esa-sceva/satcom-chunk-collection huggingface dataset:
# Combination Constraints of Multiple Fields for Implicit Modeling of Ore Bodies
## 4 Combination Constraints  
### Multiple Fields  
#### 4.1.1 Multi-Labeled Implicit Function  
For the signed distance field, the function values of \(f(\mathbf{x})\) are represented as signed distances. The function value of any point \(\mathbf{p}\) in the mineralized space is defined as the distance from \(\mathbf{p}\) to the closest point on the implicit surface \(S\). The points on the surface satisfy \(S=\left\{\mathbf{x}\in R^{3}\middle|f(\mathbf{x})=0\right\}\). The interior and exterior of the surface can be distinguished based on the sign of function values. Without loss of generality, we make the agreement that the interior of the surface is negative, and the exterior of the surface is positive, as shown in Figure 1.  
For the modeling space involving several fields, the existing multi-labeled implicit function [32; 33] can be used to define the combined field. In this definition, each sub-field is represented by a labeled implicit function. The function value of a point in each sub-field is also assigned a corresponding label, and the valued function of a point in the combined field is determined by the multi-labeled values according to the combination rules.  
The multi-labeled implicit function \(F(\mathbf{x})\) of the combined field can be defined as  
\[F(\mathbf{x})=f_{1}(\mathbf{x})\circ f_{2}(\mathbf{x})\circ\ldots\circ f_{\mathbf{n}}(\mathbf{x}) \tag{7}\]  
where \(f_{i}(\mathbf{x})\) is the implicit function of the \(i\)-th sub-field and the symbol \(\circ\) indicates a certain combination rule between fields.  
Figure 1: Schematic diagram of two signed distance fields (**c**,**d**) interpolated from two contours (**a**,**b**). The range of signed distance values corresponds to the color bar.  
The combined field is still represented by an implicit function. The modeling goal is to reconstruct the combined implicit function \(F(\mathbf{x})\). Compared with the single-field function, the main difference is that the function value of \(F(\mathbf{x})\) should be determined by several sub-functions via combination rules. As mentioned, each sub-field of the combined potential field can be expressed by different implicit functions. For example, \(f_{i}(\mathbf{x}),i=1,2,\ldots,n\) can be interpolated by different RBF-based methods. Therefore, the appropriate interpolation constraints and the corresponding implicit modeling method can be selected as needed for each sub-field.
# An Overview of Biopolymers for Drug Delivery Applications
## 2 Types of Biopolymers Used in Drug Delivery Systems
### Polysaccharides
#### 2.1.5 Hyaluronan  
Hyaluronan is a linear polysaccharide with disaccharide repeats of d-glucuronic acid and N-acetyl-d-glucosamine. Even though it is a simple linear chain, it has several important biomechanical properties [79]. Hyaluronan was demonstrated to present biocompatibility, biodegradability, high viscoelasticity, and can also be mixed with specific receptors on the cell surface [80].  
Hyaluronan has attracted attention as a drug delivery vehicle because it can recognize specific receptors that are overexpressed on the surface of tumor cells, and cancer drugs can be targeted to the tumor cells to better destroy them. Hyaluronan has been used extensively in controlled-release and targeted DDSs. Until now, most studies are only in an in vitro experimental phase; studies utilizing in vivo tests are very rare. However, it is believed that the prospect of hyaluron as a drug carrier will be larger with the discovery of new materials and the development of new technologies. The literature presents much research on hyaluron as a carrier for various drugs, but most of them are just in a laboratory study phase. Because of the complex processes involved, hyaluron is difficult to industrialize [81].
## 2 Cross-Domain Collaborative Task Allocation Model
### Mathematical Model of Target Allocation
#### 2.3.3 Based on Cluster Load Balancing  
In consideration of the varying abilities of each surface agent to transport the load within the cluster, we introduce the concept of load capacity balancing for the target distribution channel optimization in this model. To establish the target distribution channel cluster load model, we employ the root mean square of the workload in the target distribution channel on each platform, represented by Equation (4):  
\[P_{3}=\sqrt{\sum_{n=1}^{a}\left[\frac{S(n)}{r_{n}}\right]^{2}} \tag{4}\]  
where \(r_{n}\) is the number of available target allocation channels for the water surface agent \(W_{n}\) and the channel workload \(S(n)\) is assigned to the target of the water surface agent \(W_{n}\).  
