esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### DVC Analysis
#### 3.2.3 Damage Estimate  
It is evident from the analysis of Section 3.2.1 that the crack distribution area and the maximum principal strain calculated by DVC have a good correlation. So, the evaluation of internal damage is based on the relationship between the amount of damage and the  
Figure 14: Principal strain map of different sections under 50 MPa. (**a**) the first section; (**b**) the second section; (**c**) the third section; (**d**) the fourth section.  
Figure 13: Principal strain map of different sections under 30 MPa. (**a**) the first section; (**b**) the second section; (**c**) the third section; (**d**) the fourth section.  
maximum principal strain inside the specimen. According to the literature [29], the damage index is defined as Equation (2):  
\[D=\frac{\sum_{i}E_{max}A_{i}|E_{max}>0}{\sum_{i}A_{i}} \tag{2}\]  
where \(D\) is the damage index; \(E_{max}\) is the maximum principal strain of the sub-volume, and the region is defined by a step size of 15 voxel; and \(A_{i}\,|\,E_{max}>0\) refers to the positive maximum principal strain area.  
The variation in the CT image gray value can also be used as a basis for evaluating the damage. As described in Section 2.3.2, the gray value will change with the variation in the material density. As cracks form in the specimen under stress, the gray value decreases. The gray values of CT images used for DVC calculation range from 0 to 255.  
The gray value and damage index at different sections of the specimen are shown in Figure 15. It can be seen from the figure that gray values under different stresses are serrated, which is caused by the uneven distribution of materials with different densities inside the specimen. The gray value and damage index under 50 MPa are significantly lower than 30 MPa, which indicates that the specimen density decreased, and small cracks began to form as the stress increased. It is worth mentioning that the gray value and damage index of the middle position of the specimen differ greatly under different stress states, while those of the two ends differ little, indicating that the localized damage of the central position is more serious.
## 2 Operational Principle  
### Tree-Rings Data Tracking  
Nature not only provides an information sourcebook of behavior, function, color and shape which can inspire visual design and invention [26], but acts as a school for materials science and its associated disciplines such as chemistry, biology, physics or engineering [27]. Conventional analysis of fatigue damage accumulation based on the whole strain course has disadvantages of high cost, complicated sensor implementation and system integration, making it hard to use in engineering applications. In combination with a counter based on Digital Signal Processing (DSP) technology, the bio-inspired tree-rings data tracking method can record the real-time characteristics of fatigue behavior: the amplitude of strain, the number of cycles, and the stress state, potentially overcoming the disadvantages of the traditional fatigue prediction methods. The tree-rings data tracking method could record various environmental conditions; the DSP hardware technique, on the other hand, records, analyzes, and transmits the real-time characteristic data from the wireless fatigue meter, such as the amplitude of strain, the number of cycles and the stress state. With both the environmental conditions and the fatigue progressing, the analysis of fatigue damage accumulation and an early warning for potential structural fatigue failure can be estimated.
# Model-Free-Based Single-Dimension Fuzzy SMC Design for Underactuated Quadrotor UAV
## 4 Control Design
### Control Design Using Model-Free Approach  
One may see a non-linear system as mentioned in [39; 40], in implicit form:  
\[f\left(y,\dot{y},\ \ldots,\ u,\ \dot{u},\ d\right)=0 \tag{19}\]  
Moreover, in this Equation (19), \(y\ \in\ R\) is the output, \(u\ \in\ R\) is the input variable and \(d\ \in\ R\) is the bounded disturbances. Thus, the model-free control design is proposed here to approximate these dynamics of the system.  
\[\ddot{y}=F(t)+\alpha u(t) \tag{20}\]  
In above equation, \(F(t)\) is defined as unknown expression that must be observed and estimated by checking the input and output relation. Then by deducing this for model-free control strategy, Equation (20) will be turned as:  
\[\ddot{Y}_{i}=F_{i}(t)+\alpha_{i}u_{i} \tag{21}\]  
For simplification, the notation in Equation (20) is indexed with \(i\) for the correspondence of individual inputs and state variables. The most important part of this strategy is the observing and estimating the unknown terms of \(F\). Thus, the generalized form for proposed model-free control design is stated in Equation (20), where \(\ddot{Y}_{i}\) is same as \(\ddot{y}\) but illustrated as the sum of all corresponding individual inputs and state variables. Researchers have discussed this issue in several publications with different estimation techniques. This paper follows the estimation method followed by differentiator technique as mentioned in [40].  
