esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Construction of the CDI  
#### 3.1.1 Response of SIF to Different Drought Indices  
The results of the correlation analysis between various drought indices and SIF during the growing season in NEC from 2000 to 2020 (Figure 4a-g) showed that each drought index in the study area was mainly positively correlated with SIF (more than 80.9% of grids), especially in farmland and grassland, and the significant (_p_ < 0.05) positively correlated regions were mainly distributed in the central and western part of the study area, indicating that drought would suppress photosynthesis in crops. The proportion of grids with a positive correlation of VCI, VHI and CDI with SIF in the NEC exceeded 98.6%, probably because VCI and SFI are both vegetation indices of remote sensing inversion, and the contribution of VCI to VHI and CDI is larger, with the weight of VCI constituting VHI being 0.7, and the weight of VHI constituting CDI being 0.56. In some woodland areas, there was a negative correlation between SIF and TCI, SPEI, SMCI, and scPDSI. This is  
Figure 3: Probability density (**a**) and survival probability (**b**) distributions of the CDI.  
likely because forests are more resilient to short-term droughts than are crops and grass. When the main factor limiting photosynthesis in plants is not water, temperature and rainy weather with precipitation will lead to lower temperatures and reduced radiation, thus limiting photosynthesis in forests, which also indicates that these drought indices are more applicable in farmland and grassland than in woodland. In contrast, CDI and SIF were predominantly significantly and positively correlated in the forest region, suggesting that the comprehensive index extends the applicability of drought monitoring to some extent. The statistical results of the correlation analysis between each index and SIF in the farmland and grassland areas (Figure 4h) showed that the correlation between SIF and VHI (r = 0.58) increased compared to VCI (r = 0.56) after considering TCI. The correlation between SIF and SPEI (r = 0.12) was the weakest, followed by SMCI (r = 0.49). The correlation between SIF and CDI (r = 0.65) was the strongest correlation and was significantly higher than that of the commonly used drought index scPDSI (r = 0.24), indicating that CDI combines the advantages of soil, vegetation, and meteorological drought indices; considers the effect of temperature on drought; and can be used for agricultural drought identification in NEC.  
Figure 4: Correlations (**a**–**g**) and statistics (**h**) of different drought indices with SIF in NEC, 2000–2020. (Dotted areas indicate \(p<0.05\) significant correlations, dashed lines indicate median correlation coefficients of all grid points in farmland and grassland areas.)
## 1 Introduction
If the energy leakage of the resonators is balanced by the inherent viscothermal losses, the energy is trapped inside the resonator, dissipated in heat energy, and maximal absorption can be obtained [36]. This balance of energy leakage and losses is known as the critical coupling condition and, in the particular case of the reflection problem, corresponds to an impedance matching of the resonator with the surrounding medium. In this work we design 2D arrays of Helmholtz resonators with optimized absorption properties at low frequencies giving rise to an improved sound diffusion in the audible regime. These two mechanisms (absorption and dispersion) could be combined to improve the previous results presented in reference [14] where only the diffusion is playing a role in the sound mitigation process. We start the analysis with a simplified 1D model where the plane wave approximation is used to design an array of Helmholtz resonators that present perfect absorption for a targeted range of frequencies. The system is optimized by tuning the geometry of the Helmholtz resonators, i.e., by tuning the viscothermal losses of each element, and perfect sound absorption is reported analytically and experimentally. Then, the system is numerically implemented in a 2D environment in which we validate the hypothesis previously assumed in the 1D modeling to design an array of Helmholtz resonators, and we evaluate both the absorption and diffusion properties of the array. Furthermore, due to the fact that the system is discrete in 2D, it combines the diffusive properties of the array with the perfect sound absorption. These two mechanisms increase the performance of the system with respect to the one previously reported in reference [14].
