esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Piezoelectric Jet and Atomization Devices
## 2 Advances in Piezoelectric Micro Jet Devices
### Squeeze Mode  
In 1984, [PERSON] et al. [34] invented the first squeeze piezoelectric jet device. They designed the extrusion jet device utilizing the pressure wave conduction theory in the ink storage cavity. In order to improve the deformability and corrosion resistance of the ink storage cavity, PET/PTFE was chosen to make ink tubes, where the squeeze ink spray head could spray ink with a maximum viscosity of 100 m Pa.s [35]. In 2013, [PERSON] et al. [36] used the Comsol software to analyze the squeeze inkjet head, and the volume of jet ink droplets was obtained by simulation calculation. In 2017, [PERSON] et al. [37] established a mathematical model of sound propagation considering liquid viscosity based on a piezoelectric inverse effect, and the sound pressure response at the nozzle was investigated. In view of the complicated structure, there were some shortages including the high assembly process and high price of the extruded piezoelectric nozzle. In 2020, [PERSON] et al. [38] further designed a simple ring piezoelectric ceramic direct drive injection material micro-drop injection device, and the feasibility of the design was also verified by experiments.  
All of the above-mentioned piezoelectric jet devices are assigned to the type of squeeze piezoelectric micro jet devices as shown in Figure 1c. The major components of these devices mainly consist of a circular tube, piezoelectric ceramic tube, and glass tube. The piezoelectric ceramic tube is used as a brake, and the glass tube is used as a liquid storage cavity. Simultaneously, the top part of the glass tube is drawn into micropore, acting as a nozzle. In the fabrication, the most critical step is the polarization direction of the piezoelectric ceramic tube, which must be polarized along the radial direction, and the power supply is connected to the inner and outer walls of the piezoelectric ceramic tube, respectively.
# Force Performance Analysis and Numerical Simulation of Assembled Ribbed-Slab Abutments
## 3 Assembled Ribbed-Slab Abutment Model Validation and Foundation Model Analysis
### Development and Validation of Numerical Analysis Models  
#### 3.1.1 FE Simulation of Test Models  
ABAQUS 2022 software was used to establish a refined FE model of the FLP3 model. Figure 8 showcases the FE model of each component established in this study: For the bridge abutment model as a whole, the cell mesh size was 50 mm, while the mesh division of steel reinforcement was set to 100 mm, which was 1-4 times the concrete mesh. The mesh length of the flange and the bolt was 40 and 10 mm, respectively.  
In the FE modeling process, the eight-node hexahedral linear reduced the integration unit (C3D8R) was used for representing ordinary concrete, flange, and bolt units, while the two-node truss unit (T3D2) was utilized for simulating the reinforcement bars. The concrete damage plasticity model was employed to establish the C50 constitutive relationship, and the reinforcement constitutive relationship was considered as a bifurcated model, detailed parameters are shown in Tables 3 and 4. Assuming that the tensile damage is elastic and can simulate the compression of concrete, and assuming that the concrete will have two different damage modes due to stretching or compression, the characteristics of the concrete in the force and deformation can be simulated more realistically. Meanwhile, the relevant distribution of cracks can be simulated by inputting the damage factors in the model. Embedding constraints were adopted to ensure that the steel reinforcement skeleton was connected to the concrete entity. Additionally, in this setting, the FLP3 model set the contact between the concrete surface, flange surface, bolt surface, and flange surface as the surface-to-surface contact. The normal and tangential contacts assumed \"hard contact\" and friction, respectively, to simulate the bonding and sliding between different components. To simulate the bonding and sliding between different components, since the bonding quality of the structural joint cannot be determined, the friction coefficient is determined to be 0.4 through multiple validations; the structure boundary is set with fixed constraints at the bottom of the floor according to the test conditions.  
Figure 8: FE model diagram of the test specimen.
