esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Mixture Testing
#### 3.2.3 Low-Temperature Fracture Test  
The thermal stress-restrained specimen test (TSRST) cannot consider the effects of traffic loads and the propagation of cracking over time [31]. To evaluate the low-temperature behavior of asphalt mixture, disk-shaped compact tension (DCT) test, a novel fracture test method, was performed following the ASTM D7313-07 test procedure. DCT testing is controlled to acquire a constant rate of crack mouth opening displacement (CMOD), which is 1 mm/min. Testing temperature was selected to be \(-18\)\({}^{\circ}\)C. Typical load-displacement curves obtained from DCT tests are shown in Figure 4. Generally, fracture energy (G\({}_{\text{f}}\)), as one of the main fracture parameters, can be calculated from the area under the load-CMOD curve. However, it has been found by many researchers ([PERSON] et al. [32], [PERSON] et al. [33], and [PERSON] et al. [34]) that the fracture energy has its drawbacks and could not fully distinguish between the cracking behavior of the mixtures. It can be seen in Figure 4 that the mixture with high peak load (about 3.8 kN) and steep post-peak slope has very close fracture energy value with the mixture exhibiting low peak load (about 2.5 kN) and shallow post-peak slope. Therefore, different indices such as flexibility index (FI), toughness index (TI), and fracture strain tolerance (FST) are proposed to assess the low-temperature properties of asphalt mixtures.  
The FST fracture index was developed by [PERSON] et al. [33] and showed superiority and lower variability than fracture energy in assessing the low temperature cracking ability of mixtures. The FST parameter can be calculated from Equation (4).  
\[\text{FST}=\frac{\text{G}_{\text{f}}}{\text{S}_{\text{f}}} \tag{4}\]  
where G\({}_{\text{f}}\) is the fracture energy and S\({}_{\text{f}}\) is the fracture strength of DCT test; the calculation of G\({}_{\text{f}}\) and S\({}_{\text{f}}\) are shown in Equations (5) and (6).  
\[\text{G}_{\text{f}}=\frac{\int_{0}^{\text{AFinal}}F\cdot du}{t\times a} \tag{5}\]  
\[\text{S}_{\text{f}}=\frac{2F_{\text{max}}(3L-a)}{t\times a^{2}} \tag{6}\]  
where \(F\) is the test load during the test, \(u\) is the CMOD, \(\Delta\)Final is the displacement at the end of the test, \(t\) is the thickness of test specimen, \(a\) is the crack ligament length, \(F_{\text{max}}\) is the peak load, and \(L\) is the distance from loading location to specimen's boundary.
## 4 Methods  
### Data Acquisition  
The MRI raw data were acquired on a 3T MR scanner (MAGNETOM Prisma, Siemens Healthineers, Erlangen, Germany). Four healthy volunteers were scanned, resulting in four in vivo datasets. Three datasets \((D_{1},D_{2},D_{3})\) with non-rigid motion were acquired with a T1-weighted 3D GRE (fast low angle shot gradient echo) sequence, covering the breast region. The breast MRI acquisition was performed during free breathing, with the subject lying in the supine position. Further details about the imaging protocol can be found in [32]. A dataset \(D_{4}\) with subtle rigid motion was acquired using a T1-weighted 3D MPRAGE (Magnetization-Prepared Rapid Gradient Echo) sequence covering the head. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (1975, revised 2013) and was approved by the ethics committee (approval number: [CPP EST-III, 08.10.01]) under the protocol \"METHODOO\" (ClinicalTrials.gov Identifier: NCT02887053). All participants provided written informed consent prior to their inclusion in the study.  
The main parameters of the dataset of GRICS are the number of samples in frequency encoding direction, \(N_{x}\), in phase-encoding direction in the image plane, \(N_{y}\), and in the direction of slices, \(N_{z}\). The size of the matrix is \(N_{x}\times N_{y}\times N_{z}\). The number of coils in the coil array is \(N_{c}\) (\"Body 18\" and \"Spine\" coil arrays from the manufacturer; \"Head 20\" for brain MRI data); the number of shots or motion states is \(N_{t}\). In this work, the acquisition type is 3D.  
