esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Driving Force Analysis for Internunual GPP Dynamics  
#### 3.3.1 The LULCC Effect on GPP from 2001 to 2016  
To separate the effects of LULCC and the observed greening land surface, we first compared the interannual GPP trends using the \"LULCC1\" (accounting for both LULCC and  
Figure 4: Spatial pattern of differences in GPP caused by changes in V\({}_{\text{cmax}}\) for different vegetation types in different countries. (**a**) Sixteen-year averages of GPP (g C m\({}^{-2}\) yr\({}^{-1}\)) in contemporary climate simulated by FGM using PFT-specific V\({}_{\text{cmax}}\) constants. (**b**) Spatial pattern of annual mean V\({}_{\text{cmax}}\) and mean seasonality of V\({}_{\text{cmax}}\) for different PFTs. (**c**) Difference in GPP due to the two parameterizations in Europe, i.e., GPP in Figure 1a minus GPP in (**a**). The bar chart in (**c**) represents the mean difference in GPP caused by changes in V\({}_{\text{cmax}}\) for different countries. (**d**) Difference in GPP for five main vegetation types (i.e., forest, shrub, grass, crop, and wet) for different countries caused by changes in V\({}_{\text{cmax}}\).  
LAI change), \"LULCC2\" (only accounting for only LULCC), and \"LULCC3\" (accounting for only LAI change) model simulations.  
From 2001 to 2016, the vegetation cover in Europe was as high as 97% with relatively stable land cover compositions (\(\pm\)1% changes in fractions of all LULC types) (Figure 5a). However, the total number of vegetated pixels decreased by 3224 at an average rate of \(-\)159 pixels per year (the coefficient of determination (R\({}^{2}\)) = 0.5, \(p\) < 0.01), while nonvegetated pixels (including urban, water, and snow and ice) increased correspondingly. Based on the two epochs of MCD12Q1 data in the initial year 2001 and the ending year 2016, 1.8% of the total area of Europe (excluding water, snow, and ice; same hereafter) experienced land cover change from 2001 to 2016. The largest increase occurred in forested land (+0.9% of total area), while all other land cover types decreased slightly (i.e., \(-\)0.3% of the total area for shrub/crop; \(-\)0.1% of the total area for grass/barren/wetland). The proportion of forested area to total area increased from 27.4% in 2001 to 28.3% in 2016.  
Our simulations indicated that the direct LULCC effect explained little of this variation (\"LULCC2\"), with a small but significant negative effect of \(-\)1.3 Tg C yr\({}^{-1}\) (R\({}^{2}\) = 0.61; \(p\) < 0.01). In contrast, the greening effect alone explained approximately 92.8% of this variation (\(p\) < 0.01; \"LULCC1\" and \"LULCC3\") (Figure 5b). The greening effect on the interannual GPP trend was 35.5 Tg C yr\({}^{-2}\) and enhanced the total GPP by 536 Tg C yr\({}^{-1}\), with the largest contribution from forests (31.2%, 167.0 Tg C yr\({}^{-1}\)), followed by shrubs (25.4%, 136.4 Tg C yr\({}^{-1}\)), cropland (25.2%, 135.2 Tg C yr\({}^{-1}\)), grassland (17.6%, 94.3 Tg C yr\({}^{-1}\)), and wetland (<1%, 3.2 Tg C yr\({}^{-1}\)) (Figure 5b).
