esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative In Vitro Analysis of Composite Resins Used in Clear Aligner Attachments
## 3 Results
### Mechanical Testing Results
#### 3.2.1 Flat Indentation  
Flat indentation tests were used to evaluate the resistance of the composites to localized compressive stress. Figure 4a illustrates the load-displacement curves, and the key performance metrics are reported in Table 2. All the samples underlined a similar behavior for each composite type. At a constant penetration depth of 0.3 mm, the maximum load sustained by AC (585.9 \(\pm\) 22.36 N) was approximately double that of CF (290.2 \(\pm\) 5.52 N). This significant difference reflects the greater stiffness of AC, likely due to its higher filler loading and stronger polymer network.  
The elastic modulus, derived from the slope of the linear region of the curve, also favored AC (231.5 \(\pm\) 9.1 MPa) over CF (156 \(\pm\) 10.5 MPa), confirming its superior resistance to deformation. Meanwhile, the residual indentation depth was slightly lower in AC (0.09 mm) than in CF (0.11 mm), suggesting better elastic recovery. Flat indentation testing revealed that AC exhibited approximately double the maximum load capacity and a substantially higher elastic modulus than CF. These properties make AC particularly suitable for applications demanding rigid mechanical anchorage, such as load-bearing orthodontic attachments or occlusal contact zones. CF, while softer and less stiff, displayed  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Height (mm)** & **Diameter (mm)** & **Density (g/cm\({}^{3}\))** \\ \hline AC & 2.7 \(\pm\) 0.08 & 6.5 \(\pm\) 0.15 & 1.50 \(\pm\) 0.02 \\ CF & 3.0 \(\pm\) 0.04 & 6.2 \(\pm\) 0.05 & 1.30 \(\pm\) 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average dimensions and densities measured for the fabricated composite samples.  
a slightly greater residual deformation and stress relaxation. Its stress reduction rate (20%) was marginally higher than that observed for AC (18%), suggesting a more pronounced viscoelastic behavior--again consistent with a more flexible and less crosslinked network. These characteristics may be advantageous for attachments subjected to repetitive stress, bending, or interfacial movement, where material adaptability can reduce the risk of fracture or detachment.
## 4 Results
### Epipolar Geometry Results  
In this section, we show how the epipolar curves can be used to filter wrong matching points. Figure 5a shows a feature point (red mark) on the upper catadioptric image, and Figure 5b shows the corresponding point on the lower catadioptric image along with the epipolar curve shown in blue. Figures 5c,d show the same information but for the unwrapped panoramic images. For a corresponding point in the lower camera to be correct, it must lie along the epipolar curve, as shown in the images.
# A Substrate-Reclamation Technology for GaN-Based Lighting-Emitting Diodes Wafer
## 1 Introduction  
Gallium nitride (GaN) is a direct-bandgap semiconductor. It is commonly used for fabricating light-emitting diodes (LEDs) due to their wide bandgap, chemical stability, and high temperature stability. These LEDs emit from the ultraviolet to the visible range [1; 2; 3]. Sapphire (Al\({}_{2}\)O\({}_{3}\)) has become a key material for fabricating GaN-based LEDs because it serves as an excellent epitaxial substrate due to its small lattice-constant mismatch with GaN. In particular, patterned sapphire substrates (PSSs) constitute an important epitaxial substrate for GaN-based LEDs because they enhance the light-extraction efficiency and internal quantum efficiency of LEDs [4; 5; 6]. The price of a 4 in PSS is approximately $30-32 USD [7], so PSSs account for about 25% of the total cost of an LED chip. Thus, reclaiming the sapphire substrate for GaN-based LED wafers can significantly reduce the manufacturing cost of LED chips. A chemical mechanical polishing (CMP) method is currently used universally to reclaim substrates [8; 9; 10]. However, the disadvantages of CMP include process complexity, surface scrapes, and thinning of the sapphire substrate. Surface scrapes and thinning result in the performance failure of the epitaxial film. Moreover, CMP makes the PSS pattern disappear. In the present work, we study a substrate-reclamation technology to avoid these disadvantages. In a non-vacuum state, we prepare a reclaimed substrate by thermally decomposing a GaN epitaxial film in a furnace system. We also investigate the properties of decomposed GaN on PSSs and the regrown LED epilayers on the reclaimed PSS.
## 2 Materials and Methods
### Test Object and Equipment
#### 2.1.2 Vibration Signal Acquisition System  
This study focuses on a self-propelled combine harvester, with vibration tests conducted on all major working components of the machine. The vibration signal acquisition system was primarily composed of the following precision equipment:  
1. DH5902N Dynamic Signal Analyzer(Jiangsu Donghua Testing Technology Co., Ltd., Taizhou, China): This served as the core data acquisition unit, responsible for the high-efficiency digitization and preliminary processing of the analog signals received from the sensors.
