esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
## 3 Enhanced Labeling Workflow
### Seismic Attributes Selection  
In this workflow, we begin by selecting and applying several seismic attributes to the seismic data. A seismic attribute is a measure of a seismic property that helps visually enhance and quantify the geological features of interest [26]. Seismic wave can be represented by three important variables, namely amplitude, phase, and frequency [27]. Each of these variables represents different attributes that help bring out different aspects of the geological feature.  
The amplitude variable can be used to strengthen the reflection. This technique is suitable to find the potential hydrocarbon accumulation or sweet spot in the seismic data, which indicates the better net to gross reservoir sands. On the other hand, frequency variable signifies the resolution that can be used to detect thin beds and different depositional environments. The phase variable is primarily an indicator of structural discontinuities, such as faults, salts, and other geomorphological features [28].  
Seismic attribute selection begins by choosing suitable attributes for the geological feature target. Table 1 summarizes the most commonly used attributes that measure seismic properties and their specific geological applications.  
In this paper, we focus on the attribute selection for salt and channel features as per the environment of deposition on each seismic dataset. The structural seismic attributes are important to quantify and detect the salt structure. These attributes are discontinuity, dip, azimuth, curvature, and amplitude change. The salt structure identified by the high velocity and strong acoustic impedance contrasts with surrounding sediments [29]. Salt interpretation consists of two complementary steps: first, highlighting the salt boundaries in the seismic data; second, extracting the boundaries by creating three dimensional surfaces [30].  
For the channel features, seismic geomorphology attributes (i.e., curvature, dip, azimuth, etc.) can provide the channel's internal and external sinuosity architecture [31]. Seismic attributes based on the amplitude value are also show tremendous result on highlighting reservoir channel features that contain point-bar accretion and stacking pattern [32]. Lateral variations in reservoir quality and thickness follow scrollbar patterns associated with lateral migration of a moderate-to-high-sinuosity meandering river [33].
## 3 Experimental Results and Analysis
### Surface Composition Analysis after Laser Cleaning  
The surface roughness is a critical parameter for assessing the efficacy of laser cleaning. As illustrated in Figure 3b, the surface roughness of materials subjected to laser cleaning exhibits a trend of initial decrease and subsequent increase in response to the escalation of laser energy density and spot overlap rate. Optimal conditions, with a laser energy density of 4.26 J/cm\({}^{2}\) and a spot overlap rate of 75%, result in the lowest surface roughness, which is measured at 0.95 \(\upmu\)m. However, further increments in either the laser energy density or spot overlap rate contribute to surface damage and an increase in roughness.  
Figure 3c displays the X-ray diffraction (XRD) patterns of laser-cleaned samples at varying laser energy densities, maintaining a consistent spot coupling ratio of 75%. The surface phases of corroded 20 steel predominantly consist of Fe, \(\alpha\)-FeOOH, \(\beta\)-FeOOH, \(\gamma\)-FeOOH, and Fe\({}_{3}\)O\({}_{4}\)[21] with each phase exhibiting distinct relative peak intensities. This variation is attributed to the corrosion environment's NaCl content, where the CI ions induce the formation of \(\beta\)-FeOOH in the rust layer [22]. During the laser-cleaning process, the unstable \(\beta\)-FeOOH phase vanishes, and the intensities of the stable phases \(\alpha\)-FeOOH and Fe\({}_{3}\)O\({}_{4}\) gradually diminish with increasing laser energy, whereas the Fe-phase peaks intensify. At an energy density of 4.26 J/cm\({}^{2}\), both the Fe\({}_{3}\)O\({}_{4}\) and \(\alpha\)-FeOOH phases completely disappear. The reason for this phenomenon is the initial presence of metal oxides on the surface of the 20-gauge steel, which masks the iron phase peaks. As the laser eradicates the corrosion layer, the underlying substrate is progressively revealed, leading to an enhanced intensity of the iron phase. The total absence of oxide spectral lines at a laser energy of 4.26 J/cm\({}^{2}\) corroborates the thorough removal of the corrosion layer. Furthermore, the emergence of Fe\({}_{3}\)O\({}_{4}\) peaks at excessively high energies confirms the oxidative effect of laser-induced damage to the substrate.
## 5 Flight Results  
### Network Topology  
The network topology depends on the physical distances of the satellites. The relative distance of the distributed mission relies on the initial deployment parameters, orbital perturbations, and atmospheric drag control. Figure 14 shows the relatively stable distances among the nodes with a sub-kilometers drift per day, as they were placed into the same orbital plane. The drift reduction, starting at approximately day 300, is the result of a phasing maneuver by active drag control.  
