esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Selection
#### 2.2.3 Grey Model Based on Genetic Algorithm Optimization  
The process of using the least squares method to determine the optimal order has problems, such as cumbersome operation processes and local optimal solutions, resulting in unsatisfactory results. The selection of the optimal order of the fractional grey accumulation model can be intelligently searched by genetic algorithms. The algorithm has strong search capabilities, and its mutation mechanism can avoid falling into local optimization. It can introduce probabilistic thinking in natural selection, randomly select individuals, and easily combine with other algorithms to approximate the optimal solution of the function or a better local optimal solution. Because a set of candidate solutions is used instead of a candidate solution, the crossover and mutation operations will cause the candidate solution to differ from the previous solution. As long as efforts are made to maintain population diversity and avoid premature convergence, a globally optimal solution may emerge. First, the time series is transformed into individuals in the population by coding, generating an adaptability function [25]. Before reaching the maximum number of iterations, continuous selection, crossover, and variation are made to select individuals with good adaptability in the population and repeat the above operations on the subpopulation until the optimal solution is found [26]. Figure 3 shows a flowchart using genetic algorithms to optimize the grey model.  
First, the variables, including population size, crossover rate, mutation rate, genetic algebra and degree of variation factor were initialized. After generating the initial population, the gray model mentioned above is introduced for fitness calculation. This means that after decoding the individual coding string, the phenotype of the individual can be obtained. The phenotype of an individual can calculate the value of the objective function of the corresponding individual. According to the type of optimization problem, the fitness of an individual is calculated by the objective function value according to certain transformation rules, and the ultimate purpose is to determine the optimal order.
## 3 Results
### Differences in Lumbar Intramuscular Properties between Amputees and the Control Group  
Table 4 shows the differences in lumbar intramuscular properties for the three conditions (i.e., AS, NAS, CG), and the presented CG values are the mean values of the right and left sides.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Lumbar Part** & \multicolumn{3}{c}{**Control Group (\(\boldsymbol{u}\) = 13)**} & \multirow{2}{*}{**SE**} & \multirow{2}{*}{**t Value**} \\ \cline{3-3} \cline{5-6}  & & **Right** & & & **Left** \\ \hline \multirow{4}{*}{**Stiffness (N/m)**} & Upper & 282.1 \(\pm\) 38.7 \({}^{\lx@sectionsign}\) & 281.6 \(\pm\) 39.3 & 5.14 & 0.169 \\ \cline{2-6}  & Lower & 225.6 \(\pm\) 47.1 & 227.2 \(\pm\) 41.2 & 3.02 & 0.130 \\ \cline{2-6}  & SE & 8.55 & 9.68 & - & - \\ \cline{2-6}  & t value & 6.375 *** & 5.580 *** & - & - \\ \hline \multirow{4}{*}{**Frequency (Hz)**} & Upper & 15.3 \(\pm\) 1.3 & 15.8 \(\pm\) 1.3 & 0.29 & \(-\)1.807 \\ \cline{2-6}  & Lower & 14.0 \(\pm\) 1.5 & 14.3 \(\pm\) 1.6 & 0.11 & \(-\)2.432 \({}^{*}\) \\ \cline{2-6}  & SE & 0.39 & 0.41 & - & - \\ \cline{2-6}  & t value & 3.124 *** & 3.023 \({}^{*}\) & - & - \\ \hline \multirow{4}{*}{**Decrement (Ratio)**} & Upper & 1.29 \(\pm\) 0.34 & 1.13 \(\pm\) 0.25 & 0.04 & 4.487 *** \\ \cline{2-6}  & Lower & 1.02 \(\pm\) 0.20 & 1.00 \(\pm\) 0.16 & 0.03 & 0.506 \\ \cline{1-1} \cline{2-6}  & SE & 0.07 & 0.04 & - & - \\ \cline{1-1} \cline{2-6}  & t value & 3.921 *** & 3.010 \({}^{*}\) & - & - \\ \hline \hline \end{tabular} SE, standard error; \({}^{\lx@sectionsign}\) mean \(\pm\) standard deviation; \({}^{*}\) \(p<\) 0.05, ** \(p<\) 0.01, *** \(p<\) 0.001.  