2. 1A312E Triaxial Acceleration Sensors: These sensors incorporate three mutually perpendicular shear structures, enabling the simultaneous capture of acceleration signals along the X, Y, and Z axes of a Cartesian coordinate system. The sensors have a sensitivity of 1 mV/(m\(\cdot\)s\({}^{-2}\)) and a measurement range of up to 5000 m\(\cdot\)s\({}^{-2}\). To meet the precision requirements of this study, their measurement range was set to \(\pm\)500 g, with a linearity error of less than 1%, a maximum transverse sensitivity of less than 5%, and a frequency response range of 0.5-10,000 Hz.
3. Accelerometer Buses, Adapter Cables, and Magnetic Mounts: These were used for providing power to the sensors, transmitting signals, and ensuring stable installation at various measurement points.
4. Dynamic Signal Analysis Computer: This computer was equipped with the DHDAS dynamic acquisition system software and was used for real-time monitoring of the data acquisition process, setting acquisition parameters, and performing initial data visualization and storage.  
The dynamic signal acquisition system is illustrated in Figure 2. The coordinated operation of the entire acquisition system laid the foundation for subsequent high-precision and high-reliability vibration data analysis.  
Figure 2: Schematic diagram of the dynamic signal acquisition system.
# Comparative Analysis of Different Model-Based Controllers Using Active Vehicle Suspension System
## 2 Mathematical Model of Quarter-Car Active Suspension System
### Active Suspension System  
The equation of motion is defined as:  
\[M_{c}\ddot{X}_{c}=-d_{c}(\dot{X}_{c}-\dot{X}_{wh})-C_{c}(X_{c}-X_{wh})+F(t)+d \tag{1}\]  
\[M_{wh}\ddot{X}_{wh}=d_{c}(\dot{X}_{c}-\dot{X}_{wh})-d_{wh}(\dot{X}_{wh}-\dot{X} _{g})+C_{c}(X_{c}-X_{wh})-C_{wh}(X_{wh}-X_{g})-F(t)-d \tag{2}\]  
Let us assume that \(X\), \(u\) and \(u_{d}\) represent the state vector, control input and road disturbances where state variables are as follows:  
\[\begin{split} X_{1}&=X_{c}-X_{wh}\\ X_{2}&=\dot{X}_{c}\\ X_{3}&=X_{wh}-u_{d}\\ X_{4}&=\dot{X}_{wh}\end{split} \tag{3}\]  
\(X_{c}-X_{wh}\) and \(X_{wh}-u_{d}\) are vehicle suspension travel and wheel deflection. \(\dot{X}_{c}\) and \(\dot{X}_{wh}\) are vehicle body and wheel velocity respectively. In addition, \(\dot{X}_{c}\) and \(\dot{X}_{wh}\) are vehicle body and wheel acceleration. Hence, in matrix form, we can write:  
\[X=\begin{bmatrix}X_{1}\\ X_{2}\\ X_{3}\\ X_{4}\end{bmatrix}=\begin{bmatrix}X_{c}-X_{wh}\\ \dot{X}_{c}\\ X_{wh}-X_{g}\\ \dot{X}_{wh}\end{bmatrix} \tag{4}\]  
where \(u(t)=F(t)\) and \(u_{d}=\dot{X}_{g}\). Furthermore, \(d\) is bounded external disturbances, i.e., \(d\leq|s|\Delta\), where \(\Delta>0\). Hence, the equations of motion can be presented in more expedient state-space form:  
\[\dot{X}(t)=AX(t)+Bu(t)+Eu_{d}+Md \tag{5}\]  
or in matrix form, it can be written as follows:  
\[\begin{bmatrix}\dot{X}_{1}\\ \dot{X}_{2}\\ \dot{X}_{3}\\ \dot{X}_{4}\end{bmatrix}=\begin{bmatrix}0&1&0&-1\\ -\frac{C_{c}}{M_{c}}&-\frac{d_{c}}{M_{c}}&0&\frac{d_{c}}{M_{c}}\\ 0&0&0&1\\ \frac{C_{c}}{M_{wh}}&\frac{d_{c}}{M_{wh}}&-\frac{C_{wh}}{M_{wh}}&-\frac{d_{c}+d _{wh}}{M_{wh}}\\ \end{bmatrix}\begin{bmatrix}X_{1}\\ X_{2}\\ X_{3}\\ X_{4}\end{bmatrix}+\begin{bmatrix}0\\ \frac{1}{M_{c}}\\ 0\\ -\frac{1}{M_{wh}}\end{bmatrix}u(t)+\begin{bmatrix}0\\ 0\\ -1\\ \frac{d_{wh}}{M_{wh}}\end{bmatrix}u_{d}+\begin{bmatrix}0\\ 1\\ 0\\ -1\\ \end{bmatrix}d \tag{6}\]  
The model can be extracted as follows:  
\[\begin{split}\dot{X}_{1}&=X_{2}-X_{4}\\ \dot{X}_{2}&=-\frac{C_{c}}{M_{c}}X_{1}-\frac{d_{c}}{M_{c}}X_{2}+ \frac{C_{c}}{M_{c}}X_{4}+\frac{1}{M_{c}}u(t)+d\\ \dot{X}_{3}&=X_{4}-u_{d}\\ \dot{X}_{4}&=\frac{C_{c}}{M_{wh}}X_{1}+\frac{d_{c}}{M_{wh}}X_{2}-\frac{C_{wh} }{M_{wh}}X_{3}-\frac{d_{c}}{M_{wh}}X_{4}-\frac{d_{wh}}{M_{wh}}X_{4}-\frac{1}{M _{wh}}u(t)+\frac{d_{wh}}{M_{wh}}u_{d}-d\end{split} \tag{7}\]  
