esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### TE and TM Mode Analysis  
The designed MMA was simulated in both TE and TM modes. Unit cell boundary conditions were applied for TM and TE simulation. Figure 7a-d presents the absorption, permittivity, permeability, and refractive index plot for all three modes, which show a feature of uniform absorption behavior. Both modes obtain the near-uniform metamaterial properties. Table 3 shows the metamaterial properties of the proposed absorber at the resonant frequency. The DNG metamaterial property appears at 14.12 GHz, 17.53 GHz, and 19.91 GHz in TM propagation mode. However, at 12.62 GHz, SNG behavior is exhibited. The dispersion diagram also validates the metamaterial properties plotted by Equation (11) [24], where d is the MMA unit cell thickness and the propagation phase constant. Figure 8 presents the dispersion curve of the designed MMA during TM mode simulation. The positive slope of the curve represents the right-hand (R) region or SNG metamaterial. The phase and group velocity are parallel in the R region. The DNG metamaterial behavior is represented by the negative slope in the left-hand (L) region, where group velocity and phase are antiparallel. In TM mode, the upper three frequencies, 14.12 GHz, 17.53 GHz, and 19.91 GHz, are located in the L region, which represents double-negative metamaterial behavior. The SNG behavior of the lower 12.62 GHz frequency is understood from the right-hand R region. The similarity of these two methods validates the metamaterial behavior of the MMA.  
\[\beta d=\cos^{-1}\biggl{(}\frac{1-S_{11}S_{22}+S_{21}S_{12}}{2S_{21}}\biggr{)} \tag{11}\]  
Figure 7: (**a**) Absorption, (**b**) permittivity (real), (**c**) permeability (real), and (**d**) refractive index (real) of the proposed MMA.
# Numerical Study on Redbed Slope Stability Under Multi-Factor Conditions
## 3 Study of the Deformation and Instability Modes of the Slope After Excavation and Backfilling
### Plastic Zone  
As shown in Figure 8, under the condition without a contact surface, the plastic zone only exhibits a small number of shear yield elements, and their location corresponds to that  
Figure 7: Numerical calculation contour plot of contact surface: (**a**) shear stress contour plot; (**b**) shear displacement contour plot; (**c**) sliding contour plot.  
of the shear strain increment plot. Under the condition of a contact surface, tensile yield elements appear in two weak interlayers. To accurately assess the slope safety factor and potential sliding surfaces of red-bed slopes, the strength reduction method is employed. The mechanical parameters of the rock-soil mass (cohesion and tangent values of internal friction angle) are continuously reduced to bring the slope to the critical sliding state, and the reduction coefficient at this point is considered the safety factor [35]. The safety factor of the slope is calculated using the strength reduction method (\"F-O-S\" command) in FLAC3D. The safety factor for the condition without a contact surface is 1.582, while it is slightly lower at 1.58 for the condition with a contact surface.  
From the above analysis, it can be concluded that due to the relatively strong overall mechanical properties of the red-layered soft rock slope, its stability is fairly safe, and large-scale shear or tensile failures are unlikely. However, small-scale landslides are prone to occur at the top, in the heavily weathered soil layers and weak interlayers.
# An Ultra-Wideband Frequency Selective Rasorber with Low Infrared Emissivity
## 2 Analysis, Design and Experimental Verification
### Design of Infrared Emissivity Reduction
The S-parameters of IRSL-FSR under \(\pm 30^{\circ}\) TM and TE waves were calculated, and the results are presented in Figure 18. It was observed that as the incident angle increased, there was a slight decrease in the bandwidth of the high-frequency transmittance band (greater than 15 GHz), while the low-frequency absorption effect (less than 3.8 GHz) deteriorated slightly. However, overall performance requirements were still met. Therefore, the introduction of IRSL has little impact on the polarization stability and angular stability of the structure. In summary, an ultra-wideband IRSL-FSR with low infrared emissivity is designed in this section. The structure has an ultra-wideband absorption band of 2.66-8.01 GHz, an ultra-wideband transmission band of 9.06-117.94 GHz, and an infrared emissivity of  
Figure 16: The structure and working principle of the whole IRSL-FSR (**a**) and its unit cell (**b**). Figure 17: Comparison of S-parameters between FSR and IRSL-FSR. Figure 18: S-Parameters of the IRSL-FSR vary with \(\pm 30^{\circ}\) TM (**a**) and TE (**b**) wave irradiation. 0.23 in the long-wave band. The design of broadband communication and radar infrared bistestalth is realized.
