esa-sceva/satcom-chunk-collection huggingface dataset:
# Carrier Dynamics in InGaN/GaN on the Basis of Different In Concentrations
## 3 Results and Discussions  
### Band Gap of InGaN  
Recognizing that the energy gap of InGaN varies with In concentration is necessary for the design of an InGaN-based optical power supply to obtain high-efficiency luminescence with a target light wavelength from the InGaN layer. The change in the InGaN energy gap with In concentration is also an important issue in theoretical research. Unfortunately, the light emission of InGaN originates not from a uniform InGaN layer but from local structure due to In segregation, phase separation, and other reasons. These phenomena result in a considerably lower PL emission compared with the energy gap of the \"carrier\" InGaN layer and its unusability in obtaining the InGaN energy gap. In literature, the most common methods used to measure the energy gap of InGaN include absorption spectra [29; 30], optical density measurements [31], and PL excitation spectra [32; 33]. A discussion of energy gap bending parameters of In\({}_{\rm v}\)Ga\({}_{\rm 1-x}\)N as a function of x is underway. [PERSON] et al.reported a pertinent study regarding high-In-concentration InGaN samples [34]. Given the difficulty of the high-In-concentration growth of InGaN and pure In, the energy gap of In\({}_{\rm v}\)Ga\({}_{\rm 1-x}\)N with high x or x = 1 is rarely studied. Thus, the uncertainty of energy gap with high in concentration must be studied. Given the improved growth of h-InN, the effects of O and interfacial metal clusters can be avoided [35]. The InN energy gap has been measured and discussed in several papers. The new h-InN energy gap is in the range of 0.7-1.4 eV. [PERSON] et al. [36] calculated the InN energy gap of 0.58 eV. The sample we studied was an InGaN layer grown on a GaN buffer layer by using a sapphire (Al\({}_{2}\)O\({}_{3}\)) substrate. The energy gap of InGaN and In concentration was determined using eq (2) according to the reference [37]:  
\[Eg_{(x)}\ =\ 0.7x+3.4(1-x)-1.43x(1-x) \tag{2}\]  
Figure 2: Optical scheme of nondegenerate four-wave mixing (FWM) with holographic spectroscope (HBS) as beam splitter.
## 5 Conclusions  
This study investigated the potential of time-series C-band synthetic aperture radar (SAR) data acquired by the Sentinel-1 constellation with interferometric wide mode for determination of crop seeding and harvest dates. Interferometric SAR (InSAR) processing was conducted for each pair of SAR images acquired consecutively in time throughout the 2019 growing season. Preliminary results show through visual interpretation that time-series SAR backscattering revealed the seasonal development cycle of crop growth, while the change in InSAR coherence levels, either a loss of coherence from a high level or a remarkable gain of coherence from a lower level, was associated with substantial change in surface structure of a crop field induced by agricultural land management activities such as seed-bed preparation in the spring or harvest in the fall. This information can be used to deduce the dates on when these field activities happened. However, the loss of coherence can also be associated with other factors, such as temporal decorrelation due to environmental conditions or vegetation development, thus deriving these field activities through InSAR coherence change detection should be integrated with incoherence change detection through the analysis of time-series SAR backscattering as well. Change detection analysis based on time-series SAR data obtained by Sentine-1 and the RADARSAT Constellation Mission can be a research/operation tool for detecting other crop (such as lodging) and field activities that induce sudden and substantial structural changes.  
Given the encouraging results from this study, however, the authors also acknowledge the limitations of the reported research. In subsequent study, detailed field observations and in situ measurements concurrent with Sentinel-1 overpass will be collected throughout the growing season from pre-planting to postharvest to better identify all factors contributing to changes in SAR backscattering and InSAR coherence.  
Conceptualization and content curation, J.S. and J.L.; Methodology, J.S., J.L., V.P.; Interpretation and Analysis, J.L., J.S., V.P., X.G., B.Q., Q.C. and T.D., Validation, J.L., J.S., V.P. and Q.C.; Original Draft Preparation, J.S. and J.L.; Review and Editing, J.S., J.L., V.P., X.G., B.Q., Q.C., T.D., D.M., T.M., J.K. and D.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by the Sustainability Metrics Program at the Science and Technology Branch of Agriculture and Agri-Food Canada.  
The authors declare no conflict of interest.
