esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Details  
Figure 1 shows the schematic layout of the pulsed Er:Y\({}_{2}\)O\({}_{3}\) ceramic laser. A pig-tailed \(\sim\)976-nm LD with a fiber core diameter of 105-\(\upmu\)m and a numerical aperture (NA) of less than 0.15 was used to pump the Er:Y\({}_{2}\)O\({}_{3}\) ceramic. The pump light was focused into the Er:Y\({}_{2}\)O\({}_{3}\) sample with a \(\sim\)420-\(\upmu\)m spot diameter through a 25-mm-focal-length lens F1 and a 100-mm-focal-length lens F2. The confocal parameter was estimated to be \(\sim\)26 mm. The physical length of the plane-plane cavity was 28 mm. The input coupler (IC) was high-transmission coated at \(\sim\)976 nm (T \(>\) 98%) and high-reflectivity coated at \(\sim\)2.7-\(\upmu\)m (R \(>\) 99.8%). Three flat mirrors with transmissions of 5%, 8%, and 20% at \(\sim\)2.7-\(\upmu\)m and high transmissions at \(\sim\)976 nm were employed as OCs. The Er:Y\({}_{2}\)O\({}_{3}\) ceramic (developed at Jiangsu Normal university) was synthesized by the solid-state reaction method and vacuum sintering followed by hot isostatic pressing [14]. The ceramic sample had a dimension of 2 \(\times\) 3 \(\times\) 12 mm and an Er-ion concentration of 7 atom % and was uncoated. It was mounted in a copper block that was cooled by water at \(\sim\)13 \({}^{\circ}\)C to allow for effective heat removal. A dichroic mirror (DM) coated with high reflectivity at the laser wavelength and high transmission at the pump wavelength was placed between the OC and the detector to filter out the unabsorbed pump power.  
Figure 1: Schematic setup of the Q-switched Er:Y\({}_{2}\)O\({}_{3}\) ceramic laser. LD: laser diode; IC: input coupler; OC: output coupler; DM: dichroic mirror.  
The mechanical Q-switch was a 1-mm-thick rotating metal plate carved with sagittal rectangular slits (see the inset of Figure 1), of which the rotating speed and slit number were variable from 1 to 134 rounds per second and 1 to 30, respectively. It was placed close to the ceramic sample, where the diameter of the laser mode was calculated to be \(\sim\)470-\(\mu\)m. The modulation frequency was adjusted by changing the slit number or (and) the rotating speed. The slit width was optimized to be 0.6 mm. The duty ratio of the Q-switch was continually tunable by moving the plate along its radial direction. Using such a mechanical Q-switch considerably increased the time for the inversion population to accumulate.
## 2 Numerical Models and Schemes
### Control Method of Varying Angular Speed  
During the runaway process, the rotational speed of the pump-turbine runner fluctuates for acceleration and deceleration. The change in rotational speed is obtained by the hydraulic force coupling method and uses the angular momentum equation to relate rotational speed variation with the torque exerted on the turbine runner. The process can be described as  
\[\frac{dn}{dt}=\frac{30}{\pi}\frac{T}{J} \tag{1}\]  
where \(J\) is the total rotational inertia of rotating systems, \(T\) is the hydraulic torque acting on the runner, and \(t\) is the time.  
Using an Euler approximation, the runner rotational speed can be calculated as follows:  
\[n_{i+1}=n_{i}+\frac{30}{\pi}\frac{T}{J}(t_{i+1}-t_{i}). \tag{2}\]  
During transient simulations, the torque on the runner blades were extracted from the CFD solver by a user defined function (UDF) at every time-step. Given the results at the \(i\)th time step, the rotation speed at the \((i+1)\)th time step \(n_{i+1}\) can be obtained from Equation (2). Thus, the rotational speed was updated at every time-step, and the corresponding flow fields were calculated.
