esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Monitoring Systems
In situ monitoring data of the soil monitoring system and meteorological monitoring system were collected by a CR1000 data collector (Figure 1) every 10 min. The groundwater level was monitored in situ and recorded by probes (Levelogger III 3001) in SYC-01 every 10 min. The data inside were exported by Solinst software when the probe was connected with the computer. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System** & **Equipment Model** & **Factor** & **Company** & **Frequency** \\ \hline Soil monitoring system & CS650 & Volume water content & & \\ CS257 & Soil water potential & & & \\ HFP01 & Surface heat flux & & & \\ \hline Meteorological monitoring system & 034B & Wind speed and direction & Campbell Scientific Co., Ltd. & \\  & HMP155A & Humidity, air temperature & Logan, UT, USA. & Every 10 min \\  & TE525 MM & Rainfall & & \\ monitoring system & CS100 & Atmospheric pressure & & \\  & CRN4 & Net solar radiation & & \\  & 255–100 & Evaporation & & \\ \hline Groundwater monitoring system & Levelogger III 3001 & Groundwater level & Solinst Canada Ltd. & \\  & & & Georgetown, ON, Canada. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Soil, meteorology, and groundwater monitoring systems information.
# Mini-Review: Recent Technologies of Electrode and System in the Enzymatic Biofuel Cell (EBFC)  
[PERSON]  
1 Fuel Cell Institute, Universiti Kebangsan Malaysia, Bangi  
Various non-toxic chemical fuels are used in EBFC, such as glucose, lactate, urate, alcohol, amines, starch, and fructose [22], and have a specific enzyme to oxide these fuels; for example, the cellobiose dehydrogenase is used to oxide the lactose in the EBFC [23]. However, glucose is widely used because it has the advantages of being abundant, easy to obtain, cheap [24], and involved in various functions [25]. An enzyme is used in EBFC as a catalyst. In the anode, the enzyme commonly oxidizes the glucose into gluconolactone and produces electrons and protons. Glucose oxidase is widely used because it has advantages such as availability [26], high selectivity [27], and high catalytic activity to oxide glucose [25], self-discharge, low mix potential [28], and ease of use in a membrane-less EBFC system. At the same time, the enzyme at the cathode will complete the reduction reaction by using electrons and protons from the anode and then generate electricity. The working principle in EFC at the anode and cathode is given below:  
Complete oxidation of glucose:  
\[\text{C}_{6}\text{H}_{12}\text{O}_{6}+6\text{O}_{2}\to 6\text{CO}_{2}+6\text{H}_{ 2}\text{O} \tag{1}\]  
Anodic glucose oxidation (GOx):  
\[\text{C}_{6}\text{H}_{12}\text{O}_{6}\overset{\text{GOx}}{\to}\text{C}_{6} \text{H}_{10}\text{O}_{6}+2\text{e}^{-}+2\text{H}^{+} \tag{2}\]  
Cathodic oxygen reduction (laccase):  
\[\frac{1}{2}\text{O}_{2}+2\text{H}^{+}+2\text{e}^{-}\to\text{H}_{2}\text{O} \tag{3}\]  
Total:  
\[\text{C}_{6}\text{H}_{12}\text{O}_{6}+\frac{1}{2}\text{O}_{2}\to\text{C}_{6} \text{H}_{10}\text{O}_{6}+\text{H}_{2}\text{O} \tag{4}\]
## 3 Registration Algorithms Based on Probability Stochastic Distribution Model
### Random Sample Consensus (RANSAC)-Based
[78] introduced the Super-4 PCS algorithm, which simplified the process of discovering congruent 4-points sets, and made use of angle and distance as constraints. This successfully decreased the number of congruent 4-points sets, reduced the time complexity and increased the registration efficiency. The SC-RANSAC, created by [PERSON] et al. [79], was founded on spatial consistency checks. It enabled RANSAC to operate on more secure correspondences with lower inlier ratios. RANSAC's runtime is considerably improved under the same accuracy and robustness conditions. By enhancing sample efficiency and employing grouping algorithms, [PERSON] et al. [80] lowered overall running time. Although this method combines benefits of PROSAC [75] and NAPSAC [73], it incorporates some of their drawbacks, especially that the Group-SAC algorithm depends on the description between data points and the aggregate of interior points. By creating a sample sphere model, [PERSON] and [PERSON] [81] put forth a sampling sphere registration technique based on RANSAC that enhances the search efficiency of corresponding points, cuts down on the amount of time it takes to do so, and lowers the time complexity. Synthesizing the advantages of the NAPSAC, LO-RANSAC, PROSAC, and GroupSAC algorithms, [PERSON] et al. [101] optimized each step of the RANSAC algorithm and suggested a universal RANSAC framework that subjects each (non-minimum) sample to an iterative refinement process during model estimation, decreasing the number of RANSAC iterations and being consistent with theoretical expectations. Many existing algorithms, however, either fail or have extremely high computational cost due to the presence of outliers, sometimes even occupying the vast majority of assumed correspondences. For the issue of outliers in conjunction with the RANSAC algorithm, [PERSON] et al. [82] and [PERSON] [83] proposed OP-RANSAC and RANSIC for outliers respectively. In addition to being quick, they can withstand outliers by over 95%. In particular, RANSIC outperforms other state-of-the-art algorithms on point cloud registration problems by recalling nearly all outliers. To sum up, RANSAC-based registration methods frequently avoid the need to calculate intricate geometric features and have high robustness to noise, but the selection of corresponding points is a very challenging operation. Generally speaking, when point cloud features are highly obvious, additional geometric feature constraints may be introduced to minimize the time complexity of finding related connections.