The model presented in (7) has numerous advantages. Due to its simple, and low dynamic order it helps in understanding the dynamic behavior of vehicle. Furthermore, it also provides good basis for designing the active damping controllers.
# Trust and Mobility-Based Protocol for Secure Routing in Internet of Things
## 2 Background
### Attacks in RPL
#### 2.2.2 Blackhole Attack  
Blackhole attack is among the severe and high-impact RPL attacks [39]. In a Blackhole attack, attacker node(s) drop all the packets, rather than forward to the destination, thus triggering a DoS attack in the network, as demonstrated in Figure 3. As an effect of the Blackhole attack, end-to-end (E2E) delay is increased, and packet delivery ratio (PDR) is reduced. If the packets are manipulated by the malicious node before forwarding, that variant of the Blackhole attack becomes even more devastating. It results in a misleading route advertisement in addition to a reduced PDR. The description and investigation of other RPL attacks are beyond the scope of this paper. Table 1 summarizes a description of the attacks under consideration.
## 1 Introduction
### FRT Capability and DRES Reaction During Grid Faults
#### 3.5.2 Measurement and Quantification
In such cases, a portion of the FSS should be charged in order to provide the required active power. In the worst case it can be assumed that the fault caused the voltage to drop to \(V_{4}\) (see top of Figure 12) while the DRES is programmed to inject a current similar to Figure 11. The required ultracapacitor size is the determined by:  
\[ \begin{split} E^{\prime\prime}_{\text{FRT}}&=V_{4} \cdot(I^{\prime}\cdot t_{\text{subtr}}+I^{\prime}\cdot t_{\text{tr}})\\ C^{\prime\prime}_{\text{FSS}}&=\frac{2E^{\prime\prime}_{ \text{FRT}}}{V^{2}_{\text{FSS\_rated}}}\end{split}  \tag{20}\]  
where, \(I^{\prime\prime}\) is the RMS value of the fault current during the subtransient time duration \(t_{subtr}\) and \(I^{\prime}\) is the RMS value of the fault current during the transient time duration \(t_{tr}\). Thus, in the worst case,  
\[C^{\text{Total}}_{\text{FSS}}=C_{\text{FSS}}+C^{\prime\prime}_{\text{FSS}} \tag{21}\]  
Equation (18) represents the availability cost that has to be remunerated. This cost involves only the additional FSS and the associated DC/DC converter installation cost. Due to the small time-scales involved during the faults the operational costs are negligible. On the other hand, Equation (17) represents the actual contribution of a DRES during a fault and can be used for additional remuneration of this AS in the cases where the contribution with controllable currents is requested. The remuneration coefficient that will be applied to (18) should be evaluated on a cost-benefit analysis of this AS within a distribution system.
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 3 Methodology
### Phase 2: Incremental Mutual Information Selection (IMIS)  
Mutual information-based incremental selection (IMIS) is employed to extract the most salient feature subset for the ransomware detection model. This technique leverages mutual information to quantify the relevance of candidate features while mitigating redundancy with already selected features. Its non-parametric nature renders it adaptable to both linear and non-linear relationships, and categorical and continuous data types. Unlike correlation, which is limited to linear dependencies, mutual information captures the full spectrum of variable relationships, making it a robust tool for feature selection in complex datasets characterized by intricate, non-linear dependencies.  