Among them, the target allocation channel workload formed by various allocation schemes for an agent in the cluster can be expressed as Equation (5):  
\[S(n)=\mu S_{Intra}(n)+(1-\mu)S_{Inter}(n) \tag{5}\]  
where \(S_{Intra}(n)\) is the internal workload of the water surface agent \(W_{n}\), that is, the number of target allocation channels assigned to the main targets of the agent. In a target allocation scheme, a water surface agent primarily focuses on attacking four main targets using loads, while considering other targets as cooperative targets. The cooperative workload of the water surface agent, denoted as \(S_{Inter}(n)\), represents the number of target allocation channels assigned to the cooperative targets. Additionally, \(\mu\) represents the weight coefficient of the internal workload of the cluster within the total workload. In this research, the value of \(\mu\) was taken as 0.3 during the simulation calculation, as the collaborative workload typically requires more resources and is generally not greater than 0.5. If each water surface agent has 16 available target allocation channels, the cluster workload is 1 when there is no cooperation or full cooperation, corresponding to \(\mu\) being equal to 1 or 0, respectively. In the case of local cooperation (\(0<\mu<1\)), the cluster workload is less than 1.
# Manipulating Stress Responses during Spaceflight Training with Virtual Stressors
## 4 Materials and Methods
### Dependent Variables  
The study used both physiological and subjective indices of stress. The dependent variables are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dependent Variable** & **Sensor/Metric** & **Features/Type** & **Description, Association** & **Frequency** \\ \hline \multirow{6}{*}{Physiological stress response} & \multirow{2}{*}{Electrocardiogram (ECG) and Heart Rate Variability (HRV)} & HR & Heart rate & \\ \cline{3-5}  & & HRV & & The root mean of the sum of the squares of differences between beat intervals & \\ \cline{3-5}  & & & & beat intervals & \\ \cline{3-5}  & & (HRV) & & Proportion of the successive normal to normal beat intervals & \\ \cline{3-5}  & & & that differ more than 50 ms & \\ \hline \multirow{2}{*}{Physiological stress response} & Continuous Non-Invasive Blood Pressure & SBP & Systolic Blood Pressure & \\ \cline{3-5}  & & DBP & Diastolic Blood Pressure & \\ \hline \multirow{2}{*}{Physiological stress response} & Electrodermal Activity (EDA) & EDA & Tonic component (0–0.16 Hz) & \\ \hline \multirow{2}{*}{Subjective Stress} & Post Stress Task Reaction Measure (PTSR) & \multirow{2}{*}{9-point Likert scale} & After trial \\ \cline{3-5}  & & & & \\ \hline Subjective Stress & Free Stress Comparison & 100-point scale & & After experiment \\ \hline \multirow{2}{*}{Subjective Stress} & Short State Stress Questionnaire (SSSQ) & 24 items, 5-point Likert scale & Engagement, Distress, Worry & Before, after trial \\ \hline \multirow{2}{*}{Workload} & \multirow{2}{*}{NASA TLX} & \multirow{2}{*}{6 items, 21-point scale} & Mental demand, Physical demand, Temporal demand, Effort, and Frustration & After trial \\ \hline \multirow{2}{*}{Arxiety} & State-trait anxiety & 20 items, 7-point Likert & & \\ \cline{3-5}  & inventory (STAI) & scale & & After trial \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of dependent variable sensor/metrics and frequencies.  
Figure 5: VR-ISS emergency fire (low, medium, high stressor scenarios). Reproduced with permission from (45), IEEE, 2020.
## 3 Numerical Modeling  
### Case Study  
A full-scale submarine was chosen as a target body in this study. Three stern-rudder plane configurations, including Cross-plane rudders, X-plane rudders, and Y-plane rudders, with the same span of 7.4 m were considered. The stern-rudder plane configurations were created from the NACA0020 profile. Figure 2 presents the geometry and main dimensions of the rudder plane configurations and the submarine. The definitions of hydrodynamic forces and moments acting on the Cross-rudder plane, X-rudder plane, and Y-rudder plane configurations are shown in Figure 3. The two opposite blades in the X-rudder plane and Cross-rudder plane configurations are all deflected about the same rotational axis, and are parallel and 0.578 m from the leading edge. Meanwhile, a pair of blades in the Y-rudder plane configuration are rotated about the rotational axes with the same distance from the leading edge but in different directions. The rudder deflection, \(\delta_{r}\), is defined for each type of the rudder plane configuration, as shown in Equations (19)-(21). Furthermore, the angle of deflection of each stabilizer, \(\delta_{s}\), is described in the left-hand equations. The hydrodynamic forces acting on each rudder plane configuration are defined in Figure 3.  