## 1 Introduction
[18], through uniaxial tests, found that the correlation between fracture filling roughness and crack propagation paths exhibits a clear threshold effect. Both [PERSON] et al. [19] and [PERSON] et al. [20] showed through their tests that different fracture characteristics significantly influence the mechanical properties and failure modes of grouted fracture specimens. In summary, both domestic and international scholars have conducted extensive research on the mechanical properties of rock, focusing on factors, such as freeze-thaw cycles, fracture characteristics, grouting materials, and confining pressure conditions, resulting in numerous findings. However, most existing studies treat these factors as independent variables, neglecting their mutual influence and interaction. In complex geological environments, the interaction of multiple factors may amplify or diminish the effect of a single factor. Additionally, most current research on fractured rock masses focuses on straight fractures, with less attention given to the rough fracture surfaces commonly found in natural rock masses. Therefore, this study employs the joint roughness coefficient (JRC) to quantify fracture roughness. Through single-factor comparison experiments and orthogonal test designs, this study systematically analyzes the effects of factors, such as freeze-thaw cycles, fracture dip angle, roughness, grouting material, and confining pressure on rock's mechanical properties, with a focus on the interaction and synergistic effects of these factors. The interaction mechanism of rock's mechanical properties in complex environments was revealed, and the findings are expected to provide a theoretical basis and data support for the stability and disaster prevention of grouted reinforced slopes in seasonal frost areas.
# Exploring Metalloprotease from _Dunaliella_ sp.: Production, Regulation, and Structural Insight
## 3 Results and Discussion
### Isolation and Identification of Microalgae  
Identifying and classifying microalgae is crucial for unlocking their future applications [37]. Researchers emphasize the significance of native microalgal strains [58]. Local strains are likely to require fewer resources, such as fertilizers and water, due to their adaptation to local conditions. This aspect not only supports environmental sustainability but also promotes local economies by encouraging the use of indigenous biological resources [59]. A green microalga was isolated from the southern region of Tunisia, where microalgal diversity remains relatively unexplored. The collected samples exhibited an average total salinity of 346 g/L and a pH of 6.9. The isolated strain was identified as _Dunaliella_ sp. based on morphological and phylogenetic analysis. The isolated microalga lacks a rigid cell wall and is enveloped by a thin, elastic membrane that allows them to adapt effectively to fluctuating osmotic pressures in their environment [60]. This flexibility in adaptation accounts for the range of shapes observed, including cylindrical, ovoid, spindle-shaped, and approximately spherical forms. The cell size measures around 8-18 \(\upmu\)m in diameter. These characteristics are specific to the genus _Dunaliella_. The 185 rRNA gene amplification and sequencing revealed that the isolated strain belongs to the family Dunalielaceae within the genus _Dunaliella_. It exhibits close similarity (>93%) to _D. salina_, _D. quartoleta_, and _D. polymorpha_. No specific species identity was assigned to the isolated strain, but the phylogenetic tree analysis (Supplementary Data) could suggest that the strain has diverged evolutionarily from _Dunaliella salina_.
## 1 Introduction
Note that the model addressed in this paper has a Rotational Elastic Actuator (REA) installed [47], in order to consider interaction with the environment. Second, the constraint forces in the axial direction are formulated and are added into the dynamic model. A solution for the second key point is to tune the weighting matrices, depending on the side-slipping, in order to converge the side-slipping as soon as possible. An idea for this solution is to include the side-slipping velocity into the weighting matrices. The control system designed in this paper consists of a state observer and a nonlinear quadratic regulator, both based on the State-Dependent Riccati Equation (SDRE) [48; 49]. As the state observer, the Luenberger observer is adopted, in order to verify the theoretical effectiveness of our ideas without any noise. To estimate the state accurately, establishing an accurate model is crucially important (i.e., the model must include accurate constraint forces corresponding to the actual side-slipping situation). To accomplish this, an algorithm to estimate the occurrence of side-slipping and the side-slipping link is constructed. The algorithm discriminates both the occurrence of side-slipping and the side-slipping link from the estimated state and observed output. The weighting matrices of the Luenberger observer are tuned depending on the discrimination result. Rapid and accurate state estimation are obtained. Additionally, in the nonlinear quadratic regulator, the discrimination result is utilized to construct an accurate state-space representation. Furthermore, control inputs are obtained by including the velocities in the axial direction into the weighting matrices. This paper is organized as follows: In Section 2, the dynamic model of a five-link snake-like robot with passive side-slipping switching is derived. The passive side-slipping is represented by utilizing the constraint forces formulated by the Projection Method. Next, the Luenberger observer based on SDRE is designed in Section 3. The algorithm to estimate the generation of side-slipping is also constructed. In Section 4, the head position control system which adapts to the side-slipping is designed. The idea to include the velocities in the axial direction into the weighting matrices of SDRE, which is also proposed in detail. The effectiveness of the designed Luenberger observer and the designed head position control are verified through numerical simulation in Section 5. Section 6 concludes the paper.
