esa-sceva/satcom-chunk-collection huggingface dataset:
# The Progress of Cobalt-Based Anode Materials for Lithium Ion Batteries and Sodium Ion Batteries
## 3 Preparation Methods of Synthesizing Cobalt-based Active Materials
### Electrodeposition  
Electrodeposition, as a method for separating metals or alloys from their compounds, has been used in the preparation of cobalt-based electrode materials [18; 38; 43; 84; 85; 86; 87; 88; 89]. [PERSON]'s group prepared the Co-Sn alloy by depositing Sn and Co atoms through the co-electroplating method, according to moderate time and current density [84]. Figure 7e indicates that the deposited Co-Sn alloy covered the nickel substrate with certain roughness and protuberances. [PERSON]'s group reported a novel nanoarchitectured Sn-Sb-Co alloy through direct electrodeposition on a Cu nanoribbon array [85].  
Figure 7: Preparation methods and morphology characterizations of cobalt-based composites. (**a**) Schematic illustration of the preparation of CoP\({}_{3}\)@C microspheres. (**b**) HRTEM image of the CoP\({}_{3}\)@C composite [80]. (**c**) SEM image of the SnCoC-2 sample [34]. (**d**) SEM image of the Sn\({}_{30}\)Co\({}_{30}\)C\({}_{40}\) material [81]. (**e**) SEM image of the Co-Sn alloy after electrodeposition [84]. (**f**) Schematic illustration of the preparation of the Sn-Sb-Co alloy. (**g**) SEM images of the SnSbCo-3 electrode; the insets show a corresponding high-magnification image (top-right) and cross-sectional photos of the electrode (bottom-right) [85]. (**h**) SEM image of the porous SnCo alloy [86]. (i) SEM image of Co\({}_{3}\)O\({}_{4}\) thin films synthesized by electrochemical deposition [87]. (**j**) SEM image of the Sn-Co alloy deposited on a Ni nanocon-array [38].  
A schematic diagram of the preparation of 3D nanoarchitectured Sn-Sb-Co alloy is shown in Figure 7f. SEM images (Figure 7g) clearly reveal the 3D array nanostructure and thickness (1500 nm) of the Sn-Sb-Co alloy.  
A porous Sn-Co alloy was fabricated by [PERSON]'s group via electrodeposition the Sn-Co ally on the porous Cu film prepared by electroless plating [86]. SEM image (Figure 7h) indicates that the Sn-Co alloy was uniformly covered on the porous Cu film. Porous Co\({}_{3}\)O\({}_{4}\) nanostructured thin films were synthesized via a facile electrodeposition method (Figure 7i) [87]. SEM image reveals that the sample was composed of small nanoflakes of about 30-40 nm in thickness. [PERSON] et al. successfully a synthesized nanoarchitectured Sn-Co alloy electrode through a two-step electrodeposition [38]. The SEM image in Figure 7j evidently indicates that there is sufficient room between the Sn-Co cylinders
## 2 Materials and Methods
### Research Methods
#### 2.3.4 Data Verification  
The MOD17A3 HGF dataset contains annual quality control data (NPP_QC), and its quality control measures include data filtering, outlier handling, spatial interpolation, and data correction. This study conducted a statistical analysis of NPP-QC data in the northwest region of Liaoning Province over the past 21 years, dividing the credibility of NPP data into three levels, namely high, medium, and low [41], and calculating their percentages (Table 2). The results showed that the cumulative percentage of high- and medium-reliability NPP data in the northwest region of Liaoning from 2001 to 2021 reached 99%, indicating that the application of these data in the northwest region of Liaoning is highly accurate.
## 5 Q-Learning-Based CCM-SPS Resource Allocation Scheme Proposed for JCS Sidelink
### Q-Learning-Based CCM-SPS Scheme for JCS Sidelink  
After detecting a resource collision, a consecutive collision elimination mechanism based on Q-learning is proposed to mitigate consecutive resource collisions. Vehicles interact with the environment in real-time and intelligently determine the optimal actions given the current state.
