esa-sceva/satcom-chunk-collection huggingface dataset:
# Physico-Chemical Characterization of Sesame/Rapeseed Oil Mixtures
## 3 Results
### Chemical Characterization of the SO/RSO Mixtures
According to literature data, the crystallization of vegetable oils takes place at three characteristic temperatures, corresponding to the phase transitions of saturated fatty acids (around \(-10\)\({}^{\circ}\)C), monounsaturated fatty acids (around \(-35\)\({}^{\circ}\)C) and polyunsaturated fatty acids (around \(-54\)\({}^{\circ}\)C) [37]. According to the results presented in Figure 1 and Table 5, these three peaks are distinguishable for rapeseed oil and mixtures with a higher concentration of rapeseed oil (up to a ratio of 30/70). These observations may be related to the almost two times higher content of monounsaturated fatty acids in rapeseed oil compared to sesame oil (see Table 1). It is clearly seen from Figure 1 that as the concentration of rapeseed oil increases, the low-temperature peak shifts to higher temperatures from \(-53.2\)\({}^{\circ}\)C to \(-40.9\)\({}^{\circ}\)C and its enthalpy increases more than twice (from \(-27.85\) J/g to \(-58.38\) J/g). At the same time, the high-temperature peak shifts to lower temperatures (from \(-9.3\)\({}^{\circ}\)C to \(-18.0\)\({}^{\circ}\)C), and its enthalpy decreases (from \(-5.575\) J/g to \(-0.308\) J/g). These trends may be related to the different fatty acid compositions of the two pure oils. According to the results presented in Table 5, linear relationships with a very high correlation coefficient were established between the crystallization temperatures of the mixtures and the content of sesame oil, as well as between the enthalpies of crystallization and mixtures' composition. During the heating, the DSC curves of both oils and their mixtures show a double endothermic peak, corresponding to the oil melting phenomena--Figure 2. The peak is asymmetric, and its shape changes with sesame oil concentration. At high sesame oil content, a distinct low-temperature peak is observed, which decreases with increasing rapeseed oil concentration. It transforms into a shoulder to the main peak at rapeseed oil concentration above 50%. There is a linear dependence between the melting temperature of the mixtures and their composition with a high correlation coefficient. The good correlation dependences between the temperatures of the phase transitions of the SO/RSO mixtures and their composition give reason to consider that the DSC method can be used in the identification of the composition of mixtures between sesame oil and rapeseed oil.
# Contribution of Gray and White Matter Abnormalities to Cognitive Impairment in Multiple Sclerosis
###### Abstract  
Patients with multiple sclerosis (MS) commonly exhibit cognitive impairments (CI). However, the neural mechanisms underlying CI remain unclear. The current study applied diffusion tensor imaging (DTI) and voxel-based morphometric (VBM) magnetic resonance imaging (MRI) techniques to evaluate differences in white matter (WM) integrity and gray matter (GM) volume between MS patients with CI and MS patients with cognitive preservation (CP). Neuropsychological assessment and MRI were obtained from 39 relapsing-remitting MS (RRMS) patients and 29 healthy controls (HCs). Patients were classified as CI or CP according to cognitive ability, and demographic characteristics and MRI images were compared. Compared with HCs, MS patients exhibited widespread damage in WM integrity, and GM loss in several regions. Compared with CP patients, CI patients exhibited more extensive WM impairments, particularly in the corpus callosum, cerebellar peduncle, corona radiata, optic radiation, superior longitudinal fasciculus, anterior limb of the internal capsule, and cingulate, as well as decreased GM volume in the bilateral caudate, left insula and right temporal lobe. MS patients with CI exhibited more significant structural abnormalities than those with CP. Widespread impairments of WM integrity and selective GM atrophy both appear to be associated with impaired cognition in RRMS.  
