esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In the  
Figure 2: Left: Hydroxylation of DMPO into DMPO–OH and mechanism of nucleophilic addition of water on DMPO in the presence of Cu, also leading to the DMPO–OH adduct, as described by [PERSON] et al. [15]; Right: Typical EPR spectrum of DMPO–OH [14; 16; 17]. case of Cu(II), the metal binds to DMPO and the polarization of the DMPO's double bond promotes the nucleophilic attack of H\({}_{2}\)O (Figure 2). To confirm that the observed DMPO-OH adduct originates from HO\({}^{\bullet}\) addition and not from these above-mentioned side reactions, an indirect method using scavenger molecules can be applied. The selected scavengers should be more reactive towards hydroxyl radicals than DMPO (rate constants higher than that of the spin-trap, k(DMPO/HO\({}^{\bullet}\))) and could be added at high concentrations. Among scavengers, dimethyl sulfoxide (DMSO) and formate (HCOO\({}^{-}\)) are often used, and the resulting adducts are DMPO-CH\({}_{3}\)\({}^{\bullet}\) with a typical six-line signal (a\({}_{\rm N}\) = 16.1 G, a\({}_{\rm H}\) = 23 G) and DMPO-COO\({}^{\bullet-}\) with another six-line pattern (a\({}_{\rm N}\) = 15.75 G, a\({}_{\rm H}\) = 19.09 G), see Figure S1 (Supplementary Materials). While the spin-trapping technique has been extensively used to study hydroxyl radical formation by H\({}_{2}\)O\({}_{2}\)/Cu(I) [15] and H\({}_{2}\)O\({}_{2}\)/Cu(II) systems [21] especially in biology at physiological pH, less information is available when alkaline solutions are considered. In this context, this paper deals with the investigation of the decomposition of hydrogen peroxide into hydroxyl radicals in the presence of copper(II)-phenanthroline complexes in both neutral and alkaline aqueous media.
# Image-Based Dedicated Methods of Night Traffic Visibility Estimation
## 3 Proposed Visibility Estimation Methodology  
In this section, the proposed visibility estimation process is to be demonstrated. Firstly, obtain the atmospheric transmissivity of the current environment. Then, combine the DCP algorithm with brightness and contrast to obtain image visual visibility indicators. Finally, merge the multiple visibility indicators by MNLR analysis. The obtained visibility results are calibrated to span the range [10, 10000], where the lower limit corresponds to the worst visibility possibly, and the upper limit corresponds to the visibility which has no adverse effects on people's travel.
# Adaptation in Gait to Body-Weight Unloading
## 1 Introduction
Besides having acute effects on gait performance, exposure to BWU for a short period of time in the order of a few minutes and subsequent reloading can alter movement characteristics [29; 30]. Further, prolonged exposure to BWU for a longer period of time in the order of a few months can also produce significant changes in movement characteristics that last for a significant period of time as seen in crewmembers returning from space [20; 31; 32; 33; 34]. Due to the fact that adaptive changes produced during spaceflight is coupled with changes in structural and functional characteristics of the neuromuscular system, we cannot compare it with short-term changes that we observe on the ground that are solely produced by reduced body load. However, studying the adaptive changes to short-term BWU can improve our understanding of the mechanism adopted by the sensorimotor system during adaptation. Also, with the increase in use of BWU for treadmill training in patients with neurological impairments, studying the adaptive changes to short-term BWU may provide valuable information to practitioners who administer gait or balance rehabilitation using it [21; 35; 36]. The main objective of this study was to investigate changes in locomotion produced by 30 min of walking at a reduced body weight, which roughly relates to that of Martian gravity (38% of normal body weight). Locomotion was assessed by measuring temporal gait parameters, changes in joint angles, and neuromuscular activation of the lower limbs before, during, and after the adaptation session. We also assessed the adaptive changes in subjective body-weight perception as a function of movement context (walking, standing, and sitting). Body-weight perception was assessed using a customized weight scale. It was hypothesized that the effect of unloaded walking on weight perception would vary as a function of movement context.