The network topology is determined by: (i) The orbital configuration; (ii) the satellite operating mode and desired QoS; and (iii) possible resource differences between the satellites. The orbital configuration is the most heavily weighted factor affecting the basic network topology. In a first-order approximation, the edge weight is linearly down-scaled from the propagation distance. A time snapshot of the communication graph is given in Figure 15. This case refers to a complete graph, since the maximum distance of 400 km was not exceeded. Each pair of nodes could be interconnected, either directly or through a master node, without multi-hop routing.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{6}{c}{**Scenario**} & \\ \cline{2-7} ISL **Distance** & **10 km** & **65 km** & **148 km** & **400 km** & **Unit** & **Remark** \\
**Pointing Offset** & **45 \({}^{\circ}\)** & **45 \({}^{\circ}\)** & **30 \({}^{\circ}\)** & **0 \({}^{\circ}\)** & & \\ \hline Frequency & 2266 & 2266 & 2266 & 2266 & MHz & \\ TX power output & 27 & 27 & 27 & 27 & dBm & \\ TX losses & 1.5 & 1.5 & 1.5 & 1.5 & dB & cable and connector \\ TX antenna gain & 0 & 0 & 5 & 7.5 & dBi & pointing offset \\ RX antenna gain & 0 & 0 & 5 & 7.5 & dBi & pointing offset \\ RX losses & 1.5 & 1.5 & 1.5 & 1.5 & dB & \\ RX sensitivity & \(-116.50\) & \(-116.50\) & \(-116.50\) & \(-116.50\) & dBm & \\ Roll-off factor & 0.25 & 0.25 & 0.25 & 0.25 & - & \\ Symbol rate & 80 & 80 & 80 & 80 & kbps & \\ RX noise factor & 3.5 & 3.5 & 3.5 & 3.5 & dB & \\ Link budget & 140.50 & 140.50 & 150.50 & 155.50 & dB & \\ Free space path loss & 119.55 & 135.81 & 142.95 & 151.58 & dB & \\
**Margin** SNR & **20.96** & **4.69** & **7.56** & **3.93** & dB & DBPSK+ CC r=0.5 \\ \hline SNR required & & & 4.00 & & dB & DBPSK+ CC r = 0.5 \\ SNR required & & & 6.00 & & dB & DBPSK+ CC r = 0.75 \\ SNR required & & & 9.00 & & dB & DQPSK+ CC r = 0.75 \\ SNRrequired & & & 13.00 & & dB & 8 ADPSK+ CC r = 0.75 \\ SNR required & & & 17.00 & & dB & 16 ADPSK+ CC r = 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Link budget of S-NET ISL for various distances and pointing offsets.
## 4 Development of Machine Learning Models
### Regression-Based Machine Learning Techniques
#### 4.1.1 Linear Regression (LR)  
The LR technique identifies the linear relationship between the predictor and response variables. The present study used a quadratic model containing linear and squared terms for each predictor and products of pairs of distinct predictors. Maximum Likelihood estimation was used to formulate the robust model equations. It was solved using Iteratively Reweighted Least Squares (IRTS) to improve the fit by minimizing the effect of outliers by including weight as an additional scale factor.
## 5 Conclusions  
This study effectively investigated the optimal parameters of injection molding temperature and plasticizer material to enhance the mechanical and biodegradation performances of PLA-sago starch miniplates implants. Several key findings can be drawn to conclude as follow:  
* Optimal conditions for miniplate fabrication were identified at 170 \({}^{\circ}\)C for PLA/sago starch with 10% PEG, yielding favorable mechanical properties and a degradation rate of 7.97% over 28 days. For PLA/starch with 20% PEG, optimal conditions were at 150 \({}^{\circ}\)C, resulting in specific mechanical properties and a degradation rate of 10.22%.
* An antagonistic interaction between the sago starch and PEG plasticizer was indicated when blended into the PLA-based implant, affecting its mechanical properties.
* However, the role of sago starch concentration in the mixture resulted in an increased biodegradation rate.
* The addition of PEG plasticizer also had an impact on mechanical properties, yet it was attributed to the formation of an interphase layer by the PEG plasticizer, enhancing interaction between PLA and starch interfaces, indicated via SEM images.