## 5 Results and Discussion
### Overlapping Triples and Nested Entities Experiment  
To validate the effectiveness of the model in addressing overlapping triples, further experiments were conducted on CMelE and DuIE2.0 datasets. The dataset's test data were partitioned into four subsets based on different overlapping types: normal, EPO, SEP, and SOO. Experiments were conducted on each of these four sub-datasets. The experimental results are depicted in Figure 2.  
The analysis of the CMelE dataset reveals that the SEO data show normal levels, while the EPO, SOO, and normal data exhibit an overall lower performance. Such a phenomenon may stem from the uneven distribution of the dataset, with a higher prevalence of SEO data and a scarcity of other datasets, leading to lower overall results. However, The GPRL model's performance is compromised when dealing with normal-type data, possibly due to error propagation. In this scenario, the relationship filter might extract inaccurate relationships, leading to incorrect ternary extractions. Despite this setback, the F1 values achieved for other types of overlapping data surpass those of other models. This outcome highlights the effectiveness of GPRL in addressing overlapping and nested entity challenges.  
In the analysis of the DuIE2.0 dataset, it is observed that all data types are within normal ranges. This points out that the lower performance in the CMelE dataset is due to the dataset itself rather than the model. When normal triples are extracted from DuIE2.0, performance drops again. When SEO triples are extracted, the model's performance is slightly better than the baseline model, and a significant improvement is observed over the baseline model when EPO triples are extracted. EPO involves entity pair overlap, which is a more complex form of overlap compared to SEO. This suggests that other baseline models exhibit decreased performance when handling more complex overlap scenarios, while GPRL performs better in such intricate situations. Furthermore, GPRL also stands out in extracting SOO triples, proving its ability to recognize nested entities better than others. Nevertheless, all models displayed a decreasing trend in addressing SOO challenges, indicating that there is still ample room for improvement in the recognition of nested entities.  
In the training process, GPRL and GPLinker presented better in the training process, while TPLinker_plus and PRGC incurred high training costs due to their intricate model structures. This further underscores the superiority of GPRL over other baseline models  
Figure 2: Overlapping ternary and nested entity result diagrams.  
across various aspects. The GPRL model proposed in this research excels in addressing overlapping ternary and nested entity challenges, demonstrating efficient and accurate extraction of ternary information compared to its counterparts.
# Large-Scale Modeling of Absorbing Aerosols and Their Semi-Direct Effects
## 3 Results
### Absorbing Aerosol Distribution
East Asia, US and Europe are strongly affected by anthropogenic aerosol emissions. In Central Europe and US anthropogenic aerosol emissions were reduced in recent decades by air pollution control measures [52]. AAOD in East Asia is higher in the model compared to the observations in summer and fall months. In the annual mean, AAOD values reach 0.02 in the observations and in the model. The annual means of the OMI and model AAOD distributions are shown in Figure 2. Model results are compared for the BASE and GFAS cases. In the annual mean, AAOD values reach regional values as high as 0.02 in observations and in the BASE case model. In the GFAS case the AAOD reaches annual mean values above 0.05 in the biomass burning region in Central Africa. Overall the model agrees with the regional distribution of the absorbing aerosol optical thickness compared to the OMI data, but has lower values for AAOD compared to the OMI observations for the BASE case, and higher values for the GFAS case. It should be noted that the model results are available at T63 resolution which is lower than the resolution of the OMI AAOD dataset at \(1^{\circ}\times 1^{\circ}\) resolution. Some subgridscale variability of the aerosol distribution may not be captured by the model, which may lead to an underestimate of simulated maximum AAOD. This discrepancy may be an indication that the aerosol absorption in the model is too low in the BASE case. This possible underestimation implies that the results for aerosol radiative effects can also be considered a lower limit for the effects simulated with this model setup. The GFAS case shows regionally higher AAOD values compared to the OMI retrievals and thus this 'high BC' case is used as an upper limit for BC forcing.