## 2 Materials
### International Soil Moisture Network  
ISMN is a data-hosting facility developed and still maintained by several universities [55; 56; 57]. It is supported by the European Space Agency's (ESA) Earth Observation program. The ISMN stations include soil texture properties and SM values in time, freely available at [[https://ismm.geo.tuwien.ac.at/](https://ismm.geo.tuwien.ac.at/)]([https://ismm.geo.tuwien.ac.at/](https://ismm.geo.tuwien.ac.at/)) (accessed on 17 August 2022). When we started the algorithm development, the total number of available stations was 1611 after 2017, when S1 data became available. The locations of the stations cover different climates and ecoregions. However, \(\sim\)70% of the available stations were located in the USA (see Figure 1).  
In addition to the station locations, in Figure 2, we present the ternary distribution of the soil data. Ternary distribution depicts the data in a 3D space, making it simpler to understand relations. Figure 2 shows that most soil samples are located in the loam class, followed by sandy loam, clay loam, and silty loam.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Category** & **Feature Description** & **Spatial Res.** & **Temporal Res.** \\ \hline Climate Data 1  & T(\({}^{\circ}\)C), ET (mm) \& P (mm) & 1 to 5 km & Daily \\ Satellite Data 2 (S1) & VV, VH \& VH/VV & 10 m & 6–12 days \\ Satellite Data (SMAP) & Surface \& Subsurface SM (mm) & 10 km & 3 days \\ Soil Texture & Sand, Clay, Silt (\%) & Point-wise & Constant Values \\ Topographical Data 3  & H (m), S (\({}^{\circ}\)), A (\({}^{\circ}\)), HS (\({}^{\circ}\)) & 30 m & Constant Values \\ Soil Moisture Data & SM of top 5 cm (m\({}^{3}\)/m\({}^{3}\)) & Point-wise & 15 min & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data used in this research provided with its descriptions, spatial, and temporal resolutions.  
Figure 1: The spatial distribution of ISMN sites. Red dots display the distribution of 103 stations with reliable data.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Feature** & **Mean** & **Std** & **Feature** & **Mean** & **Std** \\ \hline Temperature (T) & 8.81 & 11.03 & Sand & 42.81 & 13.91 \\ Evapotranspiration (ET) & 2.80 & 1.96 & Clay & 18.77 & 6.90 \\ Precipitation (P) & 2.64 & 11.01 & Silt & 38.42 & 10.87 \\ VV & 0.019 & 0.019 & Elevation (H) & 1400.48 & 1150.57 \\ VH & 0.088 & 0.076 & Slope (S) & 7.55 & 7.16 \\ VH/VV & 0.229 & 0.281 & Aspect (A) & 162.99 & 104.83 \\ SMAP SM (Surface) & 14.70 & 8.62 & Hillshade (HS) & 180.10 & 23.09 \\ SMAP SM (Subsurface) & 52.56 & 37.97 & Soil Moisture (SM) & 0.18 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The statistics of features used in the study.  
Along with the soil texture and SM data, the metadata of each station includes land cover based on the ESA CCI land cover product [58] and Koppen-Geiger climate classes [59]. It should be noted that these data were used only for the evaluation of the model performance w.r.t. varying land cover and climate class of the stations, not for training the model.
## 4 Simulation Experiment  
### Simulation Method  
To verify the validity of the MTF calculation method, a fast-steering mirror (FSM) and vibration platforms are usually required to give rise to sinusoidal vibration and linear image motion. In this paper, we used a mixed signal of sinusoidal and linear motion to actuate the FSM, which generated a complex motion at the focal plane in one direction. Figure 4 shows the experimental optical system [21; 22]. However, the results may be inaccurate because of the pointing error of FSM and the aberrations of the optical system, when the motions are complicated.  
Considering the actual working process of the FSM in an optical system, we proposed a simulation method to calculate the MTF in an optical system affected by vibration. In the simulation experiments, all of the data and simulation images were obtained from ZEMAX (2009, ZEMAX Development Corporation, USA and 2009) and MATLAB(R2012b, MathWorks, USA and 2012) [23; 24]. We designed a simple optical system based on Figure 4 using ZEMAX as shown in Figure 5a. It was made of two groups of system: FSM and convergent lens. The point spread function (PSF) of the system and its data are seen in Figure 5b,c. According to the PSF acquired in ZEMAX, the actual process of the real system can be simulated by a simulation method programmed by MATLAB. The simulation method used ZEMAX to get the PSF of the optical system, and used MATLAB to simulate the effects of the image motions on the PSF. In that case, the MTF of the optical system affected by the image motion can be calculated by the PSF. The method simulates the influence of image motion on the MTF of the optical system in a period of time. The intuitive explanation for this method is as follows. Ideally, the reciprocating motion of the FSM causes the system PSF response to move spatially, without affecting the distribution of the intensity. These displacements are integrated during the exposure. The PSF for the image motion is obtained by adding up and normalizing the PSFs of the optical system with multiple instantaneous moments, which are sampled in a single exposure time by changing the position of the intensity matrix based on MATLAB. Thus, the MTF for the image motion can be calculated by Equation (25).  
\[\text{MTF}=\int\limits_{-\infty}^{\infty}\text{PSFexp}(-\text{i}2\pi\text{fx })\text{dx} \tag{25}\]  
Figure 4: Sketch of the experimental setup. FSM: fast-steering mirror.