\[F(t)\approx\hat{F}(t)=F(t-\varepsilon)=\ddot{Y}(t-\varepsilon)-\alpha u(t-\varepsilon) \tag{22}\]  
The term \(\varepsilon\) is defined as a small-time delay. Since the change in angular velocities and acceleration is bounded thus, this will lead \(F(t)\) towards bounded values in Equation (22) and measurable using small delay and difference term as \(F(t-\varepsilon)\). In this way, the model-free control can be stated as mentioned in Equation (23).  
\[u_{c}=-\frac{\hat{F}-\ddot{Y}_{d}+u}{\alpha} \tag{23}\]The term \(\ddot{Y}_{d}\) is the required second derivative of the output state, and it should be smooth, whereas \(u_{c}\) is the feedback for measuring the tracking error. By manipulating Equations (22) and (23), one may get the relation as:  
\[\ddot{e}+u_{c}=F-\hat{F} \tag{24}\]  
where \(\ddot{e}\) is defined as \(Y-Y_{d}\), known as trajectory tracking error.
## 4 The Performance of the Optimized SVR Model on Driving Decision-Making
### Sensitive Analysis of Road Conditions on Driving Decisions
In the low and middle traffic density range (4-28 km/h), road visibility \(\delta\) has the greatest effect on driving decision, then followed by adhesion coefficient \(\mu\), road curvature \(\rho\), and road slope \(\tau\). So, we can conclude: in the low traffic density range, driving decision-making is mainly restricted by the road conditions, in consequence, results are easy to be wrong without considering road conditions. on the other hand, with the high traffic density range, driving decision-making is mainly limited by vehicles states, so even if road conditions are not taken into account, the reasoning results are less affected. Take the change rate of driving decision in low traffic density range in Figure 9b as an example, when all of the road parameters are taken as the standard values, the average rates of free driving, lane changing, and car following are about 0.469, 0.262, and 0.269, respectively, in the low traffic density range. If \(\delta\) is changed to 100, the average rates of three driving decisions are changed to about 0.078, 0.034, 0.888, respectively, which means that about 61.9% of samples change their decisions when the road visibility is changed from 1000 m to 100 m. Similarly, if \(\tau\) is changed to 4%, then the average rates of three driving decisions are changed to 0.515, 0.238, and 0.247, respectively, which means that about 4.6% of samples change their decisions when the road slope is changed from 0 to 4%. The same is true for the analysis of driving decisions corresponding to the changes in other two parameters. Thus, it can be seen that road conditions are important indexes that cannot be ignored in DDM for autonomous vehicle.
# Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks +
## 3 System Model
### Task Model
According to Equation (2), the execution of a task \(k\) without predecessors (\(\mathcal{P}(k)=\emptyset\)) can start immediately, while the start time of a task \(k\) that depends on predecessor tasks (\(\mathcal{P}(k)\
eq\emptyset\)) equals the maximum finish time \(FT_{j}\) of the respective predecessor tasks \(j\in\mathcal{P}(k)\).
## 2 Materials and Methods
### Signal Enhancement
#### 2.2.2 Amplifier Board  
In Section 2.2.1, the LED strip signal was directly injected into the multimeter. However, in this sub-section, the signal was first fed into a multifunctional amplifier board (TW-MF2 CAB Sglux: Berlin, Germany); this amplifier board is mostly applied in blind Ultraviolet (UV)-detector photocurrent amplifications. This amplifier board is commercially available in electronics online stores. The board's dimensions are 60 mm \(\times\) 45 mm \(\times\) 12 mm and it was powered with a single power supply of 26 V--Figure 3. As elaborated in Figure 3b, this board is composed of circuit components, which include resistors, transistors, capacitors, potentiometers, and operational amplifiers. Before injecting any signal into the amplifier board, the potentiometers 1 and 2 were adjusted to ensure that the board's dark current is below 1 mV, as recommended by the manufacturer. The post-amplifier board signal was then transported through the electric cable and injected into the digital multimeter.  