## 5 Results and Discussion
### Intermodal Scheduling Case Study  
To evaluate the practical applicability of the proposed model and algorithm, a case study is conducted using a 35-node intermodal transport network, as detailed in Section 4.3. The IGWOHHO algorithm is employed to solve the multi-objective scheduling problem, with a focus on total cost, transport time, and carbon emissions. The analysis compares the scheduling results obtained from different algorithms, examines the influence of varying objective weights on optimization outcomes, and assesses the impact of carbon tax rates on scheduling decisions.
# A Decentralized Blockchain-Based Trust Management Framework for Vehicular Ad Hoc Networks
## 2 Methods
### Trust Metrics Aggregation  
It is not practical for each vehicle to have different trust metrics in different vehicles. This may lead to a non-consistency problem between different calculated local trust metrics because each monitor will update the trust metrics of its neighbors based on its own experience with them. Besides, this may facilitate the badmouthing attack where a vehicle overestimates or underestimates its neighbors. Therefore, each monitor \(m\) has to forward the trust metrics list of its monitored vehicles to the miners. As aforesaid, the miners are random vehicles selected on the road. Hence, periodically, each SendTransTimer period, monitor \(m\) transmits a copy of its database List_Local_Tm(m) to the nearest miner in its neighborhood. Afterward, the miner proceeds to the aggregation of the received local trust metrics into global trust metrics. The aggregation is a time-driven process. Periodically, the miner will go through the received databases and looks for trust metrics of each vehicle \(v\), then it computes one global trust metric denoted \(TM(v)\) for \(v\) as shown in Equation (2):  
\[TM(v)=\frac{\left(TM(v)_{old}+\sum_{i=1}^{N}T_{i}(v)\right)}{N+1} \tag{2}\]  
where \(N\) is the number of monitors of vehicle \(v\) and \(TMold(v)\) is the old trust metric of \(v\) stored in the blockchain. Initially \(TMold(v)\) is equal to 0.1. Similarly to the local trust metric, the aggregated \(TM\) is a value in the interval (0-1). If vehicle \(v\) gets \(TM(v)=1\), it is considered trustworthy. After aggregation, the miner records the new aggregated trust metric \(TM(v)\) in a local database, to be packed later in a block. Afterward, the miner proceeds to the subsequent task which is the block generation and validation.
# Experimental Study on Ice Melting of Bridge Tower Crossbeams under Different Conditions
## 3 Results and Discussion
### Crossbeam Ling Test
#### 3.1.1 Influence of Ambient Temperature on Crossbeam Icing  
By carrying out model tests for working condition 1, working condition 2, and working condition 3, the icing process of the crossbeam was continuously filmed, and the corresponding ice column histograms were drawn by Origin software, as shown in Figure 12. The first is for the ice on the top surface of the beam, which is sparsely distributed and soft; the second is for the ice layer adhering to the wall surface of the beam, which is hard and dense, but not uniform in thickness; and the third is for the ice column attached to the bottom edge of the beam, which is of varying length and uneven in distribution.  
The raindrops form longer icicles at the junction of the sides and bottom of the crossbeam, and it is evident that the icing coverage of the sides of the crossbeam varies at different ambient temperatures, as shown in Figure 13. As can be seen from the figure, at an ambient temperature of \(-5\)\({}^{\circ}\)C, the sides of the crossbeam have fewer ice drops, accounting for about 5% of the side area, and the icicles at the junction of the sides and the bottom are shorter and thinner; at an ambient temperature of \(-10\)\({}^{\circ}\)C, the sides of the crossbeam have many ice drops, accounting for about 40% of the side area; at an ambient temperature of \(-15\)\({}^{\circ}\)C, the sides of the crossbeam are almost covered with ice drops, accounting for about 80% of the side area, and the icicles at the intersection of the sides and the bottom are long and thick. This means that the lower the ambient temperature, the greater the ice coverage on the sides of the beam, making it more difficult to melt the ice.  