# The HD-Domain Metalloprotein Superfamily:
### HD-Domain Oxygenases
The substrate scissile bond is positioned above one of the iron sites, leaving the second site open for oxygen binding. PhnZ and TmpB contain an YxxE loop (green) in their primary sequence that is located vicinal to the active site, which upon substrate binding undergoes a conformational change to allow for oxygen binding and catalysis. forming a FeIII/FeIII superoxo species that initiates oxidative cleavage by abstracting a hydrogen atom from the substrate. Following turnover, the active mixed-valent FeII/FeIII form is regenerated [12], and thus, there is no need for an external reducing system to reactivate the enzyme, which is a feature unique to HD-domain containing oxygenases. Unlike MIOX, PhnZ and TmpB sequences contain a transient YxxE loop involved in substrate binding. Prior to substrate binding, the tyrosine (Y24 in PhnZ and Y30 in TmpB) is oriented toward the active site and binds to the Fe2 site, while the glutamate (E27 in PhnZ and E33 in TmpB) faces away from the active site [13; 15]. Substrate binding induces a conformational change, positioning the glutamate within hydrogen bonding distance to the amino group of the substrate and causing the tyrosine-iron bond to break [13; 15]. Dissociation of the tyrosine frees the Fe2 site for O2 binding and subsequent turnover and most likely serves as a protective mechanism to prevent oxidative inactivation of the active site (Figure 6). Collectively, HD-domain oxygenases have catalytic and structural features that differ significantly not only from that of other nonheme-iron oxygenases, but also of HD-domain hydrolases. This divergence is useful as it can provide some descriptors to distinguish oxygenases from hydrolases within the HD-domain family. It is likely that these characteristics are conserved among all HD-domain oxygenases and may provide a critical first step into the characterization of other HD-domain proteins of unknown function.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 3 Material and Methods
### Reference Data  
The reference data come from three sources: (1) farmer's declaration from 2006 (_Registre Parcellaire Graphique_ in French), (2) field campaigns performed during 2006, and (3) a reference map obtained with a semi-automatic procedure [61]. From these three reference sources, we extracted a total of 13 classes representing three winter crops (wheat, barley and rapeseed), five summer crops (e.g., corn, soy and sunflower), four natural classes (grassland, forests and water) and urban areas. Note that the reference map (3) is only used to extract the urban class, which is stable over time, and we checked the associated polygons visually.  
Table 1 displays the total number of instances per class at pixel- and polygon-level. It shows great variations in the number of available instances for each class where grassland, urban surfaces, wheat and sunflower predominate.  
The reference data are randomly split into two independent datasets at the polygon level where 60% of the data is used for training the classification algorithms and 40% is used for testing. To statistically evaluate the performance of the different algorithms, this splitting operation is repeated five times [62]. Hence, each algorithm is evaluated on five different train/test splits. To also take the variations from weights' initialization into account, we train the neural networks five times on each train/test split. The presented results are then averaged over the 25 runs (\(5\times 5\)).
## Materials and Methods
### Terrestrial Laser Scanning Data  
In order to extract the canopy structure factors of coniferous and broad-leaved mixed forest around the flux tower of Dinghushan Nature Reserve, a Trimble TX8 terrestrial laser scanner was used to scan the vegetation and to obtain the point cloud data. The scanner utilized a vertical rotating mirror based on a horizontal rotation with a scanning speed of 1 million points/second. The maximum standard measurement range was 120 m, the minimum measurement range was 0.6 m, and the noise of the measurement range was \(<\)2 mm. The angle range of the scanning field was 360\({}^{\circ}\)\(\times\) 317\({}^{\circ}\).  