The datasets were carefully chosen by selecting different cases with varying coil numbers and dimensions. The study aims to comprehensively evaluate and understand the impact of these variables on the performance. Specifically, the datasets include different combinations of \(N_{x}\), \(N_{y}\), \(N_{z}\), \(N_{c}\), and \(N_{t}\), reflecting real-world scenarios with distinct spatial resolutions and complexities.
# Light-Induced Advanced Oxidation Processes as PFAS Remediation Methods: A Review
## 3 Advanced Oxidation Processes (AOPs) for PFAS  
### Heterogeneous Photocatalysis Materials  
Heterogeneous photocatalysis involving semiconductor catalysts provides one of the best remediation techniques for PFAS remediation by involving UV-Vis light and sometimes oxidation reactants [52; 53; 54; 55]. The most important advantage of using photocatalysis is that both photo-oxidation and photoreduction pathways operate in tandem to effectively decompose PFAS to shorter-chain perfluoro carboxylic acid (PFCA) by the stepwise elimination of CF\({}_{2}\) groups. Finally, these PFCA intermediates completely mineralize to CO\({}_{2}\) and fluoride ions through repeating similar steps. The photocatalytic material, along with appropriate opto-electronic features, must also be chemically stable, recyclable, non-toxic, and available with a low price tag. Based on these requirements, Ti, Ga, In, and Bi-based compound photocatalysts have been primarily implemented in the literature to remove PFAS from wastewater (Table 1).
## 5 Results and Discussion  
### Applications of WRF-uGA System  
The optimization results showed that the fitness function converged to the minimum in the 25 th generation (Figure 4). The resulting optimal combination of physical parameterization schemes consisted of the MYNN2 PBL scheme, Noah-MP surface scheme, RRTMG shortwave radiation scheme, and RRTMG longwave radiation scheme. The theoretical evidence for the optimal combination of the physical parameterization schemes in coastal regions is as follows: (1) The Noah-MP scheme better represented the land-surface variables leading to more realistic simulations of the sea breeze and boundary layer characteristics at coastal regions ([PERSON] and [PERSON], 2018; 2018); (2) The MYNN2 PBL scheme has been shown to more accurately reproduce the PBL structure in coastal regions ([PERSON] et al., 2019; 2019; 2019).
## 3 Numerical Analysis  
### Finite Element Model  
In this study, the CEL method was adopted to simulate the deformation of a plate against free fall into water using ABAQUS, which is a commercial finite element analysis program. The dynamic explicit solver of ABAQUS was applied for the simulation [30]. Figure 3 shows the computational domain and boundary conditions of the finite element model. Water, which undergoes a significant deformation, was modeled in the Eulerian domain, whereas the steel box, whose deformation was relatively insignificant, was modeled in the Lagrangian domain. Water was assumed to be viscous and compressible fluid. Air was not modeled separately, and the empty space was set to the initial void region. The displacement and rotation of the wall of the water tank were all fixed. The initial deformation condition of the steel plate was not considered in the simulation. In the simulation, to enable efficient calculations, the steel box was placed 0.001 m from the free water surface, and the secondary velocity considering the actual drop height, i.e., 1 or 2 m, was applied in the simulation. Both the water and steel box meshes were constructed with an optimized size via a mesh sensitivity study (see Section 3.2). The steel box was modeled using shell elements. The penalty contact method between the Lagrangian elements and the Eulerian elements was used in Abaqus.  
The multilinear plasticity model was applied to the material model of the steel box, as shown in Figure 4 with Table 2, to calculate the amount of plastic deformation on the bottom plate due to impact after its fall onto the water tank. The material properties of each plate based on thicknesses of 3, 5, and 8 mm were obtained from [PERSON] et al. [3].  
