esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic and Quantitative Assessment of Blood Coagulation Status with an Oscillatory Rheometer
## 3 Results
### Heparin Titration  
The addition of increasing concentrations of heparin to citrated blood prolonged the CT, changed the linear slopes of the elastic and viscous shear moduli and changed the value of the shear moduli at the end of the measurement (300 s) (see Table 1, Appendix A Figures A1 and A2).  
The oscillatory rheometer provided a CT of (40.3 \(\pm\) 1.2) s for 0.5 IU/mL heparin in citrated blood, which was similar to that measured by KC 1A (40.5 \(\pm\) 1.3) s. Equally, the other concentrations of heparin yielded similar CT between oscillatory rheometer and ball coagulometer, which were not significantly (ns) different from each other except for 1.5 IU/mL heparin (Table 1, Appendix A Figure A3). More importantly, the comparison of the diverse heparin concentrations showed that each concentration had a significantly different CT and could therefore be differentiated from each other. Comparing linear slopes of elastic and viscous shear moduli with the diverse heparin concentrations, it was quite obvious that with increasing heparin concentration the slopes decreased (Table 1, Figure A4a,b) as was expected with a lower clot formation over a certain time period. Interestingly, after 300 s both elastic and viscous shear moduli of the control measurement without heparin, 0.5 IU/mL and 0.8 IU/mL heparin were not significantly different from each other, while the two higher heparin concentrations 1.0 IU/mL (G\({}^{\prime}\) = (1.8 \(\pm\) 0.5) Pa, G\({}^{\prime\prime}\) = (6.5 \(\times\) 10\({}^{-1}\)\(\pm\) 0.7 \(\times\) 10\({}^{-1}\)) Pa) and 1.5 IU/mL (G\({}^{\prime}\) = (0.5 \(\pm\) 0.3) Pa, G\({}^{\prime\prime}\) = (3.0 \(\times\) 10\({}^{-1}\)\(\pm\) 0.5 \(\times\) 10\({}^{-1}\)) Pa) showed significantly decreased values (see, Table 1, Figure A4c,d). This indicates that concentrations below 1 IU heparin did increase clotting time but no measurable effect on fibrin network and clot firmness was observed. Moreover, higher heparin concentrations led to higher standard deviations. A higher difference between ball coagulometer and oscillatory rheometer could be observed at citrated blood with a concentration of 1.5 IU/mL heparin. Concentrations of 2.0 and 3.0 IU/mL were not repeatable by ball coagulometer or oscillatory rheometer, and are thus not shown here. In fact, at heparin concentrations above 2 IU/mL, other methods (e.g., Heptest-Stat assay) are used to guide heparin dosing in clinical practise [23].  
These experiments show that the oscillatory rheometer and the reference medical device provided comparable CT results. Furthermore, the rheometer visualised the coagulation process by the change of viscous and elastic shear moduli over a certain time period. By increasing heparin concentrations the parameters changed towards prolonged CT values, lower linear slopes of viscous and elastic components and lower viscous and elastic shear moduli values.
# Shear Resistance Evolution of Geogrid Reinforced Expansive Soil Under Freeze-Thaw Cycles
## 3 Results and Discussion
### Friction Angle and Cohesion  
Friction angle and cohesion are two important parameters of soil shear strength. They can be used to assess the stability and bearing capacity of soils and are critical to the design and construction of geotechnical engineering. The Mohr-Coulomb circle theory was used to plot the Mohr stress circle and strength envelope to obtain the angle of internal  
Figure 8: Variation of normalized elastic modulus versus F-T cycles in this study, (**a**) \(\sigma_{3}=100\) kPa; (**b**) \(\sigma_{3}=200\) kPa; (**c**) \(\sigma_{3}=300\) kPa.  
Figure 9: Normalized elastic modulus in present paper compared to the previous studies [9; 34; 39; 43].  
friction and cohesion. The variation patterns of internal friction angle and cohesion with the increase in the number of F-T cycles are given in Figure 10.  
As can be seen in Figure 10a, the friction angle of the unreinforced soil and reinforced soil decreased with the increase in the number of F-T cycles, whereas the friction angle of the unreinforced soil decreased significantly, and that of the reinforced soil was relatively stable, which is basically in agreement with the previous study [28; 43]. With the increment of reinforced layers, the friction angle increased, and that for freeze-thaw specimens was more pronounced compared to unfrozen-thaured specimens. The findings indicated that the reinforced soils exhibit better resistance to freezing and thawing. After the sixth F-T cycle, the friction angle decreased by about 7.6%, 5.9%, 4.5%, and 5.1% for 0-, 1-, 3-, and 5-layer GRES. During freeze-thaw cycles, the mineral skeleton adjacent to ice lenses and the freezing front was subjected to compression. The size of the particles was reduced, the surface was smoother, and the friction between the particles was reduced. However, geogrids could restrain the soil surrounding particles and provide additional confinement on a macroscopic scale. This phenomenon can be the reason of friction angle decrease, and the geogrid reinforced soils exhibit better performance during freeze-thaw cycles.  
