esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Scalable Blockchain Trust Management System (SBTMS)
### Step 1: Calculating Confidence Score and Message Reliability
\[P(O|c_{s})=\prod_{s^{\prime}=1}^{N}P\big{(}o_{s^{\prime}}|c_{s}\big{)}=\prod_{ s^{\prime}=1}^{N}o_{s^{\prime}} \tag{7}\]  
\[P(O|\overline{c_{s}})=\prod_{s^{\prime}=1}^{N}P\big{(}o_{s^{\prime}}|c_{s} \big{)}=\prod_{s^{\prime}=1}^{N}\big{(}1-o_{s^{\prime}}\big{)} \tag{8}\]  
\(P(\overline{m}\mid C)\) in Equation (2) is calculated using by Equation (9). \[P(\overline{m}|C)=\frac{P(\overline{m})\times\prod_{s=1}^{N}P(c_{s}|\overline {m})}{P(\overline{m})\times\prod_{s=1}^{N}P(c_{s}|\overline{m})+P(m)\times \prod_{s=1}^{N}P(c_{s}|m)} \tag{9}\]  
where \(P(m)\), \(P(c_{s}\mid m)\) and \(P(c_{s}\mid\bar{m})\) are equal to \(1-p(m)\), \(c_{s}\), and \(1-c_{s}\), respectively. Furthermore, \(P(C\mid\overline{m},O)\) is given by  
\[P(C|\overline{m},O)=P(C|O)=\prod_{s=1}^{N}P(c_{s}|O) \tag{10}\]  
Finally, the receiver vehicle sends the calculated reliability to the nearest RSU as \((v_{r},v_{s},m,P(m|O,C))\), where \(V_{r}\) is the receiver's ID, \(V_{s}\) is the sender's ID, \(m\) is the message's ID, and \(P(m|O,C)\) is the calculated reliability level by \(V_{r}\) for the message \(m\). Algorithm 1 summarizes undertaken activities by each vehicle. ```
Input :Event Output :Rate value whilevehicle (running)do if(see an event)then Send message to other vehicles; end if if(Vehicle receive a message)then Forward message and set counter=counter+1; Calculate \(c_{s}\); Send query for nearest RSU to get \(o_{s}\); Calculate \(P(m\mid O,C)\); Send message reliability to RSU; end if end while
```  
**Algorithm 1**Vehicle's activities.
## 5 Discussion and Conclusions
### Conclusions  
Based on remote sensing images in the late 1970s, the early 1990s, 2004 and 2012, the LULC datasets in the headwaters of the Yangtze River and Yellow River were developed, and LULC and macro-ecological situations and changes were contrastively analyzed by applying the transition matrix and LULC change index. The main conclusions can be summarized as follows:  
1. While the dominant LULC type was grassland in the headwaters of both the Yangtze River and Yellow River, the area percentage of the grassland in the headwaters of the Yellow River was 17.00% more than in the headwaters of the Yangtze River, especially the high-coverage grassland; in addition, permanent ice and snow were found only in the headwaters of the Yangtze River, with their percentage being 2.03%.
2. During the past 30 years, in the headwaters of the Yangtze River, the grassland and wet- and marshland area increased at the expense of sand, gobi and bare land and desert; in the headwaters of the Yellow River, significant reduction of grassland coverage, shrinkage of wet- and marshland and the consequential expansion of sand, gobi and bare land were noticed, and the change magnitude was more dramatic overall. From the late 1970s to the early 1990s, shrinkage of wet- and marshland and desertification were the main LULC change types in the headwaters of the Yangtze River, while an expansion of sand, gobi and bare land and a decline in grassland coverage were the main LULC change types in the headwaters of the Yellow River. From the early 1990s to 2004, desert changing to wet- and marshland was the main LULC change type in the headwaters of the Yangtze River, while a further grassland deterioration, wet- and marshland shrinkage and significant expansion in sand, gobi and bare land were noticed in the headwaters of the Yellow River. From 2004 to 2012, the sand, gobi and bare land and desert changing to wet- and marshland and grassland were the main LULC change types in the headwaters of the Yangtze River; in the headwaters of the Yellow River, an increase in grassland coverage, expansion of the wet- and marshland and a decrease in the sand, gobi and bare land area were the main LULC change types.
3. According to the LULC change index, during the past 30 years, the macro-ecological situation in the headwaters of the Yangtze River has experienced a process of degeneration, slight melioration, and continuous melioration, in that order; while the headwaters of the Yellow River has experienced a process of degeneration, obvious degeneration, and slight melioration, in that order. In addition, the change magnitude in the headwaters of the Yellow River has been more dramatic than in the headwaters of the Yangtze River.