\end{table}
Table 3: Comparison of lumbar intramuscular properties between the right and left sides in the control group.  
In the upper lumbar region, the stiffness of the AS and NAS tended to be 14.4% greater than that of the CG (F value = 3.291, \(p<0.05\)). In addition, the frequency of the AS and NAS tended to be 10.9% higher than that of the CG (F value = 4.455, \(p<0.05\)). Especially in the NAS, there were significant differences among the three conditions. In the post-hoc analysis ([PERSON]'s HSD), the stiffness and frequency of NAS significantly increased compared to the CG (\(p<0.05\)). On the other hand, there was no statistical difference in the stiffness and frequency of the lower lumbar region.  
In addition, the decrement values of the NAS were less than those of the AS or CG (decreased by 12.4% and 6.6%, respectively), but were not statistically significant.
## 4 Comparison Results
### Compared Results with Nominal Hydraulic Parameters  
Firstly, the nominal hydraulic parameters is adopted in simulation with the uncertain nonlinearities \(\Delta_{2}=\Delta_{3}=0\). The cylinder position demands are selected as \(y_{d}=25(\sin(0.8\pi t)+\sin(0.4\pi t)+\sin(0.2\pi t))\) mm and \(y_{d}=25(\sin(1.6\pi t)+\sin(0.8\pi t)+\sin(0.4\pi t))\) mm. The initial states of two control schemes are \(x_{1}(0)=20\) mm, \(x_{2}(0)=0\) mm/s, \(x_{3}(0)=0\) bar. The proposed controller comparison with PI controller are shown in Figures 3-6. The controller based on prescribed performance constraint has the steady tracking errors \(\Delta x_{1}=0.01\) mm in low frequency demand and \(\Delta x_{1}=0.05\) mm in high frequency demand respectively, which is better than the PI controller \(\Delta x_{1}=0.5\) mm and \(\Delta x_{1}=2\) mm in corresponding frequency demand. Since the constraint holding technique is adopted in the proposed controller, the tracking error of the cylinder position is not always beyond the prescribed constraint \(\rho(t)e_{\rm min}<e(t)<\rho(t)e_{\rm max}\). Thus, these comparison results indicate the advantage of this prescribed performance constraint technique.  
Figure 4: The cylinder position responses \(x_{1}\) by prescribed performance constraint controller with \(\Delta_{2}=\Delta_{3}=0\), the demand \(y_{d}=25(\sin(0.8\pi t)+\sin(0.4\pi t)+\sin(0.2\pi t))\).  
Figure 8: The cylinder position responses \(x_{1}\) by prescribed performance constraint controller with hydraulic parametric uncertainties and the external load, the demand \(y_{d}=25(\sin(0.8\pi t)+\sin(0.4\pi t)+\sin(0.2\pi t))\) mm.  
Figure 9: The cylinder position responses \(x_{1}\) by PI controller with hydraulic parametric uncertainties and the external load, the demand \(y_{d}=25(\sin(1.6\pi t)+\sin(0.8\pi t)+\sin(0.4\pi t))\) mm.
# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 4 Methods
### Preretinal Space Segmentation
#### 4.1.2 UNet  
UNet is an architecture proposed by [33] that obtains good accuracy in semantic segmentation of biomedical images. It consists of encoder and decoder paths, each with five levels of two convolution blocks. Each block incorporates a \(3\times 3\) px convolution followed by ReLU (Rectified Linear Unit) activation function. Between each of the five encoding level, a downsampling \(2\times 2\) px max-pool operation with a stride \(2\times 2\) px is applied. Simultaneously, each level doubles the number of feature channels. Consequently, the feature maps are upsampled with a \(2\times 2\) px up-convolution in the decoder path while halving the number of feature channels.  
