esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
The methodology described here aims to develop land use recommendation and limitation mapping, as well as land use limitation degree mapping, for the southeastern margin of the Ria de Arosa (scale 1:50,000) (Coordinate system: Projected coordinates, ETRS89/UTM Zone 29N). To achieve this, comprehensive fieldwork was conducted, structured in various campaigns with specific objectives focused on improving the understanding of the physical environment and the external processes affecting it. This preliminary fieldwork was complemented by a photo-interpretation analysis of orthophotos (both historical aerial photographs from the American flight series (1956-1957), with an estimated spatial resolution between 0.5 and 1 m per pixel, and recent ones from the Spanish National Aerial Orthophotography Plan (PNOA), with a spatial resolution of 25 cm per pixel), along with an analysis of a high-resolution digital elevation model (DEM) (\(1\times 1\) m pixel) produced from LIDAR satellite data. The integration of these sources allowed for a detailed recognition, greater accuracy in synthesis, and improved resolution of the thematic maps of the physical environment factors, which play a key role in this study.  
Simultaneously with the field data collection, an exhaustive bibliographic and digital review was carried out, incorporating relevant thematic maps, such as the Spanish Forrest Map (MFE) and the Land Occupation Information System of Spain (SIOSE). All this information was integrated into a rigorous and structured geodatabase.  
Subsequently, spatial analysis was performed using a Geographic Information System (GIS) (ArcGIS Pro 3.4.2 ) (Figure 2). This GIS methodology allows for the integration and overlay of the different thematic maps of the study area through spatial analysis techniques. It is based on a multi-criteria decision-making (MCDM) analysis through the combination of multiple layers of information, applying quantitative methods (Weighted Overlay Analysis--WOA) and qualitative methods (Qualitative Overlay Analysis--QOA) depending on the nature of the data.  
By combining both cartographies, the land use recommendation and limitation mapping and the land use limitation degree mapping are produced. This methodological approach enables the development of a transparent and replicable process, facilitating its interpretation and application in various contexts. Its objective is to provide a clear and accessible tool for territorial planning, allowing the direct identification of priority areas for public agencies, environmental stakeholders, and the public, even for those without prior knowledge of environmental geology and geological hazard management.  
Figure 2: Methodological scheme.
## 3 Results and Discussion
### Controlling the Particle Distribution with Cylindrical Vector Beams
In the Table 1, we take the brightest fringes close to the dark annulus as the main peaks. The peak intensities are normalized by the intensity of the main peak, respectively. The light intensity of focused radially polarized beam is more concentrated in the main diffraction spot, as seen in Table 1. Furthermore, the radially polarized beam can be focused to a spot size significantly smaller than for linearly polarized beam [30]. The spot with a smaller size and stronger intensity will generate a stronger gradient force for the radially polarized beam. The gradient force generated by the central spot drives most of the particles into the center, which changes the whole diffraction pattern. The intensities of the high-order diffraction fringes are weak, and only the central spot can trap the particles. Finally, the particles are assembled to a  
Figure 5: Images of the 1 \(\upmu\)m particles assembly formed by the diffraction patterns of the cylindrical vector beams. (**a**) azimuthally polarized beam. The particles form a two-fan-shaped structure at the interface; (**b**) radially polarized beam. Most of the particles are driven into the center and form a compact structure at the interface. The amount of 1 \(\upmu\)m particles in the solution is 2 \(\times\) 10\({}^{8}\) cm\({}^{-3}\). Laser power is 450 mW; scale bar, 10 \(\upmu\)m.  
compact structure, as shown in Figure 5b. It can be seen that the size of the two-dimensional particles aggregation is much larger than the size of the central spot, but the particles still form a stable structure. For optical tweezers in the water, the particles far from the trap center can be attracted by the forces from the high order fringes. The attracted particles move towards the trap center and are finally trapped at the central spot. This is why optical tweezers can trap the particles far from the trap center. Furthermore, only the central spot has sufficient axial gradient force, and the other diffraction fringes do not have sufficient axial force to confine the particles in the water, so the fringes can not form stable three-dimensional traps, and the particles can only be trapped by the central spot in the water.