###### Abstract  
Market demand for palatable plant-based meat alternatives is on the rise. One of the challenges is formulating products with sensorial characteristics similar to conventional meat. In this study, the effect of myoglobin on the aromatic profile of plant-based meat alternatives was assessed. Plant-based burgers were made with soy-textured protein, supplemented with three levels of myoglobin (0, 0.5 and 1.0%, the latter two mimicking endogenous myoglobin levels in meat), and grilled for 12 min at 250 \({}^{\circ}\)C. To evaluate the aromatic profile of the compounds, raw and grilled samples were subjected to headspace solid-phase microextraction (HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS). Principal component analysis (PCA) analysis was then performed to visualize the interaction between grilling and myoglobin addition, and the effect exerted on the resulting aromatic profile. Myoglobin significantly affected several classes of volatile compounds, either by itself or in conjunction with grilling. A notable increase in aldehydes and a decrease in hydrocarbons were noted after adding myoglobin. As expected, an increase in pyrazines was observed after grilling. The results suggest myoglobin positively influences the aromatic profile of plant-based meat alternatives, contributing to a profile closer to the one of conventional meat.  
myoglobin; plant-based meat alternatives; HS-SPME-GC-MS; volatiles; PCA-analysis; aroma; Maillard reaction; lipid oxidation; aldehydes; pyrazines +
Footnote †: journal: _foods_
# Performance of Warm-Mixed Flame Retardant Modified Asphalt Binder
## 5 Results and Analysis of Superpave Evaluation Index Test of Asphalt Binder
### Dynamic Shear Rheological Test  
The dynamic shear rheological test was employed in this paper to evaluate high temperature rheological properties of three kinds of asphalt binders. Original asphalt binder and rolling thin film oven tested (RTFOT) binder were studied, strain controlled mode was adopted, the frequency was 10 rad/s, and the high-temperature performance was mainly evaluated with anti-rutting factor (G*/sin8). The test results are shown in Figures 3 and 4.  
Figure 4: Results of the dynamic shear rheological test of rolling thin film oven tested (RTFOT) aged asphalt binder.  
Figure 3: Results of the dynamic shear rheological test of original asphalt binder.  
As seen in Figures 3 and 4, the addition of flame retardant and Sasobit can obviously improve the G*/sin\(\delta\) of asphalt binder. For example, for the original asphalt binder, Sasobit modified asphalt binder and warm-mixed flame retardant asphalt binder with different flame retardant concentration exhibited G*/sin\(\delta\) (at 64 \({}^{\circ}\)C) that were 1.10, 1.13, 1.19, 1.20, and 1.25 times that of SBS modified asphalt binder, respectively; for the RTFOT aged asphalt binder, Sasobit modified asphalt binder and warm-mixed flame retardant asphalt binder with different flame retardant concentration exhibited G*/sin\(\delta\) (at 64 \({}^{\circ}\)C)that were 1.04, 1.10, 1.11, 1.13, and 1.22 times that of SBS modified asphalt binder, respectively. These results indicate that both flame retardant and Sasobit can significantly improve the high-temperature resistance deformability of asphalt binder; and as for warm-mixed flame retardant asphalt binder, G*/sin\(\delta\) increased with the increase of flame retardant concentration, indicating that the concentration has a positive influence on the high-temperature performance, which is consistent with the penetration test results.  
According to the above test results, the high-temperature PG grading made for SBS modified asphalt binder, Sasobit modified asphalt binder and warm-mixed flame retardant asphalt binder. The results are shown in Table 8.  
It can be inferred from Table 8 that the high temperature PG grading of asphalt binder has been improved by one grade after the addition of Sasobit and flame retardant, and as for the warm-mixed flame retardant asphalt binder, its high-temperature grading remains unchanged despite the increase of the concentration of flame retardant. The above test results indicate that the addition of flame retardant and Sasobit is beneficial for improving the high-temperature performance of asphalt binder.
# Evaluation of Fire Blight Resistance of Eleven Apple Rootstocks Grown in Kazakhstan Fields
## 3 Results
### Genetic Determinants Influencing Resistance to Erwinia amylovora in Apple Rootstocks
The two-year survey showed that the rootstocks G.41, G.16, and '62-396' demonstrated the best resistance to fire blight despite outbreaks in neighboring plots of other rootstocks in both 2021 and 2022. It is important to mention that G.16 carries the resistant allele for only one SNP marker; nevertheless, its DSI was zero. Fire blight outbreaks were recorded for 'MM106', positive for marker AE10-375, and non-marker resistant rootstock types in the fields (Table 4). The greater spread of infection occurring in 2022 is because of increased air humidity, temperature, and precipitation. Our assessment is that the natural spread of the pathogen among the fields and of resistance of rootstocks to _E. amylovora_ was not fully fledged because infected trees were immediately eliminated following identification based on PCR testing.