The forest canopy structure was scanned and a total of six stations were arranged. The distance between every two stations was greater than 10 m (Figure 2). More than three target balls were placed between every two stations, and the three balls were not in the same row. The target ball was used as the feature point; the point cloud data stitching and registration between the two stations were completed. The point cloud data collected by the TLS scanning was like a hemispherical sphere with angular information. With the spectrometer at the center, the points within a radius of 10 m were selected for extraction of LAI\({}_{\rm e}\). First, the TLS data was divided into different parts according to the azimuth angle of 45\({}^{\circ}\). Second, we visually resolved each part and manually measured the height of the highest point on the ground, and manually removed the highest point and the points below the highest point. Finally, the residual points of the 3D point cloud data were projected onto the XOY plane. Third, the porosity of each part was calculated by the volume element method, and the second part of LAI\({}_{e}\) was calculated. Among them, based on [PERSON]'s law, the extinction coefficient was set to 0.5. In order to explore the effects of vegetation canopy structure on PRI and LUE, the LAI\({}_{e}\) under different classifications were calculated by the TLS data; the PRI spectral observation data of each record was graded based on the observed azimuth, divided into one every 45\({}^{\circ}\), and sliding was performed at an average of 10\({}^{\circ}\); there were 33 levels.
## Abbreviations  
\begin{tabular}{l l} d & Estimated object distance from LiDAR \\ \(I\) & Depth value function at pixel coordinate in the transparent Sparse Depth Image \\ \(u,v\) & Pixel coordinates in the image \\ \(\mathbf{R_{\text{rect}}}\) & Rectification matrix \\ \(\mathbf{R}\) & Rotation matrix \\ \(\mathbf{T}\) & Translation vector \\ \(X,Y,Z\) & Coordinates of points in 3D LiDAR data \\ \(z_{\text{min}},z_{\text{max}}\) & Minimum and maximum elevation values of LiDAR data \\ \(c_{\text{min}},c_{\text{max}}\) & Minimum and maximum color values for depth visualization \\ \(D_{\text{Bbox}}\) & Depth data extracted from the bounding box \\ \(d_{\text{min}}^{\text{Bbox}}\) & Minimum depth value within the bounding box \\ \(D_{\text{SegMask}}\) & Depth data extracted from the segmentation mask \\ \(d_{\text{min}}^{\text{segMask}}\) & Minimum depth value within the segmentation mask \\ \(x_{c},y_{c}\) & Central coordinates of the bounding box \\ \(R\) & Set of pixels inside the region for depth extraction \\ \(x_{c}^{\prime},y_{c}^{\prime}\) & Centroid coordinates of the polygonal segmentation mask \\ \(m\) & Number of cells in the grid (grid size) \\ \(w_{\text{cell}},h_{\text{cell}}\) & Width and height of grid cells \\ \(d_{\text{grid}}\) & Depth values extracted from the grid cell centers \\ \end{tabular}  
\(\Delta x_{1},\Delta y_{1}\) Differences in top-left coordinates of KITTI and YOLOv8 bounding boxes \(\Delta x_{2},\Delta y_{2}\) Differences in bottom-right coordinates of KITTI and YOLOv8 bounding boxes TP True Positive FP False Positive
## 3 Results and Discussion  
In an initial endeavor, we carried out the asymmetric Hantzsch reaction by condensing equimolar amount of 5-trifluoromethyl-1,3-cyclohexanedione, methylacetoacetate, unsubstituted benzaldehyde and ammonium acetate at 50 \({}^{\circ}\)C in trifluoroethanol. The reaction reached completion in 25 min (monitored by tlc) with 98% yield. Next, we extended the reactions using substituted aromatic aldehyde. The reactions were complete in 25 min as before with excellent yield regardless of the nature of the substituents on the aromatic ring. However when the reaction was carried out using butylaldehyde, the reaction was over in 50 min albeit in lower yields (entries 15, 16, Table 1)  
\begin{table}
\begin{tabular}{|c|c|c|c|c|} \hline
**Entry \({}^{a}\)** & **R** & **R\({}_{1}\)** & **Product** & **Yield (\%) \({}^{b}\)** \\ \hline
1 & & **Me** & **4a** & 98 \\ \hline
2 & & **Et** & **4b** & 92 \\ \hline
3 & & **Me** & **4c** & 91 \\ \hline
4 & & **Et** & **4d** & 90 \\ \hline
5 & & **Me** & **4e** & 90 \\ \hline
6 & & **Et** & **4f** & 89 \\ \hline
7 & & **Me** & **4g** & 95 \\ \hline
8 & & **Et** & **4h** & 96 \\ \hline
9 & & **Me** & **4i** & 97 \\ \hline
10 & & **Et** & **4j** & 93 \\ \hline
11 & & **Me** & **4k** & 99 \\ \hline
12 & & **Et** & **4l** & 91 \\ \hline
13 & & **CHO** & **Me** & **4m** & 97 \\ \hline
14 & & **Et** & **4n** & 84 \\ \hline
15 & & **Me** & **4o** & 79 \\ \hline
16 & & **Et** & **4p** & 72 \\ \hline
17 & & **CHO** & **Me** & **4q** & 90 \\ \hline
18 & & **Me** & **4r** & 90 \\ \hline
19 & & **Et** & **4s** & 97 \\ \hline
20 & & **Et** & **4t** & 98 \\ \hline
21 & & **Et** & **4u** & 97 \\ \hline \end{tabular} \({}^{a}\) All reactions proceeded to completion  
\({}^{b}\) Yield after recrystallization  
\end{table}
Table 1: List of Compounds Synthesized and their percent yields.  