Figure 3: Computational domain and boundary conditions.  
The \(U_{s}-U_{p}\) EOS was used to simplify the properties of water in terms of density and dynamic viscosity, as shown in Table 3. Furthermore, it was used to simulate the Navier-Stokes flow without considering turbulent flow. As the primary force component should be pressure for water impact, a suitable approximation can be obtained using this EOS [27].  
The parameters for the speed of sound of water (c\({}_{0}\)), constant coefficient(s) of the impact velocity equation, and material constant (\(\Gamma_{0}\)) of the Gruneisen EOS are provided in Table 3[27].
## 3 Feedforward Control Considering Non-Uniform Creep Difference and Hysteresis Nonlinearity
### NCD Mechanism Analysis and Separation Basis
Under ideal conditions, without the influence of dynamic creep, the reduction in displacement under the S-shaped trajectory driving voltage should be faster than that under the triangular wave voltage. However, due to the accumulation of displacement differences in the previous voltage intervals, the S-shaped trajectory driving voltage can only narrow the gap with the triangular wave voltage without surpassing it. Finally, when the voltage exceeds Marker Point 4, the rising speed of the S-shaped trajectory driving voltage decreases again, and the NCD becomes significantly stronger, causing the displacement difference between the two to gradually widen once more. In summary, the dynamic response characteristics of the S-shaped trajectory driving voltage vary significantly across different voltage intervals compared to the triangular wave voltage, and its displacement changes are jointly influenced by the rising speed and the NCD. From the above analysis, the variation trend of the NCD component of a piezoelectric actuator under an S-shaped trajectory driving voltage can be determined. Based on this trend, the NCD component of piezoelectric actuators under different speeds and maximum voltages can be predicted. Consequently, the NCD component can be separated from the complex nonlinearity of a piezoelectric actuator as a predetermined input, reducing the output nonlinearity of PAMTs to only the hysteresis characteristics of piezoelectric actuators.
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 2 Literature Review
### Optimization and Comparative Analysis of Controllers  
Recent research by [22] presents a comparison between two optimization techniques, Artificial Bee Colony (ABC) and Artificial Bee Colony with Multileitte Guidance (ABCMG), for optimizing a PID controller on a 3-DoF robot. Their results demonstrate that the MGABC algorithm outperforms the traditional ABC based on multiple performance indicators. Furthermore, the optimized system exhibits robustness under various disturbance conditions, suggesting that the proposed techniques and their objective function are viable for PID controller optimization in robotic applications.  
Research presented in [23] addresses the nonlinear behavior challenges in robot manipulators. The study implements and evaluates five controllers on a 3-DoF robot: predictive, hyperbolic sinusoidal, sliding mode, a hybrid predictive + hyperbolic cosine, and adaptive. The researchers established a communication interface between MATLAB/Simulink and the robot to generate trajectories for analyzing the dynamic performance using Residual Mean Square indicators.  
In [24], researchers design and assess four controllers: two classical (Computed Torque and PID) and two advanced (fuzzy logic and predictive) implementations for a SCORBOT-ER V plus robot model. Their simulation incorporates gravity compensation, inverse motor models, and belt movement effects in each joint. Controller evaluation is based on trajectory curves, joint errors, and three performance indices.  
Notable advances in the field include novel algorithms and controllers recently developed for robotics. Contemporary approaches such as Artificial Bee Colony, Genetic, Particle Swarm, Harmony Search, and Differential Evolution algorithms offer new opportunities for control applications in robot manipulators. The latter two studies provide comprehensive analyses through controller comparisons and detailed supporting annexes. Building upon these works, this research presents three advanced controllers, alongside a PID control for comparison, including the simulation of obstacle avoidance along the robot's trajectory.
