esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results and Discussion
### Comparison between KK and Coherent Systems in the Presence of Scintillation
Therefore, at a weak received optical intensity level, the SNR decreases significantly resulting in the BER degradation. For example, in the uplink scenario, for \(K=10\) and lens diameter of 0.25 m at transmitter and receiver, the BER of \(6.8\times 10^{-5}\) in the absence of turbulence shown in Figure 4, increased to the mean BER of \(2\times 10^{-2}\) in the presence of turbulence (see Figure 10). Therefore, turbulence limits the information rate of FSO communications. Also, to make FSO link more reliable in the presence of strong turbulence in both KK and coherent transceivers, FEC codecs such as low-density parity-check (LDPC), or polar codes are required [35; 36; 37]. In summary, in Table 2, we show the qualitative comparison between KK, coherent and intensity-modulated direct-detection (IMDD) systems. Here, the cost of a KK system is lower than that of a coherent system since expensive ECL and LO laser are not required for the KK system. In KK system, Hilbert transform is implemented in digital domain to extract the signal phase and hence, the computational cost of the KK system is higher than that of the IMDD system. The mode mixing efficiency is acceptable in KK and IMDD, typically more than 80%. However, for the coherent detection in FSO communication systems in the presence of atmospheric turbulence, the data-LO mode mixing efficiency is poor [20; 21; 22]. The KK system enables the simultaneous amplitude and phase modulation similar to coherent detection at low hardware cost and yet, it does not suffer from poor mode mixing efficiency in the presence of atmospheric turbulence.
## 3 Extraction of Intermittent Failure Feature
### Experiments for Intermittent Failure Severity Extraction  
The severity of intermittent failure is extracted from the contact resistance data. Therefore, the four-wire method is introduced to measure contact resistance as shown in Figure 9.  
The current passing through our electrical connector is driven by a voltage source and the voltage drop between the two terminals of the pins of the connector is recorded with a multimeter. Then, the electrical connector is mounted on the vibration stage to simulate the practical operating environment of the connector. By monitoring the contact resistance on-line, the curve of contact resistance can be obtained.  
The next step is to post-process the obtained contact resistance. If the resistance exceeds the failure threshold, the difference between the resistance and the threshold is recorded. If the resistance does not exceed the threshold, the intermittent failure value is set to 0. After that, the intermittent failure curve can be obtained. Next, to satisfy the input requirements of the HSMM model, the generalized severity of intermittent failure is proposed in the following part.
## 3 Results and Discussion
### Swelling and Thermodynamics Analysis
Then, since the local minima of the swelling energy corresponds to the thermodynamic stable states, it can be deduced that the Na- and Cs-MMT will form the single- and double-layer stable hydrated states when the number of interlayer water molecules reach 40 and 80 respectively. The double-layer hydrate for Cs-MMT is in the most stable state. Thus, the Cs\({}^{+}\) ions can inhibit swelling. The global minima of Na-MMT is located at \(n=160\). After forming the first and second hydration layer, Na-MMT will continue expansion to the osmotic swelling stage, namely that the Na\({}^{+}\) ions promote swelling in contrast with Cs\({}^{+}\). For Ca-MMT, its role in promoting expansion is even more  
Figure 2: The simulated and experimental basal spacing of montmorillonite as a function of water content (\(g\) denotes molecular mass). pronounced. The intermediate state of the first or second hydration layer will not form during the swelling process, and the osmotic swelling stage will rapidly and directly develop. The nanoscale elastic properties of these stable state water content systems will be further simulated in the following sections.
# UKF-Based State Estimation for Electrolytic Oxygen Generation System of Space Station
## 2 Operation Analysis and Working Principle of EOGS  
EOGS is one of the essential core systems of ECLSS in long-term manned mission. From the perspective of energy conversion, the EOGS converts the input electrical energy into heat and chemical energy, and the input deionized water is reacted to generate hydrogen and oxygen, as well as un-electrolyzed water. Therefore, the dynamic model of the actual system can be established by analyzing the processes and principles of the material and energy exchange of the actual system.