Upon completion of the synthesis of fluorinated hexahydroquinoline derivatives, we explored the synthesis of hexahydroacridinedone under similar conditions (Scheme 2 below). The reaction of two equivalents of 5-trifluoromethyl-1,3-cyclohexanedione with one equivalent each of aromatic aldehyde and ammonium acetate proceeded with good yield to give 5a and 5b, as shown in Scheme 2.  
**Scheme 2.** Synthesis of fluorinated hexahydroacridinedone from 5-trifluoromethyl-1,3-cyclohexanedione, aldehydes, and ammonium acetate.  
The Infra-red spectra of all the products showed intense signals for carbonyl (1740-1690 cm\({}^{-1}\)) and NH (3500-3300 cm\({}^{-1}\)) functional groups. The detailed results are given in Table 1. The structure of **4d** was established by single crystal X-ray analysis (Figure 1) [12] and IR, NMR, and elemental analysis. The crystal data has been deposited in the Cambridge Crystallographic Data Center as supplementary publication number, CCDC 843861. This is the first report of such synthesis using 5-trifluoromethyl-1,3-cyclohexanedione, rather than dimedone.
## 4 Discussion
### Extending Techniques to Determine the Viability
#### 4.2.2 Morphostructure Determined by TEM in the Viability Assessment  
In some publications, electron microscopy was used to assess both the cell shape and morphostructural changes of _H. pylori_. The suggestion of the reduced integrity of the bacterial  
Figure 6: Physiological and morphological changes of coccoid _H. pylori_ forms contributing to difficulties in the proper interpretation of viability during the antibacterial activity determination. Symbols: extracellular DNA (eDNA), outer membrane vesicles (OMVs).  
cell structure based on observations using transmission electron microscopy (TEM) is, however, at risk of over-interpretation (Figures 6 and 7).  
Research using _Escherichia coli_ showed that a cell population consisting mainly of bacteria in the VBNC state had a lower number of ribosomes and a higher degree of protein oxidation than a population capable of a rapid resuscitation [77]. It was noticed that the reduction in ribosomes and DNA amount in VBNC _E. coli_ cells gave the impression of an empty cytoplasm, while in both studies where this process was observed cell death was excluded [78; 79]. In the first case by the lack of staining of such cells with propidium iodide [78], in the second case by the possibility to resuscitate them in culture media [79]. It is suggested that a very thin sectioning during TEM preparation may contribute to the apparent lack of small, highly condensed nucleoid and protein aggregates, giving the effect of empty cytosol (ghost cells) (Figure 6) [80]. Formation of these structures is promoted by the reduction in iATP and is crucial for the survival of microorganisms under stressogenic conditions [81].  
Although these reports concerned microorganisms other than _H. pylori_, it cannot be ruled out that a very similar process may also take place in this bacterium. This hypothesis may be partly confirmed by observations indicating an ability to generate free hydroxyl radicals and the endogenous oxidative stress-dependent aggregation of proteins and DNA by coccoid _H. pylori_ forms [82; 83].