## 3 Results  
### Accelerations Results  
The acceleration data of U14 basketball players (ages 13-14) collected during official competition was categorized into five intensity zones based on magnitude. From a total of approximately 74 million recorded actions, soft acceleration (\(<\)0.374 m/s\({}^{2}\)) accounted for the largest proportion, representing 43.4% of all data (\(\approx\)32.2 million instances), with a mean value of 0.146 m/s\({}^{2}\). Low acceleration (0.374-0.811 m/s\({}^{2}\)) comprised 18.7% of the dataset (\(\approx\)13.9 million), with an average of 0.603 m/s\({}^{2}\). Moderate acceleration (0.811-1.545 m/s\({}^{2}\)) represented 32.2% (\(\approx\)23.9 million), with a mean of 1.019 m/s\({}^{2}\) (Table 2).  
Higher intensity efforts were less frequent. High acceleration (1.546-3.496 m/s\({}^{2}\)) constituted 5.3% of the total (\(\approx\)3.9 million), with an average of 2.074 m/s\({}^{2}\), while maximum acceleration (\(>\)3.496 m/s\({}^{2}\)) was rare, comprising only 0.5% of actions (\(\approx\)372,000), with a mean value of 4.920 m/s\({}^{2}\) (Table 2).  
Approximately 44% of all game actions performed by U14 players occur under low-intensity acceleration conditions, specifically within the range of 0 to 0.374 m/s\({}^{2}\). When considering all actions with accelerations up to 1.545 m/s\({}^{2}\) (encompassing soft, low, and moderate intensities), this proportion rises to 94.25% of the total. In contrast, high-intensity accelerations (1.546-3.496 m/s\({}^{2}\)) account for only 5.25% of actions (\(\approx\)3.9 million), with a mean value of 2.074 m/s\({}^{2}\). Maximum-intensity accelerations (\(>\)3.496 m/s\({}^{2}\)) are rare, comprising just 0.5% of actions (\(\approx\)371,600), with a mean of 4.920 m/s\({}^{2}\). Overall, only 5.75% of game actions are performed at high or maximum acceleration intensities (Figure 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Accelerations (m/s\({}^{2}\))** & **Soft** & **Low** & **Moderate** & **High** & **Maximum** \\ \hline Cluster centers & 0.146 & 0.603 & 1.019 & 2.074 & 4.920 \\ Ranges & \(<\)0.374 & 0.374 to 0.810 & 0.811 to 1.545 & 1.546 to 3.496 & \(>\)3.497 \\ n & 32,247,093 & 13,860,517 & 23,938,208 & 3,901,757 & 371,596 \\ \% & 43.39 & 18.65 & 32.21 & 5.25 & 0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the five-k means cluster of the variable acceleration.
# Full-Abstract Biomedical Relation Extraction with Keyword-Attentive Domain Knowledge Infusion
## 2 Related Work
### Document-Level RE  
Document-level RE has recently gained increasing interest in the NLP community since documents provide richer semantic information than sentences. Several datasets that focus on document-level RE have been developed, such as CDR [35; 36], DocRED [37], and GDA [38], which have driven the development of innovative models. One line of prior efforts [5; 6; 7; 10] explored ways to conduct inter- and intra-sentence reasoning [25; 39], a major challenge in document-level RE.  
[PERSON] et al. [10] employed a maximum entropy (ME) model and a CNN model for inter- and intra-sentence RE, respectively. Bi-affine Relation Attention Network (BRAN) [40] stacks a series of transformers [41] followed by head and tail MLPs and a bi-affine operation that encodes the pairwise token prediction in a 3D tensor. Graph neural networks (GNN) [5; 6; 7; 8] have also been a popular choice due to their intuitive modeling ability in RE, where named entities and relations can be modeled as nodes and edges in a graph. [PERSON] et al. [5] developed a GNN-based model to capture both local and non-local dependency between entity mentions.  