* The higher injection molding temperatures were found to generally accelerate the degradation rate and break elongation percentage, which play a key role in balancing both mechanical and biodegradation performances of the PLA/sago starch/PEG miniplates.  
The study significantly contributes to enhancing the value of agricultural products, especially sago starch, in the medical field by improving miniplate products biomaterial through advanced fabrication methods.  
Conceptualization, Y.W., S.S. and A.A.; methodology, Y.W. and S.S.; software, M.C.; validation, A.A., S.S. and P.K.; formal analysis, Y.W. and M.T.; investigation, M.T. and M.C.; resources, Y.W., M.C. and A.A.; data curation, Y.W. and M.T.; writing--original draft preparation, Y.W.; writing--review and editing, M.T. and Y.W.; visualization, M.C. and M.T.; supervision, Y.W., P.K. and A.A.; project administration, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Universitas Indonesia PUTI Grant 2022 NKB-1354/UN2.RST/HKP.05.00/2022.  
The data supporting the conclusions of this article will be made available by the authors on request. Further inquiries can be directed to the corresponding author/s.  
The authors declare no conflicts of interest.
## 3 Preliminaries
### Elliptic Curve Cryptography  
\(F_{P}\) represents the finite field and \(p\) represents a large prime number that denotes the size of a finite field. An elliptic curve \(E\) over the finite field \(F_{P}\) is defined by \(y^{2}=x^{3}+ax+b(mod~{}p)\), where \(a,b\in F_{P}\), \(\left(4a^{3}+27b^{2}\right)(mod~{}p)\
eq 0\). \(G\) is the additive group with prime order \(q\). \(P\) is the generator of the addition group \(G\). The infinity point O and the points of the curve form an elliptic curve additive group G. The difficult problems based on curves are shown below.  
* Elliptic Curve Discrete Logarithm Problem (ECDLP): The random points \(Q=x\cdot P\) on \(E\), where \(Q,P\in G\), and \(x\in Z_{q}^{*}\). The computation of \(x\) is hard in polynomial time.
* Computational Diffie-Hellman Problem (CDHP): The random point \(Q=x\cdot P\) and \(R=y\cdot P\) on \(E\), where \(Q,R,P\in G\) and \(x,y\in Z_{q}^{*}\). The computation of \(xyP\in G\) is hard in a polynomial time when \(x\) and \(y\) are two unknown random numbers.
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 2 Elixir Programming Language
### Elixir's Programming Model
#### 2.3.1 Polymorphism via Protocols  
Both Elixir and Erlang achieve polymorphism through pattern matching and function dispatch. However, Elixir introduces the powerful concept of _protocols_, enhancing flexibility and intuitiveness. Protocols define a set of functions that any data type can implement, enabling generic operations for different types and implementations. Elixir dynamically recognizes and calls the corresponding specific implementation, demonstrating inherent polymorphism. Additionally, protocols can have \"fallback to Any\" mechanisms, providing default implementations for unknown types. This promotes code reusability and simplifies handling heterogeneous data structures.
# Using Aspect-Level Sentiments for Calling App Recommendation with Hybrid Deep-Learning Models
## 3 Methods and Proposed Approach
### Deep-Learning Algorithms
#### 3.6.4 Recurrent Neural Network  
A recurrent neural network, an artificial neural network, is used for the prediction of sequential and time-series data. It uses previous input data from its memory to predict the current input data. Other deep neural networks are based on the assumption that input and output are independent, while an RNN uses the previous output for the upcoming inputs as they have some sort of sequence. In this way, an RNN makes predictions and is known as a multiple feed-forward neural networks as it passes information from one pass to another. It is widely used in applications of natural language processing, speech recognition, and image captioning. However, the vanishing gradient problem, a drawback in RNN, halts the learning of long data sequences [37].
## 3 Results and Analysis
### Sliding Velocity and Ring down Count  
Figure 6 shows that the ring down count (RDC) responds to the strain of backfill materials during the experiment. The AE curve can be clearly divided into four stages: normal stage, normal-limit transition stage, limit stage and failure stage, which correspond to the initial constant deformation, constant-accelerating transition deformation, initial accelerating deformation and later accelerating deformation. Before 70 s, the sliding body is in initial constant deformation and the AE is in the normal stage; the velocity and RDC are both at a low level. The change scale of the velocity and RDC is small, and the two curves are relatively stable. At about 70 s, the sliding body changes from constant deformation to accelerating deformation, and AE changes from normal stage to limit stage. The velocity and RDC rise to a small extent synchronously. From 70 s to 80 s, the sliding body is in the initial accelerating deformation, and AE is in the limit state. This stage lasts for a short time, which is a sign of the landslide entering a dangerous state. At about 80 s, the sliding body enters into the later accelerating deformation, and AE goes through the failure stage. The velocity and RDC rise sharply and reach the peak value, and the slope is completely unstable.  