According to Figure 10b, the cohesion of the unreinforced and reinforced soils decreased with the increase in the number of F-T cycles, but the reinforced soils behaved more stably. After the sixth F-T cycle, the cohesion decreased by about 24.5%, 21%, 14.3%, 14.5% for 0-, 1-, 3-, and 5-layer GRES. Thus, reinforced soils have better freeze-thaw resistance than unreinforced soils. After freeze-thaw cycles, the pore space increases and the distance between soil particles increases, which may be the main reason for the decrease in cohesion. Geogrids are able to limit the development of cracks around them and therefore show better resistance to freezing and thawing.
# Throughput Analysis of IEEE 802.11 WLANs with Inter-Network Interference
## 2 Background and Motivation  
### IEEE 802.11 Distributed Coordination Function Background  
The IEEE 802.11 Distributed Coordination Function (DCF) is the fundamental mechanism for medium access in WLANs. IEEE 802.11 DCF is a random access scheme, based on the carrier sense multiple access with collision avoidance (CSMA/CA) protocol. In the IEEE 802.11 DCF, a transmission node senses a wireless channel before transmitting data. If the channel is used by a transmission process, the channel's state is busy. That means the neighbor nodes, which are in the carrier-sensing range, are transmitting. The node hence defers the transmission process until the channel becomes idle.  
The first step of transmitting an IEEE 802.11 frame is the Backoff-Timer (BT) decrement, during which the initial value of BT is randomly chosen between 0 and the Contention Window (CW). Transmission nodes decrease their BT values only when the channel is in the idle state. If the channel is busy, the decrement of BT will be stopped. If the BT reaches zero, the node starts to transmit a frame, whose duration is expressed as DATA frame. An Acknowledgement (ACK) frame is sent from the receiver to the sender after waiting for the Short Interframe Space (SIFS) interval if the data transmission is successful. The sender confirms the transmission success by receiving the ACK frame from the reception node. If the ACK frame will not arrive at the sender, it recognizes that the frame transmission is failed. Moreover, the sender renews CW value and resets the initial value of BT.
## 5 Results and Discussion
### Sample Size Calculation Using the Neyman Allocation Method
#### 5.2.1 Sample Size Calculation for CF4 Tier 3 DRE Using the Neyman Allocation Method  
In this study, the total number of scrubbers required for the Tier 3 development of target gas was obtained to confirm the information related to the population. The number of CF4 scrubbers, considering scrubber types, is presented in the following Table 12.  
To stratify and calculate the sample size using Neyman allocation, information regarding the standard deviation for the characteristic of the substance in each stratum is required in addition to the stratum population. Because the purpose of this study was to identify the sample size for developing the DRE values, the mean and standard deviation of Tier 3 DRE for CF4 gas by scrubber type were determined and presented in Table 13.  
The results of DRE calculations by scrubber type indicate that the plasma type had the highest value at 0.970, followed by the catalytic type at 0.945, and the burn type had the lowest value at 0.768, along with the largest standard deviation.  
The calculation of the sample size using Neyman allocation considered the same error level (1%) and confidence level (95% confidence interval) as the previous method of calculating the sample size using CV. The sample size was calculated according to Neyman  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification** & **Surn** & **Plasma** & **Catalyst** \\ \hline Mean & 0.768 & 0.970 & 0.945 \\ SD & 0.115 & 0.008 & 0.032 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Tier 3 DRE mean and standard deviation by scrubber type in CF4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **Scrubber** \\ \hline Burn & 420 \\ Plasma & 2546 \\ Catalyst & 2 \\ Total & 2968 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Number of scrubbers by scrubber type for DRE development in CF4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification** & **Surn** & **Plasma** & **Catalyst** \\ \hline Mean & 0.768 & 0.970 & 0.945 \\ SD & 0.115 & 0.008 & 0.032 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Tier 3 DRE mean and standard deviation by scrubber type in CF4.  
allocation considering the characteristics (standard deviation) of the strata, and the sample size per stratum is shown in Table 14 considering the proportion of the population in each stratum. After calculating the sample size, it was found that a total of 21 samples were required for the development of CF4 DRE, with two burn types, 18 plasma types, and one catalyst type by stratum.