## 1 Introduction
[PERSON] [17] addressed the problem of the vibration resistance of MEMS IMUs in high-g shock environments. He embedded gyroscopes and accelerometers into a symmetric hexahedral frame and utilized a viscoelastic-damping damper to achieve internal vibration damping. [PERSON] et al. [18; 19; 20; 21] addressed the problem of damper-damping matching in a vehicle's steel plate spring suspension system. They established mathematical models of the suspension system for the optimal damping ratio, and the root mean square values of dynamic deflection and vibration velocity. The optimal damping ratio of the damper in the suspension system was determined. [PERSON] [22] proposed four new passive vibration control damping forms based on inertia-spring-damping (ISD) structures. Through the establishment of dynamic equations and the acquisition of a steady-state amplitude amplification factor, the optimal damping ratio and stiffness ratio of the system were obtained. [PERSON] and [PERSON] [23] presented a new dry friction shell shock absorber design. Such shell shock absorbers are projected to be used in the mining, oil and gas industries. The development of the above damping methods is more mature, and they have good damping effects in related fields. However, these methods are mostly applied to vibration suppression in precision instruments and have not yet been utilized in the more severe environment of low-frequency, high-intensity impact vibrations. Accordingly, this study proposes a vibration-damping system, which is based on non-dominated sorting genetic algorithm (NSGA II) parameter optimization. This method is designed to solve the problems relating to the significant longitudinal vibrations in pneumatic DTH hammer drilling processes. First, the forced vibration model of a short-section circuit unit under complex drilling conditions was constructed and analyzed. Then,the vibration-damping model of the circuit unit was established via model simplification, the separation of variables, the superposition of vibration shapes, etc. For the problem relating to the complexity of the model parameter solution, the forced vibration full response function of the circuit unit was used for analysis. This study proposes two key parameters, the logarithmic attenuation rate of the amplitude and the displacement amplification factor, with both affecting the performance of the vibration-damping system. NSGA II is used to find the optimal stiffness-damping parameters of the system. The optimal stiffness and damping parameters of the system are derived using NSGA II(Surrogates and Evolutionary Algorithms Laboratory, Indian Institute of Technology, Surat, India). Finally, a vibration-damping scheme adapted to the drilling conditions of a DTH hammer was designed. It effectively suppress the vibration amplitude of the circuit unit in a violent vibration environment. This enhances the efficiency and safety of the DTH hammer drilling operations.
## 2 Open Challenges When SS Meets ISAC
### Interference Management  
ISAC systems suffer from self-interference and cross-interference with other users, making real-time mitigation techniques essential. Cognitive radio techniques can help adapt transmission parameters dynamically, but robust algorithms are still needed.
## 5 Methodology
### Texture Feature Extraction
#### 5.1.1 Two-Level 2D DWT  
Wavelet transform (WT) is a linear transform developed from Fourier transform, where the basic functions are sinusoids, but the wavelet functions vary with frequency and limit duration, thus offering better resolutions along the frequency scale [27; 28]. In the beginning, the development of WT was for signal processing, but not for image processing, until [PERSON] and [PERSON] provided the discretization of WT and established the connection between WT and the multiresolution theory, respectively. This paper regards an auspicious pattern image as the change in a discrete signal along a 2D scale. A 2D DWT can decompose the auspicious pattern image into many scales, which range from the roughest scale to the finest [29; 30; 31]. Through a decomposition of 2D DWT, which is implemented by consecutive low-pass (L) and high-pass (H) filtering through one-dimensional convolution, the auspicious pattern image (\(\mathrm{L}\)\(\mathrm{m}\), \(\mathrm{n}\)) can be divided into sub-band images, including an approximation image (\(\mathrm{L}\)\(\mathrm{L}\)) and three detailed images in horizontal (\(\mathrm{H}\)\(\mathrm{L}\)), vertical (\(\mathrm{L}\)\(\mathrm{H}\)), and diagonal (\(\mathrm{H}\)\(\mathrm{H}\)) orientations [28]. The parameters of \(\mathrm{m}\) and \(\mathrm{n}\) denote the number of pixels of the image in the row and column directions, respectively.  