# Large Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis
## 7 Integrating LLM-Informed Strategy with TANGO Control System at CELLS ALBA Synchrotron
### Enhanced Functionalities in TANGO  
* Real-time Analysis and Adaptation: The integration can allow for real-time analysis of XPS data, with the LLM providing rapid interpretations. This capability enables immediate adjustments to experiments based on the LLM's feedback.
* Automated Data Flow and Processing: The entire process, from data acquisition to analysis and storage, can be automated, ensuring a streamlined workflow. This automation reduces manual intervention and speeds up the research process.
* Scalable and Updatable System: The integration framework can be designed to be scalable, accommodating increases in data volume and complexity. The LLM server can be updated or retrained as needed to keep up with advancements in AI and XPS analysis techniques.
## 4 Theoretical and Experimental Analyses
### Security
#### 4.3.2 Histogram  
A histogram serves as a visual representation of the pixel distribution. The greater the uniformity in pixel distribution, the more robust and resistant the scheme becomes against statistical analysis attacks. The histograms of Peppers are shown in Figure 7. To show the pixel distribution more clearly, scatterplots and 3D histogram distributions were also tested. The results show that the pixel distribution of the share image is more uniform, which indicates that the scheme has high security. In addition, the pixel distributions of the reconstructed image and original image remain consistent, indicating that the carrier image is fully recoverable. In addition, we tested the scheme with the threshold value changed. As depicted in Figure 7, for \(T\) = 3 and \(T\) = 4, the reconstructed image maintains an identical pixel distribution to the original image, which shows that the proposed scheme has threshold variability.
# Impact of Subsurface Drainage System Design on Nitrate Loss and Crop Production
###### Abstract  
Subsurface (or tile) drainage offers a valuable solution for enhancing crop productivity in poorly drained soils. However, this practice is also associated with significant nutrient leaching, which can contribute to water quality problems at the regional scale. This research presents the findings from a 4-year tile depth and spacing study in central Illinois that included three drain spacings (12.2, 18.3, and 24.4 m) and two drain depths (0.8 and 1.1 m) implemented in six plots under the corn and soybean rotation system (plots CS-1 and CS-3: 12.2 m spacing and 1.1 m depth, plots CS-2 and CS-4: 24.4 m spacing and 1.1 m depth, and plots CS-5 and CS-6 18.3 m spacing and 0.8 m depth). Our observations indicate that drain flow and NO\({}_{3}\)-N losses were higher in plots with narrower drain spacings, while plots with wider drain spacing showed reduced drain flow and NO\({}_{3}\)-N losses. Specifically, plots set up with drain spacings of 18.3 m and 24.4 m showed significant reductions in drain flow compared to plots featuring a 12.2 m drain spacing. Likewise, plots characterized by 18.3 m and 24.4 m drain spacings (except CS-4) showed better NO\({}_{3}\)-N retention and lower leaching losses than those with 12.2 m spacing (CS-1 and CS-3). Crop yield results over a 3-year period indicated that CS-2 (wider spacing plot) showed the highest productivity, with up to 13.6% higher yield compared to other plots. Furthermore, when comparing plots with the same drainage designs, CS-2 and CS-4 showed 5.1% to 2.6% higher corn yield (3-year average) compared to CS-1 and CS-3, and CS-5 and CS-6, respectively. Overall, a wider drainage system showed the capacity to export lower nutrient levels while concurrently enhancing productivity. These findings represent that optimizing tile drainage systems can effectively reduce nitrate losses while increasing crop productivity.  