## 4 Experiment
### Evaluation Metrics  
#### 4.2.1 Classification Evaluation  
To evaluate the performance of different shadow compensation results on classification accuracy, the Overall Accuracy (OA), Average Accuracy (AA), and Kappa are used. OA refers to the ratio of the number of correctly classified samples to the total number of samples, and it is the most direct performance metric, representing the ability to classify correctly. AA is the average value of the accuracy of all categories. It accounts for the classification accuracy of each category, making it fairer for imbalanced data. The Kappa is a metric that measures classification accuracy, taking into account the possibility of random classification. Higher values of OA, AA, and Kappa represent better classification performance in hyperspectral images. By randomly selecting different training samples for \(10\) repeated experiments, the average values of OA, AA, and Kappa are obtained, providing a detailed assessment of the classification result after shadow compensation.  
Figure 3: The Houston dataset. (**a**) Hyperspectral image. (**b**) Classification map. (**c**) Class name.
## 2 Dynamical Modeling
### System Dynamic Equations in State-Space Form  
For intake, exhaust, and in-cylinder oxygen concentration, rearranging the above equation into state-space form,  
\[\begin{bmatrix}\Phi_{int}(k+1)\\ \Phi_{exh}(k+1)\\ \Phi_{cyl}(k+1)\end{bmatrix}=\begin{bmatrix}A_{11}&A_{12}&0\\ 0&A_{22}&A_{23}\\ A_{31}&0&A_{33}\end{bmatrix}\begin{bmatrix}\Phi_{int}(k)\\ \Phi_{exh}(k)\\ \Phi_{cyl}(k)\end{bmatrix}+\begin{bmatrix}B_{11}\\ B_{21}\\ B_{31}\end{bmatrix} \tag{14}\]  
where \(A_{ij}\) and \(B_{ij}\) can be found at Appendix A.  
For commercial applications, the intake and exhaust oxygen concentration can be measured by sensors. Thus, the measurement equation owns a form as,  
\[\mathbf{Y}(k)=\underbrace{\begin{bmatrix}1&0&0\\ 0&1&0\end{bmatrix}}_{\mathbf{H}}\begin{bmatrix}\Phi_{int}(k)\\ \Phi_{exh}(k)\\ \Phi_{cyl}(k)\end{bmatrix} \tag{15}\]  
Since the ICOC is predicted by the dynamical equations, to distinguish it from the model derived in the following section, here we call Equations (14) and (15) as \"dynamical model\".  
To be noted, too many variables of gas masses are involved in this system and they can only be obtained either by integrating the mass flow rate, as speed-density Equation (5) and orifice Equations (11) and (12), or by looking up maps, as \(\eta_{v}\), or by combining these two strategies, as \(m_{com}\) and \(m_{tur}\). Almost none of them is directly measured by physical sensors in real engines, thus the accuracy of the final estimation result of the ICOC is vulnerable to model uncertainties.  
As aforementioned in the introduction section, to improve the accuracy and robustness of this system, \"virtual measurement\" strategy is introduced in the following section.
# Two-Dimensional Full-Waveform Joint Inversion of Surface Waves Using Phases and Z/H Ratios
## 3 Joint Inversion of the Rayleigh Wave Phase and the Z/H Ratio  
As was demonstrated by the sensitivity analysis described in the previous section, the phase and the Z/H ratio of a Rayleigh wave had different sensitivities to the S-wave velocity structure, and they provided complementary constraints on the structure. Thus, joining the two data sets will help reduce the non-uniqueness of the inversion.  