The amplifier board was set to a two-channel sequential amplification configuration. In the first amplification stage, the amplifier mode-line 1 was set to a transimpedance amplifier, and its gain factor setting-line 1 was \(10^{7}\) V/A. In the second amplification stage, the amplifier mode-line 2 was set to a voltage amplifier with a pre-gain of 10 V/V. The gain factor setting line 2 was also adjusted to a voltage gain of 10 V/V. Therefore, the second amplification stage had a total voltage gain of 100 V/V. This amplification combination produced a dark current of 0.8 mV, which was below the operational dark current of 1 mV, as recommended by the amplifier board manufacturer.  
However, the dark current increased to 1.3 mV when the second amplification stage setting was adjusted to have a total gain of 1000 V/V. Therefore, the total amplification gain in the second stage was maintained at 100 V/V, hence a total amplifier board gain of \(10^{9}\) V/A.  
Figure 2: Signal Amplification by LED chip number/active area increment. Note: This image only shows how the LED strips were reconnected successively—it was taken when five strips were reconnected. During the experiments, the number of LED strips (active area) reconnected was as stated in the different sections of this report.  
Figure 3: Signal Amplification using an Amplifier Board: (**a**)-Image of the amplifier board, (**b**)-Schematic of the amplifier board circuitry (extracted from [19]).
_Appl. Sci._**2015**, \(5\), 36-47; doi:10.3390/app5010036  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Ion Acceleration by Short Chirped Laser Pulses**  
**[PERSON] 1, [PERSON] 2, [PERSON] 1 and [PERSON] 1,*  
\({}^{1}\) Max Planck Institute for Nuclear Physics, Saupfercheckweg 1, 69029 Heidelberg, Germany;  
E-Mails: [PERSON]SS] ([PERSON]-X.L.); [PERSON]_ADDRESS] ([PERSON].)  
\({}^{2}\) Ziegelhauser Landstrasse 19, 69120 Heidelberg, Germany; E-Mail: [PERSON]]  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]S];  
Tel.: +49-6221-516-170; Fax: +49-6221-516-152.  
Academic Editor: [PERSON]  
_Received: 18 December 2014 / Accepted: 12 February 2015 / Published: 17 February 2015_  
**Abstract:** Direct laser acceleration of ions by short frequency chirped laser pulses is investigated theoretically. We demonstrate that intense beams of ions with a kinetic energy broadening of about 1\(\%\) can be generated. The chirping of the laser pulse allows the particles to gain kinetic energies of hundreds of MeVs, which is required for hadron cancer therapy, from pulses of energies in the order of 100 J. It is shown that few-cycle chirped pulses can accelerate ions more efficiently than long ones, _i.e._, higher ion kinetic energies are reached with the same amount of total electromagnetic pulse energy.  
**Keywords:** laser acceleration; chirped pulses; ion acceleration  
**PACS classifications:** 52.38.Kd, 37.10.Vz, 42.65.-k, 52.75.Di, 52.59.Bi, 52.59.Fn, 41.75.Jv, 87.56.bd
## 3 Advances in Control Strategies
### AI-Driven Control  
AI--particularly deep reinforcement learning (DRL) and neural network surrogates--has transformed robot control by enabling predictive, self-optimizing, and context-aware systems. [PERSON] et al. [37] reduced the training time by 25% relative to Proximal PolicyOptimization baselines. [PERSON] et al. (2018) achieved a 30% improvement in task success over classical image-based visual serving. [PERSON] et al. (2018) increased throughput by 15% versus heuristic scheduling. [PERSON] et al. (2018) improved packing efficiency by 20% compared with rule-based bin-packing. [PERSON] et al. (2018) cut error rates by 10% relative to CNN-only perception pipelines. [PERSON] et al. (2018) reviewed ROS-based DRL for industrial robotics, highlighting a 22% adaptability gain across benchmark scenarios. Extending to cross-domain relevance, [PERSON] et al. (2018) reported a 15% tracking-error drop versus a non-dead-zone-compensated PID, and [PERSON] et al. (2018) demonstrated a 20% efficiency improvement in smart-city resource planning, illustrating transfer pathways for large-scale decision layers that can cascade into industrial control stacks.  
