esa-sceva/satcom-chunk-collection huggingface dataset:
# Measuring Method of Node Importance of Urban Rail Network Based on H Index
## 3 Method for Calculating the Importance of Urban Rail Network Nodes Based on \(h\)-Index
### Evaluation Standard  
(1) Intersection degree  
**Definition 5**: _Use \(top_{i}^{n}\) to denote the sequence set, consisting of the first \(n\) nodes arranged in descending order of node importance in index \(i\). Then the intersection degree of the two sorting sequences \(X\) and \(Y\) is defined as:_  
\[Sim(X,Y,n)=\frac{\text{card}(X\bigcap Y)}{n}=\frac{\text{card}\Big{(}top_{i}^{ n}\bigcap top_{j}^{n}\Big{)}}{n}\]  
Among them: \(card(X\bigcap Y)\) represents the number of elements in the set of \(X\bigcap Y\). The greater the degree of intersection, the more similar the ranking results of the two sequences are.  
For instance, assume that \(X\) is a set sorted by BC index, \(Y\) is a set sorted by CC index, the first 10 elements of \(X\) and \(Y\) after sorting are \(X=\{1,2,3,4,5,6,7,8,9,10\}\), \(Y=\{2,6,8,10,15,17,29,11,16,21\}\), the intersection of \(X\) and \(Y\) has four elements \(\{2,6,8,10\}\), then \(Sim(X,Y,10)=4/10=0.4\).  
(2) Intersection _rank_ value  
**Definition 6**: _Use \(equiv_{i}^{v}\) to indicate the sort value of the \(v\)-th node in \(top_{i}^{n}\). Then the rank values of the respective sequences under the intersection of the two sorting sequences \(X\) and \(Y\) are defined as:_  
\[rank_{i}^{n}\Big{(}X\bigcap Y\Big{)}=\frac{\sum_{v\in X\bigcap Y}\text{seq}_{ i}^{v}}{\text{card}(X\bigcap Y)}\]  
The difference in _ank_ value between the two indicators is \(\Delta rank(i,j,n)=rank_  
\(Y\) sets as (2, 6, 8, 10) and (1, 2, 3, 4), respectively. Then \(rank^{10}_{X}(X\cap Y)=(2+6+8+10)/4=6.5\), \(rank^{10}_{Y}(X\cap Y)=(1+2+3+4)/4=2.5\), \(\Delta rank(X,Y,10)=rank^{10}_{X}-rank^{10}_{Y}=4>0\), it means that the node identified by indicator \(X\)(BC index) is more important than the node identified by indicator \(Y\)(CC index).  
**4. Case Study**  
The importance of the traffic network nodes is studied in Beijing's 2016 urban rail network. As shown in Figure 3.
###### Abstract  
In a power grid with multiple two-state loads, the total power can vary over a significant range. This results in the inability to supply this system from a low-power source. The solution is an algorithm that shapes energy demand depending on its availability. For this purpose, a new load distribution method is proposed based on introducing a buffer between the temperature controller output and the heater and filtering the load using a master Proportional-Integral (PI) controller. The aim of the work was to evaluate the quality of the developed algorithm for limiting power peaks in the power grid. The research was conducted on a model of the Creep Test Laboratory with 389 heaters simulated in MATLAB Simulink R2023b. The algorithm was tested with various settings of the master controller parameters. By experimentally adjusting these parameters, a ten-fold reduction in peak power was achieved. The standard deviation for the L1 phase was reduced from 7.6 kW to 0.6 kW. Similar results were obtained for phases L2 and L3. The tested control system tracked changes in the average power value by changing the number of loads switched on and by frequency-modulating the signal when the change was less than the power of a single load. It was demonstrated that the controlled delayed switching of electrical loads can modify the shape of the total electric power without affecting their operation. The proposed solution features a low computational complexity, which allows its implementation in various systems.  
