esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Development
### Geometric Model
#### 2.2.2 Governing Equations
* Species conservation equation \[\frac{\partial(\varepsilon c_{an,ca})}{\partial t}+\
abla\cdot(\varepsilon \overrightarrow{u}c_{an,ca})=\
abla\cdot(D_{an,ca}^{eff}\
abla c_{an,ca})+S_{ k},\] (9)  
\[S_{H_{2}}=-\frac{M_{H_{2}}}{2F}i_{an}, \tag{10}\]  
\[S_{O_{2}}=-\frac{M_{O_{2}}}{4F}i_{ca}, \tag{11}\]  
\[S_{H_{2}O}=\frac{M_{H_{2}O}}{2F}i_{ca}, \tag{12}\]  
The species conservation equation is shown in Equation (9), and \(S_{k}\) is the species source term. \(S_{k}\) is zero in the flow channel and in GDL, and can be simplified in CL as shown in Equations (10)-(12). \(c_{an,ca}\) is the constituent concentration, and \(D_{an,ca}^{eff}\) is the effective diffusion coefficient of the constituent in anode or in cathode. * Charge conservation equation \[\
abla\cdot(\sigma_{e}\
abla\phi_{e})+S_{e}=0,\] (13)  
\[\
abla\cdot(\sigma_{m}\
abla\phi_{m})+S_{m}=0, \tag{14}\]  
\[i_{an}=i_{an,ref}(\frac{C_{H_{2}}}{C_{H_{2},ref}})^{\gamma_{an}}(e^{\frac{g_{ nn}F}{2F}\eta_{an}}-e^{\frac{g_{nn}F}{2F}\eta_{an}}), \tag{15}\]  
\[\frac{\partial(\varepsilon\rho c_{p}T)}{\partial t}+\
abla\cdot(\varepsilon \rho c_{p}\overrightarrow{u}T)=\
abla\cdot(k^{eff}\
abla T)+S_{Q},\]0  
\[\frac{\partial(\varepsilon\rho c_{p}T)}{\partial t}+\
abla\cdot(\varepsilon \rho c_{p}\overrightarrow{u}T)=\
abla\cdot(k^{eff}\
abla T)+S_{Q},\]1  
The charge conservation equation is shown in Equations (13) and (14). \(\phi_{e}\) and \(\phi_{m}\) represent solid and membrane potential, respectively. \(S_{e}\) and \(S_{m}\) represent electron and proton current, respectively, and only exist in CL. The values of \(S_{e}\) and \(S_{m}\) could be calculated by the Butler-Volmer equation, as shown in Equations (15) and (16). And the subscripts of \({}_{\rm an}\) and \({}_{\rm ca}\) represent anode and cathode, respectively. The relationship between electrode potential, solid potential, and membrane potential is shown as Equation (17). * Liquid water formation and transport equation [29] \[\frac{\partial(\varepsilon\rho c_{p}T)}{\partial t}+\
abla\cdot(\varepsilon \rho c_{p}\overrightarrow{u}T)=\
abla\cdot(k^{eff}\
abla T)+S_{Q},\]2 (18) Important assumptions have been made to solve the equations above. (1) Laminar and incompressible flow in flow channels. (2) Steady conditions of operating PEMFC. (3) Reactive gases as ideal gas. (4) Homogeneous porous medium. (5) Neglected gravity.
# Dual Residual Denoising Autoencoder with Channel Attention Mechanism for Modulation of Signals
###### Abstract  
Aiming to address the problems of the high bit error rate (BER) of demodulation or low classification accuracy of modulation signals with a low signal-to-noise ratio (SNR), we propose a double-residual denoising autoencoder method with a channel attention mechanism, referred to as DRdA-CA, to improve the SNR of modulation signals. The proposed DRdA-CA consists of an encoding module and a decoding module. A squeeze-and-excitation (SE) ResNet module containing one residual connection is modified and then introduced into the autoencoder as the channel attention mechanism, to better extract the characteristics of the modulation signals and reduce the computational complexity of the model. Moreover, the other residual connection is further added inside the encoding and decoding modules to optimize the network degradation problem, which is beneficial for fully exploiting the multi-level features of modulation signals and improving the reconstruction quality of the signal. The ablation experiments prove that both the improved SE module and dual residual connections in the proposed method play an important role in improving the denoising performance. The subsequent experimental results show that the proposed DRdA-CA significantly improves the SNR values of eight modulation types in the range of \(-12\) dB to \(8\) dB. Especially for 16 QAM and 64 QAM, the SNR is improved by \(8.38\) dB and \(8.27\) dB on average, respectively. Compared to the DnCNN denoising method, the proposed DRdA-CA makes the average classification accuracy increase by \(67.59\)-\(74.94\%\) over the entire SNR range. When it comes to the demodulation, compared with the RLS and the DnCNN denoising algorithms, the proposed denoising method reduces the BER of 16 QAM by an average of \(63.5\%\) and \(40.5\%\), and reduces the BER of 64 QAM by an average of \(46.7\%\) and \(18.6\%\). The above results show that the proposed DRdA-CA achieves the optimal noise reduction effect.  