# Harmonized Autonomous-Human Vehicles via Simulation for Emissions Reduction in Riyadh City
## 1 Introduction  
The rapid urbanization of cities in the Gulf region, particularly in Saudi Arabia, has placed increasing pressure on transportation infrastructures, leading to traffic congestion, elevated carbon emissions, and degraded air quality. Riyadh, as a fast-growing metropolis, exemplifies these challenges with its high vehicle dependency and limited adoption of sustainable mobility solutions. In response, the integration of AVs into urban transportation systems is gaining attention as a potential catalyst for improved traffic efficiency and environmental performance. When properly coordinated, AVs offer the potential to reduce reaction time, improve traffic flow, and optimize fuel usage; however, for the foreseeable future the majority of road networks will feature mixed traffic composed of both AVs and traditional human-driven vehicles (HVs). The transitional phase of AV adoption necessitates robust frameworks capable of managing these heterogeneous fleets in order to harness the benefits of AVs without compromising safety or throughput. Recent advances in vehicular communication, specifically V2V, V2I, and V2N technologies, enable connected and automated vehicles (CAVs) to make collaborative driving decisions. Nevertheless, simulating such coordinated behavior in large-scale urban settings remains computationally challenging. Traffic microsimulation platforms such as the Simulation of Urban Mobility(SUMO) platform can serve as powerful tools for evaluating policy and infrastructure interventions, but require enhancements to reflect the coordination and intelligence enabled by V2X technologies. This study introduces a harmonized AV-HV coordination framework, termed HARMONY-AV, which approximates communication-driven behavior using SUMO and its TraCI interface. The framework is tested on a major corridor in Riyadh Prince Mohammed bin Salman bin Abdulaziz Road using real traffic data and emissions models. By simulating four AV penetration scenarios (0%, 10%, 30%, 50%), we quantify the effects of increasing AV integration on key performance indicators such as the travel time, stop frequency, average speed, and CO\({}_{2}\) emissions.  
The contributions of this work are threefold:  
* Proposing a reproducible SUMO-based simulation pipeline tailored for mixed AV-HV traffic scenarios in Saudi Arabia;
* Modeling V2X-inspired AV behavior through realistic parameter tuning and API-based coordination;
* Demonstrating the environmental and mobility benefits of AV coordination under varying levels of penetration.  
By offering a scalable and context-aware simulation framework, this study supports data-driven planning for future smart mobility deployments and provides practical guidance for policymakers and urban planners seeking to reduce emissions and improve traffic efficiency in mid-sized Gulf cities.