## 2 Research on Flexural Strengthening with Textile Reinforced Concrete
### Calculation Model
#### 3.3.2 Design Tables for Calculation  
The iteration process described before can be time-consuming. Therefore, dimensionless design tables can be developed, e.g., References [46; 47]. Analogous to RC calculation tables, specific application limits must be taken into account, e.g., the concrete strength class and the kind of steel reinforcement. In the case of flexural strengthening with TRC, the characteristic material properties of the textile reinforcement (stress-strain relation) must be considered as well. That means, for every kind of textile, special design tables have to be created. For textiles that are frequently used, however, it is worth the effort to generate such tables because overall the handling is much more efficient compared to the iteration. For rectangular cross sections made of normal strength concrete and reinforcing steel acc. to EC2 [41; 48; 49] and carbon reinforcement acc. to Reference [25], (ed. 2016, characteristic ultimate strain \(\epsilon_{\text{tuk}}=0.0075\)), design tables were already developed ([PERSON] [46], stress-strain relation according to Figure 3a; [PERSON] [47] has used the more linear stress-strain relation of modern textiles acc. to Figure 3b, as well as higher design values for the tensile strength of carbon). In Table 2, a stress-strain distribution with constant Young's modulus according to Figure 3c was used, recommended by the authors and [PERSON] et al. [23]. Table 2 does not include reductions in regard to durability, temperature, and permanent load when determining the design value of the carbon material strength; for discussion, see Section 3.4. In addition, in Table 2, strain hardening of the steel reinforcement was not considered (horizontal branch of \(\sigma\)-\(\epsilon\) curve after reaching the yield strain); the preload \(\epsilon_{\text{t0}}\) is assumed to be zero. The handling is easy. First, it is assumed that the steel strains \(\epsilon_{\text{s1}}\) and \(\epsilon_{\text{s2}}\) reached or exceeded the yielding point. With Equation (6), the design bending moment \(\mu_{\text{t}}\) can be calculated:  
\[\mu_{\text{t}}=\frac{\text{M}_{\text{Edt}}+\text{F}_{\text{s1d}}\cdot\text{z} _{\text{ts1}}+\text{F}_{\text{s2d}}\cdot\text{z}_{\text{ts2}}}{\text{b}\cdot \text{f}_{\text{cd}}\cdot\text{d}_{\text{t}}{}^{2}} \tag{6}\]  
Forces and moment can be calculated as follows:  
\[\text{F}_{\text{s1d}}=\text{A}_{\text{s1}}\cdot\text{f}_{\text{yd}};\;\text{ F}_{\text{s2d}}=\text{A}_{\text{s2}}\cdot\text{f}_{\text{yd}};\;\text{M}_{ \text{Edt}}=\text{M}_{\text{Ed}}-\text{N}_{\text{Ed}}\cdot\text{z}_{\text{t1}}\]  
The textile reinforcement ratio \(\omega_{\text{t}}\) and the strains \(\epsilon_{\text{t}}\) and \(\epsilon_{\text{c2}}\) can be taken from Table 2. In the next step, the previous assumption for the strains \(\epsilon_{\text{s1}}\) and \(\epsilon_{\text{s2}}\) (yield range) must be checked with Equations (7) and (8).
## 2 Results
### The Binding Site for ppGpp on H-NS  
_E. coli_ H-NS is a protein with 137 amino acids. The three-dimensional structure of H-NS full sequence from any sources has not been reported yet. For _E. coli_, the NMR structures of fragment 2-58 (1 LR1), fragment 2-47 (1 NI8), and fragment 1-47 complexed with Hha (2 MW2) are available at the protein data bank. A structure of C-terminal fragment 91-137 (1 HNR) is also presented. The fragment 91-137 is monomeric, while all of the other fragments are described as dimers. The three-dimensional structures of three N-terminal fragments are very different, suggesting a flexible nature of H-NS. The docking with GalaxyWeb server did not provide the binding pocket when the above four structures were used as templates.  
H-NS from _Salmonella typhimurium_ is 95% identical to that from _E. coli_. The structure from an x-ray crystallographic study of H-NS fragment 1-83 (3 NR7) is available as a monomeric peptide. Only two amino acids, L79 and V82, are different in the fragment and the docking with GalaxyWeb server suggests the binding site for ppGpp. As shown in Figure 4, the amino acids at the N-terminal of H-NS are proposed to interact with ppGpp. Since the structure of the whole H-NS is not available, the relationship between binding site and the fluorophore W109 is unknown. The structural flexibility of H-NS suggests that H-NS may undergo a structural transition in the presence of ppGpp.  
Figure 3: Determination of the dissociation constant \(K_{\rm D}\) for ppGpp on H-NS by fluorescence spectrometry. (**a**) The emission spectra of H-NS in the presence of ppGpp. While H-NS (10\(\upmu\)M) was excited at 280 nm, the emission spectra in the presence of various concentrations of ppGpp were recorded at 25 \({}^{\circ}\)C. (**b**) The difference in fluorescence (\(\Delta\)F(area)) between 322 and 336 nm against ppGpp concentrations. The \(K_{\rm D}\) for ppGpp was calculated as 80 \(\pm\)11 \(\upmu\)M.  
\begin{table}
\begin{tabular}{c c} \hline
**Ligand** & \(K_{\rm D}\) (\(\upmu\)M) \\ \hline ppGpp & 80 \(\pm\) 11 \\ GDP & 72 \(\pm\) 6 \\ GTP & 120 \(\pm\) 25 \\ cAMP & 270 \(\pm\) 40 \\ ATP & NS 1  \\ \end{tabular}
\end{table}
Table 3: The \(K_{\rm D}\)’s for nucleotide ligands on H-NS.
# Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches
## 5 Discussion
For example, the models trained on FakeRom performed well when tested on FakeRom data but struggled significantly when tested on the NEW dataset, suggesting that FakeRom is more limited in scope. In contrast, models trained on the NEW dataset performed well not only on their own data but also on the FakeRom and combined datasets, underscoring the superior generalization capabilities offered by the NEW dataset. Another notable finding is the clear advantage of Transformer-based models such as BERT and RoBERTa. These models, when trained on the NEW dataset, achieved the highest performance metrics across accuracy, precision, recall, and F1 score. For instance, BERT trained on NEW reached 96.5% accuracy on the combined test set, while RoBERTa achieved 94.4% accuracy. The ability of these models to handle complex, nuanced patterns in text data makes them well suited for fake news detection, especially when trained on diverse datasets like NEW. The current methods and datasets exhibit several significant limitations in Romanian fake news detection. The models show considerable performance decline in cross-dataset scenarios, with accuracy dropping from 85.1% to 20.2% when tested on different datasets, indicating poor generalization capabilities. This is particularly evident when models trained on FakeRom are tested on content from the NEW dataset, suggesting insufficient diversity in training data to handle varied sources of fake news. Technical constraints also pose significant challenges. The models face computational resource limitations, requiring restricted batch sizes (8 for BERT, 16 for RoBERTa) due to memory constraints. Early stopping and hyperparameter tuning are limited by available computational resources, potentially preventing optimal model performance. The datasets themselves present limitations in terms of diversity and coverage, with FakeRom lacking sufficient variety to help models generalize effectively across different sources. The Romanian language content faces specific challenges, including limited dataset size and insufficient coverage of regional linguistic variations. These results clearly show that the NEW dataset offers more diverse and representative examples, allowing models trained on it to generalize more effectively across different datasets, whereas the FakeRom dataset is more limited in scope and leads to poorer cross-dataset performance.
# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
## 3 Methodology of the GRFN
### Construction of MMPSO Algorithm for Body Dimension Prediction
#### 3.3.3 Multi-Swarm Dynamic Regroup Strategy  
The independent learner group employs a dynamic regrouping strategy for multiple subswarms to generate learning exemplars. First, the dynamic regroup learning strategy of multiple subswarms is used to maintain population diversity. In each iteration, the velocity of particles \(i\) within each dynamic subgroup is updated using Equations (29) and (30) to enhance the global exploration performance:  
\[v_{id}^{(t+1)}=w_{2}v_{id}^{(t)}+c_{4}\tau_{4}(pb_{id}^{(t)}-x_{id}^{(t)})+c_{5 }\tau_{5}\beta_{i}^{ST}(pb_{sb,d}^{(t)}-x_{id}^{(t)}), \tag{29}\]  
\[\beta_{i}^{ST}=\left\{\begin{array}{ll}1,&\text{if }\alpha_{i}>0.5\\ 0,&\text{otherwise}\end{array}\right., \tag{30}\]  
where \(\omega_{2}\) is a self-adjusting inertial weight that controls the search range and improves the global search capability. \(c_{4}\), \(c_{5}\) are constants of 1.49445, and \(\beta_{i}^{ST}\) represents the coefficient of the social learning tendency of the independent learner group subpopulation as perceived by particle \(p_{i}\). When \(\alpha_{i}>0.5\), \(\beta_{i}^{ST}=1\) represents the social cognitive ability perceived by \(p_{i}\); otherwise, there is no social ability. Simultaneously, a nonuniform Gaussian mutation operator is applied as an update perturbation to augment the subpopulation's global search performance of the independent learner group during early iterations and improve the local exploitation ability in later iterations.  
\[\widetilde{\chi}_{id}^{(t)}=\left\{\begin{array}{ll}\chi_{id}^{(t)}+\Delta( t,N((ub_{d}-X_{id}^{(t)}),\sigma^{2}(t)),&\text{rand}<0.5\\ \chi_{id}^{(t)}-\Delta(t,N((X_{id}^{(t)}-lb_{d}),\sigma^{2}(t)),&\text{rand} \geq 0.5\end{array}\right., \tag{31}\]  
\[\Delta(t,y)=y\cdot(1-\text{rand}^{(1-t/T_{\text{max}})^{2}}), \tag{32}\]  
\[\sigma^{2}(t)=\sigma_{0}^{2}\cdot\exp(-(t/T_{\text{max}})), \tag{33}\]  
where \(\Delta(t,y)\) adaptively adjust the step length. \(\sigma_{0}^{2}\) is the initial variance and the symbol \(\sigma^{2}(t)\) denotes the particle's mutation amplitude in the \(t\)-th iteration.