For adjoint tomography of the Rayleigh wave phase [32], we sought to minimize the travel-time misfits between the observed waveform and the synthetic waveform generated using SPECFEM2D. In this study, the frequency-dependent travel-time misfits can be written as:  
\[\chi_{RP}(m)=\frac{1}{N_{c}}\ \sum_{i=1}^{N_{c}}\frac{1}{M_{i}}\sum_{j=1}^{M_{i} }\int\Bigg{(}\frac{\Delta T_{j}\ (\omega,\ m)}{\sigma_{T,j}\ (\omega)}\ \Bigg{)}^{2}d\omega\, \tag{14}\]  
where \(m\) is the model vector, \(\chi_{RP}\) is the misfit function for the Rayleigh wave phase, \(N_{c}\) is the number of periods, \(M_{i}\) is the total number of misfit measurements in the \(i\)-th period, \(\Delta T_{j}\ (\omega)\) is the frequency-dependent phase delay between the \(j\)th pair of observed data and the synthetic data at frequency \(\omega\) in the ith period, and \(\sigma_{T,j}\ (\omega)\) is the corresponding measurement uncertainty. Similarity, the frequency-dependent Z/H ratio misfit is defined as follows:  
\[\chi_{ZH}(m)=\frac{1}{N_{c}}\ \sum_{i=1}^{N_{c}}\frac{1}{M_{i}}\sum_{j=1}^{M_{i }}\int\Bigg{(}\frac{\Delta zh_{j}\ (\omega)}{\sigma_{zh,j}\ (\omega)}\ \Bigg{)}^{2}d\omega\, \tag{15}\]  
where \(\chi_{ZH}\) is the misfit function for the Rayleigh wave's Z/H ratio, and \(\Delta zh_{j}\ (\omega)\) is the Z/H ratio variation measured between the jth pair of the observed data and synthetic data with an uncertainty of \(\sigma_{zh,j}\.\)  
Figure 4: The effect of the local heterogeneities on the Z/H-ratio kernel: (**a**) Z/H-ratio kernel in the model containing a flat interface (dashed line); (**b**) Z/H-ratio kernel in the model containing an interface variation (dashed curve), which follows a cosine function with a maximum amplitude of 5.0 km; (**c**) differences between the Z/H-ratio kernels in (**a**,**b**).  
Thus, the objective function of the joint inversion based on the adjoint-state method is defined as follows:  
\[\chi(m)=\ \omega_{RP}\,\chi_{RP}(m)+\omega_{ZH}\,\chi_{ZH}(m)\, \tag{16}\]  
where \(\omega_{RP}\) and \(\omega_{ZH}\) are the weights used to balance the two data types to prevent the results from being dominated by either one. The joint-inversion scheme is described in Figure 5.
## 2 Results and Discussion
### Real Rice Samples Application of HS-SPME  
To evaluate the efficiency of the proposed method in real samples, the developed HS-SPME was applied to assay the key-marker volatile compounds in several aromatic rice samples, including aromatic rice from Indonesia (Pandan Wangi and Mentik Wangi), India (Basmati) and Thailand (Jasmine). Volatile profiles were obtained from these samples, then compared in order to establish differences. The results of real sample application experiments is shown in Figure 2.  
Figure 2: The relative levels of key-marker volatile compounds in tested rice grain samples (**a**) and the proportion of the compounds contributing to the total aroma compounds (**b**).  
The four tested rice samples are considered as aromatic rice varieties in the national and international market [46; 47]. Pandan wangi and Basmati had the highest proportion of 2-acetyl-1 pyrroline, whilst hexanal and 2-pentylfuran were the most prominent volatile compounds for Jasmine and Mentik Wangi. The different levels of key-marker volatile compounds in aromatic rice samples could be due to different regions for cultivation [48].  
Since 1983, 2-acetyl-1 pyrroline is regarded as the solely most important compound in rice, especially fragrant or aromatic rice [7]. However, it was not the case for Kao Dok Mali 105 or the so-called Thai Jasmine rice and Mentik Wangi. Apart from 2-acetyl-1-pyrroline, other key-marker volatile compounds were also counted as important compounds that affect the quality of aromatic rice, including hexanal and 2-pentylfuran. The result also disclosed that Jasmine rice has a markedly higher amount of key-marker compounds compared with other tested aromatic rice samples.