Keywords:load shifting; power shortage; virtual energy storage (VES); scheduling; electric heating load; creep test; off-grids; PI controller +
Footnote †: journal: Applied
## 7 Conclusions  
In conclusion, as the global requirements for food production increase alongside a varying climate, various new technologies could sustainably alleviate numerous challenges in disease management by lessening dangerous chemical inputs and endorsing swift detection and inhibition of phytopathogens. Endophytic research has demonstrated potential in the role of sustainable approaches in agriculture owing to the growth promotion and phytopathogen biocontrol proficiencies of microorganisms associated with plants. Research into these microorganisms is especially vital, as they exhibit numerous compensatory advantages (naturally occurring agents) compared to currently implemented chemical approaches. However, with advances in endophytic research came increased discovery of the challenges and limitations facing research approaches associated with endophytic research. These challenges were mainly linked to the complexity of microbiomes associated with plants, the antagonistic effects that control or suppress pathogens that are affected by (1) interaction between microbial biocontrol isolates and host plants, (2) pathogenic infection, and (3) environmental factors. Thus, recent research has been focused on specialized techniques in order to understand the mechanisms and bioactive compounds associated with endophytes and mode of action of microbial biocontrol in vivo (host). Hence the current review highlighted the implementation of the CRISPR/Cas9 systems, nanotechnology and omics approaches and how these advancements in techniques have bridged the gap regarding the challenges. Through the use of these technologies' important genes, proteins and secondary metabolite compounds were identified to understand plant-microbe interactions, but also highlights candidates which could be exploited for the improvement of biocontrol agents for the enhancement of phytopathogen tolerance. To fully optimize endophytic technologies in order to implement them in the agricultural industry, we need to understand microbial interactions with plants in order to exploit them for improved plant pathogen management.  
Conceptualization of manuscript. [PERSON], A.K. and M.K. Wrote the manuscript: [PERSON], [PERSON], L.-A.N., J.M., L.O., N.H., A.O., A.I.D. and M.N. Edited and checked the manuscript: [PERSON], A.K., M.K. and A.I.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of South Africa (NRF). The NRF provided financial support for this work to A.G. (Grant number: 129493), A.K. and M.K. (Grant numbers: 107023, 115280, 116346 and 109083). A.G. was also supported by the CRF fund of the University of the Free State.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Modeling and Dataset Building
### Traffic Flow Sample Data Collection  
Before collecting samples, it is necessary to establish a mapping relationship between the numbered sections of the actual city traffic road network and the positions in the sample matrix. As shown in Figure 3, if a situation arises during the mapping process where a road segment does not exist in reality, zero padding can be applied.  
Figure 2: Sample Data Formation.  
Due to the complex terrain of cities and the varied traffic demands, urban traffic networks often involve many uniquely shaped intersections during their design and construction. The road segments associated with these intersections require specific handling when establishing mapping relationships, with the processing rules outlined in Table 3.  
This article utilizes VISSIM's secondary development technology to obtain the number of queued vehicles at key phases for each road segment within any given simulation cycle. This data is used to create a detection data matrix, which serves as input to calculate the corresponding green light time data matrix for each road segment. The dimensions of the detection data matrix match those of the control data matrix, allowing the two matrices to be combined into a single sample matrix, as illustrated in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Prototype** & **Optimized** & **Remarks** \\ \hline Y-shaped intersection & & & Move inclined sections to horizontal \\ T-shaped intersection & & & Perform zero-padding for traffic data where sections do not exist \\ \hline Boundary segment & & & Move inclined sections to horizontal and perform zero-padding for non-existent sections \\ \hline \hline \end{tabular}
\end{table}
Table 3: Image Processing of Special-shaped Links.  
Figure 3: Link Number Mapping Relationship.  
Here, (a) is a digital schematic diagram of the simulated road network, primarily reflecting the physical relationships between nine intersections within the network. The control methods include single-intersection signal control, arterial coordinated signal control, and multi-intersection sub-region coordinated control, fully illustrating the variations in traffic flow at actual intersections. (b) is a concatenated matrix of the queue lengths at the inbound lanes of each intersection and the signal timing for various phases in the simulated road network. To facilitate a unified feedback loop for the GAN model's data input and output, longer queue phases should correspond to longer passage durations.  
By modifying the random number seed in VISSIM and simulating traffic conditions during different periods, such as peak and low times, the above steps were repeated to collect samples. Utilizing data augmentation techniques, a total of 3000 sample sets were generated to prepare for the training of the GAN model.