# Research on Personalized Course Resource Recommendation Method Based on GEMRec
## 3 Graph-Enhanced Multimodal Recommendation Method: GEMRec
### Interpretable Methods for Graph-Semantic Enhancement
#### 3.4.1 SHAP-Based Feature Importance Analysis  
SHAP (SHapley Additive exPlanations) is a game theory-based method used to interpret the predictions of machine learning models. In our course recommendation system, SHAP values help us understand the contribution of each feature to the recommendation decision.  
We define a recommendation model f, which maps input features x to a recommendation score y, where x may include features such as the student's historical learning records, interest preferences, and node attributes within the knowledge graph. The core idea of the SHAP method is to calculate the marginal contribution of each feature to the final prediction. For a feature i, its SHAP value \(\phi_{i}\) is defined as follows:  
\[\phi_{i}=\sum\
olimits_{S\subseteq N}\frac{|S|!(|N|-|S|-1)!}{|N|!}\left[f(S \cup\{i\})-f(S)\right] \tag{36}\]  
where N is the set of all features, S is a subset of features excluding iii, and \(f(S)\) represents the model's prediction when only using the feature subset S.  
However, directly computing SHAP values has exponential complexity, making it challenging to apply in large-scale recommendation systems. To address this, we use an approximation algorithm that employs Monte Carlo sampling to estimate SHAP values. For each feature, we randomly select mmm feature subsets and compute the average marginal contribution as follows:  
\[\phi_{i}\approx\frac{1}{m}\sum\
olimits_{j=1}^{m}\left[f(S_{j}\cup\{i\})-f(S _{j})\right] \tag{37}\]  
where \(S_{j}\) is the j-th randomly selected feature subset. This allows us to approximate the importance of each feature in the recommendation decision.  
Figure 4: Workflow of interpretable methods for graph-semantic enhancement.
## 5 Conclusions  
In this paper, a distributed cooperative particle swarm optimization (DCPSO) algorithm was proposed to generate a set of spatial paths for multi-UAVs that execute formation rendezvous missions. The quantic PH curve was used as the path because of its curvature continuity. In the DCPSO algorithm, the particles in each sub-swarm updated their positions and velocities separately, then they communicated with other sub-swarms to modify their fitness to realize simultaneous arrival under constraints. Some key points are illustrated as follows:  
(1) Considering the kinematic constraints of UAV and collision avoidance, the proposed path planning method could generate simultaneous arrival paths for UAV formation rendezvous.  
(2) Compared with the cooperative co-evolutionary algorithm (CCGA), the proposed algorithm could jump out of local optimum easily and had a better stability.  
Figure 6: Paths of UAVs for formation rendezvous in 3-D environments. (**a**) 3-D view; (**b**) top view.  
Figure 7: Curvature and torsion variations with respect to path length. (**a**) Curvature; (**b**) torsion.  
**Author Contributions:** Conceptualization, X.Z. and Z.Z.; methodology, Z.S.; software, Z.S.; formal analysis, Z.S.; writing--review and editing, Z.S. and F.Y.  
**Funding:** This research was funded by Civil Aircraft Special Project (MJ-2015-F-009), National Key R&D Program in Shaanxi Province (2018 ZDCXL-GY-03004).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Numerical Analysis of the Take-Off Performance of a Seaplane in Calm Water
## 3 Numerical Model
### Grid Generation and Refinement  
The grid design and topology and appropriate refinements were found crucial in obtaining reasonable results. Trimmed cell mesher was applied to provide high-quality hexahedral cells in both the regions due to the hexahedral cells aligned with the flow direction and interface making less numerical diffusion. For accurately describing the boundary layer flow, prism mesh with 10 layers and a stretching ratio of 1.2 was generated near the wall. The first layer was set to keep wall y+ in the range from 30-100 and All y+ Wall Treatment was utilized in all simulations.  