Multi-Agent and Bio-Inspired AI Control  
Multi-agent DRL and bio-inspired neural paradigms are accelerating collaborative control. [PERSON] et al. (2018) introduced a multi-agent DRL framework for persistent monitoring that improved coordination by 22% under operational constraints. [PERSON] et al. (2018) employed spiking neural networks (bio-inspired) for 3D arm control, yielding a 17% gain in energy efficiency ([PERSON] et al., 2018). [PERSON] et al. (2018) surveyed deep learning in soft robotics, reporting \(\sim\)15% computational overhead savings across representative benchmarks and pointing toward scalable bio-inspired control ([PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2019). [PERSON] et al. (2019) improved buoy-winch coordination by 20% using DRL--evidence for synchronization benefits in industrial analogues ([PERSON] et al., 2018). [PERSON] et al. (2019) achieved a 20% harvesting efficiency increase in agri-robotics, and [PERSON] et al. (2019) enhanced mobile-robot navigation by 18%, both underscoring the adaptability of biologically inspired strategies under multi-agent interaction loads. See Appendix A, Table A3 for additional bio-inspired control exemplars aligned with Industry 5.0.
## 3 Results
### Estimation of Concentration from Turbidity  
The turbidity-concentration relationship was analyzed from the data that were used for the model calibration and validation to estimate the concentration from the turbidity that was predicted using the developed models (Figure 12).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{4}{c}{**Variance Inflation Factor**} \\ \cline{2-5}  & **(RGB Gray)** & **(RGB)** & **(RG)** & **(GB)** & **(RB)** \\ \hline R & 1606 & 53 & 3.4 & - & 17.2 \\ \hline G & 155 & 26 & 3.4 & 8.4 & - \\ \hline B & 3369 & 130 & - & 8.4 & 17.2 \\ \hline GR & 1028 & - & - & - & - \\ \hline \end{tabular}
\end{table}
Table 2: Results of multicollinearity assessment for the developed models.  
Figure 11: Model verification results: (**a**) RGB Gray scale model, (**b**) RGB model, (**c**) RG model, (**d**) GB model and (**e**) RB model.  
Figure 12: Turbidity-concentration relationships based on average suspended sediment particle size (**a**) power-law, (**b**) second-order polynomial, and (**c**) first-order polynomial.  
The proposed turbidity-concentration relationships showed similar R\({}^{2}\) values. Considering that the size of the particles that were used for the suspended sediment injection was smaller than 0.044 mm, it was difficult to estimate the concentration from turbidity when the average particle size was increased by suspended substances other than the injected red clay. In particular, the section with low turbidity before suspended sediment injection exhibited a large deviation. Among the developed models, the RG model that showed the highest predictive power with no multicollinearity was used. Figure 13 shows the SSC that was estimated using the predicted turbidity by the model based on the presented turbidity-concentration relationship that is most commonly used (power-law).  
In the case of the SSC that was estimated using the predicted turbidity by the developed model, the RMSE value ranged from 20 to 21 ppm, thus confirming the possibility of estimation of the SSC from color changes in images. In case of estimating the SSC based on this method, the predictive power is expected to further increase if larger amounts of data on SSC and turbidity are used.
# Numerical Study of Aircraft Wake Vortex Evolution under the Influence of Vertical Winds
## 3 Results
### Numerical Simulation Verification  
To validate the accuracy of the numerical simulation results, a comparative analysis is conducted among the simulation outcomes under calm wind conditions, radar detection findings from the literature [32], and calculations derived from the Hallock-Burnham theoretical model [24]. The tangential velocity of the vortex at a single point in the Hallock-Burnham model is  
\[\mathrm{V}_{\emptyset}=\frac{\mathrm{r}\Gamma}{2\pi(\mathrm{r}^{2}+\mathrm{r }_{\mathrm{c}}^{2})}, \tag{6}\]  
and the vertical velocity at any point \((\mathrm{x},\mathrm{y})\) in the flow field is  
\[\mathrm{V}_{\mathrm{y}}=\frac{\Gamma_{1}(\mathrm{x}-\mathrm{x}_{1})}{2\pi \Big{[}(\mathrm{x}-\mathrm{x}_{1})^{2}+(\mathrm{y}-\mathrm{y}_{1})^{2}+ \mathrm{r}_{\mathrm{c}1}^{2}\Big{]}}+\frac{\Gamma_{2}(\mathrm{x}-\mathrm{x}_{2 })}{2\pi\Big{[}(\mathrm{x}-\mathrm{x}_{2})^{2}+(\mathrm{y}-\mathrm{y}_{2})^{2} +\mathrm{r}_{\mathrm{c}2}^{2}\Big{]}}, \tag{7}\]  
where \(\Gamma_{i}\) denotes wake vortex circulation and \(\mathrm{r}_{i}\) denotes the radius of the vortex core. As can be seen in Figure 8, with the change in radial distance, the tangential velocity shows a trend of increasing and then decreasing; the tangential velocity is the largest at the vortex  
Figure 7: Convergence status of computational parameters.  