## 5 Experimental Results
Since the model assumes that every phrase is accurate unless it finds a mistake in the sentence, a precision of 100% was to be anticipated. By successfully navigating the fuzziness of concepts, the incorporation of neutrosophic logic within precise ontology yielded an 80.2% sensitivity. The accuracy loss is eliminated, and language concepts are conveyed effectively. An additional set of experiments (comparative study with a fuzzy ontology model) was carried out to prove that neutrosophic logic coupled with an ontology builder is an effective tool for modeling semantic characteristics in the realm of computer-assisted essay grading. In this context, we compared our suggested method to one that employs fuzzy ontology to represent semantic characteristics for automatic essay scoring, as discussed in the author's previous work [53]. The results in Table 10 confirm the research hypothesis that using the neutrosophic ontology for modeling an essay's semantic features will enhance the system's correctness in terms of \(kp\). The suggested combination achieved a 4% increase in \(kp\) when compared to the nearest combination that shields between fuzzy logic and ontology. For each case in the overlapping region, the neutrosophic ontology specifies the level of truth, indeterminacy, and falsehood. Therefore, it yields superior results when compared to fuzzy ontology. Only the instance's fuzzy membership value is provided by a fuzzy ontology. In neutrosophic sets, all three measures (Truth, Falsehood, and indeterminacy) are independent, and how one affects another in decision-making depends on their sum. If sum < 1, we have incomplete information (we do not know all information), while if sum = 1, we have complete information, as in an intuitionistic fuzzy set.
## 3 Results and Discussion
### Air-Core Dynamics  
The mean parameters of the internal flow are important for the geometric design of the atomizer. However, instabilities are commonly presented and for some geometries, they may change the breakup nature or generate an unstable spray, as documented elsewhere [1; 25; 26]. The steady simulations, in principle, can only indicate the calculation stability or the convergence rate, which may or may not be linked with real flow instabilities. Therefore, this part focuses only on transient models. The simplest way to detect flow instabilities is to measure the rate of change of the air-core diameter or its position. From the experimental data, the most prominent are the air-core contractions and extractions at a given position as seen in Figure 11. The same dominant frequencies were also observed for the air-core boundaries, but with slightly less prominence. Its frequencies, shown in Figure 12, are almost identical along the air-core length and peaked roughly at \(f=110\), 240 and 480 Hz for \(Re=1000\), 2000 and 4000, respectively. This leads to an almost constant value of the Strouhal number over the range of \(Re\) values:  
\[St=\frac{fD}{v_{p}} \tag{1}\]  
where \(D\) is a characteristic dimension, which can be the swirl chamber diameter and is constant here. The same behaviour was documented in [37], where the value of \(St\) depended on the atomizer constant and was independent of the operating regime. Note here that no dominant frequency was observed in the measured LDA data.  
The CFD time-resolved results did not match the experimental data well. The turbulence models, including the LES model, subdued the air-core surface waves and almost no fluctuations can be detected, see the smooth air-core structure in Figure 11. The laminar model exhibits the largest amplitudes of the air-core fluctuations as reported in Figure 13 where the frequencies for two locations, \(d_{ac2}\) and \(d_{ac11}\), are shown. Nevertheless, the air-core temporal behaviour is different from the experimental data, since no dominant frequencies and a rather smooth air-core surface is presented in the bottom part of the swirl chamber, see Figures 11 and 13 left. The top part of the air-core at the location \(d_{ac2}\) exhibits periodical fluctuations with a dominant frequency of 470, 930, and 1900 Hz for \(Re=1000\), 2000, and 4000, respectively and the same frequencies are also observ  
Figure 11: The air-core surface from laminar simulation and LES (right).  
Figure 12: Frequency spectra of air-core diameter fluctuations at location \(d_{ac11}\). Experimental data.  
component near the air-core. The frequencies are roughly 4 times higher compared to the frequencies from the experiment, but it follows the same trend of constant \(St\). The turbulence intensity of the swirl velocity, based on the velocity standard mean deviation, reached values from 0.5 to 2% for the laminar simulation. These values are roughly 4-times lower compared to the experimental data. It tends to increase towards the top of the swirl chamber and also with increasing \(Re\), which is the opposite behaviour to the experiment.