subsurface drainage; drainage design; nitrate loss; crop productivity 2024 14 10180. [[https://doi.org/10.3390/appl42210180](https://doi.org/10.3390/appl42210180)]([https://doi.org/10.3390/appl42210180](https://doi.org/10.3390/appl42210180))
## 3 Methodology
### Joint-Feature Decoder  
The joint-feature decoder is to model temporal dynamics based on the joint features of local and remote-sensing data and then outputs short-term PV power. As shown in the bottom diagram of Figure 1, the decoder is staked with \(L_{\text{de}}\) decoder layers, and each layer contains three attention modules (i.e., self-attention, cross-domain attention, and cross-attention) to determine the correlations from different perspectives. The outputs of two encoders are integrated as the input of cross-attention by ConvFusion: \(\mathbf{Z}_{\text{en}}=\text{ConvFusion}(\text{Concat}([\mathbf{Z}_{\text{RS}}, \mathbf{Z}_{\text{LS}}]))\). The inputs of the decoder contain initialized seasonal part \(\mathbf{X}_{\text{de}}^{0}\) and trend part \(\mathbf{T}_{\text{de}}^{0}\), which are decomposed from the latter half of \(\mathbf{X}_{\text{LS}}^{0}\) and concatenated with scalar placeholders (zeros for the seasonal part and means for the trend part). The details in the \(l\)-th decoder layer are  
\[\begin{split}&\mathbf{Z}_{\text{de},1}^{l}=\text{Attention}( \mathbf{X}_{\text{de}}^{l-1}),\\ &\mathbf{Z}_{\text{de},2}^{l}=\text{CrossDomainAttention}( \mathbf{X}_{\text{de}}^{l-1},\mathbf{Z}_{\text{RS}}),\\ &\mathbf{Z}_{\text{de},3}^{l}=\mathbf{X}_{\text{de}}^{l-1}+\text{ ConvFusion}(\text{Concat}([\mathbf{Z}_{\text{de},1}^{l},\mathbf{Z}_{\text{de},2}^{l}])),\\ &\mathbf{S}_{\text{de},1}^{l},\mathbf{T}_{\text{de},1}^{l}=\text{ SDBlock}(\text{Attention}(\mathbf{Z}_{\text{de},3}^{l},\mathbf{Z}_{\text{en}})+\mathbf{Z}_{\text{de},3}^{l}),\\ &\mathbf{X}_{\text{de}}^{l},\mathbf{T}_{\text{de},2}^{l}=\text{ SDBlock}(\text{FeedFoward}(\mathbf{S}_{\text{de},1}^{l})+\mathbf{S}_{\text{de},1}^{l}),\\ &\mathbf{T}_{\text{de}}^{l}=\mathbf{T}_{\text{de}}^{l-1}+\mathcal{ W}_{1}^{l}*\mathbf{T}_{\text{de},1}^{l}+\mathcal{W}_{2}^{l}*\mathbf{T}_{\text{de},2}^{l}, \end{split} \tag{11}\]  
where \(\mathbf{Z}_{\text{de},i}^{l},i\in\{1,2,3\}\) is the intermediate feature; \(\mathbf{S}_{\text{de},1}^{l},\mathbf{X}_{\text{de}}^{l}\) denote the seasonal parts in \(l\)-th layer; \(\mathbf{T}_{\text{de},1}^{l}(i\in\{1,2\}),\mathbf{T}_{\text{de}}^{l}\) denote the trend parts in \(l\)-th layer; \(\mathcal{W}_{i}^{l}\in\{1,2\}\) denotethe project functions for the trend parts. After \(L_{\text{de}}\) decoder layers, the final prediction \(\hat{\mathbf{y}}\) is from the sum of two parts: \(\mathcal{W}*\mathbf{X}_{\text{de}}^{L_{\text{de}}}+\mathbf{T}_{\text{de}}^{L_{ \text{de}}}\), where \(\mathcal{W}\) is the projector for the seasonal part.
# Impact of Mega Constellations on Geospace Safety
## 2 Models and Assumptions
### Starlink Constellation Configuration and Selection of Target Satellites
#### 2.3.2 Target Satellite Parameters  
Three different types of satellites were selected as target satellites for the calculations, with the aim of assessing the impact on LEO satellites of different volumes, orbital altitudes, and orbital inclinations, with orbital data and satellite parameters from SpaceTrack and DISCOweb, as shown in Table 3. The orbital data for the three satellites were obtained from two rows of SpaceTrack elements TLE [29].