## 3 Numerical Simulation of the Debris Impact on Satellite Tank
### Shock Wave Propagation  
Figure 6 exhibits the pressure nephogram of the shock wave in the satellite tank at 53 \(\upmu\)s, 125 \(\upmu\)s, 160 \(\upmu\)s, and 248 \(\upmu\)s. When the debris penetrates the satellite tank, a hemispherical shock wave is formed in front of the debris and a cavity is formed behind the debris. The velocity of the shock wave is higher than that of the debris, so the debris lags behind the shock wave. In the propagation process, the shock wave exerts a pressure load on the tank walls. The irreversible energy loss occurs on the wave front, which results in the continuous decrease in shock wave pressure. At the same time, the debris squeezes the liquid hydrazine in front, and a local high pressure zone is generated. When \(t=\) 248 \(\upmu\)s, the shock wave reached the back wall of the satellite tank. After the shock wave transmission, the high pressure hydrazine in front of the debris acts on the back wall, and further exerts a pressure load on it.  
Figure 5: The velocities of debris at different times obtained by the simulation results.  
Figure 6: The pressure nephogram of the shock wave in the satellite tank at 53 \(\upmu\)s, 125 \(\upmu\)s, 160 \(\upmu\)s, and 248 \(\upmu\)s.  
In order to study the pressure load exerted by the shock wave on the tank walls, element A-element G with the angles of 0\({}^{\circ}\), 30\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), 120\({}^{\circ}\), 150\({}^{\circ}\), and 180\({}^{\circ}\) to the impact line were selected respectively, as illustrated in Figure 4. The pressure-time curves from element A to element G are plotted in Figure 7.  
Element A was located at the impact point on the front wall. The pressure of the initial shock wave was the highest, which reached 1.22 GPa. In the propagation process, the shock wave compressed liquid hydrazine and exerted a pressure load on the tank walls. The energy of the wave front was irreversibly dissipated, the shock wave pressure decreased rapidly before element C, and decreased slowly after element C. Due to the superposition of the incident wave and reflected wave, the shock wave pressure increased from element D to element G. As the angle between element D and the impact line was 90\({}^{\circ}\), the incident angle of shock wave was the smallest, the pressure of the reflected wave was the smallest, and the pressure of the superimposed wave front was also the smallest, which was only 260 MPa. Affected by the circumference, the incident angle from element D to element G increased gradually. Element G was located at the exit point on the back wall, and the shock wave reflection at element G was close to the positive reflection. After the superposition of the incident wave and reflected wave, the shock wave pressure reached 390 MPa.
# Effects of Water-Reducing Agents on the Mechanical Properties of Foamed Phosphogypsum
## 3 Results and Discussion
### Effect of the Water-Reducing Agents on the Dry Density of the Foamed Phosphoglycansum Samples  
Figure 5 presents the dry densities of the foamed phosphogypsum A and B samples with different water-reducing agent dosages. The dry density of the foamed phosphogypsum samples first decreased and then increased when the water-reducing agent contents were increased. The dry density of the samples was found to be the lowest when the water-reducing agent content was 0.4%. The lowest dry densities of the phosphogypsum A and B samples doped with FDN were 0.65 g/cm\({}^{3}\) and 0.66 g/cm\({}^{3}\), respectively. The samples doped with FDN had lower dry densities than the foamed phosphogypsum samples mixed with PCE with the same water-reducing agent contents.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Sample** & **Dosage of Water Reducer (\%)** & **Two-Hour Flexural Strength (MPa)** & **Absolute Flexural Strength (MPa)** & **Two-Hour Compressive Strength (MPa)** & **Absolute Compressive Flexural Strength (MPa)** \\ \hline Phosphogypsum B doped with FDN & 0.1 & 0.73 & 1.16 & 1.1 & 2.13 \\ \hline Phosphogypsum B doped with PCE & 0.1 & 0.69 & 1.05 & 1.08 & 1.84 \\ \hline Phosphogypsum B doped with FDN & 0.2 & 0.74 & 1.17 & 1.15 & 2.16 \\ \hline Phosphogypsum B doped with PCE & 0.2 & 0.70 & 1.1 & 1.10 & 2.06 \\ \hline Phosphogypsum B doped with FDN & 0.3 & 0.8 & 1.29 & 1.23 & 2.33 \\ \hline Phosphogypsum B doped with PCE & 0.3 & 0.72 & 1.21 & 1.14 & 2.14 \\ \hline Phosphogypsum B doped with FDN & 0.4 & 0.82 & 1.48 & 1.28 & 2.60 \\ \hline Phosphogypsum B doped with PCE & 0.4 & 0.73 & 1.25 & 1.16 & 2.20 \\ \hline Phosphogypsum B doped with FDN & 0.5 & 0.77 & 1.23 & 1.19 & 2.22 \\ \hline Phosphogypsum B doped with PCE & 0.5 & 0.71 & 1.16 & 1.12 & 2.10 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mechanical properties of the foamed phosphogypsum B samples with different water-reducing agent dosages.  