The core principle of IMIS is to utilize entropy as a measure of the information content a candidate feature contributes towards the target label. High information content translates to high relevance. Subsequently, redundancy between the candidate and already selected features is quantified, and high redundancy leads to a diminished feature significance. This ensures the selected feature set maximizes relevance while minimizing redundancy. Notably, IMIS incorporates incremental learning, enabling dynamic re-evaluation of feature significance upon the arrival of new data, thus empowering the detection model to adapt to evolving ransomware behaviors.  
The mutual information feature selection process involves computing mutual information scores between each feature and the target variable, followed by a ranking of features based on these scores. Features exhibiting the highest scores, indicative of maximum information content, are prioritized for model training, while those with low scores are discarded, resulting in a streamlined and potentially more performant model. The culmination of this phase is the derivation of an IMIS-selected feature set, comprising the most relevant and non-redundant features, which serve as input for the detection model in Phase 3.
## 2 Mathematical Model of Multi-Satellite Scheduling
### Constraints
#### 2.3.3 Energy Constraints  
Energy constraints are manifested in the following two aspects: (1) the discharge depth of the battery for each discharge activity cannot exceed 20%; (2) the satellite must achieve the energy balance of the circle during each illumination ground period. That is, the discharge energy of the battery pack during the grounding can be fully replenished during the subsequent illumination period.  
The mathematical expression is shown in Equation (4), and the meaning of the symbols is explained in Table 1.  
\[\forall\ k\in\ K,\ t_{\text{C}k},\ t_{\text{E}k}\in\ [\ T_{gk^{\prime}},\ T_{ \text{E}k}], \tag{4}\]
## 1 Introduction
By assessing this, the risk of vibration-induced damage to the IGBT module and its components can be effectively monitored and mitigated;
* Random vibration analysis was conducted to assess the module's response to unpredictable and fluctuating vibration environments, providing a more comprehensive understanding of its dynamic behavior. To carry out this analysis, finite element analysis (FEA) using ANSYS 2023 R1 was utilized to simulate the vibrational forces acting on the IGBT module, allowing for a detailed examination of its structural response under varying conditions. This approach ensures a more accurate prediction of the module's performance and potential vulnerabilities to dynamic forces;
* The deformation pattern, lifetime, and damage distribution of the IGBT module were analyzed using ANSYS simulation tools to assess the impact of vibration on the module's lifespan. This analysis provided insights into how the vibrational forces within the wind turbine nacelle affect the IGBT module, helping to predict potential failure points and optimize its design for enhanced durability and performance in such dynamic environments. The structure of this paper is organized as follows. Section 2 covers our changes to the modal analysis, while Section 3 focuses on the random vibration analysis. In Section 4, the load frequencies in a wind turbine are discussed. Section 5 provides details on how to model the IGBT module in ANSYS. The simulation results are presented in Section 6, and, finally, Section 7 concludes the paper by summarizing key findings.
# Effects of Manure Waste Biochars in Mining Soils
## 4 Discussion
### Metal Mobility in Amended Mining Soils  
The application of all biochars reduced, in general, the mobility of trace metals in the three soils (Z1, Z2, and Z3) of the Zarandas-Andalusia area. This can be related to the addition of biochars with high pH, leading to a liming effect in acid soils, decreasing metal mobility [22]. [PERSON] et al. [57] indicated that biochar acts on the heavy metal available fraction, thus achieving a reduction in their mobility in the soil solution. The reduction of the Pb mobility in the Zarandas-Andalusia area could be due to Pb precipitating at soil pH 4-8 and its mobility being reduced in alkaline conditions [58]. Furthermore, previous studies observed that biochar addition raised the pH and increased the soil organic matter content, which favors the immobilization of some metals in acid polluted soils [59; 60]. It is important to note that the Z1 soil showed the highest mobile Zn content (1.46 mg\(\cdot\)kg\({}^{-1}\)) (Table 4). It is well known that Cu and Zn adsorption can be reduced by the competition between both metals for the same adsorption sites [61] and, consequently, the higher Zn content could explain the increase in Cu mobility in some cases after biochar addition. Moreover, metal adsorption on biochar will depend not only on soil and biochar pH, but also on surface functional groups and pore size distribution [56] (see data in Table 1). In our research, the highest effect of biochar on metal mobility was obtained in soils from the Zarandas-Andalusia area characterized by acid pH and, in the particular cases of Z2 and Z3, low Coxi content. This is in agreement with [PERSON] et al. [62], who indicated that Cu tends to the formation of organic complexes with organic matter, which facilitates the reduction of its mobility. Therefore, the application of manure biochars to this type of multi-contaminated acid soil can be an adequate strategy to reduce the mobility of trace metals and, therefore, their possible toxicity of metals for soil microorganisms and plants. In the case of the Portman-Murcia area, the main result was the reduction of Pb and Cd mobility in P1 and P2 soils, while, in the Mijarojos-Cantabria area, the main result was the reduction of Zn and Cd mobility due to the causes mentioned above. Indeed, the alkaline pH of the soils of these two areas, except P2 soil, could be the main factor that limits the reduction of trace metal mobility in these two areas.