The approximation image (\(\mathrm{L}\)\(\mathrm{L}\)) can be further decomposed in the next level to obtain the images of \(\mathrm{L}\)\(\mathrm{L}\)\(\mathrm{H}\)\(\mathrm{L}\), \(\mathrm{L}\)\(\mathrm{H}\), and \(\mathrm{H}\)\(\mathrm{H}\), with the sizes of (\(\mathrm{m}\)\(\mathrm{2}\), \(\mathrm{n}\)\(\mathrm{2}\)). For the decomposition of each level, the alternative convolutions of the approximation image and the low-pass or high-pass filters in the column or row directions are operated using a downsampling function by 2. Thus, the 2D DWT finally produces the pyramid representations of the  
Figure 3: Scheme of texture feature extraction from auspicious pattern imagery.  
sub-band images, which occupy the same amount of storage as the original auspicious pattern image [32].  
Several studies in the literature indicate that the 2-level 2D DWT (i.e., 2D DWT in the second level) can robustly extract the details of the image texture features from the original images [23; 24; 28; 33]. After decomposition in the second level, the 7 sub-band images, including 6 detailed images (HL1, LH1, HH1, HL2, LH2, and HH2) and one approximation image (LL2), are obtained. The indices of \"1\" and \"2\" signify the first and second levels, respectively. Among the above 6 detailed images, HL2, LH2, and HH2 were chosen to calculate the paper's GLCMs.
# An Aeromagnetic Compensation Algorithm Based on a Temporal Convolutional Network
## 4 Results and Discussion
### Compensation Algorithm Resource Consumption Test Results  
In this section, we compare the time cost and floating-point operations (FLOPs) of MagTCN and the other compensation algorithms mentioned above to ensure that the proposed method can meet the requirements of real-time aeromagnetic compensation. The hardware and hyperparameters used in the experiment are basically the same as those in Section 3.2. In addition, we set the batch size to 1 to simulate the real-time environment and selected 30,000 inputs to test the average time cost.  
As shown in Table 6, BPNN and WilliamsNN have lower average time costs and FLOPs due to their simple structures. The average time cost and FLOPs of BPNN are 0.428 ms and 0.238 MB, respectively, and those of WilliamsNN are 0.428 ms and 0.238 MB, respectively. ResNet and MagTCN take more time, 1.240 ms and 1.837 ms, respectively, but still meet the real-time requirements at a sampling frequency of 10 Hz (<100 ms). Their FLOPs can also be achieved by most graphics processing units [31].
## 2 Development of Seepage Field Prediction Methods
### Deep Learning-Based Method for Seepage Field Prediction
Compared with the traditional yield prediction task, seepage field prediction places greater emphasis on the distribution pattern and physical consistency in the spatial-temporal dimension, and, thus, it presents significant differences in application goals, problem focus, and model design. Currently, the Deep Learning architectures employed have gradually expanded from the early time-series modeling networks, such as LSTM and GRU, to include multiple types, such as Convolutional Spatio-temporal Networks (CSNs), Graphical Neural Networks (GNNs), Physically Informative Neural Networks (PINNs), and Differential Convolutional Neural Networks (PD-CNNs), among other structures, and they continuously enhance the physical interpretability and prediction accuracy of the models by introducing control equation residuals, boundary condition constraints, and attention mechanisms. There are obvious differences in the applicability of traditional methods and Deep Learning methods in reservoir seepage field prediction (see Table 2). Traditional methods usually rely on explicit seepage mathematical models and parameter assumptions, and, although they have good interpretability and engineering controllability in the conventional development stage, it is difficult to accurately capture, by these methods, the complex nonlinear relationships and dynamic evolution processes in the high WC stage, with the increase of reservoir nonhomogeneity, the intensification of well network perturbation, and the high degree of reconfiguration of seepage paths. Deep Learning methods, on the other hand, do not rely on predefined physical model structures, they have automatic feature-extraction capabilities, and they can flexibly cope with prediction tasks under multivariate and multi-source input conditions. In particular, without assuming the reservoir type and applicability to multiple neural network structures (e.g., DNNs, CNNs, LSTMs, PINNs, GNNs, etc. ), they demonstrate stronger scenario adaptability and prediction accuracy, and they have a broader prospect of application in dynamic identification and response modeling of seepage fields during high WCPs. \begin{table}
\begin{tabular}{p{56.9 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline Traditional method & Good physical consistency, strict adherence to conservation laws, and strong interpretability; suitable for theoretical deduction and program design in the early development stage. & Its performance is limited in dealing with nonlinear seepage response and complex working conditions (such as non-homogeneous, fracture, fracture block, and complex well network perturbation) in high-water-bearing periods, and it often needs to simplify the assumptions, which results in the prediction accuracy being limited by the completeness and accuracy of the physical parameter acquisition.