### Electrosorption  
In 1989 [PERSON] defined \"electrosorption\" as the reversible adsorption, or the reversible retention, of ions, molecules, and particles from a liquid phase on or near an electronic conducting surface as a function of the electric potential difference between the surface and the liquid [9]. From this first definition, which was related only to liquid-solid adsorption, [PERSON] and [PERSON] broadened the definition to all those phenomena in which this surface-binding process is promoted, or aided, by the presence of an electrical field [10]. However, while this definition seems to cover every scenario in which a polarization of a conductive substrate (due to an application of an electrical current) induces an attraction of oppositely charged species to the surface, literature research highlights that this process is mainly used in capacitive and pseudocapacitive deionization for separation [11; 12; 13; 14], and energy storage [15; 16]. Other reported applications are for the removal of dangerous contaminants, such as organic ions and heavy metals, or enhanced liquid chromatography sensing techniques [17]. The scope of the electrosorption process is, therefore, closely linked to the presence of a liquid phase and a solid phase. A schematic representation of such process is reported in Figure 1.  
Difference between electrosorption and adsorption from the gas phase is well described by [PERSON] [18], [PERSON] [19], and [PERSON] [20]. Table 2 briefly reports these differences. However, this term has never been used to define the phenomenon in the gas phase. The main reason is that the air is not electrically conductive and the species in the electrodes are barely adsorbed and not solvated. However, in case of a weakly-ionized plasma, the charged species in the air gap between the electrodes can conduct electricity, and the electric field may displace the molecules previously adsorbed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Term** & **Definition** \\ \hline Adsorptive & The adsorbable substance in the fluid phase \\ Adorbent & Solid on which adsorption occurs \\ Adsorbate & The substance in the adsorbed state \\ Adsorption & The increase of the concentration of species in the vicinity of an interface \\ Chemisorption & Adsorption involving chemical bonding \\ Physisorption & Adsorption without chemical bonding \\ \hline \hline \end{tabular}
\end{table}
Table 1: Terms related to the adsorption process.
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 7 Experimental Results
### ILP Solver vs. Smart Heuristic  
This section presents the results obtained when comparing the ILP solver with the heuristic proposed in this work. Due to the model scalability and time constraints, it took approximately 50 min to obtain a solution considering a scenario with just a few requests, even executing the model in a server equipped with 48 cores and 500 GB of RAM. Hence, we only consider 10 SFC requests in the experiment, given that the time to find the best solution increases as the number of requests grows.  
Figure 4 depicts the energy consumed in the nodes to execute the requested SFCs. Regarding energy consumption, the ILP tends to select nodes that consumed less energy to place the VNF instances in comparison with our heuristic.  
In addition to energy consumption, we also compare the ILP solver and the heuristic approaches regarding resource consumption (i.e., CPU and memory). Analyzing Figures 5 and 6, we can see that the optimal solution and the heuristic approach performed similarly in terms of resource consumption. Although the resource consumption obtained by the ILP was slightly lower, the heuristic approach can provide a good (but not optimal) solution in a shorter time than the ILP, even in scenarios with a higher number of nodes. This fast response time is essential considering that one of the appeals of using edge computing is lower latency for applications.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Edge nodes & 32 \\ Edge node CPU & 5000 (IPS) \\ Edge node Mem & 10 GB \\ VNF types & 3 \\ VNF min CPU & 1000 (IPS) \\ VNF min Mem & 3000 \\ SFC types & 3 \\ VNFs in SFC & 2, 3 \\ Number of users & 1000 \\ Number of SFC requests & 1000 \\ SFC instance timeout & 2 min \\ VNF instance timeout & 2 min \\ Packet size & 50 IP \\ VNF max share & 3 SFC Requests \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation parameters.
