esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Studies on the catalytic properties of gold nanostructures in oxidation reactions showed that such nanoparticles stabilized and well dispersed on various supports (metal oxides [1; 2; 3; 4; 5; 6], carbon materials [7; 8; 9; 10; 11; 12], metal-organic frameworks [13], zeolites [14], modified aluminums [15; 16], ionic liquids [17], etc.) are able to successfully catalyze several oxidation reactions, as well as the selective partial oxidation of cyclohexane.  
The preference for cyclohexane as a model substrate arises from the significance of the oxidized products (cyclohexanol and cyclohexanone) in the production of adipic acid, a building block for different processes in the pharmaceutical, chemical, and food industries. In fact, adipic acid is an important chemical commodity produced globally (over 3.5 million metric tons/year, growing ca. 5%/year) [18; 19] mainly in the synthesis of Nylon-6,6 polyamide (primary use), but can also be used in the production of polyurethane resins and polyester [18].  
Despite the efforts made to prepare and investigate Au nanocatalysts without any support to assess the activity of pure Au without support effects, the application of gold nanoparticles (Au NPs) in solution as catalysts for oxidation reactions is scarce, mainly due to aggregation issues that induce low reproducibility in the results. So far, [PERSON] et al. [20] has disclosed the use of Au NPs stabilized by a 1-dodecanethiol monolayer to catalyze the aerobic oxidation of cyclohexene to produce cyclohexene oxide (11%), alkyl hydroperoxide (84%), and allyl ketone (5%).  
It is known that catalytic properties of metal NPs are size- and shape-dependent [21; 22]; therefore, the ability to control sizes and morphologies of the Au NPs is an important issue in nanomaterial science. In this work, cetyltrimethylammonium chloride (CTAC)-stabilized Au nanotriangles (Au NTs) were synthetized by a seed-mediated approach and \"face-blocking\" growth mechanism [23].  
The above CTAC-stabilized Au NTs were tested for the microwave (MW)-assisted oxidation of neat cyclohexane to ketone and alcohol (KA) oil under mild conditions using a \"green\" oxidant (aqueous _tert_-butyl hydroperoxide, TBHP), in contrast to the current inefficient industrial harsher conditions [18; 24]. To our knowledge, this is the first time that the successful use of Au NTs for the oxidation of cyclohexane is disclosed.
## 2 Motivation and Preliminary
### Preliminary  
Roundtrip is a method based on cycle GAN and its framework is shown in Figure 2[20]. It contains four parts: generator network G and H, discriminators D\({}_{\mathbf{X}}\) and D\({}_{\mathbf{Z}}\). \(\mathbf{Z}\) is a latent variable drawn from normal distribution; \(\mathbf{X}\) denotes historical samples; \(\widetilde{\boldsymbol{Z}}\) and \(\widetilde{\mathbf{X}}\) denote generated latent variables and generated virtual samples. H transforms latent variables to virtual samples and G transforms historical samples to latent variables. Both of them are nonlinear transforms. D\({}_{\mathbf{X}}\) is employed to discriminate real and virtual samples. Likewise, discriminator D\({}_{\mathbf{Z}}\) discriminates real and virtual latent variables. D\({}_{\mathbf{X}}\) and D\({}_{\mathbf{Z}}\) determines their authenticity (real 0, fake 1). By minimizing the following six objective functions, G, H, D\({}_{\mathbf{X}}\), and D\({}_{\mathbf{Z}}\) achieve the optimal configuration.  