# Mitigation of Electro Magnetic Interference by Using C-Shaped Composite Cylindrical Device
## 2 Experimental and Simulation Setup
### Characterization  
A field meter was applied to detect electromagnetic waves. It was purchased from Chauvin Arnoux Co., Ltd., Dewsbury, England with serial no. 100539 GHK. An internal isotropic-magnetic-field probe scanned electromagnetic waves from 10 Hz to 30 kHz and  
Figure 1: Measurement of the electromagnetic field in the C-shape high-permeability material. (**a**) Instrument setup. The C-opening in the vertical (i.e., the Y-axis) direction (**b**) and horizontal (i.e., the X-axis) direction (**c**). The line currents ran in the coil as indicated by the Z-axis.  
from 200 nT to 40 mT. The measurements were carefully calibrated before experiments. For example, the distance between the sensor and the C-shaped high-permeability material was 7 cm, as shown in Figure 1. The realistic experimental setup can be observed in Supporting Information Materials. In Figure 2, the measurement depended on the distance in the experiments.
## 1 Introduction
Furthermore, we also validated the relation of the strength of turbulence, called refractive index (\(C_{n}{}^{2}\)) with the FSO performance. Based on data collected from the sensors and by adopting the Blaunstein model (BKB) [13], we will analyze how the variation of refractive index during the different times of the day affects the FSO performance. The paper is organized in the following sections: In Section 2, an overview of the complete installed system at QU and the application, that has been designed to measure the performance of FSO link under harsh climate, is presented. The behavior of FSO link along with detailed statistical analysis during winter and summer season at Qatar is presented in Section 3. In Section 4, the fluctuations of refractive index in Qatar during the winter season (as the environmental parameters are not within the limits of BKB model for the summer season) using the BKB model is presented which is also used to validate the results in Section 3. All the statistics and data from the previous sections were used to form a closed form expression relating the Packet Delivery Ratio (PDR) with the major environment parameters during the summer season, as they do not affect the PDR much during the other seasons. Finally, a conclusion is presented in Section 5.
# Paneth Cells and Lgr5+ Intestinal Stem Cells in Radiation Enteritis
## 2 The Structure and Functions of the Small Intestine
### Enterocytes  
Absorptive enterocytes constitute around 80% of cells in the epithelium of the small intestine [9; 15; 17; 40; 46]. Following their production from differentiating Lgr5\({}^{+}\) ISCs, the enterocytes migrate outwards along the crypt-villous axis to reach the outer surface of the epithelium of the small intestine. Enterocytes have villi and microvilli on their luminal surface to enhance the capacity for absorption of nutrients. The function of enterocytes is not limited to digestion and absorption of food as they also participate in innate immunity in the gut [40]. The enterocytes undergo apoptosis and get exfoliated after 3-5 days following their generation from the Lgr5\({}^{+}\) ISCs [63]. Ionizing radiation leads to a much more rapid turn-over and exfoliation of enterocytes and other IECs.  
Despite the lining of the small intestine being made of a single layer of columnar-shaped enterocytes, it can perform the dual function of absorption of nutrients and prevention of passage of harmful pathogens. Together with junctional proteins, enterocytes and other epithelial cells make the luminal epithelium of the small intestine selectively impermeable [40]. The other roles of the enterocytes include sampling of nutrients and the gut microbiota to enhance transcription and synthesis of specific enzymes which are needed for digestion and absorption. Enterocytes use pathogen recognition receptors (PRRs) to sample the gut microbiota [40]. Enterocytes can de-differentiate and become stem cells and replace the Lgr5\({}^{+}\) ISCs, if the reserve stem cells and Paneth cells have been fatally damaged by high-dose ionizing radiation [58].  