# High-Performance Color-Converted Full-Color Micro-LED Arrays
## 2 Experimental Section  
Figure 1 illustrates the device structure of color-converted full-color micro LEDs based on mono-blue-color micro LEDs. It consists of bottom blue micro-LED arrays and a layer of two color (green and red)-conversion materials. The total array size is \(100\times 100\) pixels in a chip area of \(10\times 10\) mm\({}^{2}\). The chip size of the blue micro LED is \(60\times 100\) um\({}^{2}\) and the pitch length of each pixel is \(300\) um. A GaN-based LED structure (operating at a dominant emission peak of \(450\) nm) was grown on a sapphire substrate using metal-organic chemical vapor deposition. The LEDs consisted of a \(2\) um Si-doped n-GaN layer, a molecular quantum well (MQW) active layer consisting of five periods of undoped InGaN wells and undoped GaN barriers, and a \(0.15\) um Mg-doped p-GaN layer. To fabricate the LEDs, the p-GaN layer was etched using an inductively-coupled plasma (ICP) that utilized Cl\({}_{2}\) and BCl\({}_{3}\) source gases until the n-GaN layer was exposed for n-type contact. Further, the LEDs were fabricated using a \(200\) nm-thick indium tin oxide (ITO) layer as a transparent current spreading layer. Layers of Cr and Au with thicknesses of \(30\) nm and \(100\) nm, respectively, were deposited by e-beam evaporation as the n- and p-pad electrodes, respectively. The SiO\({}_{2}\) passivation layer was coated on the wafer for array electrode fabrication. Further, the SiO\({}_{2}\) layer on the n- and p-pad was selectively etched by buffer oxide etchant (BOE) until the n- and p-pad electrodes were exposed to the array electrode. Layers of Cr and Au with thicknesses of \(100\) nm and \(500\) nm, respectively, were deposited by e-beam evaporation as the n- and p-array electrodes, respectively. To avoid the color-crosstalk effect, black matrix was deposited between the blue LEDs by using simple lithography techniques.  
To fabricate conformal color-conversion layers, green, and red perylene bisimide were mixed with organic insulator material, a solution of acrylic resin, and a photoactive compound (PAC) with a positive tone of diazonaphthoquinone (DNQ). Perylene bisimide is known to have strong absorption in the visible region and high fluorescence quantum yields (QY) in diluted solutions, and high photochemical stability [12; 13; 14; 15]. N,N'-bis (4-bromo-2,6-diisopropylphenyl)-1, 6, 7, 12-tetra [4-bromophenyloxy] perylene-3, 4, 9, 10-tetracarboxylic diimide and N,N'-bis(4-bromo-2,6-diisopropylphenyl)-1, 6, 7, 12-tetra [4-Iodophenoxy] perylene-3, 4, 9, 10-tetracarboxylic diimide were used as the color-conversion layers for green and red emissions, respectively [12; 13; 14; 15]. With the conventional lithography process, red and green color conversion layers (CCLs) were successfully deposited on blue LED.
## 5 Implementation and User Scenario
### Functional Details  
AI-MDD-UX provides a dynamic and adaptive shopping experience for e-commerce platforms by facilitating real-time UI updates. Users interact with the e-commerce platform via a web page, mobile app, or VR interface. The front-end captures real-time interaction data (e.g., clicks, scrolls, session time) to analyze user engagement. This data is continuously monitored, allowing the system to adjust the UI dynamically based on pre-defined rules. The system applies K-Means++ clustering to segment users based on their interaction patterns. Once clusters are established, GANs generate adaptive UI layouts tailored to each group. The generated UI models undergo validation through multi-agent reinforcement learning, ensuring that the layouts align with user preferences and optimize engagement. Once validated, the layouts are passed to the code generation layer, where they are transformed into platform-specific specific. For instance, Figure 6a illustrates an example of automatic code generation for web (Reactjs), while Figure 6b shows code generation for mobile (Flutter).  
Figure 6: Code generation for (**a**) Web (React.js) and (**b**) Mobile (Flutter).
## 1 Introduction
[6] also presented magnetometer calibration with the genetic algorithm for industrial micro-controllers. Remotely operated underwater vehicles use attitude and heading reference systems (AHRSs). AHRSs use magnetometer and accelerometer data to determine the heading of a vehicle. For genetic algorithm calibration, bias errors, gain errors, and misalignment errors were considered. This method was verified with simulated and collected magnetometer data. It is shown that the genetic algorithm shows an improvement over the recursive least square method after testing on a mission. Most research has attempted to use two-step estimation [2], standard minimization [1], the hyper least square method, strict ellipsoid, and the three-step method [3]. However, in every case, the magnetometer calibration conditions are different. The majority of previous authors have used error models. The accuracy of calibration depends on the true magnetic field or the model used. [PERSON] and [PERSON] stated that the genetic algorithm shows an improvement in magnetometer calibration. Genetic algorithms have been applied to many areas successfully [7] (p. 3). Genetic algorithms are suitable for solving problems unable to be solved easily using classical methods such as least square methods [6; 7; 8]. Genetic algorithms are known as effective candidates for parameter selection for non-linear, non-continuous problems wherein a large number of unknown parameters must be found while local maxima can exist in the search domain. In order to observe the performance, we used the genetic algorithm for magnetometer calibration. The purpose of the paper is to use a genetic algorithm to calibrate the on-orbit magnetometer data collected by CubeSats. Our study focuses on calibrating on-orbit magnetometer data using the genetic algorithm. This method is tested with magnetometer data observed by BIRDS-3 CubeSats. This study discusses the accuracy and interchangeability of the answers found using the genetic algorithm for selected regions. Even though there remain limitations, this study demonstrates the level of calibration accuracy that can be achieved using the genetic algorithm for magnetometers installed in CubeSats. The novelty of the present work concerns applying the genetic algorithm to low-latitude regions (where the true magnetic field is expected to be similar to a model magnetic field) of on-orbit magnetometer data and checking the interchangeability of the parameters with other on-orbit data measured from the same satellite. Moreover, this work considers the reasons for the high oscillations seen in the total magnetic field measured on orbit. The present work can contribute to calibrating the on-orbit magnetometer data collected by CubeSats, aiding CubeSat developers in overcoming limitations when calibrating magnetometers. This paper consists of six sections. Section 1 is the introduction; Section 2 describes the materials; Section 3 describes the methodology used in this study; Section 4 describes the results obtained using the aforementioned methodology; Section 5 is the discussion; and Section 6 is the conclusion.