Similarly, [PERSON] et al. [6] designed a GNN model, named GLRE, that encodes and aggregates global and local entity and relation representation. [PERSON] et al. [7] proposed an edge-oriented (EoG) GNN that leverages multi-instance learning to enhance intra- and inter-sentence reasoning. Compared to the prior studies that focused on modeling and reasoning, our work focuses on domain knowledge infusion, which has not been extensively explored in the field of document-level RE. One relevant work is by [PERSON] et al. [12], which injected domain ontology knowledge into the model, resulting in performance gains. On the other hand, our work investigates the role of keywords, which is a novel method of knowledge infusion.
## 5 The INS-Aided Acquisition Experiments with Different Grade INS
### Velocity and Position Error Using Different INS  
In order to analyze the effects of the INS errors on the performance of Doppler shift and code phase estimations conveniently, the initial position and velocity are assumed known without errors. To calculate the Doppler shift error and code phase error, the relative position and velocity errors between the receiver and the satellite caused by the gyro and accelerometer errors are illustrated in Figures 9-12 with the four different grades of INS, and the maximum errors in 180 s are further listed in Table 4. From the above results, we can draw the conclusion that the velocity and position errors increase as the INS decrease in quality.  
Figure 11: Velocity and position errors of tactical grade INS: (**a**) the velocity error curve of tactical grade INS; (**b**) the position error curve of tactical grade INS.  
Figure 12: Velocity and position errors of inertial grade INS: (**a**) the velocity error curve of inertial grade INS; (**b**) the position error curve of inertial grade INS.  
Figure 10: Velocity and position errors of civil grade INS: (**a**) the velocity error curve of civil grade INS; (**b**) the position error curve of civil grade INS.
## 4 Analysis and Comparison
### Comparison of Algorithms  
In this section, we mainly focus on three dimensions for packet detection algorithms: the detection rate, the synchronization accuracy, and computational complexity. To make the comparison reasonable, we use the uniform simple decision rule. The IEEE 802.11 ah protocol is used for the simulation experiments. The added noise signal is AGWN and the SNR changes from 0 to 10. To obtain a reliable result, each value is averaged by 10,000 times simulation experiments.
# Experimental Tests and Aeroacoustic Simulations of the Control of Cavity Tone by Plasma Actuators
## 3 Comparison and Validation
### Sound-Pressure Spectra  
Figure 13 shows the measured and predicted sound-pressure spectra both with and without control, where the frequency resolution is \(\Delta\)St = 0.1 (\(\Delta f=156\) Hz), where St represents the Strouhal number based on the cavity length and the freestream velocity. The spectra were averaged 9374 and 18 times, respectively, in the experimental and computational results. The predicted fundamental frequency around St = 1.0 agrees well with that measured both with and without control. The sound-pressure level at the fundamental frequency also is in good agreement with that measured, both with and without control, where the level is reduced by the control. The predicted and measured reduction levels were 5 and 6 dB, respectively.  
The discussions in the previous and present subsections confirm that the present computations adequately predict the flow and sound fields, both with and without control.  
Figure 11: Vortices induced by actuators in the absence of a freestream flow: (**a**) visualized flow in the experiment; and (**b**) the predicted velocity vectors color-coded by values of the vertical velocity.  
Figure 12: Predicted and measured mean velocity profiles with and without control (\(x/L=0.05\), \(z=z_{\rm pa}\)).
## 3 Results
### PSGE Regulated the NF-\(\kappa\)B-Mediated Inflammatory Responses  
The effect of PSGE on pro-inflammatory cytokines such as TNF-\(\alpha\) and interleukin 6 (IL-6) was investigated in LPS stimulated RAW 264.7 cells. PSGE significantly suppressed IL-6 levels at 100 and 200 \(\mathrm{\SIUnitSymbolMicro g}\)/mL and TNF-\(\alpha\) levels at 200 \(\mathrm{\SIUnitSymbolMicro g}\)/mL (Figure 3A,B). Notably, at 200 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, PSGE inhibited IL-6 more strongly (0.55-fold), similar to DXM, compared to TNF-\(\alpha\) (0.75-fold). Based on these anti-inflammatory effects of PSGE, we determined  
Figure 1: Liquid chromatography-mass spectrometry chromatogram and cell viability. (**A**) Baicalin, (**B**) Baicalein, (**C**) Wogonin, (**D**) _Scutellaria hicalensis_ Georgi extract (SGE), (**E**) Processed _Scutellaria hicalensis_ Georgi extract (PSGE).  