## 2 Modeling Methodology
### Soil Dynamic Damping and Nonlinear Model  
Soil nonlinearity significantly influences the dynamic behavior of a pile and superstructure. If a simple elastic or elasto-plastic model as Mohr-Coulomb criteria is employed, implementation of an additional damping is necessary to properly represent the hysteretic characteristics ([PERSON] & [PERSON] (2006) [28], [PERSON] et al. (2008) [29], [PERSON] et al. (2012) [30], [PERSON] et al. (2013) [31], [PERSON] & [PERSON] (2019) [23]). In this study, the hysteretic damping model was applied to consider both the nonlinearly varying soil modulus and energy dissipation. The hysteretic damping model expresses the tangential elastic modulus of the G/G\({}_{\rm max}\)\(-\gamma\) curve as a function of shear strain, as shown in Equation (5) (Itasca Consulting Group, 2006 [17]). \(\rm L_{1}\) and \(\rm L_{2}\) are coefficients that determine the rate and onset of the decrease in the G/G\({}_{\rm max}\) value in the G/G\({}_{\rm max}\)\(-\gamma\) curve. In this study, the values of \(\rm L_{1}\) and \(\rm L_{2}\) that simulated the experimental results most accurately were determined to be \(-3.65\) and \(0.5\) by comparing the G/G\({}_{\rm max}\)\(-\gamma\) curve of Nevada sand derived through indoor experiments ([PERSON], 1970 [32] and [PERSON] et al., 2001 [33]) with those derived from the changes in \(\rm L_{1}\) and \(\rm L_{2}\) through iterations ([PERSON] et al., 2016 [34], Figure 3).  
\[\rm M_{t}=s^{2}\ (3-2s)-(6s(1\ \text{-}\ s))/(L_{2}\ \text{-}\ L_{1})\log_{10}e \tag{5}\]  
where \(\rm M_{t}\) is the tangential elastic modulus, \(\rm s=(L_{2}-L)/(L_{2}-L_{1})\), \(\rm L=log_{10}\gamma\), \(\rm L_{1}\) and \(\rm L_{2}\) are arbitrary constants, \(\gamma\) is the shear strain, and \(\rm e\) is the void ratio.  
The initial shear modulus of the soil depends on the magnitude of the confining pressure according to the soil depth. In this study, the initial shear modulus was calculated using Equation (6) proposed by [PERSON] (1972) [35].  
\[\mathrm{G_{max}=AF(e)(OCR)^{k}\ P_{a}{}^{(1-n)}\ (\sigma^{\prime}{}_{m})^{n}} \tag{6}\]  
where F(e) = 1/(0.3 + 0.7e\({}^{2}\)), e is the void ratio, k is the overconsolidation ratio index (0 for sand), \(\sigma^{\prime}{}_{m}\) is the average principal stress (\(\sigma^{\prime}{}_{m}\) = (\(\sigma^{\prime}{}_{1}\) + \(\sigma^{\prime}{}_{2}\) + \(\sigma^{\prime}{}_{3}\))/3, \(\mathrm{P_{a}}\) is the atmospheric pressure; empirical constants A and n are the empirical coefficients determined to be 247.73 and 0.567, respectively, through bender element tests under various confining pressures ([PERSON], 2009 [36]). The distribution of the initial shear elastic modulus based on depth was input by applying the above relationship to every element using the FISH function, which is a built-in function of FLAC3D.
# Photonic Stopband Filters Based on Graphene-Pair Arrays
## 2 Graphene-Pair Arrays
For \(\hbar\omega,\ |\mu\ |\gg k_{B}T_{g}\), the interband scattering term can be simplified as  
\[\sigma_{\rm inter}=i\frac{e^{2}}{4\pi\hbar^{2}}\ln\frac{2|\mu|-\hbar(\omega+ i\tau^{-1})}{2|\mu|+\hbar(\omega+i\tau^{-1})}. \tag{4}\]  
The interband and intraband transitions items both involve \(\mu\) and \(\omega\), that is to say, the surface conductivity of graphene is a function of the chemical potential and the incident  
Figure 1: Schematic of graphene-pair arrays. The synthesized arrays are constructed by graphene sheets and dielectrics. The spatial period of graphene pairs is given by \(d=d_{1}+d_{2}\). frequency of light. In THz wave band, the interband transitions are dominant, as the excited photon energy \(\hbar\omega\) is lower than \(2\mu\) and the optical performance of graphene is similar to that of metals. In this case, the surface conductivity of graphene can be reduced to a Drude model. The one-dimensional periodic photonic crystal based on graphene pairs arranges along the Z-axis. A light normally impinges on the structure from the left and transmits from the right. By ignoring the nonlinear effects of light, we give the linear transmission and reflection spectra of the periodic systems with the forward transmission matrix method (FTMM) [26]. The chemical potential of graphene is the Femi level, so the chemical potential can be controlled by chemical doping or an external gate voltage on graphene. As a positive value of voltage loads on graphene, the chemical potential of graphene increases, while the chemical potential of graphene decreases for a negative voltage biasing on graphene. Therefore, one can modulate the surface conductivity of graphene flexibly by a bias voltage on graphene.