Keywords:multiple sclerosis; cognitive impairment; diffusion tensor imaging; voxel-based morphology +
Footnote †: journal: Medical Sciences
## 3 The Proposed Model
### Hyperparameter Optimization
The time-control model was utilized for switching between the ocean current, passive and active motions, and comprised a constant \(c_{0}\). A mathematical expression of the time-control mechanism is given as:  
\[c(t)=\left(1-\frac{t}{t\max}\right)*(2*r-1) \tag{23}\]  
where \(t\) refers to the present evaluation, \(t\max\) indicates the maximal evaluation, and \(r\) represents an arbitrary value lying within \([0,1]\) as illustrated in Algorithm 1. ```
Determine the objective function \(f(X)\), \(X=(x_{1},\ \ldots,x_{d})^{T}\)  Fix the searching space, population size (\(nPop\),) and maximal iteration \((\max_{int})\)  Initialize the population of JF, \(X_{i}(i=1,2,\ldots,\ np_{op})\), utilizing a logistic chaotic map  Compute the quantity of food at all \(X_{i},f(X_{i})\)  Define the JF at place presently with most food \((X^{*})\)  Initializing time: \(t=l\)  Repeat  For \(i=1:\)nPop do  Compute the time control \(c(t)\) utilization  If \(c(t)\geq 0.5\): the JF follows the ocean current (1) Define the ocean current (2) Novel place of JF was determined Else: the JF moves inside a swarm  If rand(0,1) \(>(l-c(t))\): the JF displays type A motion (passive motion)  (1) Novel place of JF was determined Else: JF displays type \(B\) motion (active motion)  (2) Define the direction of JF  (3) Novel place of JF was determined  End if  End if  Verify the boundary condition and compute the quantity of food at novel place  Upgrade the place of JF \((X_{i})\)  and place of JF presently with the food \((X^{*})\)  End for \(i\)  Upgrade the time: \(t=t+1\)  Still end condition was met (e.g., \(t>(\max_{int}))\)  Output the optimal outcomes and visualize (JF bloom) End
```  
**Algorithm 1**Pseudocode of AJO algorithm  
This study established an AJO technique for a suitable selection of network weights from the CNN-ABLSTM method with a minimized mean square error (MSE). The MSE mathematical model is determined as:  
\[MSE=\frac{1}{T}\sum_{j=1}^{L}\sum_{i=1}^{M}\left(y_{j}^{i}-d_{j}^{i}\right)^{2}, \tag{24}\]  
where \(M\) and \(L\) represent the resultant values of layers and data, respectively, and \(y_{j}^{i}\) and \(d_{j}^{i}\) signify the attained and the appropriate magnitudes to the \(j^{th}\) unit from the resultant layer of networks from the time \(t\).
# Numerical Simulation of Vacuum Leak Jet and Jet Noise
## 3 Discussion
### Comparison of Vacuum and Atmospheric Environment
#### 3.1.2 Aeroacoustic Characteristics  
Different external space conditions determine completely different acoustic characteristics. As shown in Figure 7, it is obvious that two wave series travel in opposite directions in vacuum environment and cannot reach far field. Why such disturbance propagation is formed in vacuum environment will be discussed in the next section. Nevertheless, in a subsonic jet under atmospheric environment, the compressibility of the gas is weak, which leads to the absences of shock cell structure as well as changes in the characteristic impedance inside and outside the shear layer. So, a directional quadrupole source is formed at the leak hole exit, which continuously radiates to the far field.  
Figure 6: Density contour with pressure contour map of different external environment. (**a**) vacuum environment; (**b**) atmospheric environment.  
Figure 7: Pressure fluctuation contour of different external environment (The arrow marked in the figure represents the direction of fluctuation wave propagation.). (**a**) vacuum environment; (**b**) atmospheric environment.