Figure 2: Cell viability and effects of PSGE against LPS-induced NO and PGE\({}_{2}\) production and related iNOS, COX-2, mPGES-1 and 15-PGDH expression in RAW 264.7 cells. (**A**) Cell viability of PSGE in RAW 264.7 cells. RAW 264.7 cells were treated with 50\(-\)1000 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of PSGE for 24 h and cell viability was determined by MTT assay. (**B**) NO levels were measured by Griess assay, (**C**) PGE\({}_{2}\) levels were measured by ELISA assay, (**D**) Protein expression and quantitative analysis of (**E**) iNOS, (**F**) COX-2, (**G**) mPGES-1 and (**H**) 15-PGDH expression relative to \(\beta\)-actin. RAW 264.7 cells were pre-treated with indicated concentration of PSGE or DXM for 2 h followed by LPS for 24 h. DXM was used as positive control. Data shown represent mean \(\pm\) SEM (\(n=3\)). (\(\#\#\)\(p<0.001\) vs. control; \({}^{*}\)\(p<0.05\), \({}^{**}\)\(p<0.01\), and \({}^{**}\)\(p<0.001\) vs. LPS).  
whether PSGE could modulate nuclear translocation of NF-kB, a transcription factor associated with pro-inflammatory responses. PSGE significantly down-regulated the nuclear expression of NF-kB p65 by 0.70-fold (at 100 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL) and 0.57-fold (at 200 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL) respectively compared to LPS-treated cells (Figure 3C,D). Inhibitor kappa B-alpha (Ik\(\alpha\)) phosphorylation involved in the regulatory steps for LPS-mediated NF-kB activation was determined by Western blot analysis. Ik\(\alpha\) phosphorylation increased by 40-fold in LPS-treated cells but PSGE treatment significantly attenuated this effect dose-dependently (Figure 3E,F). Next, we examined the role of Heme Oxygenase-1 (HO-1) on NF-kB p65 nuclear translocation. PSGE treatment augmented HO-1 expression by 1.55-fold (at 100 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL) and 3-fold (at 200 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/mL) compared to LPS-treated cells (Figure 3E,G). Our data suggested that PSGE upregulated HO-1 expression, maintained the retention of Ik\(\alpha\)/NF-kB complex, and prevented the release of NF-kB into the nucleus.
## 1 Introduction
Hexagonal models allow for a better movement representation in 2D due to the uniform distance in any direction. Figure 1: Both the ants and the Unmanned Aerial Vehicles (UAV) forage resources. They must be able to identify dynamic resources with limited energy and temporal constraints. (**a**) Ants foraging; (**b**) UAV performing Data Foraging-Oriented Reconnaissance (DFORE). We assume the aerial vehicle has limited movement capabilities, and for this reason, the aerial vehicle needs to be recharged as many times as needed at a base station. The required movement capacity of the aerial vehicle per trip to explore an operational environment is identified based on the size of the hexagonal grid. The algorithm accomplishes the temporal constraints that are defined for DFORE. It is proved in a formal way that the algorithm satisfies such constraints. A computational cost analysis and a simulation are presented to show the viability of our solution. We compare our proposed algorithm to MULES [10], adapted to the foraging reconnaissance task, which is a random walk with uniform distribution algorithm to collect data from the environment. MULES has similarities with our proposed mechanism, they both use indirect communication through an intermediary in our case artificial pheromones and in MULES the use of a mobile data relay. We measured the number of trips by each algorithm in different conditions. The comparison with MULES is justified since this proposal is the baseline algorithm for indirect communication among mobile sensors. Also, this algorithm is extensively used in recent works to solve problems like patrolling, source location privacy, data collection, etc. [11; 12; 13]. The organization of this document is as follows: A survey of recent literature is presented in Section 2. In Section 3, the preliminaries are explained along with the system model. Our proposed solution is presented in Section 4. The analysis of the proposed algorithm is shown in Section 5. In Section 6, the proof to validate our proposed algorithm is discussed. A series of experiments are shown in Section 7. The discussion of our algorithm is presented in Section 8. Conclusions are presented in Section 9. As a quick guide to follow this work, the notation is presented in Table 1.