Keywords:signal denoising; convolutional autoencoder; channel attention; residual connection; AWGN 2023
## 3 Results and Discussion
### Quantitative Differences between FKBT and 1Y-KBT
The sensory effect of \(\upbeta\)-damascenone in wine is especially concentrated. On the basis of GC-O results and OAVs, many researchers have concluded that \(\upbeta\)-damascenone contributes greatly to wine's aroma. However, sensory tests have indicated that the threshold of \(\upbeta\)-damascenone is easily affected by the matrix. Its thresholds in water and wine are 0.004 and 7 \(\upmu\)g/L, respectively, but its concentration in wine is 1-1.5 \(\upmu\)g/L; thus, \(\upbeta\)-damascenone does not affect wine's aroma directly and may contribute only indirectly to the fruity aroma of wine [24]. Compared with studies on the sensory effect of \(\upbeta\)-damascenone in wine, those on its effect in tea aroma are few. Research studies about the \(\upbeta\)-damascenone sensory effect indicated to us that checking the actual sensory effect of aroma volatiles was necessary. The concentrations of two cucumber-like aroma volatiles, namely (E,Z)-2,6-nonadienal and areal, in the FKBT infusion were 1.9-folds higher than those in the 1Y-KBT infusion, but still <5 ug/L. The OAVs of (E,Z)-2,6-nonadienal were 49 and 26 in the FKBT and 1Y-KBT infusions, respectively, but renal made no contribution to either KBT infusion aroma, with an OAV <0.1. In addition, (E)-2-nonenal, frequently detected in various teas through GC-O, contributed slightly to both KBT aromas, with OAVs of 2 and 1 in the FKBT and 1Y-KBT infusions, respectively. Although dihydroactinidiolide was reported to be an aroma volatile in several teas [3, 25, 26], we did not perceive its aroma. However, the concentration of dihydroactinidiolide in the FKBT infusion was only 60% of that in the 1Y-KBT infusion according to standard addition, supporting our previous conclusion that dihydroactinidiolide was a reliable indicator of storage duration.
# A Novel Approach to Enhance Crude Oil Recovery Ratio Using Selected Bacterial Species
## 2 Materials and Methods
### Gas Chromatography Hydrocarbon Analysis  
The collected oil fractions from the water flood before and after the incubation with bacteria strains were analyzed by GC. This was done to assess the effect of different bacterial species on the biodegradation of Bouri crude oil and to determine whether the biotransformation significantly depended on temperature as an important parameter.  
The crude oil was extracted from core column after 12 weeks of incubation using liquid-liquid extraction techniques by a mixture of \(n\)-hexane and dichloromethane (1:1) for GC analysis. The extracted solution was then dried through solvent-washed anhydrous sodium sulfate in Whatman #50 paper. The remaining organic phase of the extract was removed with a dichloromethane solvent and put in a sealed flask for subsequent analysis. Gas chromatography analyses were carried out on the initial sample (time = 0 day) and at the end of the experiments (time = 84 days) to evaluate the potential of biotransformation rate, as described by [16; 17]. Total petroleum hydrocarbon and \(n\)-alkane concentrations were determined using a DHA (Carbon Distribution) Model No: CP3800.