# Fair Benchmark for Unsupervised Node Representation Learning
## 6 Conclusions  
We propose a fair and comprehensive framework--FURL--to evaluate and rank existing unsupervised node representation learning methods. We design three tasks with different applications and hyperparameter-tuning settings. An integration schema is proposed to compute an overall rank based on the results in the three tasks. Currently, FURL has covered thirteen unsupervised NRL algorithms and eight datasets.  
In the future, we plan to include more NRL methods and datasets. We will extend this work by adding (1) more classical and state-of-the-art unsupervised node representation learning algorithms and (2) larger networks. The extended work will be dedicated to not only comparing the performance results on tasks, but also considering the efficiency (e.g., parameter efficiency, memory efficiency, learning efficiency) of unsupervised NRL methods. We will also explore to analyze the trend of performance while scaling the graph size, which will enable us to obtain deeper insight into the generalization ability of the algorithm.  
Conceptualization, X.H. and Y.X.; methodology, software, Z.G.; formal analysis, writing--original draft preparation, S.C.; validation, Z.Q. and C.Y.; writing--review and editing, X.H. and F.D.; supervision, X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded in full by the Hong Kong Polytechnic University, Start-up Fund (project number: P0033934).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Cora** & **Flickr** & **BlogCatalog** & **Citeseer** & **Pubmed** \\ \hline DeepWalk & 0.8011 & 0.1018 & 0.7496 & 0.5797 & 0.7328 \\ metapath2 vec & 0.8199 & 0.5643 & 0.7325 & 0.6382 & 0.7937 \\ node2 vec & 0.8518 & 0.5246 & 0.6982 & 0.6276 & 0.8239 \\ NetMF & 0.8408 & 0.5822 & 0.6902 & 0.5734 & 0.8230 \\ ProNE & 0.8459 & 0.6323 & 0.7133 & 0.6639 & 0.8194 \\ \hline GAE & 0.7858 & 0.0961 & 0.4425 & 0.5669 & 0.8144 \\ CAN & 0.8737 & 0.3979 & 0.7871 & 0.7126 & 0.8399 \\ DGI & 0.8399 & 0.2267 & 0.7167 & 0.7116 & 0.8545 \\ FeatWalk & 0.8413 & 0.5947 & 0.9122 & 0.6994 & 0.8488 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Results in Task 3 using TPE to tune hyperparameters, in terms of macro-averaged F1.
# The Improvement and Application of the Geoelectrochemical Exploration Method
## 4 Results and Discussion  
### Case Study on Yingezhuang Gold Deposit  
The DL-CHIM method was applied at Yingezhuang gold deposit from Shandong, 210 gold deposit from Xinjiang, and Daiyinzhang gold polymetallic deposit from Shanxi, China. The Yingezhuang gold deposit is located in the East of China, whose ore body is situated at a depth of between 100 m and 600 m. The average grade of the ore body is 4.03 g/t with a thickness ranging from 2 m to 10 m [13]. Figure 3a,b show the results obtained using the DL-CHIM method. It can be seen from Figure 3 that the DL-CHIM method identifies distinct anomalies of Au over the gold ore bodies, and that cationic species anomalies of Au have highest values (41.05 ppb) above the buried ore bodies, and anion anomalies of Au have high intensity and good continuity, which corresponds closely to ore body. Anionic species anomalies and cationic species anomalies of Au are much wider and have better continuity (sample numbers from 18 to 54), which can give anomalous responses from deep-seated mineralization. Anionic and cationic species anomalies of Au enable the identification of the position of the deeply buried ore bodies. The maximum anomalous value in cationic species anomalies of Au clearly corresponds to the gold-rich ore body of Yingezhuang gold deposit. In the thick transported covered terrains (sample numbers from 18 to 36), and anion anomalies of Au can show better results for concealed gold deposits in depth because gold may mainly exist as micro-nano particles in surface soils, which are negatively charged and can migrate to the designated electrode and be adsorbed in the foam sample under the action of an artificial electric field. The analytical results of this field work are tabulated in Table 1.  