core, and the rate on both sides of the vortex core is dropping and tends to zero. Due to the differences in models and measurement locations, the specific values of the velocity are also different; it can be remarkably observed that the velocity distribution patterns obtained from the numerical simulation closely align with both the Hallock-Burnham theoretical model and the radar detection findings.  
Our study validates its numerical model by comparing results with those from the literature, which investigates the aerodynamics and vortex evolution of A320 under various conditions. Using similar O-H-type structured grids and the SST k-\(\omega\) turbulence model, our research aligns with the numerical simulation conditions of [12]. The comparison at 60 m height with stationary wall conditions shows great agreement in the wingtip vortices' horizontal and vertical displacements, as illustrated in Figure 9. This consistency confirms the accuracy of our model and bolsters the reliability of our simulation approach for capturing the airflow dynamics around aircraft.  
Figure 8: Tangential velocity distribution of the wake vortex. (**a**) Numerical simulation results; (**b**) H-B theoretical modeling results; (**c**) radar detection results.
## 4 Verification Experiment Results and Analysis
### Real Vehicle Experiment  
The NMPC algorithm proposed was also applied to an autonomous vehicle, which won third prize in the 2019 Intelligent Connected Vehicle Competition organized by the Guangzhou municipal government. The vehicle was developed by the South China University of Technology and is shown in Figure 12. Figure 13 shows the technical architecture which can be divided into three parts:  
Figure 11: Lateral deviation of LQR.  
Figure 10: Trajectory tracking effect of LQR.  
Figure 9: Mean square errors and maximum deviations for different speeds.  
Software, hardware, and vehicle. The software architecture significantly impacted control efficacy. The localization module and perception module could send data to the planning module to generate a local target trajectory. Ignoring changes in speed for the time being, lateral controls play a vital role in the control module. The NMPC algorithm proposed above was applied to the lateral control module. Finally, the first steering angle of the solution sequence was sent to the CAN bus.  
Results and Analysis  
There is a scene of entering a parking lot through continuous right-angle turns in the competition, as shown in Figure 13(a). Figure 13(b),\(c\) show the reference trajectory and the curvature of the trajectory. It can be seen that the curvature of the trajectory increases rapidly when making a right-angle turn. In order to follow the actual driving rules and guarantee the safety, the turning speed is limited to 8 km/h.  
Figure 12: Autonomous vehicle.  
Figure 13: Technical architecture.  
The result can be seen from Figure 13(b) that the vehicle can follow the reference trajectory during the whole process. Although there is slight deviation around 0.4 m at the lager turning rate, it is also within an allowable range, which meets the requirements of unmanned vehicle control. This also fully proves the algorithm's ability to follow the trajectory with large curvature.
# ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
## 2 Literature Study
This study introduces a KAN model grounded in the Kolmogorov-Arnold theorem. By incorporating a learnable activation function into the deep learning framework, the model integrates interpretability with superior ECG signal classification performance. Specifically, by substituting traditional linear weights with nonlinear weights, the KAN preserves the benefits of deep learning for processing large-scale data while improving adaptability to intricate functional relationships. The primary advantages are as follows:1. Integration of automation and interpretability: The KAN not only automatically extracts multi-layered features from ECG signals but also offers clear explanations for the physical significance of these features. 2. Enhanced capability to address the complexity of ECG data: The KAN's structural design is particularly suited for processing multi-view and multi-scale features inherent in arrhythmia signals. 3. Reliability and practical applicability: By improving the model's adaptability to the distribution and characteristics of ECG data, the KAN achieves higher classification accuracy, offering a dependable foundation for clinical diagnosis. In summary, the KAN model not only retains the strengths of deep learning but also addresses its interpretability limitations, offering a novel approach to ECG signal classification. The following section delves into recent ML and DL in arrhythmia research, highlighting the KAN's innovations and advancements in tackling ECG classification challenges.