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 5 PPP Challenges and Solutions  
The experimental results demonstrate the advantage of PPP as a global precise GNSS solution with high accuracy. However, achieving the full potential of PPP requires addressing several challenges. A predominant challenge is the protracted convergence time, often exceeding 20 min in standard DF-PPP, attributed to the time necessary for carrier-phase ambiguity resolution. Multiple factors influence this convergence duration, including the number of visible satellites, satellite geometry, correction product quality, receiver multipath environments, and prevailing atmospheric conditions. Extensive research is underway to expedite PPP convergence, aligning it with the demands of real-time applications. Recent studies have indicated that employing triple-frequency, multiconstellation, and adopting ionospheric corrections instead of IF combinations could potentially shorten PPP convergence time [72; 88; 89]. Moreover, fixing the integer ambiguities can also reduce the convergence time to some extent compared with the float solution; nonetheless, it  
Figure 8: Position errors in the kinematic test.  
Figure 7: Number of visible GPS/GLONASS satellites observed during the kinematic test.  
necessitates supplementary corrections and more complicated algorithms. A state-of-the-art method that combines the advantages of PPP and RTK and allows faster ambiguity resolution is the PPP-RTK.  
Another significant challenge for PPP is the performance deterioration or potential inability to provide a solution in challenging GNSS environments like underpasses, within tunnels, or amid towering buildings. Although this limitation is not exclusive to PPP and pervades all GNSS positioning modes, its ramifications are magnified in PPP due to the requisite reconvergence time to reclaim the original precision. This particular constraint is of paramount concern in scenarios like land vehicular navigation. Therefore, the fusion of PPP with other navigation systems is employed to ensure the continuity of the navigation solution and reduce the PPP reconvergence time. Two examples of this fusion are PPP/INS integration [9; 11; 90] and PPP/INS/Vision/LIDAR integration [91] for navigation in GNSS-challenging environments.  
Conceptualization, M.E. and U.I.; methodology, M.E.; software, M.E.; validation, M.E. and U.I.; formal analysis, M.E.; investigation, M.E.; resources, A.N. and M.K.; data curation, M.E.; writing--original draft preparation, M.E.; writing--review and editing, U.I., A.N. and M.K.; visualization, M.E. and U.I.; supervision, A.N. and M.K.; project administration, A.N.; funding acquisition, A.N. and M.K. All authors have read and agreed to the published version of the manuscript.  
This research is supported by a grant from the Natural Sciences and Engineering Research Council of Canada (NSERC) under grant number: RGPIN-2020-03900.  
Not applicable.
## 3 Separate Analysis per Individual Components
### Thermal and Morphological Analysis of Oil
#### 3.2.2 Morphological (Volume) Tests  
To characterize the volume change caused by laser excitation, a Michelson interferometer was mounted (Figure 12), having as illumination source a THORLABS brand LED (model LED635L) that emits light with a central spectral output (\(\lambda_{LED}\)) of 635 nm, a half-height width (FWHM) of 15 nm, an approximate coherence depth of 25 \(\upmu\)m, and a half viewing angle of 7\({}^{\circ}\). Since the test object is the same oil-filled container, the LED's low  
Figure 11: Comparison of thermal behavior and refractive index of oil over time. The asterisk indicates the point that was analyzed in Figure 6.  
Figure 10: Oil temperature profile vs thermocouple position. Values shown as crossed out in Figure 9, were here removed and replaced by the interpolation curve.  
coherence allows interference only with the first surface, where the oil is exposed and the volume change is concentrated; this way, spurious interference from other surfaces is avoided. For these experiments, the same \(\lambda_{Ex}\) excitation laser and camera that were used to analyze the refractive index were used again. In addition, the experiment sequence (without excitation, with excitation, and finally, in relaxation) was repeated. As in the temperature experiment, only the oil was analyzed.  
In Figure 13, the change in the surface topography of the exposed oil at different times is presented. Figure 13a shows the displacement map when measured at 5 s, when the laser was still off; Figure 13b shows measurement at 10 s, when the laser was turned on; Figure 13c-e represents measurements made at 15, 20, and 25 s, respectively; finally, Figure 13f corresponds to the topography measured when the laser was off.  
Figure 12: Michelson interferometer used to study volume changes: (**a**) experimental setup photo; (**b**) scheme.  
Figure 13: _Cont._  
For the Michelson interferometer used in these experiments, the topography values are obtained from the phase maps through the following equation:  
\[t_{\phi}=\frac{\phi\lambda_{LED}}{4\pi}, \tag{7}\]  
where \(t_{\phi}\) is the change of sample topography.  
By integrating the volume under the topography at each point, the total volume of each measurement can be obtained. Due to interferometer instability, the measurements present a detuning error; however, volume behavior corresponds adequately to laser excitation time, and can be related to the result of a refractive index-temperature test, since oil expands with heat. Figure 14 shows, as a function of time, the change in volume against the change in temperature and refractive index.  