## 1 Introduction
In previous research studies on approximate LS and AS between two unidirectionally coupled chaotic systems without time-delay coupling, the true time-delay terms are first replaced by using their first-order approximations, which results in parameter mismatch between the drive and response systems. However, the value of the lag or anticipation time in approximate LS and AS significantly affects the synchronization condition for the two chaotic systems with such parameter mismatches. To guarantee synchronization, the value has to be limited to a very finite range. Furthermore, extending the result derived for two chaotic systems to larger arrays must also take into account possible instabilities caused by additional spatial modes. To overcome the disadvantage, the nature of the evolution of approximate LS and AS needs to be further studied in detail. In this paper, approximate LS and AS between two unidirectionally coupled hyperchaotic Chen systems [27] without time-delay coupling are investigated. A hyperchaotic system is classified as a chaotic system with more than one positive Lyapunov exponent or unstable direction, which generates high complexity. We first analyze the synchronization condition for exact LS in two unidirectionally coupled hyperchaotic Chen systems with time delay in the signal transmission from the drive systems. Differently from most research studies, LS is regarded as a special type of GS, which can be studied by using the auxiliary system approach [28; 29]. We show that the synchronization condition for exact LS analytically obtained in this paper is effective for any value of time delay in the signal transmission between two hyperchaotic Chen systems. Under such conditions, approximate LS and AS are discussed by replacing the true time-delay terms with their Taylor expansions up to the third order. Our research provides an effective method for recreating the past signals or predicting the future signals of a hyperchaotic Chen system by using its current signals. The rest of this paper is organized as follows: In Section 2, two unidirectionally coupled hyperchaotic Chen systems with time delay in signal transmission are introduced. In Section 3, the synchronization condition for exact LS in the drive-response system is derived by regarding LS as a special type of GS, in which the auxiliary system approach and Laplace transform, combined with the successive approximation approach [30] in the Volterra integral equation theory, are used. In Section 4, numerical simulations are presented to verify the effectiveness of the synchronization condition for exact LS obtained in this paper. Approximate LS and AS are discussed in Sections 5 and 6, respectively. The conclusions are finally drawn in Section 7.
# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 4 Experimental Results and Analysis
### Ablation Study  
We conducted an ablation study to verify the effectiveness of the proposed band selection. There are two modules in the proposed methodology known as Partition and Ranking. We conducted experiments by varying the modules (replace Proposed partition based on VIS, NIR, and SWIR regions into Coarser partition into 3 regions and replace Proposed ranking based on Entropy, NDVI, and NDWI into random selection). The ablation analysis for Indian Pines and Salinas data is shown in Table 7. The accuracy values show that the proposed band selection method has a positive impact on the hyperspectral classification.
# Thermal Interaction and Cooling of Electronic Device with Chiplet 2.5D Integration
## 3 Simulation Verification
### The Effect of Convective Heat Transfer Coefficient  
Fixing the spacing between chiplets as 0.1 mm and varying the convective heat transfer coefficient to be 2 kW/m\({}^{2}\)K, 5 kW/m\({}^{2}\)K, 10 kW/m\({}^{2}\)K, 20 kW/m\({}^{2}\)K, and 50 kW/m\({}^{2}\)K, the other parameters are kept constant. Figure 10 illustrates the temperature rise of the active and passive chips with different convective heat transfer coefficients. It is found that the convective heat transfer coefficient has a significant effect on chiplets. As the convective heat transfer coefficient decreases, both the temperatures of the active chip and the passive chip increase. A greater convective heat transfer coefficient is necessary to mitigate the thermal interaction between chiplets.  
The above simulation results demonstrate that the temperature rise of the passive chip increases as the spacing decreases. Utilizing high thermal conductivity TIM can effectively decrease the temperature rise of both active and passive chips and mitigate the thermal interaction between chiplets. Increasing the thermal conductivity of the interposer decreases the temperature of the active chip, increases the temperature of the passive chip, and exacerbates the thermal interaction. High convective heat transfer coefficients can effectively decrease the active and passive chip temperature, as well as mitigate the thermal interaction. To mitigate the thermal interaction, chiplet 2.5D integration necessitates the use of high thermal conductivity TIMs and liquid cooling. The results obtained from the simu  
Figure 10: Relationship between chiplet temperature rise and different convective heat transfer coefficients.  