# Channel Estimation Based on IOTA Filter in OFDM/OQPSK and OFDM/OQAM Systems
## 5 Simulation Results  
### The Performance of the OFDM/OQPSK System  
Figure 4 represents BER performance over Rayleigh fading channels with different Doppler spread [18]. At the target BER of \(10^{-3}\), the IOTA filter channel estimation method under Doppler spread 20 Hz outperforms the IOTA channel estimation method under Doppler spread of 40, 60, and 80 Hz by about 0.4, 5.5, and 9.7 dB \(E_{\text{b}}/N_{0}\) gains. At the target BER of \(2\times 10^{-4}\), under the Doppler spread of 20 Hz, the ideal channel estimation (ICE) method outperforms the IOTA channel estimation method under Doppler spread of 40, 60, and 80 Hz by about 0.4, 6.0, and 9.8 dB \(E_{\text{b}}/N_{0}\) gains. The reason is that when the channel is under fast mobile velocity, the Rayleigh channel is under the condition of severe frequency selectivity, and thereby it causes large amounts of bit errors.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Specifications** \\ \hline Symbol duration \(T_{\text{s}}\) & QPSK: 1/6000 s, 16-QAM: 1/3000 s \\ Symbol rate & 12 kbit/s \\ Guard interval (GI) length & OFDM-IOTA: 0, CP-OFDM: 128 symbols \\ System model & OFDM-IOTA / CP-OFDM \\ The length of FFT & 512 \\ Carrier frequency & 2 GHz \\ Fast fading model & Jakes spectrum \\ Number of multi-paths & 6 \\ Code rate & 1/2 \\ \(\alpha\) & 0.995 \\ Inter-leaver & Block inter-leaver (300 OFDM symbols) \\ Doppler spread & 20, 40, 60, 80 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main simulation parameters in this paper.  
The accuracy of channel estimation can be evaluated by the NMSE, which is expressed as:  
\[\text{NMSE}=\sqrt{\sum_{n=0}^{N-1}\left|\hat{\mathbf{h}}_{m,n}-\mathbf{h}_{m,n}^{\text{ ICE}}\right|^{2}/\sum_{n=0}^{N-1}\left|\mathbf{h}_{m,n}^{\text{ICE}}\right|^{2}}, \tag{9}\]  
where \(\mathbf{h}_{m,n}^{\text{ICE}}\) is the ICE result on the \(m\)-th subcarrier of the \(n\)-th OFDM/OQPSK symbol which can be obtained under the condition of no AWGN. \(\hat{\mathbf{h}}_{m,n}\) is the estimated CIR for the IOTA filter method on the \(m\)-th subcarrier of the \(n\)-th OFDM/OQPSK symbol.  
Figure 5 illustrates the NMSE versus \(E_{\text{b}}/N_{0}\) for the proposed OFDM/OQPSK system over Rayleigh fading channels with different Doppler spread. The figure shows that the accuracy of channel estimation can be greatly improved when the value of Doppler spread decreased. As shown in Figure 5, when the NMSE value is 0.1, the \(E_{\text{b}}/N_{0}\) gaps between the NMSE performance curves of proposed IOTA filter method with Doppler spread of 20, 40, 60, and 80 Hz are about 0.8, 3.2, and 4.5 dB, respectively. The NMSE simulation results verify that IOTA filter has good orthogonality properties in time and frequency domains, which can be utilized as a wavelet platform in the proposed OFDM/OQPSK system.
## 3 Results and Discussion
### ESEM Observations  
Table 6 shows the ESEM observations of the microstructure of the GPCM and the asphalt binders. The homogeneity of the GPCM was assessed using ESEM. The GPCM observations in Table 6a show that the GP particles were well dispersed in the mastic.  
Previous studies on binder microstructure using ESEM show that its fibril structure can be correlated to fundamental changes in the physical and chemical properties of the binder [24]. Thus, visual comparisons were conducted on the different binders based on their relative fibril sizes and abundance. ESEM observations were obtained at three different magnifications of 10, 40, and 50 um for a more detailed view of the microstructure. It can be noted from the 5%PCM pictures in Table 6e-g that the PCM modification of the asphalt binder had a visible impact on the microstructure of the binder. The 5%PCM's fibril size was relatively large with a sparser structure than the control binder shown in Table 6b-d.  