## 5 Conclusions  
The Acacia _decurrens_ plantation holds significant importance in the study area. Over the past few decades, the rapid expansion of these plantations has brought about both environmental and economic implications. Accurately predicting the spatial distribution of Acacia _decurrens_ plantations is essential for concerned authorities to develop effective management policies.  
This study successfully employed high-resolution satellite data and advanced techniques to assess the spatial distribution of small-scale Acacia _decurrens_ plantation forests. The thorough evaluation of multicollinearity using the VIF method was pivotal, leading to the exclusion of a substantial portion of variables to address collinearity issues, and enhanced the robustness of the SDM. Among the six algorithms utilized, RF emerged as the standout performer, exhibiting near-perfect agreement with AUC above 0.9 and TSS above 0.8. The ensemble model further identified that significant portions of the study area were covered with Acacia _decurrens_ plantations and emphasized the critical role of vegetation indices (CVI, ARVI, and GI) in determining its distribution and proliferation, underscoring their high relative importance. This study also revealed that the decision to establish plantations is influenced by environmental considerations such as elevation, with areas of moderate elevation and proximity to roads being favorable for plantation activities. The geographical rarity of Acacia _decurrens_ in certain areas was attributed to the prevalence of agricultural practices such as small-scale irrigation.  
Ultimately, this research demonstrates the effectiveness of integrating PS imagery-derived variables with environmental factors within SDM, offering valuable insights for identifying and managing small-scale Acacia _decurrens_ plantation forests and providing comprehensive information for efficient decision making in land use planning and forestry management at a local scale.  
Conceptualization, B.A., J.S.-M. and J.R.; methodology, B.A.; software, B.A.; validation, B.A.; formal analysis, B.A.; investigation, B.A.; resources, B.A., J.S.-M. and J.R.; data curation, B.A.; writing--original draft preparation, B.A.; writing--review and editing, B.A., J.S.-M. and J.R.; visualization, B.A.; supervision, J.S.-M. and J.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data from this are available from corresponding author upon reasonable request.  
The authors are sincerely thankful to Swansea University for providing PlanetScope images.
## 3 Results and Discussions
### Simulation Study
#### 3.1.1 First-Order Process Model
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Controllers** & \(K_{p}\) & \(K_{i}\) & \(K_{d}\) & \(\lambda\) & \(\mu\) & \(\beta\) & \(\gamma\) & **OS (\%)** & **Settling Time (\(t_{s}\), Seconds)** & **Rise Time (\(t_{r}\), Seconds)** \\ \hline PID & 17.5 & 4.12 & 9.46 & - & - & - & - & 0.7406 & 98.3306 & 66.4899 \\ PI-PD & 17.5 & 4.12 & 9.46 & - & - & - & - & 3.7814 & 117.7727 & 32.3616 \\ FOPID & 17.5 & 4.12 & 9.46 & 0.98 & 0.02 & - & - & 0 & 86.9470 & 30.8176 \\ FOPI-FOPD & 17.5 & 4.12 & 9.46 & 0.98 & 0.02 & - & - & 0 & 138.7361 & 68.7531 \\ COPI-PD & 17.5 & 4.12 & 9.46 & 0.98 & - & 0.01 & - & 3.7814 & 117.7864 & 32.3687 \\ PI-COPD & 17.5 & 4.12 & 9.46 & - & 0.02 & - & 0.01 & 3.7642 & 117.6919 & 31.7754 \\ COPI-FOPD & 17.5 & 4.12 & 9.46 & 0.98 & 0.02 & 0.01 & - & 0.0318 & 139.1953 & 69.9659 \\ FOPI-COPD & 17.5 & 4.12 & 9.46 & 0.98 & 0.02 & - & 0.01 & 0 & 138.5352 & 68.5506 \\ COPI-COPD & 17.5 & 4.12 & 9.46 & 0.98 & 0.02 & 0.01 & 0.01 & 0.0014 & 138.9981 & 69.7455 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Controller parameters and transient response for first-order system. Figure 3: First-order process model’s step response with compared controllers. The set-point changes ensure that the step response reaches its steady-state value. These set-point variations include positive and negative changes, and they assess the controller's performance effectively. Notably, the proposed COPI-COPD controller outperforms the other controllers, making it more suitable for varying set-point processes. The study involves deliberate exploration of disturbance rejection by injecting a disturbance of 0.5 magnitude into the controller structure for 100 s. This replicates real-life scenarios where external disturbances affect the entire process plant. Figure 5 provides a graphical representation of the adeptness of each controller structure in effectively quelling the disturbance and enabling precise control of the output back to the set point, as depicted in Figure 4's step response. The empirical validation reveals that the COPI-COPD controller outperforms its competitors, exhibiting better overall performance and emphasizing its robustness and resilience in real-world operational contexts. Complex sequential controllers, particularly the FOPI-COPD, are sturdy tools in set-point tracking, improving control dynamics and higher performance depending on dynamic operating requirements. Figure 4: First-order process model’s tracking response with compared controllers. Figure 5: First-order process model’s disturbance response with compared controllers.