\[\delta_{r\_Cross}=\frac{(\delta_{2}+\delta_{4})}{2} \tag{19}\]  
Figure 2: Main dimensions of the stern rudder configurations (in meters).  
\[\delta_{r\_X}=\frac{(\delta_{1}+\delta_{2}+\delta_{3}+\delta_{4})}{4} \tag{20}\] \[\delta_{r\_Y}=\frac{\delta_{1}+\delta_{2}+\delta_{3}}{3}\] (21) \[\delta_{s\_Cross}=\frac{(-\delta_{1}-\delta_{3})}{2}\] (22) \[\delta_{s\_X}=\frac{(-\delta_{1}+\delta_{2}-\delta_{3}+\delta_{4} )}{4}\] (23) \[\delta_{s\_Y}=\frac{(\delta_{2}-\delta_{3})}{2} \tag{24}\]
## The Ocean
### Observability of the Coastal Ocean Dynamics  
Satellite altimetry represents a mature technology in the open ocean but its exploitation remains an issue in the coastal ocean for different reasons. Land contamination in the altimeter footprint lead to a signal which is much more complex to analyze than offshore and often lead to the rejection of the altimetry measurements during the standard processing step. Geophysical corrections are significantly less accurate in the coastal area. The radiometer measurements used to derive the wet-troposphere correction becomes perturbed by land 25-50 km from the coast (depending on the instrument). The coastal ocean dynamics have also shorter space and time scales than the scales of open ocean dynamics which are mostly resolved by altimetry. Consequently, coastal altimetry data are largely discarded in standard products distributed to users and then largely unexploited. However, having as many accurate observations of the coastal ocean as possible is crucial for improving our understanding and monitoring of physical ocean processes that impact a wide range of applications (coastal area management, risk prevention, fisheries, marine safety and shipping, pollution, etc). In the context of climate change, we need to observe and understand how the coastal sea level variations, in particular extreme events such as storm surges, but also those associated to much lower time scales, impact the coastal morphology (via erosion). In the last years, a lot of efforts have been dedicated in the coastal altimetry community in order to extend the observational capabilities of altimeters always closer to the coastline (e.g., [[http://www.coastalal.eu](http://www.coastalal.eu)]([http://www.coastalal.eu](http://www.coastalal.eu))). The launch of the SARAL/AltiKa altimetry mission, with an instrument able to provide accurate sea level measurements at a higher spatial resolution and closer to the coastline, is a major step forward.
## Appendix A
\begin{table}
\begin{tabular}{l l l} \hline
**Description** & **Name** & **Values** \\ \hline Process variables & & \\ \hline Anoxic reactor volume (m\({}^{3}\)) & \(V_{1}\) & 2000 \\ Aerobic reactors volume (m\({}^{3}\)) & \(V_{2}\) & 3999 \\ Active heterotrophic biomass concentration (gr COD/m\({}^{3}\)) & X\({}_{\text{B,H}}\) & 2500 \\ Active autotrophic biomass concentration (gr COD/m\({}^{3}\)) & X\({}_{\text{B,A}}\) & 150 \\ Dissolved oxygen concentration (gr COD/m\({}^{3}\)) & S\({}_{\text{O}}\) & 0.1–5 \\ Nitrate and nitrite concentration (gr N/m\({}^{3}\)) & S\({}_{\text{NO}}\) & 0.1–5 \\ NH\({}_{4}\) + NH\({}_{4}\) concentration (gr N/m\({}^{3}\)) & S\({}_{\text{NH}}\) & 0–14 \\ Readily biodegradable substrate concentration (gr COD/m\({}^{3}\)) & S\({}_{\text{S}}\) & 0–40 \\ Influent flow rate (m\({}^{3}\)/h) & Q\({}_{in}\) & 45–2500 \\ Effluent flow rate (m\({}^{3}\)/h) & Q\({}_{e}\) & 45–2500 \\ Readily biodegradable substrate concentration in the influent (gr COD/m\({}^{3}\)) & S\({}_{\text{Si,in}}\) & 45–140 \\ Ammonium compounds concentration in the influent (gr N/m\({}^{3}\)) & S\({}_{\text{Ni,In}}\) & 8–55 \\ Internal recycle flow (m\({}^{3}\)/h) & Q\({}_{a}\) & 180–2000 \\ Oxygen transfer coefficient (1/h) & \(KLa\) & 0–10 \\ \hline Biological processes parameters & & \\ \hline Oxygen saturation concentration (gr/m\({}^{3}\)) & S\({}_{\text{O,atf}}\) & 8 \\ Heterotrophic max. specific growth rate (1/day) & \(\mu_{H}\) & 4 \\ Half saturation coefficient for heterotrophs (gr COD/m\({}^{3}\)) & K\({}_{\text{S}}\) & 10 \\ Oxygen saturation coefficient for heterotrophs (gr COD/m\({}^{3}\)) & K\({}_{\text{OH}}\) & 0.2 \\ Ammonia saturation coefficient for heterotrophs (grN/m\({}^{3}\)) & K\({}_{\text{NH}}\) & 1 \\ Oxygen saturation coefficient for autotrophs (gr COD/m\({}^{3}\)) & K\({}_{\text{O,A}}\) & 0.4 \\ Heterotrophic yield (gr cell COD formed/gr COD oxylated) & Y\({}_{\text{H}}\) & 0.67 \\ Autotrophic yield (gr cell COD formed/gr N oxydized) & Y\({}_{\text{A}}\) & 0.24 \\ Nitrogen fraction in biomass (gr N/gr COD) & \(i_{\text{QB}}\) & 0.08 \\ \hline \end{tabular}
\end{table}