The damage to enterocytes following ionizing radiation accounts for, among others, the malabsorption of nutrients which is seen in patients who have acute and chronic RE. Acute radiation injury leads to swelling, premature aging, and apoptosis of the enterocytes. An increase in the rate of apoptosis leads to an imbalance between replacement and loss, which explains the villous atrophy, shortening of the villi, and the loss of microvilli which are features of RE [65]. Concurrently, damage due to ionizing radiation to the small intestine leads to a reduction in the rate of synthesis of junctional proteins [66]. A consequence of a net loss of enterocytes and the impaired synthesis of junctional proteins is the creation of gaps on the luminal surface of the epithelium that increases permeability of the intestinal epithelium to bacteria and their endotoxins leading to their translocation [67]. The increase in the permeability of the epithelium results in local inflammation which may become systemic due to a spill-over of endotoxins and pro-inflammatory into the systemic circulation that may cause multiple organ dysfunction and death [67]. A reduction in the ability of enterocytes to collaborate with the Paneth cells and tuft cells to monitor and control the luminal organisms may also contribute to the dysbiosis, that is commonly seen in patients who have acute or chronic RE [3].
## 2 Materials and Methods
### Data Analysis
#### 2.4.2 EEG Analysis  
EEG data were processed using MATLAB R2024a-based software EEGLAB (version 2023.4) [40]. After reducing the sampling rate to 256 Hz, high- and low-pass filters were applied to retain a frequency range from 0.5 to 80 Hz.  
For the analysis of brain activity recorded during both tests, only the last 90 s of every 3-min step were used to minimize the possible influence of adaptation processes due to changes in load. A 50 Hz notch filter was applied, followed by an independent component analysis [41]. Recurring artefacts, such as those from eye blinks, eye movement, and muscular activity, were identified and removed. The cleaned data were then transformed into power spectra using a fast Fourier transform, and power spectral densities were calculated for two key frequency bands: alpha (\(\alpha\): 8-12 Hz) and beta (\(\beta\): 12-30 Hz).  
In addition, the power spectrum for each electrode was averaged across specific scalp zones. The frontal zone included electrodes F3, F1, Fz, F2, and F4; the central zone included C3, C1, Cz, C2, and C4; and the parietal zone included P3, P1, Pz, P2, and P4. To obtain an overall measure of brain activity, the data from all electrodes were also averaged to calculate the total brain power (total power, TP). Finally, exercise data were expressed as percentage changes from baseline PSD measures to account for day to day and between-subject variability, as reported in a previous study [25, 42].
## 4 Results and Discussion
### PS and DS Distribution  
After MT-InSAR processing, 4769 PS targets and 6234 DS targets were retrieved (Figure 6). The spatial densities of the targets in the area were calculated as 95.14 PS/km\({}^{2}\) and 124.36 DS/km\({}^{2}\) by considering the whole landmass of the peninsula (approximately 50 km\({}^{2}\)). Most of the targets are located at low altitudes (up to approximately 100-150 m a.s.l. for PS and up to approximately 350-400 m a.s.l. for DS), where there are urban settlements, and also on geological formations which exhibit high coherence, such as volcanostals. PS targets have a coherence greater than 0.70, while the DS have coherence greater than 0.79.  
Very dense coverage of PS is observed at the Mavri Petra andesitic dome north of Kameni Chora village due to the strong coherence over areas with sparse vegetation and bare ground, in particular over large blocks of agglomerate and cinder (Figure 7). The Mavri Petra unit has the greatest density of PS with 1241 PS/km\({}^{2}\) and a density of DS of 198 DS/km\({}^{2}\). The density of PS on Mavri Petra is even greater than in Methana town, which is an urban area (see Section 4.5). Metha  
Figure 6: Line-of-sight (LOS) deformation velocity in 2015–2019 from the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) analysis for the (**a**) Persistent Scatterers (PS) and (**b**) Distributed Scatterers (DS) datasets, overlapped onto satellite optical imagery (c) 2020 TerraMetrics, © 2020 Google).  
and 479 DS/km\({}^{2}\). The lowest density of PS was observed in alluvial areas due to much lower coherence, with 156 PS/km\({}^{2}\) and 195 DS/km\({}^{2}\). Volcanoclastic units have densities of 397 PS/km\({}^{2}\) and 784 DS/km\({}^{2}\).