Figure 3: General scheme of the processing pipeline: each 2D slice from a set of 3D scans is processed by the network to obtain four prediction masks (one for each segmentation class), from which 3 area borders are calculated: posterior cortical vitreous (PCV), inner limiting membrane (ILM), and retina pigment epithelium (RPE). Predicted segmentations \(\hat{I}\) are compared with the ground truth data \(I\), and the difference between them (in the form of loss) is used to update the network weights. During the test phase, error metrics of Dice score, Mean Absolute Error (MAE), and Topology Incorrectness Index (TII) are computed.  
One of the beneficial procedures introduced in UNet is a skip connection: e.g., the feature maps at the end of each encoder level are concatenated to the upsampled decoder maps before being processed by the convolution blocks. Such operation allows preserving relevant information from the input features. The probability maps are obtained after applying a final \(1\times 1\) px convolution after the last decoder block, transforming the 64 element feature matrix into a segmentation mask for each desired class.
## 3 Results and Discussion
### Contribution Scores  
The contribution of each functional module of targets to a particular disease through a set of common pathways can be evaluated using the CS of each module. The CSs of the four functional modules to 38 diseases were calculated using Equation (1) (see Section 2.3) and tabulated in Figure 7. The sum of the CSs from each of the four modules for a particular disease was 1 or unity. The larger the CS value of a module, the greater the contribution of the module to a disease became.  
As shown in Figure 7, the health benefits of 11 out of 32 Group I diseases (Table S5) were attributed to a single functional module (CS = 1.00) [i.e., primary congenital glaucoma (h01203) was associated with the targets in Module 1; breast cancer (H00031), endometrial cancer (H00026), gastric cancer (H00018), glioma (H00042), non-small cell lung cancer (H00014), pancreatic cancer (H00019) were connected with the targets in Module 2; malaria (H00361), inflammatory bowel disease (H01227), and ulcerative colitis (H01466) were related to the targets in Module 3; and autosomal dominant nocturnal frontal lobe epilepsy (H00807) was accredited to the targets in Module 4]; whereas the health benefits of the other 21 Group I diseases were attributed to the targets in two or three or even four functional modules. Similarly, the medical harms of six Group II diseases (Figure 7, Table S5) came from the targets of one functional module [i.e., Module 4 for alcohol dependence (H01611), cocaine addiction (H0000A), and nicotine addiction (H0000D)] or targets of two functional modules [i.e., Module 2 and Module 4 for amphetamine addiction (H0000B) and morphine  
Figure 6: (**A**) The target-pathway-disease interaction network and (**B**) The target-(Pathway)-Target interaction network with modularity partition by Gephi with Louvain algorithm, where the nodes were targets and the edges were the shared pathways of these targets.  
addition (H0000C)] or targets of three functional modules [i.e., Module 1, Module 2 and Module 4 for Schizophrenia (H01649)]. Since _Camabis_ addiction has not been documented in the KEGG database and the protein targets of Module 4 are prone to cannabinoid ligands and associated with various substance dependence including alcohol, cocaine, nicotine, amphetamine, and morphine addiction, it is reasonable for one to speculate that _Camabis_ addiction shares the same pathways and protein targets of Module 4. This may explain why the use of _Camabis_ could cross-sensitize the addictive effects of nicotine and morphine [44,45].