Grid refinements were utilized for obtaining an accurate description of the flow field, particularly in the place where large flow gradient and complex flow occurred. Anisotropic hexahedral cells were used in grid refinements which allow specifying the cell size in each coordinate direction, which could achieve desired accuracy substantially reducing the number of grids in comparison with the isometric refinement approach. As demonstrated in Figure 5, besides the refinement in the air flow field, there were four refinement zones that are important for the description of water flow:  
* The water surface zone. A sharp free surface is the precondition of accurate estimation of hydrodynamic performance. The grids located at water level should be well-refined  
Figure 4: Computational domain dimensions and boundary conditions.  
to improve the resolution to capture the water surface. Note that a uniform cell height should be adopted at the free surface to avoid the smearing of the incoming water surface which is the mean source of numerical ventilation [18].
* The surrounding fuselage zone. In order to capture the complex flow near the hull, especially the spray flow at chine and the separation flow at step, the grids that encased the immersed surfaces of seaplane in the overset region were refined.
* The Kelvin wave zone. For capturing the Kelvin wave pattern induced by the seaplane, a triangular zone with a cusp angle of 39\({}^{\circ}\) was refined downstream of the seaplane. Otherwise, low-resolution grids would smear the wave patterns, resulting in an underestimation of wave resistance.
* The overlapping interface. A critical factor in successfully applying overset meshes is to have sufficient cells with uniform size and topology across the overlapping zone between background and overset regions. To improve the accuracy of interpolation, the overlapping interface in the background should be refined to match the size and the distribution of the grid at the interface of overset region, and the overlapping zone must contain at least 4-5 cell layers in both background and overset meshes.
# Self-Guided Pixel-Wise Calibration for Low-Light Image Enhancement
## 4 Experiment
### Experimental Settings  
**Comparison Methods.** Our method is compared to 10 state-of-the-art LLIE methods, which can be categorized based on the following three classes: traditional methods: LECARM [31], SDD [32], and GCP [33]; supervised methods: RetinexXIP [34], RetinexNet [35], and DRBN [36]; and unsupervised methods: Zero-DCE [20], RUAS [18], SCI [19], and PairLIE [17].  
**Benchmark Datasets.** Our method is tested on the LOL [35], SICE [30], MEF [37], LIME [10], DICM [38], and ExDark [39] datasets. In more detail, LOL [35] is the first dataset containing image pairs taken from real scenes for low-light enhancement. It contains 500 pairs of low-/normal-light images, where a validation subset of 15 pairs is selected. Moreover, SICE [30] consists of real captured multi-exposure sequences and corresponding high-quality synthesized reference images. In the Part 2 subset of SICE, the first three low-light images from each sequence are selected and they are resized to \(1200\times 900\times 3\), resulting in the use of a total of 687 pairs of low-/normal-light images. Furthermore, MEF [37] contains 17 image sequences with multiple exposure levels. As for LIME [10], it consists of 10 testing images. In addition, DICM [38] collected 69 images with commercial digital cameras. Finally, ExDark [39] is specifically built for low-light image recognition tasks, comprising 7363 low-light images, with a total of 12 annotated categories.  
**Evaluation Metrics.** The Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) [40] are deployed as supervised assessment metrics to perform objective evaluations for both LOL and SICE datasets. For datasets without reference images, no-reference image quality assessment metrics are applied, such as the Natural Image Quality Evaluator (NIQE) [41] and the No-Reference Contrast-Distorted Images Quality Assessment (NR-CDIQA) [42]. Based on these metrics, higher PSNR, SSIM, and NR-CDIQA values reflect better image quality, while lower NIQE values indicate improved visual quality.  
Experiment CategoriesTo comprehensively evaluate the effectiveness of the proposed method, four types of experiments are carried out: qualitative evaluation, quantitative evaluation, ablation study, and application test.
###### Abstract  
At present, numerous reflector antennas have been constructed worldwide on land. However, there are few applications of reflector antennas directly set off the coast. To expand the application region of reflector antennas, a floating cylindrical reflector antenna (FCRA) driven by the moving mass was developed to implement the elevation angle adjustment. Firstly, the structure design is introduced in detail. The design parameters are stated and analyzed to obtain the kinematic relationship while considering the water surface constraint. Then, the effects of each variable on the rotation capacity and structural stability are discussed. Further, the feasibility of the elevation angle adjustment process is demonstrated by using a prototype model test and software simulation. Finally, the deformation analyses and shape sensing of the back frame are carried out on the basis of the inverse finite element method (iFEM). We concluded that this new structure is feasible and expected to sit off the coast. In addition, the iFEM algorithm with sub-region reconstruction was proved to be suitable for the shape sensing of the over-constrained FCRA during the angle adjustment process via several quasi-static sampling moments.  
floating antenna; geared back frame; moving mass; iFEM; deformation reconstruction Article