\[\widetilde{\boldsymbol{Z}} =\text{H}(\boldsymbol{X}) \tag{6}\] \[\widetilde{\mathbf{X}} =\text{G}(\boldsymbol{Z}) \tag{7}\]  
\[\left\{\begin{aligned} &\underset{\text{D}_{X}}{\text{min}}\;L_{1}= \text{E}_{\mathbf{X}\[PERSON] P(\mathbf{X})}\Big{[}\text{D}_{X}^{2}(\boldsymbol{X} )\Big{]}+\text{E}_{\mathbf{Z}\[PERSON] P(\mathbf{Z})}\Big{[}(1-\text{D}_{\mathbf{ X}}(\widetilde{\mathbf{X}}))^{2}\Big{]}\\ &\underset{\text{D}_{Z}}{\text{min}}\;L_{2}=\text{E}_{\mathbf{X} \[PERSON] P(\mathbf{X})}\Big{[}(1-\text{D}_{Z}(\widetilde{\boldsymbol{Z}}))^{2} \Big{]}+\text{E}_{\mathbf{Z}\[PERSON] P(\mathbf{Z})}\Big{[}\text{D}_{Z}^{2}( \boldsymbol{Z})\Big{]}\\ &\underset{\text{G}}{\text{min}}\;L_{3}=\text{E}_{\mathbf{Z} \[PERSON] P(\mathbf{Z})}\Big{[}\text{D}_{X}^{2}(\widetilde{\mathbf{X}})\Big{]}\\ &\underset{\text{H}}{\text{min}}\;L_{4}=\text{E}_{\mathbf{X} \[PERSON] P(\mathbf{Z})}\Big{[}\text{D}_{Z}^{2}(\widetilde{\mathbf{Z}})\Big{]}\\ &\underset{\text{G},\text{H}}{\text{min}}\;L_{5}=\text{E}_{ \mathbf{Z}\sim P(\mathbf{Z})}\big{[}\|\;\mathbf{Z}-\text{H}(\text{G}( \widetilde{\boldsymbol{Z}}))\;\|_{2}^{2}\big{]}\\ &\underset{\text{G},\text{H}}{\text{min}}\;L_{6}=\text{E}_{ \mathbf{X}\sim P(\mathbf{X})}\big{[}\|\;\mathbf{X}-\text{G}(\text{H}(\mathbf{X }))\;\|_{2}^{2}\big{]}\end{aligned}\right. \tag{8}\]  
where \(L_{1}\) and \(L_{2}\) aim to obtain well-performed discriminators; \(L_{3}\) and \(L_{4}\) supervise G and H to generate high-quality data, namely \(\widetilde{\boldsymbol{Z}}\) and \(\widetilde{\mathbf{X}}\); \(L_{5}\) and \(L_{6}\) measure the cycle consistency. It is noted that when \(L_{3}\) and \(L_{4}\) decrease, the generated data will move away from the  
Figure 1: Illustration of problem about sampling strategy and generative strategy.  
decision boundary and stay in high-density areas, which will produce data with high confidence but of low quality.
# Design of a Compact Planar Magnetic Levitation System with Wrench-Current Decoupling Enhancement
## 2 System Design Procedure
### Star Array Configuration  
After the square coil was designed, the arrangement of the planar coil array was considered. With a disc magnet mover, only five degrees of freedom can be controlled except for rotation about the vertical axis or yaw motion. The minimum number of coils  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Parameters** & **Value** & **Unit** \\ \hline
**Square coil** & & & \\ Outer width & \(D_{o}\) & 75.00 & mm \\ Inner width & \(D_{i}\) & 31.75 & mm \\ Outer radius & \(R_{o}\) & 27.625 & mm \\ Inner radius & \(R_{i}\) & 6.00 & mm \\ Magnet wire diameter & \(d\) & 1.31 & mm \\ Vertical winding turns & \(N_{vert}\) & 30–90 & turns \\ Height & \(H\) & \(d(N_{vert})+10\%\) & mm \\ \hline
**Disc magnet: 4-inch dia. \(\times\) 1/2-inch-thick neodymium magnet grade N50** & & \\ Diameter & & 101.6 & mm \\ Thickness & & 12.7 & mm \\ Remanence & \(B_{r}\) & 1.45 & T \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation (II): parametric simulation of the force between a square coil and a single disc magnet.  
Figure 4: Normalized Fabry factor (red \(\blacklozenge\)) and normalized z-component of the force (blue \(\blacksquare\)) on a 4-inch diameter \(\times\) 1/2-inch thickness disc magnet grade N50 with a 20 mm gap above the center of a square coil with different numbers of vertical turns.  
to control the levitation of a single disc magnet is five as shown in [22]. For the planar maglev system, the surrounding coils were added to form the planar coil array. The planar coil array stator in this design initially had 9 coils arranged in a 3 by 3 array or straight configuration. The coil pitch or distance from center to center of the coil along the \(x\) and \(y\) axes was 76.2 mm (3 inches) to maximize the packing density of the array.  
Two array configurations, namely straight and zigzag, were analyzed in this work. Both arrays had nine coils arranged with configuration parameters as detailed in Table 3. The zigzag array had a 38.1 mm (1-1/2-inch) offset for the middle row. The plots of the magnetic flux density at 20 mm above the two arrays are depicted in Figure 5. The significant difference between these two configurations can be observed at the corner between coils. The zigzag array could provide a better uniform magnetic flux density within the array than the straight array. In this work, the zigzag configuration was selected. An additional coil was added to the right side of the middle row for array symmetry.
# Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
## 2 Semantic Segmentation Models for Multi-Damage Detection  
To study the tradeoff between the model parameters and accuracy, we chose five deep segmentation models to detect multiple types of damage in concrete: three lightweight models (ENet, CGNet, and ESNet) and two heavyweight models (DDR-Slim and DeepLab V3+). Brief descriptions of these models are provided below.
# ML-Based Maintenance and Control Process Analysis, Simulation, and Automation--A Review
## 4 Discussion
### Directions for Further Research
* Deep learning architectures: further research can explore new solutions using their ability to automatically learn hierarchical representations and capture complex temporal relationships in brain signals. * Transfer learning: this involves enabling the transfer of knowledge from related tasks or domains to improve the performance of EEG classification models, especially in scenarios with limited labelled data, which can facilitate the generalisation of the model to different individuals, experimental conditions, and clinical populations. * Multimodal data fusion: integrating EEG data with other neuroimaging modalities, such as fMRI, MEG, or functional near-infrared spectroscopy (fNIRS), can provide complementary information about brain activity. Research into multimodal data fusion techniques could increase the spatial and temporal resolution of EEG-based classification models, improving their accuracy and interpretability. * Improved explainability: increasing the ability to interpret and explain EEG classification models is crucial to understanding the underlying neural mechanisms and building confidence in their predictions, as well as maintaining unambiguity between the user's intention and the BCI's interpretation of it. * Ethical considerations and user acceptance: as EEG-based ML applications become more common, it is crucial to address ethical issues related to privacy, data security,and user consent. This will translate into responsible implementation and adoption in different settings [70; 71; 72; 73; 74; 75]. It is worth noting that interdisciplinary research involving computer scientists, medical specialists, and psychologists is required to gain a comprehensive understanding of the neurophysiological and pathological mechanisms underlying the generation of specific EEG signal patterns and to integrate insights from different disciplines in order to develop more comprehensive predictive models and intervention strategies on the basis of the obtained insights (Table 9) [76; 77; 78; 79; 80]. So far, ML-based cognitive load management for human performance optimisation has been demonstrated. The accuracy of bidirectional gated network (BDGN)-based cognitive workload classification for EEG signals was 98%, confirming the effectiveness of the suggested BDGN approach. It is a practical solution to scheduling cognitive workload management solutions in Industry 5.0 applications [51; 52; 53]. In the case of neurodegenerative diseases and their assessment based on EEG signals, lower values were achieved. The Tresnet network yielded an accuracy of 86.9% for Alzheimer's Disease, which outperforms previous approaches [81; 82; 83; 84; 85; 86].
# APT Detection via Hypergraph Attention Network with Community-Based Behavioral Mining
###### Abstract  
Advanced Persistent Threats (APTs) challenge cybersecurity due to their stealthy, multi-stage nature. For the provenance graph based on fine-grained kernel logs, existing methods have difficulty distinguishing behavior boundaries and handling complex multi-entity dependencies, which exhibit high false positives in dynamic environments. To address this, we propose a Hypergraph Attention Network framework for APT detection. First, we employ anomaly node detection on provenance graphs constructed from kernel logs to select seed nodes, which serve as starting points for discovering overlapping behavioral communities via node aggregation. These communities are then encoded as hyperedges to construct a hypergraph that captures high-order interactions. By integrating hypergraph structural semantics with nodes and hyperedge dual attention mechanisms, our framework achieves robust APT detection by modeling complex behavioral dependencies. Experiments on DARPA and Unicorn show superior performance: 97.73% accuracy, 98.35% F1-score, and a 0.12% FPR. By bridging hypergraph theory and adaptive attention, the framework effectively models complex attack semantics, offering a robust solution for real-time APT detection.  
seed nodes; hypergraph; HyperGAT; overlapping community; APT +
Footnote †: journal: Applied sciences
### Nb-IoT  
A LPWAN technology called narrowband-IoT (NB-IoT) coexists in licensed frequency bands with GSM or LTE [10]. NB-IoT provides a range of operating modes, such as standalone, guard band, and in-band operation, within a 200 KHz frequency bandwidth [11]. It uses orthogonal frequency-division multiple access (OFDMA) in the downlink, FDMA (Frequency Division Multiple Access) in the uplink, and QPSK modulation [12]. NB-IoT reduces LTE functionalities to the minimum amount that is required for IoT applications based on the principles of the LTE protocol. With a maximum downlink throughput rate of 200 kbps and an uplink throughput rate of 20 kbps, it permits over 100,000 devices to be connected per cell. It has 1600 bytes of maximum payload size [8].