# Hybrid Engineered Water-Polymer Flooding in Carbonates: A Review of Mechanisms and Case Studies
## 3 Engineered Water Flooding (EWF)
### Wettability Modification
#### Conditions for Engineered Water Flooding (EWF)  
Chemically altered water injection results in extra oil production from carbonate rocks if favorable conditions exist. Firstly, the salinity of the injection water should be appreciably less than the salinity of the formation water in order to disturb the initial equilibrium of the system [105]. Secondly, the injection water must contain active ions, most importantly SO\({}_{4}\)[2]-, in order to change the carbonate rock surface charge and release adsorbed oil [85]. Temperature also has a strong influence on EWF performance, with most of the studies suggesting the temperature to be in the range of 70-120 \({}^{\circ}\)C for effective oil production using engineered water [106; 107]. The reservoirs containing crude oil with organic acidic components are better candidates for EWF, as these reservoirs tend to be oil-wet and more residual oil saturation is available to be displaced by low-salinity engineered water [84].
## 3 Results  
### Adapting Methanosarcina activorans to Growth in Wastewater  
A challenge when introducing a laboratory methanogen strain to a wastewater environment is their sensitivity to changes in osmolarity. _M. acetivorans_ was originally isolated from marine sediment and grows best under high-salt conditions (400 mM NaCl). When introduced directly to a comparatively low solute environment such as wastewater _M. acetivorans_ rapidly lyses. To counteract this phenomenon, strains had to first be adapted to growth in synthetic wastewater (SWW), a complex growth medium containing mixed carbohydrates, lipids, and proteins that mimics the composition of wastewater digester solids [23]. Cultures of _M. acetivorans_ were gradually adapted by supplementing our high-salt (HS) media with 10% (\(r/v\)) of synthetic wastewater every 24 h until the media reached a ratio of 50:50 HS:SWW (8 days; final NaCl concentration was 100.06 mM). At this point the methanogens were passaged into SWW supplemented with MeOH and confirmed to grow without lysis via autofluorescence (Figure 2).
## 3 Results and Discussion
### Chronoamperometry  
The hydroxylamine oxidation on the PPy NTs/GSPE was explored by chronoamperometry (Figure 8). Chronoamperometric determinations of the variable hydroxylamine contents on the PPy NTs/GSPE were performed at a working electrode potential of 840 mV. The obtained data were used to determine the diffusion coefficient (D) of the hydroxylamine. For the hydroxylamine as an electroactive material with a certain D value, [PERSON]'s equation was used to describe the current for the electrochemical reaction at a mass-transport-limited rate:  
\[\mathrm{I=nFAD^{1/2}C_{b}\pi^{-1/2}t^{-1/2}} \tag{1}\]  
Figure 6: LSV recorded for the PPy NTs/GSPE in 0.1 M PBS at pH = 7 with 100.0 uM hydroxylamine at variable scan rates. Nos. a–g: 10, 25, 50, 75, 100, 300, and 500 mV/s, sequentially. Inset: variation of the anodic peak current versus v\({}^{1/2}\).  
In this equation, I stands for the current (A), n for the number of transferred electrons, F for the [PERSON]'s constant, A for the electrode surface area (cm\({}^{2}\)), t for time (s), C  
Figure 8: Chronoamperograms recorded for the PPy NTs/GSPE in 0.1 M PBS at pH = 7 with variable hydroxylamine contents. Nos. a–f: 0.1, 0.25, 0.4, 0.8, 1.3, and 1.8 mM of hydroxylamine. Insets: (**A**) Plots of I versus t\({}^{-1/2}\) derived from chronoamperograms a–f. (**B**) Corresponding slopes from the chronoamperogram curves vs. the hydroxylamine concentration.  
Figure 7: LSV recorded for the PPy NTs/GSPE at 10 mV/s in 0.1 M PBS at pH = 7 with 100.0 \(\mu\)M hydroxylamine. Points: data used in the Tafel plot. Inset: Tafel plot obtained from the LSV.  
for the bulk concentration (mol/cm\({}^{3}\)), and D for the diffusion coefficient (cm\({}^{2}\)/s). The experimental data of the I plot versus t\({}^{-1/2}\) are shown in Figure 8A, with the optimal fits for the various hydroxylamine contents. The achieved slopes of the straight lines in Figure 8A were plotted versus the hydroxylamine content (Figure 8B). The average D value of 4.8 \(\times\) 10\({}^{-5}\) cm\({}^{2}\)/s was based on the obtained slope and on Cottrell's Equation (1).
# Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network
## 2 Related Work
### Graph Neural Networks in Pedestrian Trajectory Prediction  
Graph Neural Networks (GNNs) offer substantial advantages in processing social interaction data and other non-Euclidean spatial structures. This network architecture effectively extracts implicit relational patterns, capturing spatiotemporal regularities and thereby enhancing the predictive performance. These capabilities make GNNs particularly well suited for modeling pedestrian interactions and environmental influences in complex, dynamic movement scenarios.  
In GNN-based pedestrian trajectory prediction methods [10; 11; 17; 31], individual pedestrians are typically represented as nodes, with their interactions encoded as edges. The two predominant GNN methodologies are Graph Convolutional Networks (GCNs) [32] and Graph Attention Networks (GATs) [33], which differ significantly in how they aggregate graph-structured information.  
A GCN employs spectral methods, relying on the Laplacian matrix's spectral decomposition, enabling feature transformations in the frequency domain. In contrast, GATs utilize a spatial domain approach, incorporating an attention mechanism to compute the adaptive attention weights between nodes, dynamically adjusting interaction strengths based on the neighboring node importance.  
Among the GCN-based pedestrian trajectory prediction methods, [PERSON] et al. [31] introduced Social-STGCNN, which utilized a spatial convolutional layer to model pedestrian social relationships and a temporal convolutional layer to capture long-term motion dependencies. Additionally, [PERSON] et al. [34] proposed the Spatio-Temporal Multi-Graph Convolutional Network (STM-GCN), which aggregated positional and velocity-based social interactions into multiple graphs, learning and predicting pedestrian trajectories via GNNs. Similarly, [PERSON] et al. [35] developed the Interaction-Aware Spatio-Temporal Graph Neural Network (D-STCNN), which extracted features through a temporal graph network and incorporated spatial graph neural networks for interaction modeling.  
In GAT-based trajectory prediction models, [PERSON] et al. [36] proposed Social-BiGAT, which extracted pedestrian interactions using GATs and predicted trajectories via a Graph Adversarial Generative Network. [PERSON] et al. [17] developed Social-TAG, which applied GATs to model pedestrians' field-of-vision features.  
In this study, we propose a Directed Weighted Graph Attention Network (DWGAT) based on GATs, which quantifies directed pedestrian interactions and aggregates them to improve the interaction modeling accuracy.
# A Channel Allocation Mechanism for Cellular Networks
## 3 Channel Allocation Mechanism in Cellular Networks
### The Number of Call Arrival (\(A_{i}\))  
An MS (i.e., (a) in Figure 4) can perform a call in the service area (i.e., cell), which is provided by BSs or Node B in cellular networks (e.g., GSM and UMTS) (i.e., (b) in Figure 4) if the cell has a free channel. The scenario and timing diagrams of call arrival processes on the road segments covered by the specific Cell\({}_{i}\) is showed in Figures 4 and 5. The first call set-up at time \(t_{0}\) is performed by a MS (in Figure 5), and the MS keeps moving to the specific cell coverage at time \(t_{1}\) (in Figure 5). The second call set-up at time \(t_{2}\) is performed by the MS (in Figure 5) before leaving the specific cell coverage at time \(t_{3}\) (in Figure 5).  