# Designing a C-ITS Communication Infrastructure for Traffic Signal Priority of Public Transport
## Materials
### Area of Study: Underground Railway Section U5  
The scope and the investigated area of interest for C-ITS G5 piloting is the above-ground section of the underground railway, i.e., the U5. This specific test site starts at the most southern stop, \"Konstablerwache\", where the intersection of \"Eschenheimer Anlage/Eckenheimer Landstrasse\" and the tunnel entrance/exit is located. The investigated area then continues north until the last stop and terminal destination of the U5, the stop \"Preungesheim\".  
The total distance between the starting point and endpoint of the U5 area of study is approximately 5.1 km, of which approximately 0.5 km is located within the tunnel and 1.2 km is located on the surface roads, where the track merges with other forms of traffic. The remainder of the selected route has its own railroad tracks with level crossings at intersections. There are currently eleven stops in total along the entire route, and nine of these stops are heavily frequented intersections for the aboveground section of the U5. Inaddition, there are four intersections at which a higher frequency of emergency vehicles can be expected to cross.  
In terms of investigative studies of the U5 and in regard to public transport prioritization, all intersections with traffic lights along the test route have been fully examined. It should be duly noted that an intersection with traffic lights may consist of several nodes and/or sub-nodes with corresponding individual signalization. Therefore, the registration chains were evaluated for the location of the individual registration points for all traffic lights. Here, the log files of the onboard computer data, which belong to the Automatic Vehicle Monitoring (AVM) System of Frankfurt's public transport provider VGF (Stadtwerke Verkehrsgesellschaft Frankfurt am Main mbH, Frankfurt am Main, Germany), were used.  
All traffic lights on the U5 are in mutual communication with the traffic control center. Here, adaptive decisions are made as to which signaling program is applied/enforced/ rendered. The logic of the local control systems for the intersections uses a form of detection when deciding whether individual signal groups in the frame should be adapted locally. There is no direct communication between the signalized intersections, e.g., for the exchange of signal plans or current information status.
## 3 Results
### Evaluation of the BESS Products in the Xinjiang  
In this study, the ecosystem model (BESS) was validated with respect to GPP and ET estimates using satellite-derived data (MODIS products) and field data collected through eddy covariance flux sites and the data from previous publications in Xinjiang. Figure S6 compares satellite-derived data with BESS GPP and ET on the multi-year average values for each pixel. There are good agreements between the BESS-simulated and satellite-derived data, indicated by R\({}^{2}\) = 0.67 and RMSE = 71.16 mm yr\({}^{-1}\) for ET, and R\({}^{2}\) = 0.76 and RMSE = 107.04 gC m\({}^{-2}\) yr\({}^{-1}\) for GPP. The results show that the BESS model performed fairly well in estimating ET, with R\({}^{2}\) of 0.68 (\(p\) < 0.001), 0.88 (\(p\) < 0.001), 0.74 (\(p\) < 0.001), and 0.62 (\(p\) < 0.001) and RMSE of 8.37.5.97, 1.37, and 3.5 for Aksu (alpine meadow), Fukang (shrubland), Jinghe (shrubland), and Korla (cropland), respectively. This model also exhibited excellent performance in estimating ET in the Yellow River Basin, China (irrigated cropland) [61]. Compared with the MODIS ET products in the Aksu, Fukang, and the Yellow River Basin, the BESS ET performed better (Figure 13) [61].  