Figure 3: Results obtained by employing the DL-CHIM method over Yingezhuang gold deposit from Shandong, China. (**a**) Anion anomalies of Au (Anode extraction); (**b**) Cation anomalies of Au (Cathode extraction); (**c**) geological base map [13].
## 3 Method  
GMN+ utilizes semantic comparison to identify code homology, revealing potential vulnerabilities within the code. The GMN+ primarily consists of three modules: the Instruction Preprocessor, Semantic Learner, and Graph Learner, as shown in Figure 1. The Instruction Preprocessor employs the advanced disassembly tool IDA Pro [41] to transform binary codes into disassembled instructions, normalizes these instructions, and extracts types of instruction opcodes and operands. Additionally, the CFGs of the binary functions are constructed during this step. The Semantic Learner generates semantic embeddings for function basic blocks using a BERT model, which is pre-trained using the Masked Language Modeling (MLM) and Instruction Type Prediction (ITP) tasks. The Graph Learner integrates these semantic embeddings with CFGs of functions to construct ACFGs. It utilizes intra-graph and inter-graph attention mechanisms to learn structural and matching information within and between functions, ultimately detecting function homology via semantic similarity.
###### Abstract  
The effectiveness of a single four-hole plate (S4 HP), perpendicularly oriented four-hole and two-hole plate (Per4H2 HP), and perpendicularly oriented double two-hole plate (PerD2 HP) for the fixation of a mandibular fracture was studied. A finite element analysis of the mandibular symbiosis fractures treated with S4 HP, Per4H2 HP, and PerD2 HP was performed. All surface nodes were fixed in the mandibular condejo region and occlusal muscle forces were applied. The maximal von Mises stress (MaxVMS) values of the plates, screws and screw holes were investigated. The displacement of the fracture site on the lower border of the mandibular symbiosis was recorded. The displacement on the lower border of the fracture sites in the S4 HP group was greater than that in the Per4H2 HP group and the PerD2 HP group. There was no eversion at the fracture site among all groups. Both the S4 HP and Per4H2 HP groups showed stress concentrations on the screws close to the fracture site. The MaxVMS increased when the number of screw holes on the mandibular anterior lower border decreased. The displacement of the fracture site and eversion with Per4H2 HP and PerD2 HP were far lower than those with S4 HP. PerD2 HP is a stable and green fixation technique for mandibular symphysis fractures.  
mandibular fracture; perpendicular double 2-hole plate; perpendicular 4-hole-2-hole plate; finite element analysis +
Footnote †: journal: Applied Sciences  
1 Department of Plastic and Reconstructive Surgery, E-Da Hospital, Kaohsiung 824, Taiwan; 1 [EMAIL_ADDRESS] (L.-R.C.); [EMAIL_ADDRESS] (Y.-P.H.)  