# Design Procedure of a Mono-Anchor Head for 2360 MPa High-Strength Steel Strand
## 4 Finite Element Modeling of Mono-Anchor Head
### Material Test of Steel for Mono-Anchor Head
#### 4.2.1 Tensile Test of Carburized 42 CrMo4  
The specimen for the tensile test was manufactured according to the standard dimensions in ISO 6892-1 [20]. The specifications of the tensile test specimen are shown in Figure 7. Three specimens were manufactured and tested for each of the two variables according to the carburizing process. The stress-strain curves for two carburizing parameters are shown in Figure 8, and the average yield and ultimate strengths of the three specimens with the same parameters were computed, as shown in Table 2. With carburization, the ultimate strength of the material increased by 40% and the yield ratio increased by 20%.
# Multi-Connectivity-Based Adaptive Fractional Packet Duplication in Cellular Networks
## 3 Multi-Connectivity in Cellular Networks
### Limitations of Multi-Connectivity
#### 3.5.3 Management of Multi-Connectivity  
Networks are becoming intelligent with the evolution of software defined networks (SDN) and (network function virtualization) NFV; however, their adaption to existing cellular networks will take time. Currently, almost all of the network operators make this decision manually based on their network key performance indicators (KPI). However, the environmental conditions change, and to incorporate these manually into a network's decision making is almost impossible. Incorrect decisions on when to activate MC and when to use SC can degrade the user experience.
###### Abstract  
In order to solve the water sealing problem of soil between pipes of long distance curved pipe-jacked technology, Freeze-Sealing Pipe Roof (FSPR) as an innovative pre-supporting method in tunnel engineering is being applied to the Gongbei Tunnel in the Hong Kong-Zhuhai-Macau Bridge. The definition of FSPR is that large diameter steel pipes are laid out in a circle around the cross section of tunnel in advance, then the artificial ground freezing method is adopted to freeze soil between steel pipes to form water-sealing curtain. An effective freeze control system, which contains master freezing tubes, enhancing freezing tubes and limiting freezing tubes, is established for building up the frozen soil curtain, maintaining its stability during excavation and controlling the volume of frozen soil to limit frost heave dynamically. An in-situ test was carried out to explore the optimal freezing scheme and control mode. The results of the test show that the principal freezing scheme of the solid pipe with hollow pipe as a complement is the most optimal scheme in active freezing phase of the real construction. Meanwhile, cold control mode is suggested to control frost heave in maintained freezing phase. The conclusions have important guiding significance for this kind of engineering construction.  
t  
tunnel; Freeze-Sealing Pipe Roof; freezing scheme; in-situ test; Hong Kong-Zhuhai-Macau Bridge
## 3 Supporting Evidence from Cognitive Psychology  
### Physiological Markers of Effort and Disengagement  
The foundational empirical evidence for the cognitive phenomena I describe comes from [PERSON] and [PERSON]'s seminal 1966 study \"Pupil Diameter and Load on Memory\" [6]. This groundbreaking research provided the first systematic demonstration that pupil dilation increases directly with cognitive load; participants holding increasing numbers of digits in working memory showed proportional increases in pupil diameter. Crucially, this physiological response exhibited the exact pattern I argue parallels LRM behavior:  
Figure 2: Average pupil dilation (perceptual duration) across time during digit span tasks of varying difficulty (6 vs. 7 digits). The inverted-U pattern shows an initial increase in physiological effort markers followed by decline when cognitive capacity is exceeded, demonstrating the physiological signature of task disengagement. Data adapted from [PERSON] and [PERSON]’s 1966 study [6], showing the relationship between memory load and pupillary response.  
effort increases with task demands until capacity limits are reached, after which the system exhibits withdrawal or plateau responses (Figure 2).  
The similarity between the findings of [PERSON] and [PERSON] and the LRM patterns documented by [PERSON] et al. [1] (Figure 1 vs. Figure 2) provides compelling evidence for my theoretical framework. Both curve families exhibit the characteristic inverted-U relationship: initial increases in effort markers (pupil dilation in humans, thinking tokens in LRMs) with increasing task demands, followed by decline when systems approach or exceed capacity limits. In humans, this decline reflects physiological disengagement when memory load becomes overwhelming; in LRMs, the analogous reduction in reasoning effort suggests similar adaptive resource withdrawal.  