## 5 Conclusions  
The problem of respiratory motion has plagued the field of free-breathing CMRA for many years and yet it remains a largely unsolved challenge. In the last decade the predominant research trend has been moving away from diaphragmatic navigators with motion models of varying complexity and towards direct motion compensation of the respiratory induced heart motion. This includes self-navigation and image based-navigation techniques, the former offering improved ease-of-use while the latter provides motion correction with more degrees-of-freedom. Furthermore, improved motion estimation accuracy allows for advanced motion correction algorithms incorporating affine and non-linear correction, obviating the need for respiratory gating. Despite the potential of some of these advanced methods to compensate for almost all of the respiratory motion, it is unlikely that they will receive widespread clinical use unless they can be seamlessly integrated into the clinical workflow, and without adding complexity to the cardiovascular MRI examination. The main aim of motion compensation in CMRA should not only be to make the scans faster and more robust towards respiratory motion, but also make the scans easier to perform so as to reduce operator dependence and requirement for specialized training. So far, advanced compensation approaches have only been used in small research studies involving healthy volunteers whereas most patient studies have been limited to simple diaphragmatic motion compensation techniques. Further work is required to assess the  
Figure 6: Schematic of a general respiratory binning, motion estimation and correction procedure. The respiratory position is measured using a navigator (NAV) for every data acquisition (ACQ), which can then be associated to a respiratory bin. As respiration is cyclical this allows multiple ACQs to be associated to a particular respiratory bin. Data from each bin can be reconstructed separately (ACQ\({}_{\text{Bin1-3}}\)) and is now respiratory resolved, although undersampled. For radial binning approaches the undersampled data for each bin can be used to estimate motion between bins [67, 68]. However, for Cartesian binning approaches additional fully sampled data can be acquired [11, 69]. The respiratory motion is typically estimated by registering ACQ\({}_{\text{Bin2}}\) and ACQ\({}_{\text{Bin3}}\) to the end-expiratory ACQ\({}_{\text{Bin1}}\) to generate the transformations \(A_{2}\) and \(A_{3}\). The motion corrected CMRA data can be obtained by applying these transformations to the CMRA data in the corresponding bins and summing the results. Note, in this example 3 respiratory bins are used, however in practice 4 to 6 bins are typically employed. Adapted from [67].  
clinical merits of these emerging approaches. To this end, a focus on integrating the new methods into clinical practice is needed, including maximizing ease of use and minimizing post-processing times.
# SatEC: A 5G Satellite Edge Computing Framework Based on Microservice Architecture
## 2 Related Work
### Drone Communications with 5G  
In addition to satellites, drones are also considered as an important complement to ground 5G networks. Due to their flexibility and inherent ability for line-of-sight (LoS) communications, drone base stations (drone-BSs) can provide broadband, wide-scale, and reliable wireless connectivity during disasters and temporary events. Drone-BSs offer a promising solution for ultraflexible deployment and cost-effective wireless services [29]. [PERSON] et al. proposed a framework for strategic placement of multiple drone-BSs that provides wireless connectivity for a large-scale ground network [30]. [PERSON] et al. presented a delay-optimal cell association scheme in an unmanned aerial vehicle (UAV)-assisted terrestrial wireless network [31]. [PERSON] et al. proposed a concept of three-dimensional (3D) cellular networks and a framework for network planning for drone-BSs as well as latency-minimal cell association for drone user equipment [29]. [PERSON] et al. investigated both the mobile-enabled drones (MED) and wireless infrastructure drones (WID) cases within the realistic constraints of 5G and discussed potential solutions for highlighted open issues, either via application of current standards, or by providing suggestions towards further enhancements [32].
## 3 Materials and Methods  
### Instrumentation  
\({}^{1}\)H and \({}^{13}\)C NMR spectra were recorded on a <<Bruker Avance III HD 400>> (400 MHz for \({}^{1}\)H and 100 MHz for \({}^{13}\)C NMR) at 40 \({}^{\circ}\)C. The chemical shifts (\(\delta\)) were measured in ppm with respect to the solvent ([D\({}_{6}\)] DMSO, \({}^{1}\)H: \(\delta\) = 2.50 ppm, \({}^{13}\)C: \(\delta\) = 39.52 ppm). The coupling constants (\(J\)) are given in Hertz (Hz). The splitting patterns of apparent multiplets associated with the averaged coupling constants were designated as s (singlet), d (doublet), t (triplet), q (quartet), m (multiplet), dd (doublet of doublets), and br. (broadened). High-resolution mass measurements (HRMS) were carried out using a BrukermicroTOF-QTM ESI-TOF mass spectrometer. GC/MS analysis was performed on an <<Agilent 7890B>> interfaced to an <<Agilent 5977A>> mass-selective detector. The melting points were determined with a <<Stuart SMP 40>>. Data sets for X-ray diffraction were collected with a <<New Xcalibur, Rubys diffractometer. Column chromatography was performed on silica gel Macherey Nagel (40-63 \(\upmu\)m). Flash column chromatography was performed over silica gel (0.04-0.063 mm), using a mixture of EtOAc and petroleum ether as an eluent. TLC plates were visualized via exposure to ultraviolet light. All the reactions were carried out using freshly distilled and dry solvents from solvent stills.  