Figure 12: _Cont._  
The maximum, minimum, and average lengths of the ice column at different ambient temperatures for the crossbeam are shown in Table 5, and the dotted line graphs drawn by Origin software are shown in Figure 14. Comparing the test results of condition 1 and condition 2, the maximum, minimum, and average lengths of the ice column increased by 7 cm, 3 cm, and 4.95 cm, respectively, corresponding to growth rates of 53.85%, 60%, and 57.36%, respectively. When comparing the results of the test in condition 2 and condition 3, the maximum, minimum, and average lengths of the ice column increased by 5 cm, 4 cm, and 4.98 cm, respectively, corresponding to growth rates of 25%, 33.33%, and 36.67%, respectively. The results show that as the ambient temperature continued to decrease, the length of the ice column continued to grow, but the growth rate decreased. This is because when the ambient temperature is too low, the rate of ice formation is further accelerated and fewer raindrops flow to the end of the ice column; most of these raindrops freeze midway, so the ice column grows more in the radial direction.
# Discrimination of Aroma Characteristics for Cubeb Berries by Sensomics Approach with Chemometrics
## 3 Materials and Methods
### Optimization of SPME Conditions  
A SPME (Supelco, Inc., Bellefonte, PA, USA) fiber (50/30 \(\upmu\)m divinylbenzene/carboxen/ polydimethylsiloxane; DVB/CAR/PDMS) was used for volatile extraction after the fiber had been conditioned at 270 \({}^{\circ}\)C for 1 h. The ground samples were passed through a 20 mesh sieve to achieve uniform particle size. A 1.5 g measure of the sieved cubeb fruits powder was placed in a 20 mL vial with a sealed cap and equilibrated in a laboratory stirrer/hot plate (model PC-420, Corning Inc. Life Science, Acton, MA, USA). Then, a stainless steel needle, housing the SPME fiber, was placed through a hole to expose the fiber in the vial [19]. Three independent extractions were done for each cubeb fruit sample.  
To improve the volatile absorption, the SPME parameters were optimized: desorption time (1, 2, 3, and 4 min); incubation time (10, 15, 20, and 25 min); extraction temperature (40, 50, 60, and 70 \({}^{\circ}\)C); extraction time (20, 30, 40, and 50 min). For each parameter investigated, the analysis was conducted in triplicate.
## 4 Simulation Study  
This section describes the simulation testing of the three control schemes in attitude reference tracking, including the NDI-PI, the NDI-DO, and the NDI-AW. During the simulations, the aircraft model in the wind tunnel as detailed in Section 2 is augmented with second-order actuator dynamics for each surface, considering the deflection saturation in Table 2 and the time delay of 10 ms. The controller and observer gains are given in Table 3, which have been tuned to ensure tracking and robust performance in the presence of significant disturbances as listed in Table 4. For the sake of comparability, the nominal and robust simulations were all performed with the same step inputs on \(\alpha_{c}\) and \(\mu_{c}\) while minimizing the sideslip angle (\(\beta_{c}=0\)) at the airspeed of 30 m/s. In contrast, step inputs were also applied to the sideslip angle in the anti-windup simulation.
# Climate Effects on Tallgrass Prairie Responses to Continuous and Rotational Grazing
## 1 Introduction
However, Landsat has a longer revisit time than MODIS (eight-day vs. daily) which makes it challenging for studying vegetation productivity. Thus, the combination of MODIS and Landsat data might be a better option to examine the responses of grassland phenology and productivity to varied grazing management under variable climate conditions. In order to study the impacts of different grazing management under variable climate conditions, this study examined the vegetation phenology and productivity of tallgrass prairies in two pairs of continuous (C) and rotational (R) grazing systems during a span of ten years (2008-2017). All paddocks in the experiment were located in the same area and thus exposed to the same climatic conditions (e.g., air temperature and precipitation). There were large annual and seasonal variations in air temperature and precipitation during the study period, which provide the opportunity to investigate the interactions of climate and grazing management on vegetation phenology and productivity. This paper focuses on the responses of vegetation phenology and productivity to climate and grazing management systems. Other aspects of these systems are discussed in [PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., etc [37; 38; 39].