Building on this foundational work, decades of subsequent research have established reliable physiological indicators of cognitive effort and disengagement. Modern pupillometry studies demonstrate that pupil dilation increases systematically with memory load and task difficulty. Crucially, this relationship exhibits an inverted-U pattern: effort increases with complexity until capacity limits are reached, after which physiological indicators plateau or decline, marking disengagement.  
Modern multimodal studies confirm this pattern across multiple physiological systems. Heart rate variability decreases under sustained cognitive load, while EEG theta power increases with working memory demands. When tasks exceed individual capacity, these markers show coordinated withdrawal patterns, physiological signatures of the decision to disengage from overwhelming cognitive demands.  
The parallel to LRM token usage patterns is striking. Just as human physiological effort markers initially scale with task difficulty before declining at overload, LRM reasoning tokens follow the same trajectory: increasing with problem complexity until a critical threshold, then counterintuitively decreasing despite maintained task demands.
# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
## 5 Experiment and Analysis
### Infrared Image Test Evaluation
#### 5.3.2 Stripe Correction Algorithm Comparison Evaluation
(**a**) Original image, (**b**) TV method, (**c**) MHE method, (**d**) CNN method, (**e**) Proposed method. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequence/Method** & **Suitcase** & **Leaves** & **People** \\ \hline TV & 28.5 & 30.8 & 24.6 \\ MHE & 27.9 & 28.7 & 22.8 \\ CNN & 27.2 & 29.1 & 23.4 \\ Proposed & **25.4** & **19.7** & **20.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: \(\rho\) values of correction algorithms for raw infrared images. To evaluate the detail protection ability of the algorithm, we introduce the value of \(AVGE\) to illustrate the detail protection ability of the algorithm. Its mathematical formula is as follows:  
\[AVGE=\frac{1}{P}\sum_{\text{p}=1}^{p}\left\|\
abla_{y}\Gamma(Q_{\text{p}})\right| -\left|\
abla_{y}Q_{\text{p}}\right\| \tag{20}\]  
where \(Q_{\text{p}}\) is the noisy image with pixel p, \(\Gamma(\cdot)\) is the denoising method, \(P\) is the number of pixels, and \(\
abla_{y}\) is the vertical gradient operator. AVGE represents the change of the gradient between the corrected image and the original image. The principle behind it is based on the understanding that stripe non-uniformity is reduced while the vertical gradient should remain unchanged. So, the AVGE value closer to 0 indicates that algorithm is better at preserving image detail. As can be seen from Tables 6 and 7, CNN algorithm and the proposed algorithm get smaller AVGE values. However, as shown in Table 6, for the image of leaves with weak texture, CNN gives bad results. In fact, the CNN algorithm failed in this case. Overall, the experiment results reveal the advantage of the proposed method in stripe correction and detail preservation.
# GNSS RTK Positioning Augmented with Large LEO Constellation
## 2 Methods
### Data Simulation
The observation noises are simulated as a zero-mean normal distribution with the standard deviation (STD) dependent on satellite elevation angle, of which the STDs for each frequency are set to 20 cm and 2 mm for code and phase observation in the zenith direction, respectively [28].
## 3 Validation of Numerical Simulation Method  
The numerical calculation is based on unsteady Reynolds averaged Navier-Stokes (URANS) equations coupling with the \(\gamma-\widetilde{\text{Re}}_{\theta t}\) transition model. The semi-implicit method for pressure-linked equations consistent (SIMPLEC [33; 34]) scheme was used for the pressure-velocity coupling. The discretized momentum and pressure correction equations are solved implicitly, whereas the velocity correction is solved explicitly, thus resulting in a semi-implicit method. Second order upwind spatial discretization was used for all variables, in combination with the least square cell-based method for the gradients. The time step is determined by Strouhal number (\(Sr\)) (Equation (21)), which is a similarity criterion to characterize the unsteady flow.  
\[Sr=\frac{fc}{U} \tag{21}\]  
where the \(f\) is the frequency of unsteady period of motion, \(c\) the chord length of airfoil, and \(U\) the velocity of flow.  
The generation of computational mesh and boundary conditions are shown in Figure 1. The computational mesh is divided by C-type structural mesh. The radius of calculation field is 50 times of the chord length.  
Figure 1: Mesh and boundary conditions. (**a**) Mesh in farfield; (**b**) Mesh around airfoil.