Figure 9: Relationship between chiplet temperature rise and different chiplet thicknesses.  
lation are consistent with the trends predicted by the collective and distributed parameter model. The parameter models can provide a reference for chiplet 2.5D integration.
# Compact SOI Dual-Mode (De)multiplexer Based on the Level Set Method
## 3 Results
### Fabrication Tolerance  
During the CMOS fabrication process, particularly in lithography and etching steps, the pattern boundary may undergo dilated expansion or eroded contraction. Shown as Equation (6), we use \(\Delta\)w to represent the deviation:  
\[\Delta w=W_{dilated}-W_{origin}=W_{origin}-W_{ eroded} \tag{6}\]  
Figure 7 shows a schematic of the device with the fabrication deviation. In order to investigate the fabrication tolerance, we swept the deviation of the waveguides and the demultiplexer in the simulation to obtain the losses at the central wavelength 1550 nm.  
Figure 6: Spectrum of TE\({}_{0}\) and TE\({}_{1}\) dual-mode demultiplexer in the simulation.  
Figure 7: Schematic diagram of the deformation of the device’s pattern caused by the lithography and etching step in fabrication.  
The results presented in Figure 8a demonstrate that the ILs of two modes were below 1.8 dB in the \(\Delta\)w range of \(-100\) nm to \(100\) nm. Particularly, when \(\Delta\)w varied from \(-40\) nm to \(40\) nm, the ILs were consistently less than \(0.49\) dB. Additionally, the CTs within the range of [\(-40\) nm, \(40\) nm] were maintained at less than \(-20\) dB. Notably, the modern manufacturing processes in commercial SOI foundries allow for the effective control of geometrical variations within a \(40\) nm range. Consequently, our device remained stable even in the presence of manufacturing deviations.  
Similarly, we also estimated the deviation in the thickness of the top Si layer of SOI wafer. The actual thickness of the device typically fluctuated within a range of \(20\) nm above and below the original \(220\) nm. As shown in Figure 8b, with the variation in thickness, the IL was below \(0.24\) dB for TE\({}_{0}\) mode and \(0.22\) dB for TE\({}_{1}\) mode, while both two modes had CTs less than \(-25\) dB. Compared with the original-structure values of \(0.16\) dB, \(0.14\) dB, and \(-30\) dB for TE\({}_{0}\) mode, TE\({}_{1}\) mode, and the CTs, respectively, we observed no significant deterioration in the results.
# Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network
## 2 Related Work
For example, [PERSON] [23] proposed the fractional autoregressive integrated moving average (FARIMA) model, whereby the parameter \(d\) in FARIMA(\(p\),\(d\),\(q\)) can be obtained. When using FARIMA for prediction, the Hurst exponent of the time-series must first be identified. Furthermore, to predict the future network traffic size, the Hurst exponent, which portrays the self-similarity property, is introduced into the prediction algorithm; however, the ARIMA model itself has limitations, and its prediction effect is poor and not suitable for non-linear prediction.
# Low-Light Mine Image Enhancement Algorithm Based on Improved Retinex
## 5 Conclusions  
To address the issues of halo blurring, poor edge preservation, and serious noise in the traditional coal mine image enhancement methods, a new image enhancement algorithm is proposed. Firstly, the initial image is transferred to the HSV space, where the improved multi-scale guided filtering and Retinex algorithm are fused to effectively extract the illumination component for the brightness component, avoiding halo artifacts while retaining the edge details. Secondly, the extracted illumination component uses the Weber-Fechner algorithm for adaptive brightness correction, and the reflection component uses a fusion method for contrast enhancement and denoising, which suppresses noise while avoiding excessive enhancement. Thirdly, the saturation component is enhanced with the improved adaptive stretching method, and the color component is adjusted to prevent color distortion. Lastly, the image is converted back to the RGB space. The algorithm in this study proves that it can enhance the brightness of low-light images in coal mines while reducing the appearance of noise and halo artifacts, and also preserve edge details. Through this comprehensive processing, the enhanced image is better in terms of visual aspects and detail retention, which can better meet the needs of mine monitoring videos.  