## 9 Metabolic Features and By-Products from _Candida stellata_ Activity
### Biotechnological Application of Extracellular Enzymes Secreted by Candida stellata
#### 9.3.5. Invertases  
Invertase enzyme, also known as \(\beta\)-D-fructofuranosidase, is commonly used in industries with numerous applications as production of lactic acid [117], fermentation of sugarcane to ethanol [118], and production of fructose syrup. Furthermore, it is employed in pharmaceutical industry, child nutrition, and fortified wines [119]. These enzymes hydrolyse the glycosidic linkage from sucrose in its respective monomers, glucose and fructose, to form \"inverted sugar syrup\" with special characteristics: 40% sweeter than sucrose, stable at high temperatures, more soluble than sucrose and higher point of boiling and lower of freezing [119]. Yeast production of these enzymes is typically studied in _S. cerevisiae_[120]. Recently, [PERSON] et al. [121] have observed that one _C. stellata_ strain (N5) isolated from Brazilian grapes is a potential invertase producer. They propose this new invertase as a promising catalytic agent for use in biotechnological processes in the food industry and alcoholic fermentations.
## 4 Case Study  
Figure 4 shows the case study's test arrangement. Dispersed generations with power factors of 0.9 lag and ranging from 1 to 5 MW have been regarded as negative loads in order to assess the proposed method. Eight load points are powered by a high-voltage distribution substation with a rating of 132-33 kV that is part of the distribution test network. The network's isolator switches, which have a maximum capacity of 25 MVA, isolate each branch. Every load point in the network has a power factor of 0.9 latency, making them all suitable sites for parking lot installations. The test network's technical specifications are shown in Table 3, which loads data in three stages [45].  
Table 4 provides a summary of the system's further technical and financial specifications. The following presumptions [18; 34] are made in our study:  
* The highest billing rate is applied to each vehicle.
* Three degrees of load conditions--light, medium, and peak load--are taken into consideration in this paper.
* The parking lot is represented in the simulation as a bus (Q = 0).
* It is considered that there are always vehicles available.
* Three degrees of initial state of charge (SOC) for electric vehicles (EVs) are assumed in parking lot modeling.
* Every battery has identical dimensions. As a consequence, the output power of the parking lot is continuous in a flat discharging condition.  
According to estimates by traditional utilities, there may be 200 or so peak hours annually during which an extra kWh of power is worth 0.5 USD/kWh. As a result, automobiles are believed to provide the network with electricity for no more than 200 h annually.  
Figure 4: Studied test network.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Section** & & & & & & \\
**from–to** & **R (\(\Omega\))** & **X (\(\Omega\))** & **L (km)** & & **Load Low** & **Load Medium** & **Load High** \\  & & & & & & **Level 1 (MW)** & **Level 2 (MW)** & **Level 3 (MW)** \\ \hline
1–3 & 1.4 & 1.5 & 1.5 & 5 & 6 & 8 \\ \hline
3–7 & 2.78 & 1.5 & 1.5 & 7.5 & 8.8 & 9.2 \\ \hline
1–2 & 2 & 4 & 4 & 8.3 & 11.2 & 9 \\ \hline
2–6 & 2.8 & 5.5 & 5.5 & 4 & 5 & 7 \\ \hline
1–5 & 1.7 & 1.7 & 1.7 & 7.5 & 8.8 & 9.2 \\ \hline
5–9 & 2.1 & 4 & 4 & 7.3 & 10.2 & 8 \\ \hline
1–4 & 2.26 & 4.5 & 4.5 & 6 & 7 & 9 \\ \hline
4–8 & 2.4 & 5 & 5 & 7.5 & 8.7 & 9.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Technical characteristics of branches and load data.