# Virtual Validation of In-Flight GNSS Signal Reception during Jamming for Aeronautics Applications
## 4 Full-Scenario Simulation
### Scenario 2: GNSS Reception during jamming
On the other hand, the GPS common patch antenna pattern is more similar to our predictions and the measured data in this case (as expected, since the actual antennas have a pattern similar to the one of a patch), but underestimates signal (and jammer) reception at low elevations, as mentioned in the Scenario 1. Figure 14: Connections made between GPS satellites and DLR’s antenna (marked as pink lines). Most importantly, the reception obtained with both antennas, i.e., isotropic and GPS patch, remains consistently stable; the oscillations observed in the measurement, mostly due to reflections at aircraft or terrain level, are not modeled by the simplified patch/isotropic antenna simulations, while they can be better estimated by the validation tool proposed in the paper. The landscape upon which the jammer antenna is positioned and the aircraft on which the receiver antenna is mounted exert a significant influence on the ultimate radiation pattern achieved by the antennas. The aspects outlined above clearly demonstrate the better way to predict the in-flight signal reception and show the advantage of our approach over conventional simulation using simplified antennas or without in-depth consideration of the installed performance. Enhancing our comprehension of the scenario and conducting thorough analyses of antenna performance installed on platforms are integral in improving our ability to forecast GNSS reception. Furthermore, gaining deeper insights into the terrain characteristics of the scenario would yield additional advantages. Presently, our approach involves utilizing the PEC platform to optimize reflections for assessing antenna performance, both aerially and terrestrial. Employing appropriate platform materials could further augment our analytical capabilities in this regard. Figure 15: Simulation and Measurement results for the GNSS reception. (**a**) Ant.1. (**b**) Ant.2. (**c**) Ant.3. (**d**) Ant.4. (Solid lines–Measurement, Dashed lines–Simulation, GS and gps-\(S\) denotes measured and simulated GPS satellite number (\(S\)), respectively).
## 4 Results and Discussions
### High-Intensity Hermite-Gaussian Beam
In order to evaluate the overall impact of Kerr effect on the focusing of Hermite-Gaussian beams, the field patterns of focal spots in the six specified planes are given in Figure 8a-f, which are of 0.06 mm, 0.04 mm, and 0.02 mm left to the nominal focal plane and 0 mm, 0.02 mm, and 0.04 mm right to the nominal focal plane for the HG\({}_{11}\) laser beams with \(E_{0}=5\times 10^{8}\) V/m. It can be seen that there are some weak speckles around the four central focusing spots and the actual focal plane of lens is left to the nominal focal plane if the Kerr effect is takes into consideration. We also find that the field patterns of focal spots gradually diverge with the increment of interval distance \(\Delta z\). In Figure 9, we also show the field patterns of focal spots in the six planes without consideration of Kerr effect by setting the coefficient \(\alpha=0\). It can be seen that the patterns of the focal spots are nearly the same in the six planes although a little enlarged in the first and last planes. Thus, the Kerr effect can affect the focusing performance of lens for the incidence of high-intensity Hermite-Gaussian beams. Figure 8: The different patterns of focal spots in the six planes, which are of (**a**) 0.06 mm, (**b**) 0.04 mm and (**c**) 0.02 mm left to the nominal focal plane, respectively, and (**d**) 0 mm, (**e**) 0.02 mm and (**f**) 0.04 mm right to the nominal focal plane for the incident Hermite-Gaussian beam with field intensity \(E_{0}=5\times 10^{8}\) V/m. Figure 7: The normalized light field intensities of focal spots in the nominal focal plane along the diagonal lines of computational domains for the incident Hermite-Gaussian beams of HG\({}_{11}\) mode with three different light field intensities, \(E_{0}=1\times 10^{8}\) V/m, \(5\times 10^{8}\) V/m and \(1\times 10^{9}\) V/m, respectively.
## 3 Methods
### Pre-Processing and Feature Extraction  
The utilized data preprocessing techniques are normalization and filtering. First, temporal filtering using a Butterworth bandpass filter was applied to extract frequency bands of interest. Next, the data was normalized using the Frobenius norm, as defined in Equations (4) and (5), accounting for variations in amplitude across trials allowing to calculate the total energy or overall strength of the signal separately for each subject, ensuring that the analysis was not biased towards any particular trial or individual.  
The variables used in the equations were also defined: \(CH\) denotes the number of channels that represent the feature vectors, \(L\) represents the length of each trial, and \(T\) represents the number of trials. \(A_{i}\) represents the Frobenius norm of the \(i\)-th feature vector, \(n\) represents the number of electrodes, and \(X\) represents the input data.  
\[\eta=\sqrt{\frac{\sum_{i=1}^{n}\lVert A_{i}\rVert_{F}}{T\times L\times CH}} \tag{4}\]  
\[Normalized\;Data=\frac{X}{\eta} \tag{5}\]  
Feature extraction is performed using PCA, a statistical technique that identifies essential features in a dataset and represents them as new variables called principal components [43; 44]. These components are selected to capture as much variation in the data as possible, and their weights are determined by calculating the eigenvectors and eigenvalues of the data covariance matrix. By selecting only the principal components with the highest eigenvalues, the dimensionalities of the data are reduced while retaining as much information as possible [45].