Figure 5: Dry densities of the foamed phosphogypsum samples with different water-reducing agent dosages. (**a**) Phosphogypsum A. (**b**) Phosphogypsum B.
## 3 Proposed Method: Adaptive Unscented Kalman Filter Using Sparse Measurements of Accelerations  
### Regularization-Based State-Space Model  
This paper aims to develop a near-real-time system identification method for structures under disastrous dynamic loads such as earthquake excitations. In particular, the method focuses on detecting sudden changes in the system parameters such as Young's modulus caused by structural damage during excitations. It is assumed that input data such as an earthquake (EQ) ground motion has been acquired accurately. Conventional filter methods such as EKF and UKF show limitations in tracking the status-history having discontinuity points because those methods using constant artificial noises cannot adapt to time-varying statistics in the measurements. To address this issue, [PERSON] et al. (2006) proposed an adaptive joint state-parameter estimation method requiring full measurement condition, which is however not available in general practice [11]. The adaptive rule was tested with simple events where just one component experiences sudden damage. On the other hand, state-space models in joint input-state estimations do not accord with the objective of this paper because those models are constructed based on modal analysis for which the system parameters are assumed to be time-invariant.  
To facilitate SI under practical measurement conditions, we aim to develop a filter that performs well even with sparse measurements. To this end, let us first consider the following state-space model proposed by [PERSON] et al. (2019) for the purpose of parameter estimation [13]:  
\[\mathbf{x}_{k+1}=\mathbf{x}_{k}+\mathbf{w}_{k} \tag{8}\]  
\[z_{k+1}=h\Bigl{(}\mathbf{x}_{k+1},\;\boldsymbol{u}_{0},\;\dot{\boldsymbol{u}} _{0},\;\boldsymbol{p}_{1k+1}\Bigr{)}+\mathbf{v}_{k+1} \tag{9}\]  
where \(\mathbf{x}_{k}\) is the state vector representing parameters to estimate, e.g., [PERSON]'s moduli at time \(t_{k}\); \(\boldsymbol{u}_{0}\) and \(\dot{\boldsymbol{u}}_{0}\) are the initial displacement and velocity respectively (hereafter, the dot means time-derivative); and \(\boldsymbol{p}_{1k+1}\) denotes the time-history of excitation up to \(t_{k+1}\), e.g., seismic loads. The function \(h(\cdot)\) represents a response function from an FE model, e.g., the Newmark method (\(\gamma=1/2,\;\beta=1/4\)). The system-process model in Equation (8) is linear, thus Equation (3) can be simplified into \(\dot{\mathbf{x}}_{k+1|k}=f\Bigl{(}\dot{\mathbf{x}}_{k|k}\Bigr{)}\) and \(\mathbf{P}_{k+1|k}^{\rm{ex}}=\mathbf{P}_{k|k}^{\rm{ex}}+\mathbf{Q}_{k}\) in this case, i.e., without deriving SPs as in Equation (2). The measurement-output model in Equation (9) predicts responses, which will be compared with the measured response. In this state-space model, full-measurement is not required because the measurement-output model adopts the response function rather than the equilibrium equation.
## 5 Arbitrary Number of Modes
### n-Mode Extension  
Finally, we can also generalize the constraints derived for two-mode systems to the problem of estimating optimally \(n\) parameters encoded in \(n\) coherent states. The derivation of these constraints follows the same reasoning as for the two-mode constraints explained in Equation (23). To keep the expressions concise, we use the following notation for the linear encoding constants:  
\[\mathcal{E}_{i}^{(j)}=\frac{\epsilon_{xi}^{(j)}+i\,\epsilon_{pi}^{(j)}}{\sqrt{ 2}}, \tag{40}\]  
where \(i\) denotes the mode number and \(j\) is the parameter number. For example, for two modes, \(\mathcal{E}_{i}^{(1)}=\epsilon_{xi}+i\,\epsilon_{pi}\) and \(\mathcal{E}_{i}^{(2)}=\eta_{xi}+i\,\eta_{pi}\), for \(i=1,2\). Equation (3) then generalizes as  
\[\alpha_{i}=\frac{x_{i}+i\,p_{i}}{\sqrt{2}}=\sum_{j}^{n}\mathcal{E}_{i}^{(j)}a _{j},\quad i=1,...,n, \tag{41}\]  
where, again, \((a_{1},a_{2},...,a_{n})\) denotes the vector of real parameters. Based on Equation (20), we generalize the expression for the SLD as  