Similarly, with GPCM modifications, the size of the fibrils became larger than the control binder and yet were smaller than the %PCM binder. The addition of glass powder was found to improve the high-temperature stability of the binder. The GP morphology, shown in Table 6a, and its physical characteristics would improve the stability of the mastic at high temperatures.  
A denser and more connected fibril was noticed when observing the PG70-28 binder under ESEM, as shown in Table 6n. Adding GPCM to the PG70-28 binder caused the loss of the interconnected network of the fibrils obtained on the PG70-28 binder. To summarize, GPCM and PCM modifiers significantly impacted the binder's microstructure by making the fibril size wider with a less patterned microstructure. These observations were reasonably consistent with the previous research findings on the softer binder observations [21].  
Figure 9: Viscosity results for all binders.  
\begin{table}
\begin{tabular}{c c c}  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: ESEM observations for all tested binders.
## 1 Introduction
Since these events, especially the storm of 2015, were the strongest in the 24 th solar cycle, they are considered a good event for checking the current theories and concepts [39]. According to the data of the Large Angle and Spectrometric Coronagraph Experiment (LASCO) on the board of the Solar and Heliospheric Observatory (SOHO), the geomagnetic storms on 17 March 2013 and 17 March 2015 originated from coronal mass ejections (CMEs) associated, respectively, with solar flare type C1.6 on 15 March 2013 at 07:12 UT and solar flare type C9 on 15 March 2015 at 02:36 UT (web page: [[https://izwl.caltech.edu/ACE/ASC/DATA/level3/icmetable2.htm](https://izwl.caltech.edu/ACE/ASC/DATA/level3/icmetable2.htm)]([https://izwl.caltech.edu/ACE/ASC/DATA/level3/icmetable2.htm](https://izwl.caltech.edu/ACE/ASC/DATA/level3/icmetable2.htm)), accessed on 1 April 2024). Both CMEs were followed by the magnetic cloud [40; 41]. It was shown in [42] that the St. Patrick's Day geomagnetic storms in 2013 and 2015 occurred at rather close solar flux levels (F\({}_{10.7}\) = 118, F\({}_{10.7}\) = 126, respectively) and were responsible for somewhat similar peak values of the auroral electrojet index (AE) equal to 2700 nT and 2200 nT, respectively. According to the database OMNI, the geomagnetic storm on 17 March 2013 started from a sudden storm commencement (SSC) at 06:04 UT with a positive SYM/H index = +33 nT, the most significant negative SYM/H index = \(-\)132 nT occurred on 17 March 2013 at 20:28 UT, and the duration of the main storm phase was equal to \(\sim\)14 h. The storm on 17 March 2015 started from SSC at 04:48 UT with a positive SYM/H index = 67 nT, the most significant negative SYM/H index = \(-\)234 nT occurred at 22:47 UT on 17 March 2015, and the main storm phase lasted \(\sim\)18 h.  
Figure 4 presents the 1-min data of the SYM/H index from March 14 to March 26 in 2013 (black) and 2015 (red) from the OMNI database. It is seen that the storm of March 2015 was more substantial in comparison with the storm of 2013, which resulted evidently from different types of solar flares that generated these storms (C1.6 and C9, respectively). It is seen in Figure 4 that the shape and the duration of the main storm phase and the recovery phase were somewhat similar.
# Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation
## 3 Proposed Method
### Parallel Processing  
The optical flow method requires no encoding information and can be performed while other frames are being encoded. In a typical situation, while the first frame, which has to be Intra, is being encoded, there is enough time for the optical flow computation for the second frame, so if enough CPU cores are available, it can be computed before the need for it arises. If a single frame delay is acceptable, this method will allow saving a significant amount of time in the main encoding loop, which has to iterate over all blocks in order. Even in the case where this one frame delay would be unacceptable, the optical flow method used can be parallelized very well, and as it performs only basic mathematical operations, can easily run on a GPU or dedicated hardware.