# Derivation of New Staggered Compact Schemes with Application to Navier-Stokes Equations
## 2 Problem Formulation and Derivation of New Compact Schemes
Here, the set of local polynomial approximations \(P_{j}\) consistent with Equation (1) is used as the basis for the calculation of all the compact approximations needed in a staggered Navier-Stokes algorithm, which is typically constituted by staggered interpolations and first derivatives, and by collocated second derivatives. The polynomials \(P_{j}\) determined by the conditions of Equations (4)-(6) can be conveniently obtained by performing an Hermitian interpolation of the grid values \(f_{j}\) and of the derivatives \(f_{j}^{\prime}\), once these last ones have been obtained by solving the tridiagonal system associated to Equation (1). Of course, this last procedure is equivalent to the one outlined above, in which the explicit and implicit conditions in Equations (4)-(6) for the whole set of local polynomials \(\left(P_{j}\right)_{j}\) are enforced. Please note that in the present procedure we assume that the values \(f_{j}\) are assigned, while the compact scheme in Equation (1) is used to get a larger number of information, which is then exploited to feed the higher-degree Hermitian interpolation. The computation of the polynomial functions \(P_{j}\) would allow one to have a complete local approximation of the function \(f\) in the interval \(\left[x_{j-1},x_{j+1}\right]\). Any information on the interpolated values, or on first or higher-order derivatives, at any point near \(x_{j}\) can be obtained analytically by evaluating \(P_{j}\) or its derivatives at the selected point. The evaluation of \(P_{j}\) and of its first derivative at staggered points and the evaluation of \(P_{j}^{\prime\prime}\) at grid points, leads to new compact-type formulae, which can be obtained explicitly, once the single compact scheme in Equation (1) has been computed.
## 3 Results and Discussion  
Production, Purification, and Characterization of \(\beta\)-N-Acetylhexosaminidasees from P. crustosum Strains  
\(\beta\)-N-acetylhexosaminidasees are an important member of the binary chitinolytic system of filamentous fungi, which is responsible for the formation of septa and hyphenation of fungal cells. The activity and excretion of \(\beta\)-N-acetylhexosaminidasees into the extracellular space are constantly controlled by the cell and depend on the stage of the fungal growth [13]. To our knowledge, no studies have investigated the enzymatic activity of Hex in _P. crustosum_ strains. In this work, Hex activity was tested in four strains of _P. crustosum_. The strain _P. crustosum_ PcHex1 was obtained from a set of microscopic fungal isolates from damaged archived materials of the Slovak National Museum in Martin (Slovak Republic). Colonies of the strain grown on CYA medium appeared a grayish green color with white margins (Figure 1A). Additionally, they had asymmetrically branched conidiophores with a typical warty stem, and the conidia were smooth and oval, arranged in 3.0-4.0 \(\upmu\)m chains when observed under the microscope (Figure 1B). Based on the micromorphological and colony characteristics, the strain PcHex1 was identified as _P. crustosum_ according to the Culture Collection of Fungi (CCF, Prague, Czech Republic). The other tested _P. crustosum_ strains were obtained as isolates from international collections of microorganisms.  
The strains of _P. crustosum_ were grown in a culture medium containing \(N\)-acetylglucosamine as the \(\beta\)-\(N\)-acetylhexosaminidasees inducer for 15 days. The highest level of enzyme activity in the medium was achieved after 11-12 days of cultivation for the _P. crustosum_ strains CCF 3437 and PcHex1 and after 13-14 days for the strains CCF 3210 and NRRL 62558 (typically 0.15-0.25 U/mL and 9-11 U/mg protein, respectively; Figure 2). These results are comparable with those of previous studies wherein the specific activity of fungal Hex from _P. oxalicum_ CCF 1959 and _Aspergillus versicolor_ CCF 2491 was 10.8 U/mg protein [14] and 11 U/mg protein (0.4 U/mL), respectively [15]. The enzyme was purified using cation exchange chromatography and stored in 1 M ammonium sulfate at 4\({}^{\circ}\)C (SDS-PAGE electrophoresis is shown in Figure S1). The specific activity of the stock enzyme was typically 15-20 U/mg protein, and after 6 months, the decrease was not more than 5% of its original activity.  
Figure 1: Colony morphology (**A**) and microscopic characteristics (**B**) of _Penicillium crustosum_ PcHex1. The scale bar represents 20 \(\upmu\)m.