\[L^{(j)}=i\,\Im\left(\sum_{i}^{n}\mathcal{E}_{i}^{(j)}\left[\hat{a}_{i}^{\dagger },\hat{\rho}\right]\right), \tag{42}\]  
where \(\Im\) denotes the imaginary part. Using this notation, the Fisher Information matrix elements can be written in a compact form as  
\[F_{jk}^{(n)}=4\,\Re\left[\sum_{i}^{n}\mathcal{E}_{i}^{(j)*}\mathcal{E}_{i}^{(k )}\right], \tag{43}\]  
which generalizes Equation (10). Following the same reasoning as for the derivation of the two-mode constraints, we can establish the \(n\)-mode constraints:  
\[\sum_{i}^{n}|\mathcal{E}_{i}^{(j)}| =1\quad\forall j,\] \[\Re\left(\sum_{i}^{n}\mathcal{E}_{i}^{(j)*}\mathcal{E}_{i}^{(k)}\right) =0\quad\forall j,k\,s.t.\,j\
eq k, \tag{44}\] \[\Im\left(\sum_{i}^{n}\mathcal{E}_{i}^{(j)*}\mathcal{E}_{i}^{(k)}\right) =0\quad\forall j,k\,s.t.\,j\
eq k,\]  
corresponding to the energy, equal precision and attainability conditions. This system of equations forms a set of \(n^{2}\) constraints imposed on the set of \(2n^{2}\) encoding constants. Moreover, the equal precision and attainability conditions together impose that the matrix \(\mathcal{E}_{i}^{(j)}\) composed of all linear encoding constants is a unitary matrix. Any optimal \(n\)-mode scheme can be constructed as follows: [PERSON] encodes the \(n\) parameters \(a_{i}\) in the \(x\)-quadrature of the \(i\)th coherent states. She applies a passive Gaussian unitary on her system and sends the output to [PERSON]. [PERSON] applies the inverse of the passive Gaussian unitary used by [PERSON] and, finally, he uses homodyne detection on the \(x\)-quadratures to optimally estimate the \(n\) parameters \(a_{i}\).
## 3 Results  
### Game Venue  
The number of individual player samples for each category in the game venue analyses was 70 for control nights, 32 for nights immediately before home games, 28 for nights following home games, 13 for nights immediately before away games, and 33 for nights following away games. Estimated marginal means (with 95% confidence limits) for each sleep variable in each night category are shown in Table 1, while the mean \(\pm\) standard deviation alongside individual data points for each sleep variable in each night category are shown in Figure 2. Analyses showed significant differences in sleep duration, with players sleeping 98 min less on nights before away games than on control nights (\(p<\) 0.001, \(g_{av}\) = 1.19 [0.60, 1.78], _moderate_ effect) and 88 min less on nights before away games than before home games (\(p=\) 0.013, \(g_{av}\) = 1.07 [0.42, 1.72], _moderate_ effect). Significant differences were also evident for sleep onset and offset times, with players falling asleep 55-71 min later after home games compared to control nights (\(p=\) 0.001, \(g_{av}\) = 0.90 [0.46, 1.35], _moderate_ effect), nights before home games (\(p<\) 0.001, \(g_{av}\) = 1.15 [0.65, 1.66], _moderate_ effect), and nights after away games (\(p=\) 0.007, \(g_{av}\) = 0.89 [0.38, 1.40], _moderate_ effect), and awakening 69-99 min earlier before away games compared to all other nights (control nights: \(p<\) 0.001, \(g_{av}\) = 1.27 [0.67, 1.86], _large_ effect; nights before home games: \(p=\) 0.014, \(g_{av}\) = 1.06 [0.41, 1.71], _moderate_ effect; nights after home games: \(p=\) 0.001, \(g_{av}\) = 1.34 [0.68, 2.00], _large_ effect; nights after away games: \(p=\) 0.037, \(g_{av}\) = 0.94 [0.30, 1.58], _moderate_ effect). Non-significant (\(p>\) 0.05) differences were apparent between all nights for WASO (\(g_{av}\) = 0.00-0.37, _trivial_-to-_small_ effects) and sleep efficiency (\(g_{av}\) = 0.08-0.54, _trivial_-to-_small_ effects).
## 4 Results and Discussion
### Advancements  
A performance comparison of the proposed pattern reconfigurable antenna with available literature is shown in Table 3. The compared results show that the proposed reconfigurable antenna has the advantages of more compact structure than the ones in available literature. In addition, the proposed reconfigurable antenna also has an acceptable range of reconfigurable angle, higher operating frequency, and the proposed pattern reconfigurable antenna does not need array architecture.