The RDC directly responds to the change of sliding velocity; linear fitting is used to obtain a quantitative relationship. The fitting result is shown in Figure 7. RDC correlates strongly with sliding velocity under linear fitting, and R\({}^{2}\) reaches 0.887. This means that the velocity of landslide can be derived from RDC, which is of great significance to the deformation monitoring of an on-site slope.  
Figure 6: Sliding velocity and ring down count plotted against time.
# Multi-User Scheduling for 6G V2X Ultra-Massive MIMO System
## 4 Simulation Results and Analysis  
### Complexity Analysis  
In this section, we compare the complexity of the subspace correlation algorithm [16], geometric angle algorithm [17], conditional number algorithm [18] and the proposed algorithm. The computational complexity is defined as the number of complex operations; complex addition, complex multiplication, and complex division are considered to be complex operations. Therefore, the calculation complexity of various user scheduling criteria is represented by Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline _coor_ & **Correlation** \\ \hline \(0.8<coor\leq 1.0\) & Very strong correlation \\ \(0.6<coor\leq 0.8\) & Strong correlation \\ \(0.4<coor\leq 0.6\) & Moderately related \\ \(0.2<coor\leq 0.4\) & Weak correlation \\ \(0.0<coor\leq 0.2\) & Very weak or no correlation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correspondence between _coor_ and correlation.  
It can be seen from Table 2 that the user scheduling criterion based on matrix vectorization and scheduling factor proposed in this paper is greater than the user scheduling criterion based on subspace correlation. This is mainly because the algorithm proposed in this paper is traversed, so it consumes a certain complexity to improve the performance of the system. Subspace correlation-based algorithms, geometric angle-based algorithms, and condition-based algorithms have the same computational complexity when satisfying \(N_{t}\leq N_{r}^{2}\).
## 2 Materials and Methods
### The Probe
#### 2.2.2 Capacitance Selection  
Our research led us to select the circular parallel plate capacitor, a choice specifically tailored to meet the demands of the soil model, operational frequency response, environmental considerations, and low voltage requirements.  
The circular parallel plate capacitor, illustrated in Figure 10, is characterized by two key parameters: the radius \(r\) and the separation distance \(d\) between the plates.  
The radius \(r\) influences the capacitor's effective area, directly impacting its ability to store electric charge. A larger radius allows for a greater surface area, which enhances capacitance, thereby improving the capacitor's performance in measuring dielectric properties.  
The separation distance \(d\) plays a crucial role in determining the electric field strength between the plates. As this distance increases, the capacitance decreases, due to the reduced electric field. Conversely, a smaller separation distance increases capacitance, allowing for more effective charge storage.  
These characteristics make the circular parallel plate capacitor particularly suitable for applications in soil sensing, where precise measurements of dielectric permittivity are essential. By optimizing both \(r\) and \(d\), we can enhance the capacitor's performance in diverse soil conditions and ensure accurate readings in various environmental scenarios.  
The total capacitance, including the edge effect, is represented by the following equation:  
\[C=\frac{\varepsilon_{0}\varepsilon_{r}\pi r^{2}}{d}+\varepsilon_{0}\varepsilon_ {r}r\bigg{(}\frac{\ln(16\pi r)}{d}-1\bigg{)}. \tag{1}\]  
In the above equation, the terms are defined as follows:  
* \(\varepsilon_{0}\): vacuum permittivity;
* \(\varepsilon_{r}\): soil permittivity;
* \(r\): radius of the circular parallel plate capacitor;
* \(d\): separation between capacitor plates.
## 2 Materials and Methods
### Characterization of Light-Induced Scattering  
The optical damage resistance of Zn:Cu:Fe:LiNbO\({}_{3}\) crystals with varying Zn\({}^{2+}\) ion concentrations was quantitatively evaluated using the light scattering energy flux threshold method. This phenomenon, characterized by the onset of pronounced internal scattering when incident laser energy exceeds a critical threshold, is a well-established indicator of optical damage in photorefractive materials. The scattering intensity exhibits saturation behavior, reaching a maximum value that remains constant despite further increases in incident energy, a characteristic known as the light-induced scattering threshold effect.  