# Fewer Dimensions for Higher Thermal Performance: A Review on 2D Nanofluids
## 2 Types of 2D Nanofluids
### Hexagonal Boron Nitride
In cases where the clustering of sheets exists, an overlap of the interfacial regions occurs between the nanosheets leading to a continuous Kapitsa region with decreased vibrational density of states in phonons. The thermal imaging analysis of the nanofluids showed that the region of high temperature spreads with an increasing load of h-BN nanosheets, forming a percolation network that promotes the conduction of heat. With increasing temperature and fraction of the nanosheets, the quantity and velocity of the heat conduction phonons increased, and the overall thermal performance was improved. Nevertheless, above 0.05% wt. the rising concentration would promote the agglomeration of the nanostructures caused by the strong van der Waals forces in the vicinity of the nanostructures, provoking saturation. Such saturation is more prominent at higher temperature values and concentrations because of the intensified phonon scattering and eminent agglomeration. Furthermore, the authors found an appreciable increase in the AC breakdown voltage with the use of the nanofluids, which is large when compared with the BN nanoparticles. The referred  
Figure 4: Schematic diagram of the preparation method of the 2D h-BN/transformer oil nanofluids. increase can be interpreted by the extended nanosheets/oil interfacial regions, also known as Maxwell Garnet regions, of the 2D fillers in terms of charge trapping behavior. The faster heating/cooling rate and the enhanced TC is consistent with the Maxwell prediction model. The improved thermal performance can be explained through the augmented phonon transfer by the large interface between the nanosheets and the transformer oil. Furthermore, the natural electrostatic repulsion between the nanosheets at diluted concentrations promoted enhanced long-term and thermal stabilities compared with their nanoparticle counterpart. Han et al. [24] synthesized h-BN nanoparticles in water nanofluids and evaluated their TC. The authors reported an improved long-term stability of the aqueous nanofluids, and their TC increased by 55% at 0.10% vol. compared with that of the water itself. Table 1 shows some of the recent studies on h-BN nanofluids. There is a research gap regarding the quantity and quality of investigation works concerned with the laminar heat transfer convective performance of h-BN nanofluids in heat exchangers, heat pipes, thermosyphons, and other devices. There is also a lack of sufficient studies on h-BN nanomaterials dispersed in base fluids, such as glycerin, paraffin, silicone oil, and green base liquids, rather than water, ethylene glycol, or mineral oil, for all potential applications. Additionally, there are only a few modest attempts in the literature to use h-BN nanosheets to synthesize hybrid phase change materials to be dispersed in ionic liquids and other types of base fluids and to be used in the thermal energy storage technological area.
# Multi-Field Models of Fiber Reinforced Concrete for Structural Applications
## 1 Introduction  
Short-fiber reinforcement is often added to concrete to improve its mechanical properties [1], particularly fracture toughness. Short fibers are also used to modify other aspects of concrete, including volumetric stability at early ages [2; 3; 4], resistance to spalling at high temperatures [5; 6], bond properties of repair materials [7], structural composite behavior with steel reinforcement [8; 9; 10], and various electrical properties [11; 12; 13]. Along with the amount of fibers, the efficacy of fiber additions often depends on the distribution of fibers and characteristics of the fiber-matrix interface.  
Effective medium theories have been developed to understand the global significance of fiber additions on the properties of interest (e.g., stiffness, strength, conductivity) [14]. The usefulness of such theories is limited, however, due to assumptions made regarding the distribution of fibers within the material volume. Numerical models have been developed to understand the individual and/or collective actions of these fibers on various performance measures. These modeling efforts have predominately focussed on the mechanical (i.e., stiffness and strength) contributions of fibers in both pre- and post-cracking conditions [15; 16; 17; 18; 19].  
This paper introduces computationally efficient, semi-discrete representations of individual fibers within cement-based matrices that participate in electro-mechanical or hygro-mechanical phenomena. Voronoi Cell Lattice Models are used to represent the composite materials [20]. The fibers are discretized within the computational domain, such that the effects of fiber spatial distribution and orientation can be explicitly represented [21]. Basic simulations are conducted to study: the use of electrically conductive fibers for self-sensing; and the influences of fibers on early-age plastic settlement. The methods are verified through benchmark comparisons with numerical and theoretical models. It is found that the models can account for unintended directional bias on fiber orientation,as may occur during material casting. With respect to plastic settlement, fibers may play competing roles: mechanical restraint offered by the fibers reduces settlement, whereas enhanced hydraulic conductivity along the fiber-matrix interface may increase settlement by facilitating the bleeding process. Although the simulation efforts are limited to small component or unit-cell models, the low computational cost of the proposed approaches suggests future applications to structural concrete components.