# Estimation of Methane Gas Production in Turkey Using Machine Learning Methods
###### Abstract  
Thetabase gas emission into the atmosphere is rising due to the use of fossil-based resources in post-industrial energy use, as well as the increase in food demand and organic wastes that comes with an increasing human population. For this reason, methane gas, which is among the greenhouse gases, is seen as an important cause of climate change along with carbon dioxide. The aim of this study was to predict, using machine learning, the emission of methane gas, which has a greater effect on the warming of the atmosphere than other greenhouse gases. Methane gas estimation in Turkey was carried out using machine learning methods. The R\({}^{2}\) metric was calculated as logistic regression (LR) 94.9%, artificial neural networks (ANNs) 93.6%, and support vector regression (SVR) 92.3%. All three machine learning methods used in the study were close to ideal statistical criteria. LR had the least error and highest prediction success, followed by ANNs and then SVR. The models provided successful results, which will be useful in the formulation of policies in terms of animal production (especially cattle production) and the disposal of organic human wastes, which are thought to be the main causes of methane gas emission.  
methane gas; global warming; economy; environment; machine learning Article  
Estimation of Methane Gas Production in Turkey Using Machine Learning Methods  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Estimation of Methane Gas Production in Turkey Using Machine Learning Methods. _Appl. Sci._**2023**, _13_, 8442. [[https://doi.org/10.3390/app13148442](https://doi.org/10.3390/app13148442)]([https://doi.org/10.3390/app13148442](https://doi.org/10.3390/app13148442))
## 2 Background and Related Work
### Consensus Mechanisms
#### 2.4.2 PoS  
PoS is not dependent on a high computation capacity. It operates according to the staked properties of the network. In general, the more money a peer receives, the higher the incentive they will have to mine and reward. This mechanism does not require extremely high computational power. Little calculating power is required, so excessive electricity consumption is reduced [27]. Several limitations are associated with the PoS protocol. For example, large investors with enormous capital can manage the network to maximise their wealth, making the rich richer. PoS is also vulnerable to 51% attacks, especially when someone has more than 50% of the network wealth. An individual can exploit the blockchain easily for personal gain, resulting in malicious stakeholders gaining the majority of the supply by taking advantage of the nothing-at-stake issue. PoS suffers from low subjectivity and is thus challenging and demanding to implement [51]. To conduct a 51% attack, an opponent must obtain 51% of all cryptocurrencies. The cost of obtaining 51% of the overall stake is substantial. In light of this, the threat level posed by the 51% attack may be lower than that posed by PoW. According to our analysis, a long-range attack can exploit PoS. Carrying out the P + Epsilon attack is impossible because a large budget is required for an attacker to donate to the minority's safety deposit. A Sybil attack can exploit PoS, and a DPoS attack can interrupt any portion of the network.
## 3 Results and Discussion
### Milling Efficiency  
From the test image output results, the number of the whole grains \(=17\) out of 22 objects, whereas, the broken grain objectsare \(=5\) out of 22 objects. Therefore, the percentage of whole grain \(=(17/22)\times 100=77.27\%\). This milling efficiency (\(\eta\)) was found for a specific roller characteristic (\(\mathrm{rpm}=2000\) and gap between roller \(\sim\)0.5 mm) of the milling machine [45]. The milling efficiencies were evaluated in a similar way for the 10 test images. Subsequently, the average of all the 10 milling efficiencies was determined, which is \(\sim\)77.3% with a standard deviation (\(\sigma\)) of 1.5. The milling efficiency (\(\eta_{\mathit{avg}}\)) derived using the image-based method was in accordance with the manual calculation results (\(\eta=76.87\%\)).
## 2 Study Area and Data
### Geostationary Himauari-8 Fire Radiative Power  
The new-generation geostationary meteorological satellite Himawari-8 was launched on 7 October 2014. It has 16 observation belts with a spatial resolution of 0.5 or 1 km in visible and near infrared bands, a spatial resolution of 2 km in infrared bands and a temporal resolution of 10 min. It provides data covering the entire global hemisphere of East Asia and Australia, and has provided remote sensing observation data to the world since 2015, including real-time and archived formats. They are available through the official JAXA website. In order to convert a twice-daily VIIRS-IM FRP product into a daily integrated FRE, information on the fire diurnal cycle is required [37; 47; 48]. Apparently, compared with the observation frequency of the MODIS satellite, four times a day, the high time resolution provided by Himawari-8 can obtain the above information more accurately. However, the spatial resolution of geostationary satellites is relatively coarse, and the presence of mixed pixels increases the deviation of FRP values. Therefore, we combine VIIRS data with higher spatial resolution to reduce the deviation and uncertainty caused by using FRP data alone.