Conceptualization, F.T. and M.W.; methodology, M.W.; software, M.W.; validation, F.T., M.W. and X.L.; investigation, F.T.; resources, F.T.; data curation, M.W.; writing--original draft preparation, M.W.; writing--review and editing, F.T. and X.L.; supervision, F.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shaanxi Provincial Science and Technology Plan Project (2020 GY-029).  
Not applicable.  
Data are contained within the article.  
The authors declare no conflicts of interest.
## 4 Evaluation
### Overhead
#### 4.2.1 Test Pipeline  
We have implemented an audio processing pipeline (Figure 4b) that resembles a real-world noise pollution monitoring sensor network (The Sounds Of New York City project or SONYC [49]), excluding the data encryption and upload functionality (bottom-left blocks with dashed lines in Figure 4b). In this setup, a mono microphone is recording one second long audio snippets that are supplied to the sound classification and sound pressure level (SPL) computation blocks for real-time audio analysis. The results are saved to CSV files alongside the raw audio data in 10 s intervals after rebuffering. The most computationally demanding is the sound classification block, where we use a CPU-only implementation of a neural network-based classification model to make the test pipeline compatible with a wider range of computing platforms that may not have GPU support. We have also placed the microphone block in its dedicated audio capture Task to avoid any overrun errors, as explained in Section 4.1.4.
###### Abstract  
This study investigated the memory-improving effects and mechanisms of action of hydrolysate from silkworm fed _Cudrania tricuspidata_ leaves (HSCT) in rats with scopolamine-induced memory impairment. Thirty-two rats were categorized into 4 groups and the experiment was conducted for 6 weeks. The experimental diet groups are as follows: control, control diet; scopolamine, control diet + scopolamine (0.7 mg/kg); HSCT, HSCT diet + scopolamine (0.7 mg/kg); and donepezil, control diet + scopolamine (0.7 mg/kg) + donepezil (1.0 mg/kg). Except for the control group, the experimental groups received scopolamine 0.7 mg/kg intraperitoneally to induce decline in memory. Animal behavioral tests such as the Barnes maze, novel object recognition test, and Y-maze were performed to investigate the effects of HSCT on memory improvement. In addition, brain acetylcholine (ACh) concentration and acetylcholinesterase-adenosine (AChE) activity were assessed to investigate the mechanism of memory improvement. Results of the animal behavior test suggested that the rats administered HSCT displayed improved memory behavior compared to those administered scopolamine (\(p<0.05\)). The concentration of ACh was increased in the HSCT group compared to the scopolamine group (\(p<0.05\)), and the activity of AChE was significantly reduced (\(p<0.05\)). These results suggest that HSCT effectively improves memory by reducing AChE activity and increasing ACh content in the brains of rats with scopolamine-induced memory impairment. As a natural compound, HSCT has the potential to be used as a material to improve memory.  
Keywords:silkworm; _Cudrania tricuspidata_ leaves; animal behavior test; memory improvement; acetylcholine; acetylcholine;acerylase +
Footnote †: journal: Applied Sciences
## 2 Equations of Motion for Flexible Multibody Systems
### Coordinate Reduction of Flexible Bodies
Neglecting the residual modes, the original generalized coordinate vector \(\mathbf{q}_{F}^{i}\) can be approximated using the reduced transformation matrix \(\overline{\mathbf{T}}_{0}^{i}\) as:  
\[\mathbf{q}_{F}^{i}\approx\overline{\mathbf{q}}_{F}^{i}=\overline{\mathbf{T}}_{ 0}^{i}\overline{\mathbf{p}}_{F}^{i}, \tag{16a}\] \[\overline{\mathbf{T}}_{0}^{i}= \begin{bmatrix}\boldsymbol{\Phi}_{d}^{i}& \boldsymbol{\
abla}^{i}\\ \boldsymbol{0}&\mathbf{I}\end{bmatrix} ,\overline{\mathbf{p}}_{F}^{i}= \begin{bmatrix} \mathbf{p}_{d}^{i}\\ \mathbf{q}_{b}^{i}\end{bmatrix} . \tag{16b}\]Using Galerkin projection with the transformation matrix \(\overline{\mathbf{T}}_{0}^{i}\), Equation (9) can be reduced as:  