As reported by [PERSON], oil volume change does not represent a variable that affects her measurements due to equipment design, and therefore, she does not stop in its analysis [24]. However, it has been proven in this work that such change also exists and should be considered, depending on the application.
# Minimum Barrier Distance-Based Object Descriptor for Visual Tracking
## 3 The Proposed Algorithm
### Structured SVM Tracking  
In this section, we incorporate our descriptor into the conventional tracking algorithm, structured SVM [4], as Struck [4] excels in the recent benchmark according to [1; 10]. Firstly, we set a searching window around the bounding box obtained in the previous frame. Then, in the searching window, our MBD-based descriptors for candidate bounding boxes are incorporated into structured SVM to estimate the maximum classification score and select the current bounding box standing for the object. Finally, we may need to update the classifier and proceed to the next frame. Specifically, Struck estimates the object bounding box \(\mathbf{r}_{t}\) in the \(t\)-th frame \(\mathbf{z}_{t}\) by maximizing a classification score. To avoid the model drift, we fuse the model of the first frame and the current frame in our algorithm. The bounding box \(\mathbf{r}_{t}\), which can maximize a classifier score, is the object, as follows:  
\[\mathbf{r}_{t}=\arg\max_{\mathbf{r}}(\gamma\langle\mathbf{w}_{0},\psi(\mathbf{z }_{t},\mathbf{r})\rangle+(1-\gamma)\langle\mathbf{w},\psi(\mathbf{z}_{t}, \mathbf{r})\rangle) \tag{7}\]  
where \(\mathbf{w}\) is the normal vector of a decision plane and \(\mathbf{w}_{0}\) is learned in the initial frame. \(\psi(\mathbf{z}_{t},\mathbf{r})\) represents the weighted feature descriptor for bounding box \(\mathbf{r}\) in frame \(t\), and \(\gamma\) is a tradeoff parameter. Maximizing a classifier score by evaluating the samples represented with weighted features can obtain the best classification.  
We adopt the good strategy mentioned in SOWP [10] to detect abrupt changes of object appearance, and we update the classifier when the confidence score \(\rho\) of the tracking result is greater than a threshold \(\theta\). A confidence score \(\rho\) is given by:  
\[\rho=\frac{1}{|S_{t}|}\underset{s\in S_{t}}{\sum}\langle s,\psi(\mathbf{z}_{t },\mathbf{r}_{t})\rangle \tag{8}\]  
where \(s\) is a positive support vector and \(S_{t}\) is the set of \(s\) at time \(t\). The confidence score \(\rho\) in the \(t\)-th frame refers to the average similarity between the MBD descriptor of the tracked bounding box and the positive support vectors. Therefore, the confidence score measures the reliability of the object in the bounding box in the current frame.  
We estimate the target scale by training a scale classifier on a scale pyramid [48]. This allows us to estimate the target scale independently after the optimal position is found. \(p*q\) stands for the object size in the current frame, and \(M\) is the size of the scale filter. Then, we extract an image patch with the size \(J_{m}\) centered around the target as follows:  
\[J_{m}=a_{m}p*a_{m}q. \tag{9}\]  
In Formula (9), \(a\) denotes the scale factor between feature layers, \(m\in\{-\frac{M-1}{2},...,\frac{M-1}{2}\}\). The value of the training example at scale level \(m\) is set to the d-dimensional feature descriptor of \(J_{m}\)[48]. Finally, the new sample is used to update the scale filter.
## 3 Discussions
The plasma electron concentration was \(1.2\times 10^{19}\) cm\({}^{-3}\), and the total acceleration distance was 800\(\upmu\)m. The length of the ionisation injection zone in all cases was \(L_{n}=200\)\(\upmu\)m.  