2 College of Medicine, I-Shou University, Kaohsiung 824, Taiwan3 Department of Biomedical Engineering, I-Shou University, Kaohsiung 824, Taiwan3*Correspondence: [EMAIL_ADDRESS]
## 4 Results
### Performance Comparison  
To compare the ensemble models using the different types of hyperparameter optimization, a violin plot was employed to visualize their performance. Violin plots offer valuable insights into the widths, lengths, and central points of the violins. Wider violins indicate a higher density of values, implying better performance, while longer violins suggest a larger spread of performance values. The central points represent the median performance, providing an estimate of the average. Outliers represent extreme values [48]. Figure 6 displays our comparison of the models' performance. It was observed that the extra tree model consistently outperformed all other models when the hyperparameters were optimized using grid search CV. This highlighted the remarkable effectiveness of the extra tree model, especially when fine-tuned using grid search CV hyperparameter optimization.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \multicolumn{4}{c}{**Optimal Hyperparameters**} \\ \hline Random Forest & \(bootstrap=\) ‘False’; \(class\_weight=\) ‘None’; \(max\_depth=\) 15; \(criterion=\) ‘gini’; \(max\_features=\) ‘sqrt’; \(min\_samples\_leaf=\) 1; \(min\_samples\_split=\) 2; \(n\_estimators=\) 200; \(random\_state=\) 42 \\ \hline Extra Tree Classifier & \(random\_state=\) 123; \(n\_estimators=\) 200; \(min\_samples\_split=\) 5; \(min\_samples\_leaf=\) 1; \(max\_features=\) ‘sqrt’; \(max\_depth=\) 15; \(criterion=\) ‘gini’; \(class\_weight=\) ‘balanced’; \(bootstrap=\) ‘False’ \\ \hline XGBoost & \(colsample\_bytree=\) 1.0; \(learning\_rate=\) 0.1; \(max\_depth=\) 7; \(n\_estimators=\) 100; \(random\_state=\) 123; \(subsample=\) 1.0 \\ \hline CatBoost & \(depth=\) 4; \(iterations=\) 500; \(l2\_leaf\_reg=\) 5; \(learning\_rate=\) 0.1; \(random\_state=\) 123 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The optimal hyperparameters for the algorithms as the result of hyperparameter optimization using grid search CV.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Cohen’s Kappa (\%)** \\ \hline Random Forest & 96.61 & 95.08 & 98.09 & 96.55 & 93.23 \\ \hline Extra Tree Classifier & 98.15 & 97.48 & 98.72 & 98.10 & 96.30 \\ \hline XGBoost & 96.301 & 94.48 & 98.09 & 96.25 & 92.61 \\ \hline CatBoost & 96.61 & 95.06 & 98.09 & 96.55 & 93.23 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The performance metrics for the algorithms using the optimal hyperparameters according to the grid search CV.  
Figure 5: The area under the ROC curve and PR curve of the algorithms using the optimal hyperparameters according to the grid search CV.
###### Abstract  
A self-homodyne coherent (SHC) transmission system that has a good prospect in terms of short-reach interconnections can simplify digital signal processing (DSP) and reduce the power consumption of laser diodes. However, the polarization control of the carrier becomes a pivotal part of these systems, and different from the traditional polarization control on a certain state of polarization (SOP), it only needs to lock the two polarization lights after the polarization beam splitter (PBS) in a state of equal power. Half-wave plates or Mach-Zehnder interferometers can accomplish the above goals. In order to evaluate the performance of these polarization control structures in the SHC system, we modeled them on the basis of theoretical analysis. Furthermore, a variable-step greedy linear descent (GLD) algorithm is proposed to solve the power fluctuation problem caused by the accelerated change of SOP near the pole of the Poincare sphere. The simulation results indicate that the variable-step GLD algorithm can effectively improve the tracking ability of the polarization control loop up to approximately 1.5 times of the GLD algorithm and the gradient descent (GD) algorithm.  
polarization control; self-homodyne coherent system; variable-step greedy algorithm; state of polarization rotation +
Footnote †: journal: _Photonics_
## 3 Fuzzy Adaptive Formation Control Design
### The Exclusion of Zeno Phenomenon  
In this part, the main content is to explain that there is no Zeno phenomenon in the fuzzy adaptive ET formation control method.  
According to [17], the event-triggered (ET) mechanism (26)-(29) dynamically determines when control signals are updated based on predefined thresholds. To ensure practicality, we must prove that the time between consecutive triggering events \(\tau=t_{i(k+1)}-t_{ik}\) is strictly positive and bounded below by a minimum interval \(\tau>0\), thereby excluding Zeno behavior.  
According to the error \(z_{irm}(t)=\tau_{im}(t)-\alpha_{im\tau}(t_{ik})\), one has  
\[\frac{d}{dt}|z_{irm}|=\dot{z}_{irm}sign(z_{irm})<\big{|}\dot{\tau}_{im}(t)\big{|},\ m=v,\omega\]  
All the signals are bounded in the NMRs and the actuators signals \(\tau_{iv}\) and \(\tau_{iu}\), are differentiable. Then, existing \(\partial\) can ensure \(\big{|}\dot{\tau}_{im}(t)\big{|}\leq\partial\) is valid, where \(\partial\) is a constant.  
By integrating, one has  
\[\int_{t_{ik}}^{t_{i(k+1)}}\big{|}\dot{z}_{irm}\big{|}dt\leq\int_{t_{ik}}^{t_{i( k+1)}}\partial dt,\ m=v,\omega\]  
It means that \(t_{i(k+1)}-t_{ik}\geq(\lambda_{\tau 1}|\tau_{im}(t)|+\lambda_{\tau 2})/\sigma= \gamma>0\) for \(\forall t\in[t_{ik},t_{i(k+1)})\). So there is no Zeno phenomenon in the fuzzy adaptive ET formation control method.