# Prompt Language Learner with Trigger Generation for Dialogue Relation Extraction
## 3 Materials and Method
### Trigger Generation  
Since triggers are absent in a significant number of examples in the dataset, we intend to consider the critical information in predicting an appropriate relation by directly generating it. Unlike the explicit span extraction method, the generative approach is expected to produce implicit answers. The purpose of the generated triggers is to enhance the relation prediction capability by feeding them as one of the input features of the model. In contrast to previous trigger-related studies, our approach employs a generative model  
Figure 1: This figure demonstrates the overall model architecture. The model solved the task with the tokens from a model’s vocabulary with the task’s formalization as an MLM problem. For example, the label words in the prompt-based fine-tuning phase are from the model’s vocabulary. Descriptions of each module (a), (b), and (c) are provided in each subsection.  
with an encoder-decoder architecture, considering both the given entity pair \((e_{1},e_{2})\) and the given dialogue \(D\).  
Our trigger generation module is illustrated in part (a) in Figure 1. This module generates \(GenTr\) (generated triggers) considering the given context and entity pair, thereby identifying the relation \(r\) using \(D\) and entity pair \(E\), i.e., \(f(D,E)\to R\). A single entity pair can include multiple relations in this process. As shown in the left part of Figure 1, the trigger generation module consists of encoder-decoder architecture by adopting the pre-trained BART [38] model. The input in fine-tuning step is constructed as \"
 \(D\) 
 \(E\) 
\". The module is taught to identify optimal triggers by defining the triggers of some examples where triggers exist as labels among the examples in the DialogRE dataset, and to generate triggers using the decoder of BART model. The number of generated triggers may be one or several, and the triggers generated in this way are also used as input features to the relation prediction module in the later step. Specifically, for given \(D,E\), the trigger generator is trained as follows:  
\[P(TR|D,E)=\prod_{i=1}^{n}p(r_{i}|D,E,tr_{<i},\theta), \tag{1}\]  
where \(TR\) is the ground-truth trigger sequence to generate and \(\theta\) is the parameter set of the trigger generation module.
# A Wide-Ranging NMR Probe for Multiscale Structures
## 2 Basic Principles and Models
### Proposed Models
#### 2.2.4 Xenon Dissolved in Polymers  
In 1993, [PERSON] et al. proposed a model for the chemical shift of xenon dissolved in polymers above the glass transition temperature [41]. They assumed that, in these particular materials, xenon experiences a liquid-like environment and, consequently, that its chemical shift can be attributed to solute-solvent interactions. Thus, they described the chemical shift in terms of van der Waals interactions, not considering the bulk susceptibility of the polymers, their magnetic anisotropy, and their permanent electric dipole moment. In their model, xenon is treated as a large diffusant molecule inside a polymer matrix,which is not only able to occupy the intrinsic free volume of the polymer but also to carve its own pockets thanks to the fluid-like behavior of the rubbery chains. By considering that, in these materials, free volume fluctuates rapidly and, generally, xenon has a high diffusion coefficient, the average free volume environment experienced by xenon atoms in the NMR time scale was modeled, and its van der Waals interaction with the neighboring polymer chains was described using a Lennard-Jones 6-12 potential. Taking into account the diameter of the polymer chains, their equilibrium spacing and the spacing induced by the presence of a diffusant molecule, they were able to calculate the theoretical chemical shifts of xenon in a series of polymers. The applicability of the model was confirmed by experimental data on various samples such as polyethylene (PE), poly(vinyl ethylene) (PVE), polydimethylsiloxane (PDMS), and many other polymers.  