\(E_{max}\) of the accelerated electrons on the acceleration distance is presented. The maximum energy \(E_{max}\) of the accelerated electron using the TH laser beam, which was focused using the OAP towards the focal plane \(z_{f}\) = 30 \(\mu\)m, reaches 130 MeV, and 170 MeV when focused towards the focal plane \(z_{f}\) = 330 \(\mu\)m. The maximum energy \(E_{max}\) of the accelerated electron using the TH laser beam which was focused using the AXP is 6% higher, and increases to 180 MeV. In the case of the AXP+STC, the maximal electron energy compared to the best OAP case increases by 12% and reaches 200 MeV. In Figure 5, the energy spectra of accelerated electrons using the TH laser pulse, which was focused using the OAP towards the focal planes \(z_{f}\) = 30 \(\mu\)m (a) and \(z_{f}\) = 330 \(\mu\)m (b), using the AXP (c), and using the AXP with the STC parameter \(\alpha\) = 0.007, are presented. The electron energy spectrum (Figure 5a) of the OAP focused on the focal plane \(z_{f}\) = 30 \(\mu\)m shows two peaks: one in the area of high energies, and the other in the area of low energies. The total charge of accelerated electrons is 30 pC. For the focal plane \(z_{f}\) = 330 \(\mu\)m, the share of low-energy electrons in the energy spectrum is lower (Figure 5b). However, the total charge of all electrons decreases to 12 pC. The average energy \(E_{>0.8 max}\) of electrons, in the interval from \(E_{max}\) to 0.8 \(E_{max}\), is 100 MeV for \(z_{f}\) = 30 \(\mu\)m and 145 MeV for \(z_{f}\) = 330 \(\mu\)m. In the energy spectrum of accelerated electrons using the AXP, as shown in Figure 5c, some electrons of low energies are also visible. However, most of the accelerated electrons are concentrated in the high-energy part of the spectrum. When compared to the case where an OAP was used to focus the TH laser beam, the average energy \(E_{>0.8 max}\) increased to 150 MeV, and the total electron charge dropped to 3 pC. The simulation results of electron acceleration using the AXP with STC are shown in Figure 5d. The average energy \(E_{>0.8 max}\) increased to 170 MeV, which is about 11% more than in the case of the AXP without STC. The total accelerated charge was 2.5 pC.
# Towards a Real-Time Description of the Ionosphere:
###### Abstract  
This paper focuses on a detailed comparison, based on the F2-layer peak characteristics _f_F2 and _hm_F2, between the International Reference Ionosphere (IRI), which is a climatological empirical model of the terrestrial ionosphere, and the IRI Real-Time Assimilative Mapping (IRTAM) procedure, which is a real-time version of IRI based on data assimilation from a global network of ionosondes. To perform such a comparison, two different kinds of datasets have been considered: (1) _f_F2 and _Im_F2 as recorded by 40 ground-based ionosondes spread all over the world from 2000 to 2019; (2) _f_F2 and _hm_F2 from space-based COSMIC/FORMOSAT-3 radio occultation measurements recorded from 2006 to 2018. The aim of the paper is to understand whether and how much IRTAM improves IRI _f_F2 and _hm_F2 outputs for different locations and under different diurnal, seasonal, solar and magnetic activity conditions. The main outcomes of the study are: (1) when ionosonde observations are considered for validation, IRTAM significantly improves the IRI _f_F2 modeling both in accuracy and precision, while a slight improvement in the IRI _hm_F2 modeling is observed for specific locations and conditions; (2) when COSMIC observations are considered for validation, no noticeable improvement is observed from the IRTAM side for both _f_F2 and _hm_F2. Indeed, IRTAM can improve the IRI _f_F2 description only nearby the assimilated ionosonde locations, while the IRI _hm_F2 description is always more accurate and precise than IRTAM one.  
International Reference Ionosphere (IRI); IRI Real-Time Assimilative Mapping (IRTAM); _f_F2; _hm_F2; ionosondes data; COSMIC/FORMOSAT-3 radio occultation data; Space Weather 2021  
1
# Optimal Energy Integration and Off-Design Analysis of an Amine-Based Natural Gas Sweetening Unit
## 4 Results and Discussion
### Direct Heat Integration  
Waste heat from turbine exhaust presents a high potential for direct energy integration, which may result in a substantial decrease in the amine unit's heating requirement as shown in Figure 7. Results indicate that direct heat integration leads to energy savings ranging from 8 to 20 MW, equivalent to 20 up to 100% of the initial requirements of the amine unit. In addition to the OPEX and environmental benefits, and if addressed in the early design phase, the direct integration may contribute to reducing the capital expenditure cost by reducing the size or even eliminating the external heating source, leading to further cost optimization. As discussed earlier, the highest energy savings are reported at high CO\({}_{2}\) content and high compression pressure ratios, due to the availability of waste heat from the compression stations combined with the high energy requirements of the amine unit.