This study assumes that the call inter-arrival time (\(t\)) is exponentially distributed with the mean \(1/\lambda\)[26; 27; 28; 29; 30; 31; 32; 33] to generate the Call Arrival. Because the distance of the cell coverage is \(l_{i}\) and the speed of the car is \(V_{i}\), the time that the car moves from one side to the other side is \(l_{i}/V_{i}\). This approach considers the MS that has twice the number of Call Arrivals, and the time entering the cell and leaving the cell to estimate the probability of Call Arrival. This study gets the number of Call Arrivals (\(A_{i}\)) on the road segments through traffic flow (\(F_{i}\)) multiplied by the probability of Call Arrival. It can be expressed as Formula (3) [27; 28; 29; 30; 31; 32; 33]:  
\[A_{i}=F_{i}\times\int_{x=0}^{\infty}\Pr\biggl{(}x<t<x+\frac{l_{i}}{V_{i}} \biggr{)}dx=F_{i}\times\int_{x=0}^{\infty}\int_{t=x}^{x+\frac{l_{i}}{V_{i}}} \lambda e^{-\lambda t}dtdx=F_{i}\times\frac{1-e^{-\frac{\lambda l_{i}}{V_{i} }}}{\lambda}. \tag{3}\]  
Figure 4: The scenario diagram for vehicle movement and call arrivals on the road segment covered by the specific Cell, [27; 28; 29; 30; 31; 32; 33].  
Figure 5: The timing diagram for call arrival on the road segment covered by the specific Cell\({}_{i}\)[27; 28; 29; 30; 31; 32; 33].
# Advantages and Limitations of Current Microgravity Platforms for Space Biology Research
## 2 Ground Microgravity Simulators
### Diamagnetic Levitation  
Another approach to simulate microgravity on Earth is the use of diamagnetic levitation. Diamagnetic levitation compensates gravity at a molecular level, by balancing the weight of water and biological tissues with the repulsive force exerted by a magnetic field (Figure 1d). While electromagnets are bigger and more powerful, they tend to heat to temperatures incompatible with standard room-temperature growth conditions, thus requiring supercooling and limiting the duration of the experiment to few hours. Permanent magnets are relatively easier to use, but their size greatly limits the type of sample that can be used in the experiment. Nonetheless, diamagnetic levitation has not only been used to study microgravity in cell culture, but also in whole organisms like plants, frogs, and fruit flies (Table 1) [36], among others.  
Diamagnetic levitation can also be used to simulate partial gravity. Indeed, the magnetic field generated by magnets is a gradient (Figure 1d). Within this gradient, a specimen can experience from zero to twice the gravity force. By placing the sample at different points in this gradient, intermediate values can be achieved, including the gravitational force of the Moon and Mars (Table 1) [37].  
Diamagnetic levitation is very effective at compensating gravity. However, it should be noted that samples withstand a strong magnetic field, in addition to microgravity, and this may influence the organism. Differences in the diamagnetic properties of cellular and tissue components and the relative position in the magnetic gradient of the sample parts will result in discrepancies in the applied force. Humans have reported vertigo-like sensations and apparent perception of movement when in the proximity of the magnetic resonance imaging scanner strong magnetic field [38]. On a cellular level, strong magnetic fields affect the orientation of biomolecules, like DNA, tubulin, and actin [39], and subcellular components, like plant statoliths [40]. Strong magnetic fields have also been shown to change the membrane potential and influence ion-channel activity [41]. Some effects of the strong magnetic field can be distinguished from the ones due to levitation by carefully designing the controls to be performed in the parts of the gradient where the sample experiences 1 g acceleration (Figure 1d).
## 4 Simulation of Non-Stationary Turbulent Wind Fields for the RSB
### NMF of the Measured Decomposed EPSDs  
The square root of the measured EPSDs in Figure 3a,c, can be factorized into the latent matrices \(\mathbf{W}\) and \(\mathbf{V}\) with the NMF. The integer \(P\) in Equation (38) is taken as 8, while the iteration tolerance is taken as \(10^{-8}\) in this factorization. The NMF results of the measured EPSDs are shown in Figure 5 with the target ones.  