The importance of this model lies in the representation of the environment experienced by xenon in the NMR time scale: in polymers, it is extremely different from rigid materials, such as silica and zeolites, widely studied at the time, in which the porosity is static and stable over time. In addition to that, this model shows that each polymer has its own characteristic \({}^{129}\)Xe chemical shift, which can be used to characterize polymers and polymer blends as well as to monitor phase separation in these materials, as will be shown in Section 5.2.
## 6 Existing and Potential Industrial Applications
### Harmless Treatment of Pollutants  
The advantages like efficient and thorough removal of high-concentration pollutants are a paramount driving force for the development of SCHC technology. Many scholars found that this flame can remove nearly 99% pollutants within shorter residence time (a few seconds). Up to now, recalcitrant compounds or intermediates controlling reaction rate include sludge [38], quinoline [41], naphthalene [51], toluene [67], acetic acid [32], and ammonia nitrogen [32]. Their research status can be found in stated contents, so no detailed introduction is given here.  
The reactor is still the core for the harmless treatment of pollutants, but there are few relevant criterions for its structure design and scale-up design. At present, these treatments have been carried out with a relatively small flow rate. The experimental results of ETH showed that the ignition temperature and extinction temperature were strongly affected by the size of the nozzle. The experiment and simulation results of the University of Valladolid showed that the propagation speed of the flame was smaller than that of gas-phase flame. In the scale-up design of the reactor, controlling the flow velocity of reactants in the reactor can ensure an excellent flame stability. In the future, the internal structure design of HFR with a vast flow that can ensure reliable ignition and stable flame will largely determine the successful commercial applications of hydrothermal combustion in the field of pollutant treatment.  
Figure 11: Schematic diagram of SCHC coupled power generation system (adapted from [21]) 1—Coal mill; 2—Pump; 3—SCHC system; 4—Ash bucket; 5—Valve; 6—Gas-liquid separator; 7—High-pressure turbine; 8—Generator; 9—Preheater; 10—Medium-pressure turbine; 11—Low-pressure turbine; 12—Condenser; 13—CO\({}_{2}\) recovery unit; 14—Water preheater; 15—Reprocessing device; 16—Circulating pump; 17—Air compressor; 18—Air preheater; 19—Steam extraction and heat recovery device.
# Effect of Water Content on Light Nonaqueous Phase Fluid Migration in Sandy Soil
## 3 Experimental Results and Analysis
### LNAPL Precipitation Law  
Applying the above experiment method, each set of experiments was repeated several times using orthogonal array testing, and the sets with oil flow during the experiment process were recorded as \(\surd\) and those without as \(\times\). Only when the water-LNAPL in the medium's pores reaches the critical value of two-phase saturation can free-phase LNAPL continue to migrate downward, and the greater the water content in the medium, the smaller this critical value becomes. The study found a certain linear relationship with the critical value of water-oil two-phase saturation, as shown by the red line in Table 4, using Origin software (v.10.1) for curve fitting and analysis of the critical oil flow data to obtain the exponential function of oil content \(y\) and water content \(x\) under oil flow, i.e.,  
\[y=15.28-0.77x \tag{3}\]  
where \(y\) is the oil content, \(x\) is the water content, and the fitted regression equation R\({}^{2}\) = 0.96, indicating that the measured data fit well with the fitted function curve.  
LNAPL exists in the unsaturated zone in the following forms: free-flowing liquid phase, adsorbed phase (adsorbed on the surface of soil particles), dissolved phase (dissolved in water), and volatile phase (volatilized in the atmosphere). Table 4 shows that oil flow has a lower triangular 0-shaped matrix. When the LNAPL and water content in the soil are above the linearly fitted oil flow point, part of the LNAPL occurs in the soil as an adsorbed phase, and part of it migrates to the groundwater as a free phase, causing groundwater pollution. When the water content of the contaminated site is below 14%, or the oil content is below 11%, LNAPL will exist in a solid phase through adsorption. When the water content in the experiment was greater than 14%, or the oil content was greater than 11%,  
Figure 6: Surface infiltration rate versus time. (**a**). Descent depth versus time, (**b**). Descent depth fitted residual plot, (**c**). Descent rate versus time, (**d**). Descent rate residual plot.