## 2 Related Works
### Deep Learning-Based Remote Sensing Imagery Methods
#### 2.2.5 Far Seg++  
[PERSON] et al. [12] introduced a foreground-aware relation network (FarSeg++) to address specific challenges in the segmentation of remote sensing imagery with high spatial resolution (HSR). FarSeg++ aims to mitigate the intra-class variance in backgrounds and reduce the imbalance between foregrounds and backgrounds.  
FarSeg++ integrates a relation module for the foreground scene to enhance the discrimination of features for the foreground. This is achieved by considering correlated contexts of the foreground and the linked object-scene relations. This relation-based approach aims to improve the feature representation of the foreground, thereby aiding in accurate segmentation.  
The optimization process is foreground-aware, concentrating on foreground examples and challenging background instances during training. This approach strives for balanced optimization in light of the foreground-background imbalance often observed in remote sensing imagery.  
To address objectness prediction issues, a decoder is used for dealing with the foreground. This foreground decoder is designed to better represent objectness, which contributes to alleviating problems related to predicting objectness in challenging remote sensing scenarios.  
Extensive experiments were conducted to evaluate the robustness and efficiency of FarSeg++. The results indicated that FarSeg++ outperforms state-of-the-art generic semantic segmentation methods. Moreover, FarSeg++ can attain a better balance between segmentation accuracy and speed, which is a practical consideration for many real-world applications.
## 3 Model Descriptions  
### Construction of Mineral Grains of Different Shapes  
The basic particle shape is a circle, but noncircular grains can be achieved by the combination of two or more circular particles using the CLUMP method. In this method, grains with irregular shapes can be represented as single objects, which means the particles within a clump may overlap to any extent, but contact forces are not generated between these particles. The mineral grain is internally equivalent to a rigid body, and there is only a slight deformation at the contact boundary between the mineral grains. Considering the complexity of real mineral grains, it is difficult to simulate each mineral grain shape. Therefore, some simple grain shapes were adopted in this study. As shown in Figure 3, three mineral grain groups of different shapes were constructed: strip, triangle, and square. There were six grains of similar shapes in each group.  
1. The strip-shaped grain was composed of two circular particles of the same radius \(R\). The six grains in this group were represented as S1-S6. From S1 to S6, the distance \(d\) between the centers of the two circular particles gradually increased, and the ratios of \(d\) to \(R\) were 0, 0.25, 0.5, 1.0, 1.25, and 1.5, respectively.
2. The triangular grain was composed of three circular particles of the same radius \(R\). The line connecting the centers of the three circular particles was an equilateral triangle with a side length of \(d\). The six grains in this group were represented as T1-T6. From T1 to T6, the value of \(d\) was gradually increased. The variation of \(d\)/\(R\) was consistent with the strip-shaped grains.
3. The square grain was composed of four circular particles of the same radius \(R\). The line connecting the centers of the four circular particles was a square with a side length of \(d\). The six grains in this group were represented as R1-R6. From R1 to R6, the value of \(d\) was gradually increased. The variation of \(d\)/\(R\) was also consistent with the strip-shaped grains.  
Taking the strip-shaped grain as an example, the circular particle S1 could be directly generated, but grains S2-S6 needed to be generated by the CLUMP method of PFC2D. In the CLUMP method, the circular particles were first created, then the circular particles were replaced with grains S2-S6. The replacements followed three principles: \"area equivalence\", \"area center equivalent\", and \"directional randomization\". This means that the area and centroid of grains S2-S6 were the same as grain S1, but their orientations were random.
# Efficiency Enhancement of GaAs Single-Junction Solar Cell by Nanotextured Window Layer
## 4 Conclusions  
In conclusion, the planar ARC and NT InAlP window SC increase the light harvesting more than the bare surface SC, which was found from the suppressed reflectance, enhanced EQE spectra, and \(J_{\text{SC}}\). In particular, the NT surface decreases the reflectance and effects across the broad \(\lambda\) range, containing the whole \(\lambda\) spectrum of the GaAs 1J SC. In addition, the NT SC was distinguished from the ARC SC by comparing the alleviated \(\eta\) loss of the NT SC with the \(\eta\) loss of the ARC SC as \(\theta\) increased, which followed the purpose of this  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **V\({}_{\text{OC}}\) (V)** & **J\({}_{\text{SC}}\) (mA/cm\({}^{2}\))** & **FF** & \(\eta\) (\%)** \\ \hline Bare & 0.91 & 21.50 & 0.71 & 13.81 \\ ARC & 0.92 & 28.94 & 0.72 & 19.07 \\ NT & 0.93 & 28.40 & 0.74 & 19.44 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Voc, Jsc, fill factor (FF), and \(\eta\) for J–V curves as shown in Figure 6.  