Figure 4: The compounds that exhibit the most favorable binding energies in molecular docking calculations in their interaction with pyruvate kinases (PK): (**a**) C. _albicans_ PK; (**b**) _E. coli_ PK; (**c**) _S. aureus_ PK; (**d**) _M. smegmatis_ PK.
# Late-Stage Optimization of Modern ILP Processor Cores via FPGA Simulation
## 5 Evaluation  
### FPGA Implementation Results  
Table 4 lists the results of FPGA implementation for the basic PNX processor. Section 4.1 discusses its parameters, which are specified in Table 3. The memory structures for the basic processor are implemented inside the FPGA. For other configurations that require additional RAM, part of the L2 cache can also be implemented in the external SRAM daughter board. The PNX processor would consume a total of about 1 million lookup tables (LUTs). This is only 36% of the total LUT resources in the VU440 chip and leaves flexible implementation for various processor core design options. This value is larger than that of the Intel Nehalem processor core, which consumes 760 K LUTs. Therefore, the Intel Nehalem processor core can also be easily implemented in one VU440 FPGA chip.  
The FPGA system can achieve timing closure at 80 MHz. To further determine the impact of clock gating and manual memory structure optimization, we implement two versions of the PNX processor on VU440 FPGA. The first version, which has no clock gating and has manual optimization, deletes the clock gating module in the RTL and performs manual structure optimization. The other version, which has clock gating conversion and no manual optimization, enables clock gating conversion in the FPGA compiler tools and does not perform manual structure optimization. A 20% speed improvement can be obtained from this manual optimization.
## 1 Introduction
The impacts of land-use change on landslide susceptibility are complex and influenced by factors such as topography, geology, climate, land-use policies, and population growth [26]. A comprehensive approach is necessary to achieve sustainability goals, which includes considering the impacts of land-use change on landslide susceptibility. Sustainable land-use planning can play a critical role in reducing landslide risks by preserving natural ecosystems, restoring degraded land, and promoting low-impact development practices [32; 33]. Accurate landslide susceptibility mapping is crucial for identifying at-risk areas and making informed land-use planning decisions. With advances in remote sensing and geographic information systems (GIS) technology, sophisticated landslide susceptibility models have been developed to capture the complex interactions between land-use change and landslide susceptibility [23; 34; 35]. Understanding the spatiotemporal evolution and influencing factors of land-use change is the key to achieving sustainability goals and mitigating landslide risks. Sustainable land-use planning and effective landslide susceptibility mapping can contribute to promoting a low-carbon, resilient future. The LSM has multiple uses, identifying unstable areas in advance, disallowing new construction in hazard-prone areas, relief operations, etc. An analytical hierarchy process (AHP)-based method has been attempted in the study area to prepare LSM due to its simplifying method of assigning ranks and weights [21; 29; 36]. Different natural hazards such as floods, gully erosion, earthquakes, and liquification can also be classified through AHP methodology [37; 38; 39]. It is a method to derive ratio scales from paired comparisons from the principal eigenvectors [29]. The study of landslide susceptibility mapping in Darjeeling is due to a combination of various factors in the area. A high-resolution DEM is used for the delineation of various thematic layers. With a high population density, significant land-use changes, and challenging topography, the study region provides an interesting case study for analyzing the complex interactions between topography, land use, and population growth contributing to landslide risk in the Himalayan region. This study has the potential to provide valuable insights for informing land-use planning decisions and reducing the impact of landslides on local communities. The current study focuses on identifying the possible landslide areas for stabilizing the citizens' safety and the area's future development. To aid in the development of more accurate and reliable landslide susceptibility models that can be applied to other regions with similar challenges, the AHP method is adopted to recognize the landslide prone areas. The published result can be utilized by West Bengal tourism for future development and controlled habitation in hazard-prone areas.