# Unraveling the Biosynthesis of Carvacrol in Different Tissues of _Origanum vulgarae_
## 2 Results
### WGCNA Reveals Gene Co-Expression Networks Associated with Carvacrol  
To evaluate the co-expression pattern of carvacrol biosynthesis-related genes in \(O\). _vulgare_ 'HS', we constructed networks using 'upstream' pathways of MVA/MEP genes and carvacrol biosynthesis genes of various TPS/CYPs and transcription factors (TFs) located in carvacrol-related or PGT-related orangered4, plum1, ivory, lightcyan, darkgrey, and skyblue3 modules (Figure 7). The co-expression network of the orangered4 module with the strongest positive correlations to carvacrol (R = 0.94) and PGT density (R = 0.95) / size (R = 0.88) contained two TPS genes, two CYP genes, one _GPPS_, and one _PDR_ (Figure 7A; Table S6). The co-expression network of the plum1 module with positive correlations to carvacrol (R = 0.73) and PGT density (R = 0.64) / size (R = 0.51) contained nine genes of one _HDS_ and eight TF genes, including three _bHLHs_, two _ZIPs_, one _MYB_, one _NAC_, and one _AP2/ERF-ERF_ (Figure 7B; Table S6). Two co-expressed hub genes of _bHLH_ and _AP2/ERF-ERF_ were presented in the ivory module, which exhibited positive correlations with carvacrol (R = 0.73) (Figure 7C; Table S6). The co-expression network of lightcyan module showing positive correlations with PGT size (R = 0.68) contained seven main TF genes: four _ZIPs_, two _MYBs_, and one _WRKY_ (Figure 7D; Table S6). In total, the high amount of metabolic pathway genes of TPS/CYP genes and TF genes of _ZIPs_ and _bHLHs_ in carvacrol-specific modules (positive correlation) suggested their significant positive roles in regulating carvacrol biosynthesis.  
With negative correlations of PGT size (R = \(-\)0.72), the darkgrey module co-expression network contained one _FPPS_, one _HMGS_, five _bHLHs_, one _ZIP_, one _WRKY_, and one _AP2/ERF-ERF_, indicating the negative effects on regulating PGT development, especially for _bHLHs_ (Figure 7E; Table S6). Another co-expression network of the skyblue3 module showing negative correlations with PGT density (R = \(-\)0.52) and PGT size (R = \(-\)0.62) contained ten TF genes, namely five _bHLHs_, three _WRKYs_, one _MYB_, and one _ZIP_, implying  
Figure 7: Co-expression networks of orangered4 (**A**), plum1 (**B**), ivory (**C**), lightcyan (**D**), darkgrey (**E**), and skyblue3 (**F**) constructed using carvacrol-related and peltate glandular trichome-related module genes in _Origanum vulgare_ ‘Hot and Spicy’.  
their negative roles in regulating PGT development, especially for _bHLHs_ (Figure 7F; Table S6). Two TPS genes, two CYP genes, six _ZIPs_, and four _bHLHs_ mainly appeared in modules positively correlated with carvacrol and PGT, suggesting their significant roles in regulating carvacrol biosynthesis and PGT development. Conversely, ten _bHLHs_ and four _WRKYs_ mainly appeared in modules negatively correlated with PGT, indicating their negative roles in regulating PGT development.
## 3 Results
### Selectivity and Linearity  
Figure 2 shows the representative MRM chromatograms of double blank, zero blank, LLOQ sample (5 ng/mL), and plasma sample after oral administration of enavogliflozin. The retention times of enavogliflozin and IS were the same at 2.74 min. Compared to the blank samples, there was no significant matrix interference in the LLOQ samples at the retention times of enavogliflozin and IS. The signal-to-noise ratio of enavogliflozin was 13.3 in the LLOQ samples (Figure 2).  
Figure 1: Product ion scan spectra of enavogliflozin and d4-enavogliflozin (IS).  
Figure 2: Representative multiple reaction monitoring (MRM) chromatograms of enavogliflozin and IS (d4-enavogliflozin) in mouse plasma samples: double blank, zero blank, lower limit of quantification (LLOQ) sample (5 ng/mL; signal-to-noise ratio (S/N) of 13.3), and plasma sample following oral administration of enavogliflozin.  
A calibration standard curve was linear within the enavogliflozin concentration range of 5-3000 ng/mL, with a coefficient of determination (r\({}^{2}\)) of 0.995 \(\pm\) 0.002 determined from linear regression analysis using a weighting of 1/concentration\({}^{2}\). The calibration standard curve parameters are as follows: slope, 0.0077 \(\pm\) 0.0021 (mean \(\pm\) SD); intercept, 0.0061 \(\pm\) 0.0159. Across all concentration ranges, the accuracy and coefficient of variation (CV) ranged from 95.50% to 107.8% and from 1.351% to 7.062%, respectively (Table 1).
# Approximation Algorithm for Shortest Path in Large Social Networks
## 2 Construction of Hierarchical Networks  
Let \(G=(V,E)\) be an undirected and unweighted network with \(n=|V|\) nodes and \(e=|E|\) edges. A path \(P_{s,t}\) between two nodes \(s\), \(t\)\(\in\)\(V\) is represented as a sequence (\(s\), \(u_{1}\), \(u_{2}\),......, \(u_{l-1}\), \(t\)), where {s, \(u_{1}\), \(u_{2}\),......, \(u_{l-1}\), \(t\)}\(\subseteq\)\(V\) and {(\(s\), \(u_{1}\)), (\(u_{1}\), \(u_{2}\)),......, (\(u_{l-1}\), \(t\)}\(\subseteq\)\(E\). \(d\)(\(s\), \(t\)) is defined as the length of the shortest path between \(s\) and \(t\).  