Figure 7: Efficiencies’ dependence on \(\theta\) for the bare surface SC, SC with SiNx ARC, and InAlP NT surface.  
study to investigate the omnidirectionality of the NT surface on SCs. The presented concept and manufacturing technique are expected to benefit high-efficiency flexible SCs.  
Conceptualization, C.-W.K. and H.-J.K.; methodology of nanotexturing, C.-W.K. and H.-J.K.; fabricating the planar solar cell, G.-Y.P.; growing solar cells by MOCVD and H.-J.K.; calculation, J.-C.S.; investigation, C.-W.K.; writing--original draft preparation, C.-W.K.; writing--review and editing, H.-J.K.; supervision, H.-J.K.; funding acquisition, H.-J.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Technology Development Program to Solve Climate Changes of the National Research Foundation (NRF) funded by the Ministry of Science ICT (2017M1A2A2048903), the Leading program of the National Research Foundation (NRF) funded by the Ministry of Science (2021M3H4A1A02051253), and Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 70300026).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results
### Results of Numerical Simulation  
Figure 6 plots the comparison between the simulation and experiment of the holding force for the upstream water depth of 85 m. The difference between the simulation and experiment was small, and it was slightly large before the gate was completely closed. The relative error can meet the precision requirements, which shows that the method adopted in this study can be used to simulate the closing process of an emergency gate.  
Figure 7 depicted the displacement of the emergency gate with time in vibration closing for the service gate height of 4.0 m. In closing process, the displacement approximately decreased with time in a step form and was intermittent motion in visual, which was consistent with the actual gate-closing process [19]. That means that the fluid-structure interaction numerical model can reflect the gate vertical vibration characteristic in the closing process. Because of the gate vertical vibration, the closing speed was slightly less than the specified speed \(v_{0}\). The falling displacement was small and the closing time was slightly increased.  
Compared to the two closing modes, the variation of the flow discharge was consistent in general. Based on the formula \(Q=\mu e_{1}B\sqrt{2 gH_{0}}\) (where \(\mu\) is the coefficient; \(e_{1}\) is the opening height; \(B\) is the orifice width; \(H_{0}\) is the moving water head; and \(g\) is the acceleration of gravity), the flow discharge has a roughly linear relationship with the opening height for the constant upstream water depth. However, Figure 8 describes the characteristics  
Figure 6: Comparison of experiment and simulation: (**a**) service gate height 1 m; (**b**) service gate height 4 m.  
Figure 7: Closing displacement with time.  
of flow discharge and velocity of the monitoring 'flow1'. It can be seen from Figure 8 that the discharge was mainly affected by the service gate height and upstream water depth. The relationship between discharge and opening rate had two regions: linear and transitionary. In the initial stage, the discharge had a linear relationship with the opening rate accompanied by regular fluctuations. The fluctuation decreased as upstream water depth increased. There was also a linear relationship with the small gate opening and it had no significant fluctuation relationship. In the middle, the relationship was nonlinear. Moreover, the velocity gradually increased as the opening rate decreased, and the velocity with a significant fluctuation was only in the small gate opening.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 5 Wire-Grid Sparse Antennas
### Overview
However, the comparison of the results of these two approaches often demonstrates excellent suitability of the wire grid [293] further, in solving practical problems, the computational cost can be significantly reduced with good simulation accuracy even compared to the experimental data [294; 295]. One of the most important advantages of using a wire grid is the possibility of obtaining results when analyzing the scattering of large objects (e.g., ships, aircraft). Thus, the analysis of how the quality of wire-grid construction influences the results of modeling such objects was performed in the paper [296]. The general principles and conditions were established, and recommendations on applicability of such meshes were given. Their implementation allows for building a better grid and takes into account the features of the structure to be analyzed, while obtaining correct simulation results including the use of nonlinear wires [297]. The use of wire grids is not limited to conductive objects. Thus, they were considered for obtaining a scattered field by lossy dielectric objects [298] and anisotropic layer structures [299]. The \"weak point\" in the use of wire grids is the modeling of near fields [300]. In this case, as shown in [17], the check of the solution accuracy based on the coincidence of the wire boundaries is not always correct.