As shown in Figure 5, the NMF of the decomposed EPSD generally fit well with the targets in along-wind and vertical directions. Furthermore, the spectra snapshots of the NMF results at \(t=200\), 500, 1200 and 1500 s are compared with the targets. As shown in Figures 6 and 7, the spectrum snapshots agree well with the targets at the specific time instants, indicating that the NMF is an effective method to factorize the measured decomposed EPSDs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wind Field** & **Position** & **Direction** & **Space Interval (m)** & **Number of Simulated Nodes** \\ \hline
1 & Main girder & \(u\) & 80.5 & 19 \\
2 & Main girder & \(w\) & 80.5 & 19 \\
3 & Left tower & \(u\) & 30 & 7 \\
4 & Right tower & \(u\) & 30 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stochastic turbulent wind fields for the RSB.  
Figure 4: Simulated points in the RSB (Unit: m).  
Figure 5: The non-negative matrix factorization (NMF) of the decomposed EPSDs and the targets.  
Figure 6: Spectrum snapshots of the decomposed EPSD (along-wind).
## 3 Results  
### Evaluation of the SWRC Using Fuentes' Model  
Table 1 presents the data values obtained from the simulation of the SWRC. As observed, the soil's equivalent particle diameter increased after contamination in almost all the models used to describe the SWRC by simulation. From these results, a decrease in \(\psi_{m}\) (evaluated with Equation (2)) can be expected. It helps to partially explain the change in the shape of the SWRC as well (Figure 1). The equivalent particle diameter reaches its maximum value with the larger pore model in all arsenic concentrations, which is 15% bigger than the minimum value obtained with the other models. After its contamination with an arsenic concentration of 25 mg/kg and 50 mg/kg, the particle diameter increased its size by 2-14% and 0.6-9%, respectively, compared to the natural soil. The physical changes attributed to chemical reactions between the arsenic and the soil elements that resulted in the formation of larger particles were verified using [PERSON] model by comparing these results with those reported in Table 1.  
Upon comparing the SWCRs (0 mg/kg, 25 mg/kg, and 50 mg/kg) resulting from simulation (Figure 1), it was observed that the SWRC modifies its shape at a high saturation rate (\(s_{r}>70\%\)), aside from the model restriction used. Under \(s_{r}=70\%\) the curve did not suffer a significant variation. These results agree with the conclusion of [PERSON] and [PERSON] [39], which remarks that the SWRC obtained from [PERSON] model behaves similarly, aside from the restriction used (Burdine, Mualem, geometric pore, neutral pore, and larger pore).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model Restriction**} & \multicolumn{3}{c}{**Equivalent Particle Diameter (\(\mu\)m)**} \\ \cline{2-4}  & **As 1: 0 mg/kg 2** & **As: 25 mg/kg** & **As: 50 mg/kg** \\ \hline Burdine & 2264.30 \(\pm\) 21.40 (0.971) 3 & 2580.00 \(\pm\) 305.40 (0.970) & 2298.50 \(\pm\) 78.10 (0.971) \\ Mualem & 2267.70 \(\pm\) 33.30 (0.970) & 2238.20 \(\pm\) 50.80 (0.970) & 2336.00 \(\pm\) 57.70 (0.971) \\ Geometric pore & 2181.00 \(\pm\) 43.60 (0.970) & 2230.00 \(\pm\) 171.70 (0.970) & 2408.20 \(\pm\) 74.90 (0.970) \\ Neutral pore & 2527.70 \(\pm\) 30.60 (0.970) & 2589.70 \(\pm\) 233.40 (0.970) & 2524.30 \(\pm\) 30.60 (0.970) \\ Larger pore & 2630.20 \(\pm\) 40.20 (0.972) & 2838.50 \(\pm\) 412.70 (0.971) & 2646.80 \(\pm\) 83.20 (0.971) \\ \hline \hline \end{tabular}  
* Arsenic concentration. 2 Natural soil. 3 Data between parenthesis denote the value of the coefficient of determination.  
\end{table}
Table 1: The equivalent particle’s diameter of natural and arsenic-contaminated soils, as obtained by the evaluated models.