## 3 Explicit Calculation of Phase Space Representations of Arbitrary Digital Holograms
### Introduction to the S-Method  
Given Equation (10), the S-method is defined with the STFT and an arbitrarily chosen frequency filter window \(v\in\mathbb{C}^{L}\) of length \(L:=2L^{\prime}+1,L^{\prime}\  
S-method. The most simplistic and efficient implementation for symmetric and real-valued frequency windows \(v\) is explained in great detail in the original contribution [12] and in [11]. It is restated in Listing 1.  
**Listing 1.** An efficient S-method implementation  
\(S\)_-method_ ( \(\mathrm{STFT}\in\mathbb{C}^{M\times N}\), frequency window \(v(l)\in\mathbb{R}^{L},l\in\{0,\ldots,L-1\}\) with peak at \(L^{\prime}+1\) )  
\(SM=|STFT|^{2}\,v(L^{\prime}+1)\)\(\triangleright\)0.th term is the spectrogram  
**For** ( \(l\in\{0,\ldots,L^{\prime}\}\) ) \(\triangleright\)_l_.th refinement  
**For** ( \(f_{\xi}^{px}\in\{1,\ldots,M-1-l\}\) )  
**For** ( \(\xi^{px}\in\{0,\ldots,N-1\}\) )  
\(u=\mathcal{Real}(STFT(f_{\xi}^{px}+L^{\prime}+1-l,\xi^{px})\overline{STFT(f_{ \xi}^{px}+L^{\prime}+1+l,\xi^{px})})\)  
\(SM(f_{\xi}^{px},\xi^{px})=SM(f_{\xi}^{px},\xi^{px})+[v(L^{\prime}+1-l)+v(L^{ \prime}+1+l)]u\)  
**return**\(SM\in\mathbb{R}^{M\times N}\)  
As with the spectrogram, there exist also extensions to the S-method [11; 44], which can improve its resolution further, but they are not considered here for the sake of clarity. Instead we note, that several other PSRs are contained in the S-method (not to be confused with the S-transform) as special cases.  
1. If \(v(0)=\delta(0),L^{\prime}=0\) one obtains the spectrogram, that is \(SM_{v,w}=SP_{w}\). Thereby \(w(m)\) is commonly normalized by Equation (13).
2. If \(v(0)=1,L^{\prime}=0\) one obtains the pseudo Wigner-Ville representation [40], which initially was constructed as restriction by windowing of the space-lags \(w(m)\) considered during the computation of the Wigner-Ville representation. It is \(SM_{v,w}=PWV_{w}\).
3. If \(v(0)=1,L^{\prime}=0\), and \(w(0)=1,M^{\prime}=0\) one obtains the regular Wigner-Ville representation, that is \(SM_{v,w}=WV\).
4. If \(v(l),w(m)\) are of even parity, and the following standard requirements hold \(\mathcal{F}(v)(\zeta=0)=1\), \(v(l)\in\mathbb{R},w(m)\in\mathbb{R}\), one obtains the smoothed pseudo Wigner-Ville representation [40], that is \(SM_{v,w}=SPWV_{v,w}\). \(\mathcal{F}\) signifies the discrete Fourier transform.
## 4 Discussion
Validity of this score will be based on its capability of identifying injured runners and their running deviation leading to the injury, and may also be used as a possible indicator for detecting running injury risk factors. Future research should, as well, evaluate the changes in the GPS post running re-education and its influence on symptoms. One restriction of the GPS is that it does not measure all foot kinematics, such as hind foot kinematics, an important factor in running injuries, rather it only measures the foot progression angle. In addition, a limitation of this study is the absence of demographic data that may have an impact on running injuries such as previous running injuries, shoe type, and running terrain. It is essential to say that the MAP score in our study presents deviations among injured runners. However, it is impossible to declare the cause and effect relationship. Those deviations may be merely a reflection of the injury.