## 7 Guidelines on Developing Biopolymeric Microparticles
### Characterization of Microparticles Loaded with Chemical and Biological Agents
#### 7.3.2 Molecular Interactions between Components in Microparticles  
Data on molecular interactions in prepared microparticles can also be obtained using FTIR. FTIR spectra of microparticles prepared with cations of variable concentrations revealed that functional groups of all components interact with each other, and this is well explained in papers by [PERSON] et al. [52; 131; 133] and [PERSON] et al. [132]. Briefly, complex intermolecular interactions can be seen, which include mainly electrostatic interactions and hydrogen bonds. An increase in the cation concentration increases the crosslinking degree of microparticles, and the presence of _T. viride_ spores diminishes it because of mechanical interactions and electrostatic repulsions between negatively charged _T. viride_ spores and free alginate chains. Herein was reported that changes in FTIR spectra show structural differences in microspheres prepared with and without biological agents. FTIR data on microparticles is in agreement with X-ray diffraction evidence and the proposed so-called \"egg-box\" model [149]. In the metal-alginate complexes, [PERSON] et al. [149] proposed a \"pseudo bridge\" unidentate coordination with intermolecular hydrogen bonds in polyglutronic regions and the bidentate bridging coordination in the polymannuronic region. Both microspheres and microcapsules show that alginate asymmetric and symmetric carboxylate peaks became broader, exhibiting gradual intensity increasing and shifting of carboxylate ions stretching vibrations (asymmetric to a lower and symmetric carboxylate vibrational peak to higher wavenumbers) with increasing cation concentrations. Also, confirmation of chitosan binding to the alginate is presented where some peaks disappear from becoming weaker because of the electrostatic interactions between superposition (two oppositely charged polyelectrolytes) of the functional groups. It is already well known that the (-COO\({}^{-}\)) groups interact with the amino (NH\({}_{3}\)\({}^{+}\)) groups of chitosan, forming a polyelectrolyte complex. Changes in the FTIR spectra of microspheres and microcapsules reveal that the presence of the chitosan layer with an increase in cation concentration differently influences the structure of microparticles. FTIR spectra can give interesting information on the behavior of components that are microparticles made from. This information can help predict encapsulated agents release behavior which is of crucial importance when preparing microparticles for various applications.
# Exploring CeO\({}_{2}\)-Doped Co/SBA-15 Catalysts for Acetic Acid Oxidative Steam Reforming
## 3 Results and Discussion
### Stability Test on Co/10 CeO\({}_{2}\)-SBA-15 and Co/20 CeO\({}_{2}\)-SBA-15 Catalysts  
Avoiding catalyst deactivation caused by harsh operating conditions has been a primary issue in the steam reforming process. Hence, the development of a stable catalyst plays a main role in hydrogen formation via AAOSR. The catalytic activity of Co/10 CeO\({}_{2}\)-SBA-15 and Co/20 CeO\({}_{2}\)-SBA-15 catalysts, with respect to acetic acid conversion and H\({}_{2}\) yield, has been investigated at 550 \({}^{\circ}\)C and under the same reaction conditions for long time-on-stream periods (50 h). Figure 8 displays the results that were achieved. The initial H\({}_{2}\) yield for the Co/10 CeO\({}_{2}\)-SBA-15 catalyst was 66.8%, and it was slightly lower (62%) for the Co/20 CeO\({}_{2}\)-SBA-15 sample. These two catalysts showed a slightly decreasing yield throughout the reaction time (50 h), reaching approximately 55% at 50 h. Similarly, the  
Figure 7: (**a**) Acetone, (**b**) methane, (**c**) CO\({}_{2}\), and (**d**) CO selectivity at different temperatures (400–700 \({}^{\circ}\)C). Reaction conditions: S/C molar ratio = 2, O\({}_{2}\)/C molar ratio = 0.0375, WHSV = 30.2 h\({}^{-1}\), P = 1 atm. acetic acid conversion curves started at around 98% for both catalysts and ended at approximately 85%. H\({}_{2}\) yields close to the thermodynamic equilibrium value (67.4%) and high conversion values were achieved. These results highlight their capacity to maintain suitable catalytic performance over a prolonged period, constituting a fundamental requirement for continuous hydrogen production. To compare the findings of this study with the previously reported studies of catalytic systems for acetic acid oxidative steam reforming, the stability and catalytic performance were compared to recent data reported in the literature (Table 3). In this way, a reliable reference framework for analyzing the feasibility of the proposed catalysts was made available. Table 3 shows that the Co/10 CeO\({}_{2}\)-SBA-15 and Co/20 CeO\({}_{2}\)-SBA-15 catalysts achieved notable H\({}_{2}\) yields, in spite of being tested under thermodynamically and kinetically less favorable conditions (550 \({}^{\circ}\)C). In the literature, most of the research on acetic acid oxidative steam reforming has focused on Ni-based catalysts, frequently operating in autothermal  
Figure 8: Evolution over time-on-stream of H\({}_{2}\) yield and conversion using Co/10 CeO\({}_{2}\)-SBA-15 and Co/20 CeO\({}_{2}\)-SBA-15 in the AAOSR. Reaction conditions: T = 550 \({}^{\circ}\)C, P = 1 atm, S/C ratio = 2, O\({}_{2}\)/C molar ratio = 0.0375, and WHSV = 30.2 h\({}^{-1}\).