Based on G (parent network), we construct a series of undirected and weighted networks with different scales. The original network G is taken as the bottom level or level 0 network. We define the role of various nodes at each level of graph construction as follows:* Normal nodes are the immediate neighbors of nodes with the highest degree centrality at each of hierarchical graph construction.
* Super nodes are condensed nodes that are represented by a single node in the next hierarchy.
* Central nodes are nodes that have been selected to absorb its neighborhood nodes. After absorption, the central nodes become super nodes.
* Sub-nodes are all other nodes that have a path to the central node, a path of radius r.  
For each level of construction, i.e., from bottom to top, we iteratively perform the following steps. A normal node with the largest degree (having lot of clusters) is selected as a central node and condensed with its normal neighbors (other than super nodes) into a super node. The edges between the normal nodes are redirected to their corresponding super nodes. Condensing of current level network is completed when all nodes are merged into super nodes. These nodes are regarded as normal nodes in the next level network. Edges between two super nodes in the previous hierarchy results in a single link for two normal nodes in the next level network. The weight of an edge between two adjacent nodes in the next level network represents the approximate distance between the nodes. The topmost network is obtained when the number of nodes in the next level network is below a given threshold \(t\).  
Figure 1 shows the process of constructing a hierarchical network. Figure 1a is the current level network whose red nodes represent central nodes. Central nodes condense with their neighbors (blue nodes) to form super nodes respectively, which results in the next level network as shown in Figure 1b. All super nodes in the previous level network (Figure 1a) are considered normal nodes in the next level network as shown in Figure 1b. The red node in Figure 1a will be selected as the central node, and condense with its neighbors into a super node in the next level hierarchy.
# Exploring the Spatiotemporal Alterations in China's GPP Based on the DTEC Model
## 3 Results
### The Effects of Climate Factors on GPP  
By counting the partial correlation coefficients between GPP and various climate factors that passed the significance test, we found that the average partial correlation coefficients between China's GPP and temperature, precipitation, solar radiation, and AOD were 0.41, 0.34, \(-\)0.29, and \(-\)0.22, respectively (Figure 6). The percent areas with a positive correlation between GPP and temperature and precipitation were larger than the areas with a negative correlation (9.5% and 9.8% vs. 0.9% and 0.2%, respectively), and the overall correlation was positive. The percent area of negative correlation between GPP and solar radiation and AOD was larger than the area of positive correlation (8.8% and 15.3% vs. 2.5% and 6.2%, respectively), and an overall negative correlation was present. At the national scale, the order of the response of GPP to climate factors in China was temperature > precipitation > solar radiation > AOD.  
However, at the regional scale, the response of GPP to climate factors exhibited significant spatial heterogeneity (Figure 6e). Precipitation had the greatest effect on GPP in the NEC and IM regions, with partial correlation coefficients of 0.46 and 0.55, respectively. In the NWC, NC, CC, SEC, and SC regions, temperature had the greatest influence on GPP, with partial correlation coefficients of 0.41, 0.43, 0.4, 0.49, and 0.41, respectively. The greatest influence on GPP in the TP region was from solar radiation, with a partial correlation coefficient of \(-\)0.49. Temperature (0.44) and solar radiation (\(-\)0.43) had the greatest impacts on GPP in the SWC region.  
Figure 5: The spatial distribution of the multiyear average of China’s GPP from 2001 to 2020 (**a**) and the spatial distribution of the GPP trend that passed the significance test (**b**).