\[\overline{\mathbf{M}}_{F}^{i}\overline{\mathbf{p}}_{F}^{i}+\overline{\mathbf{K}}_ {F}^{i}\overline{\mathbf{p}}_{F}^{i}=\mathbf{0}, \tag{17a}\] \[\overline{\mathbf{M}}_{F}^{i}=\overline{\mathbf{T}}_{0}^{i}\mathbf{M}_{F}^{i} \overline{\mathbf{r}}_{0}^{i},\ \ \overline{\mathbf{K}}_{F}^{i}=\overline{\mathbf{T}}_{0}^{i}\mathbf{K}_{F}^{i} \overline{\mathbf{T}}_{0}^{i}, \tag{17b}\]  
and the corresponding eigenvalue problem is:  
\[\overline{\mathbf{K}}_{F}^{i}\overline{\mathbf{\Sigma}}_{n}^{i}=\overline{ \chi}_{n}^{i}\overline{\mathbf{M}}_{F}^{i}\overline{\mathbf{\Sigma}}_{n}^{i} \ \ n=1,2,\cdots,\overline{N}^{i}. \tag{18}\]  
The number of DOFs for the reduced flexible body \(\overline{N}^{i}\) depends on the number of dominant modes \(N_{d}\) and the number of boundary DOFs \(N_{b}\) (\(\overline{N}^{i}=N_{d}^{i}+N_{b}^{i}\)). The number of dominant modes is much smaller than the number of residual modes, and \(\overline{N}^{i}\) can be dramatically reduced compared to the original DOFs \(N_{F}^{i}\). From the eigenvector \(\overline{\mathbf{\Sigma}}_{n}^{i}\), the original eigenvector \(\boldsymbol{\varphi}_{n}^{i}\) in Equation (10) is approximated as:  
\[\boldsymbol{\varphi}_{n}^{i}\approx\overline{\boldsymbol{\varphi}}_{n}^{i}= \overline{\mathbf{T}}_{0}^{i}\overline{\mathbf{\Sigma}}_{n}^{i}. \tag{19}\]
# Relating Sentinel-1 Interferometric Coherence to Moving Events on Grasslands
## 3 Results and Discussion
### Effect of Precipitation  
Analysis of the precipitation estimates results revealed that 3-h precipitation estimates described the interactions between the coherence and precipitation similarly to those of 1, 6, 12, and 24 h estimates. One reason for that could be the limited number of image pair samples available within 12 days after a moving event. Larger variation between 1 to 24 h estimates might have been observed, given more data. On the other hand, the precipitation can dry out rather quickly, depending on the temperature on the grassland. The optimal time window length before the image acquisitions has to be analysed with a more extended sample size of moving events. Given the similarity of the precipitation estimates of different time periods, only 3-h precipitation estimates were further used in this study.  
The relationship of coherence and 3-h precipitation estimates for the first image pair after a moving event is shown in Figure 5. When precipitation estimates were over 0.25 mm, \(\gamma_{vv}\) and \(\gamma_{vh}\) remained under 0.25 after a moving event with a few exceptions, and \(\gamma_{vv}\) had slightly higher values than \(\gamma_{vh}\). For further analysis, coherence values were divided into groups with 3-h precipitation estimates \(\leq\)0.25 mm and \(>\)0.25 mm. A threshold of 0.25 mm was chosen because it allowed all three image acquisition geometries to be divided into two groups based on 3-h precipitation estimate values, and these two groups can likely be associated with rain and rain-free conditions on the grasslands. Figure 6 shows that the medians of R160 VH and VV coherence in these two groups differ significantly. Precipitation up to 3-h before one or both of the image acquisitions changes the structure and dielectric constant of the vegetation on grassland, remarkably decreasing the coherence. Loss of coherence due to precipitation has also been reported by [PERSON] et al. [27] and [PERSON] et al. [42]. At the same time, R58 and R80 geometries did not show any significant differences--the notches around the median that represent 95% confidence intervals are overlapping. The 3-h precipitation estimate values were quite small before R80 and R58 acquisitions, and they might not have significantly affected the coherence. Additionally, the accuracy of the precipitation estimates has to be considered, as well as the small sample size. For R80, the median VH and VV coherence values were lower than 0.2 for the precipitation estimates \(\leq\)0.25 mm groups, so the drop of coherence could not be large for the precipitation estimates \(>\)0.25 mm group. The differences between VV and VH for this analysis were not significant.