The result showed that the BESS GPP performed poorly in Fukang (shrubland); the result explained only 27% of the variation in the observed GPP, and the BESS GPP overestimated the GPP, with R\({}^{2}\), slope, and RMSE values of 0.27, 0.96, and 23.01 g C m\({}^{-2}\) 16 days\({}^{-1}\), respectively (Figure 13). The BESS GPP performed fairly well in Wulanwusu (cropland), with R\({}^{2}\), slope, and RMSE values of 0.73, 0.46, and 90.04 g C m\({}^{-2}\) month\({}^{-1}\), respectively (Figure 13). However, the model performance was still better than MODIS GPP in Fukang (R\({}^{2}\) = 0.16, slope = 0.45, RMSE = 11.01 g C m\({}^{-2}\) 16 days\({}^{-1}\)) [53]. The BESS model (R\({}^{2}\) = 0.72) performed better than the MODIS product (R\({}^{2}\) = 0.0014) in estimating GPP in the grassland areas of China, such as Changling (44\({}^{\circ}\)59\({}^{\prime}\)N, 123\({}^{\circ}\)51\({}^{\prime}\)E), Dangxiong (30\({}^{\circ}\)05\({}^{\prime}\)N, 91\({}^{\circ}\)07\({}^{\prime}\)E), Haibei (37\({}^{\circ}\)37\({}^{\prime}\)N, 101\({}^{\circ}\)18\({}^{\prime}\)E), and the Inner Mongolia station (43\({}^{\circ}\)55\({}^{\prime}\)N, 116\({}^{\circ}\)68\({}^{\prime}\)E) [62].  
Figure 13: Relationship between observed data and the BESS model in Xinjiang. (**a**) The comparison between ET\({}_{\text{OBS}}\) and ET\({}_{\text{BESS}}\); (**b**) the comparison between GPP\({}_{\text{OBS}}\) and GPP\({}_{\text{BESS}}\).
###### Abstract  
4-Hexylresorcinol (4 HR) has been used as a food additive and antiseptic. The aim of this study was to evaluate whether the application of 4 HR in breast cancer cells and ovariectomized rats showed estrogen-like effects. MCF-7 and SK-BR-3 cells were treated by solvent, 1-100 \(\upmu\)M bisphenol-A (BPA), or 1-100 \(\upmu\)M 4 HR, respectively. 3-(4, 5-Dimethylthiazol-2-yl)- 2,5-diphenyltetrazolzolium bromide (MTT) assay and Western blot for extracellular signal-regulated kinase-1/-2 (ERK1/2), phosphorylated ERK1/2 (p-ERK1/2), estrogen receptor-\(\upalpha\) (ER\(\upalpha\)), and ER\(\upbeta\) were done. As an in vivo study, ovariectomized rats (\(n=15\)) received solvent, 125 mg/kg of 4 HR, or 10 \(\upmu\)g of 17-\(\upbeta\) estradiol via daily subcutaneous injection for 7 days. Blood samples were obtained for evaluation of prolactin levels. Pituitary glands and uteruses were biopsied for histological evaluation and Western blot analysis. Compared with the control group, the application of 4 HR decreased the proliferation of MCF-7 and SK-BR-3 cells, while the application of BPA increased (\(p<0.05\)). The application of BPA increased the expression of ER\(\upalpha\), ER\(\upbeta\), and p-ERK1/2, but 4 HR did not change the expression of ER\(\upalpha\), ER\(\upbeta\), or p-ERK1/2 in MCF-7 cells. In an animal model, the 4 HR group showed similar levels of ER\(\upalpha\), ER\(\upbeta\), and prolactin expression in the pituitary gland compared to the solvent only group, while the estradiol group showed higher levels. Serum prolactin levels were similar between the 4 HR and solvent only groups. Taken together, 1-100 \(\upmu\)M 4 HR did not show BPA-like behavior in MCF-7 cells, and 125 mg/kg of 4 HR daily subcutaneous injection for 7 days did not demonstrate estradiol-like effects in ovariectomized rats. Collectively, 4 HR has no estrogen-like effects on both ER\(\upalpha\)-positive cells and estrogen-deficient rat models.  
4-hexylresorcinol; estrogen; breast cancer; bisphenol A; estrogen receptor; prolactin