## Nomenclature  
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{\(A_{\text{acc}}\)} & \multirow{2}{*}{amplitude of acceleration} & \multirow{2}{*}{\(N\)} & the ensemble times of EMD \\  & & \(i\)th IMF caused by the \(m\)th EMD & \(O_{3}\) & clustering center \\ \(d_{0}\) & safety distance & \(R\) & root-mean-square value \\ \(d_{i}\) & the distance from \(i\)th sample point to the center & \(S\) & sharpness \\ \(D_{\text{b}}\) & the diameter of rolling elements & \(x(t)\) & simulation signal \\ \(D_{\text{m}}\) & the diameter of the bearing pitch & \(X\) & coordinate axis \(X\) \\ \(d_{m}\) & average exceeding distance & \(x_{i}\) & \(i\)th sample point \\ \(f_{b}\) & the characteristic frequency of the rolling elements & \(x^{\prime}_{i}\) & normalized sample points \\ \(f_{c}\) & the characteristic frequency of the cage & \(X_{\text{max}}\) & maximum sample point \\ \(f_{i}\) & rotation frequency of the inner ring & \(X_{\text{min}}\) & minimum sample point \\ \(f_{tr}\) & the characteristic frequency of the inner ring & \(y_{j}\) & \(j\)th sample point to be evaluated \\ \hline \hline \end{tabular}  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Sample**} & \multicolumn{2}{l}{**The Results by the**} & \multicolumn{2}{l}{**The Results by**} & \multicolumn{2}{l}{**The Actual**} \\  & **Presented Method** & **EEMD Method** & **Condition** & **Conclusions** \\ \hline \#1 & abnormal & ball fault & ball fault & Verified \\ \#2 & normal & / & in operation & Verified \\ \#3 & normal & / & in operation & Verified \\ \#4 & normal & / & in operation & Verified \\ \#5 & abnormal & outer ring fault & outer ring fault & Verified \\ \#6 & normal & / & in operation & Verified \\ \#7 & abnormal & retainer fault & retainer fault & Verified \\ \hline \hline \end{tabular}  
\end{table}
Table 4: The conditions of flywheels #1-#7.  
\begin{tabular}{l l l} \(f_{o}\) & rotation frequency of the outer ring & \(Y\) & coordinate axis \(Y\) \\ \(f_{or}\) & the characteristic frequency of the outer ring & \(Z\) & the number of rolling elements \\ \(l_{a}\) & abnormal index & \(Z\) & coordinate axis \(Z\) \\ \(k\) & the amplitude coefficient & \(\alpha\) & the contact angle \\ \(K\) & kurtosis & \(\eta\) & exceeding rate \\ \(n_{m}(t)\) & Gaussian white noise series & \\ \end{tabular}
# Experimental Activities on a Hydrogen-Fueled Spark-Ignition Engine for Light-Duty Applications
## 4 Results and Discussion
### Sensitivity to Combustion Phasing  
Another important parameter is the spark timing, which allows for controlling the combustion phasing. The sensitivity of combustion to this parameter is of utmost relevance, as it can enhance the flexibility of control strategies, improve vehicle driveability, optimize the functioning of the after-treatment system, and reduce costs related to its design and packaging.  
In Figure 6, the in-cylinder pressure and heat release profiles are depicted for a spark timing sweep at the low-load point, corresponding to two different dilution conditions. As the spark timing is advanced, the pressure profiles rise and move in the direction of the compression stroke, irrespective of the \(\lambda\) value. When analyzing the heat release rate profiles, a similar trend parallel to the pressure profiles is observed; they advance as the spark timing moves towards the compression stroke, becoming slightly narrower and displaying higher peak values.  
By expanding the range of \(\lambda\) values (as shown in Figure 7), we can confirm the trends described earlier. The spark timing plays a crucial role in controlling the combustion phasing, as evident from the linear trends depicted in Figure 7. As \(\lambda\) increases, the influence of the spark timing on the combustion phasing becomes more pronounced, making precise control challenging. Even minor adjustments in the spark timing can lead to significant changes in the combustion phasing. Additionally, it becomes clear that maintaining the desired combustion phasing as \(\lambda\) increases necessitates advancing the spark timing.  
Figure 8 displays the primary engine parameters for the same conditions as shown in Figure 7. Concerning the engine performance, one can observe that the trends become flatter as \(\lambda\) increases. However, the range within which the combustion remains stable becomes more limited as the dilution increases. For instance, in the case of \(\lambda\) = 1.4, there is no discernible effect of the spark timing on the combustion stability. Nevertheless, COVIMEP rapidly increases with spark timing variations at \(\lambda\) = 2.6.  
Figure 6: In-cylinder pressure and HRR profiles at 1500 rpm engine speed and low-load conditions, obtained using the PFI system, showcasing variations with different spark timing values for two dilution conditions.  
Traditionally, spark timing has also been employed to delay combustion in order to control NOx emissions. However, the influence of the spark timing on these emissions is minimized under lean combustion conditions, as illustrated in Figure 9. Lower local temperatures inhibit the NOx thermal formation mechanism, thereby diminishing the impact of delayed combustion.