## 2 Materials and Methods
### NMR Experiments  
All spectra were recorded at 298 K on a Bruker AVANCE III spectrometer (Bruker BioSpin, Karlsruhe, Germany), equipped with a Bruker multinuclear z-gradient inverse probe-head operating at the proton frequency of 400.13 MHz.  
\({}^{1}\)H NMR spectra were acquired employing the _presat_ pulse sequence for solvent suppression with 64 transients, a spectral width of 6009.13 Hz, and 65536 data points for an acquisition time of 5.5 s. The recycle delay was set to 6.55 s to achieve complete resonance relaxation between successive scans.  
Bidimensional NMR experiments, \({}^{1}\)H-\({}^{1}\)H TOCSY, \({}^{1}\)H-\({}^{13}\)C HSQC, and \({}^{1}\)H-\({}^{13}\)C HMBC, were performed using the same experimental conditions previously reported [15]. Spectra were analyzed with ACD NMR manager software ver. 12 (ACD/Labs, Toronto, ON, Canada).  
Only the molecules univocally identified were considered for the study, and their quantification was performed by integration of their NMR signals. Due to the overcrowding of \({}^{1}\)H NMR spectra, only those signals that did not overlap with other resonances were considered for integration. Quantities were expressed in \(\mu\)mol/g through comparison of the relative integrals with the reference concentration and normalized to the number of protons (TSP: 9 protons and HMDS: 18 protons) and to the fresh weight of carrots.
###### Abstract  
In this study, the identification of dominant fish species in the East Sea was conducted using the dB-difference method. The survey was conducted using the two frequencies of 38 and 120 kHz in transect 6 of the southern part of the East Sea. Information on fish species was identified using fishing gear and e-DNA, and the dominant target fish species were selected and analyzed as cod, anchovy, common squid, and herring. The dB-difference range for each fish species was set to \(-\)0.86 dB \(<\)\(\Delta\)MVBS \({}_{\text{38-120\,Hz}}\)\(<\) 0.82 dB for cod and to the range of 2.66 dB \(<\)\(\Delta\)MVBS 38-120 kHz \(<\)2.84 dB for anchovy. The dB-difference of the common squid was set to \(-\)0.36 dB \(<\)\(\Delta\)MVBS 38-120 kHz \(<\) 1.25 dB and to the range of 0.88 dB \(<\)\(\Delta\)MVBS \({}_{\text{38-120\, kHz}}\)\(<\) 2.28 dB for herring; the fish species were then identified in the echograms. When comparing the results of swimming depths by fish species and previous studies, cod was detected mainly at the bottom of the sea, and anchovy and common squid were detected mainly at a depth of 50 m. Herring was detected to be mainly distributed in water depths from 50 to 150 m.  
acoustic survey; identification of fish species; distribution of fish +
Footnote †: journal:  
techniques of hydroacoustic survey, the dB-difference method is actively utilized as a tool for evaluating the density and biomass of the target organisms, as the method identifies fish species by the difference in frequency characteristics. There are various methods for evaluating fishery resources and the density and biomass of organisms using acoustic survey, such as echo counting, school detect, and time-varied threshold, but in this study, we use dB-difference in order to determine the spatiotemporal distribution of fish species, identifying the target strength of the target fish species and the length distribution of the collected fish species [6; 7; 8; 9].  
In this study, acoustic data were collected by means of a hydroacoustic survey in the southern part of the East Sea of South Korea, and the total length-body weight information of fish species was obtained using the bottom trawl survey. Then, seawater was collected, and e-DNA was analyzed to obtain information on fish species. For the efficient management and evaluation of fisheries resources, it is very important to understand the spatiotemporal distribution of fish species. Therefore, this study identifies fish using two frequency dB-difference methods and compares the spatiotemporal distribution of each fish species with that of a prior study.