The experimental setup, illustrated in Figure 1, employed a semiconductor laser source with an adjustable attenuator for precise power control. When incident light (\(I_{0}\)) interacts with the crystal, it partitions into three components according to the following energy balance equation:  
\[I_{0}=I_{T}+I_{A}+I_{R} \tag{1}\]  
where \(I_{T}\), \(I_{A}\), and \(I_{R}\) represent transmitted, absorbed, and reflected light intensities, respectively. The scattering phenomenon is quantitatively described by the relationship between scattered light intensity (\(I_{S}\)) and transmitted light [30]:  
\[I_{S}=I_{T0}-I_{Tt} \tag{2}\]  
Here, \(I_{T0}\) and \(I_{Tt}\) denote the transmitted light intensities at the initial time and time \(t\), respectively. The scattering ratio (\(R_{s}\)) is defined as \[R_{s}=I_{S}/I_{T0} \tag{3}\]  
The scattering ratio serves as a quantitative measure of scattering intensity. The effective incident light intensity (\(I_{eff}\)) is determined by  
\[I_{eff}+I_{R}=I_{0} \tag{4}\]  
where \(R\) represents the surface reflectivity. The exposure energy (\(E_{r}\)), a critical parameter for optical damage resistance, is calculated through temporal integration of the effective intensity,  
\[E_{r}=I_{eff}\tau=(1-R)\times I_{0}\tau \tag{5}\]  
where \(\tau\) represents the scattering time constant, determined by fitting the dynamic scattering curve to the following equation:  
\[\sqrt{R_{s}}=\sqrt{R_{s,sat}}\bigg{[}1-exp\bigg{(}\frac{-t}{\tau}\bigg{)} \bigg{]} \tag{6}\]  
The saturation scattering ratio (\(R_{s,sat}\)) and time constant (\(\tau\)) provide quantitative measures of the crystal's optical damage resistance. Longer saturation times and higher exposure energies correlate with enhanced resistance to optical damage, offering crucial insights into the material's performance characteristics.
# Research on the Performance of an Aircraft Landing
## 1 Introduction
In contrast, a combination of numerical continuation and bifurcation analysis can derive the steady changes in a nonlinear system according to the changing control parameters in a fast and accurate manner [13]. The combined method can optimize the design process of complex mechanisms greatly and shorten their development time, which can be a powerful analytical tool for use in the early stage of aircraft research and development (R&D). Bifurcation theory has been applied in the aviation field for many years, mainly around a number of nonlinear dynamic problems such as the ground stability of aircraft taxiing [14; 15], the flight stability of aircrafts [16; 17], and the performance of complex lock mechanisms [18]. Regarding existing research of landing gear lock mechanisms, [PERSON] [12] was the first to use the numerical continuation algorithm combined with bifurcation theory for studying the performance of a certain landing gear lock linkage mechanism. As shown by the study, saddle node bifurcation appeared in the equilibrium solution, which corresponded to the unstable jump point of the lock mechanism, as presented in the dynamics results obtained when the lock mechanism was near the locking phase. This, in turn, explained the locking mechanism of the landing gear. Later, [PERSON] also analyzed the performance bifurcation characteristics of different lock mechanisms by targeting an integrated nose landing gear (NLG) with a combined uplock/downlock mechanism [19; 20], a spatial single-sidestay main landing gear (MLG) mechanism [21] and a dual-sidestay MLG mechanism [22] and explored the influences of the spring parameters on the lock mechanism performance. In a study conducted by [PERSON] [23], the locking and unlocking mechanisms of linkage locks were investigated, and the critical unlocking force was optimized. Existing studies focus only on analyzing the bifurcation characteristics of linkage lock mechanisms, while the performance of the hook locks examined in this paper has never been studied. Compared to linkage locks, hook locks are structurally more complicated, so it is more difficult to analyze relevant motion mechanisms. In this paper, a numerical continuation algorithm and bifurcation theory are employed to study the hook lock mechanism and to elucidate the locking principle of hook locks. Further, the influences of the landing gear weight and lock spring parameters on the performance of the lock mechanism are explored, and the minimum unlocking force of the lock mechanism is optimized, thereby establishing a novel bifurcation theory-based method for designing and analyzing hook locks for landing gear.