Figure 15. The impact of the n z on the projected II angular power spectrum. The IA is generated assuming the same setup as 2.3, while the n z are respectively: the fiducial Gaussian n z adopted in the rest of the paper (solid orange lines), broader Gaussian distributions (dashed green lines) and borader Gaussians with the superpositions of ‘catastrophic outliers’ and peaks (dash-dotted magenta lines).
Figure 15. The “Consortium” section Figure 16: The UNIPI business cards
Figure 15. Scatter plots with Xxxxxxx´s tau correlation coefficient (τ) and p values for the associations between otolith Sr:Ca, Ba:Ca and δ18O measurements. Linear interpolation (blue line) is also shown.
Examples of Figure 15 in a sentence
For cars on these three segments, they simply execute BiPP with the virtual alert location and radii.In addition to the simple example shown in Fig- ure 14 with only one intersection, Figure 15 shows a scenario in a city-grid where the operating radius en- compasses multiple intersections.
When the limits are reached, control actions can be carried out and grid forming behavior no longer has a priority.• HVDC stations have almost no inherent energy reserves.• Other system requirements such as FRT, damping torsional frequency and harmonics can restrict the grid forming behavior.The guideline defines the test scenarios to validate the overall performance of GFM: Figure 15.
As a result, we can not take any advantage of the “lazy” propagation in thesingle two-way road case.From this grid example shown in Figure 15, we il- lustrated that even if a road segment is not in the safety radius of an alert, we may still have to treat the seg- ment as if it was entirely within the safety radius and propagate the alert quickly.
If you see an error message in Step 1, resolve the issue by following the on-screen instructions (Figure 15).
Click on the Program Specific Information link in the left menu (Figure 15, Box 3).
More Definitions of Figure 15
Figure 15. The baseline representation of the Rosetta water system, using the WSM DSS The model distinguishes between different water uses (urban vs. irrigation) and different water supply sources in irrigation water supply (freshwater vs. drainage water). It has been developed using data provided by ECRI and includes: All major urban water uses in the area, which are modelled as 8 settlements/urban agglomerations. Of those: o El Rashid (Rosetta) and Edco cities have the highest priority in terms of water supply; o The remaining 6 settlements (6-October, Depono, Edfina, El Ameer, El Maadya, El Sahel) have a lowest priority (equal to 2). All settlements receive freshwater from the Nile, treated in the local drinking water treatment plants. According to the data provided by ECRI, wastewater from Rosetta and Edco cities is treated in the corresponding wastewater treatment plants and discharged to the sea and the Edco lake respectively, whereas wastewater from the remaining agglomerations is probably discharged to irrigation/drainage canals. Water use for crop irrigation is divided in two demand nodes, according to the water supply source, with priorities lower than urban water supply. The first node corresponds to freshwater use, directly from the Nile canal system. The second node receives also drainage (return flows from irrigation with freshwater) as the primary water supply source, and deficits are complemented with freshwater supply from the Nile. Drainage ends up in Edco lake, which acts as a receptor body. The Nile section of the Rosetta area is modelled through a set of river reach nodes, of which the first receives as run-off the share of Nile water that enters the area. Input data entered in the model concern the cropping pattern, crop coefficients and growing seasons, precipitation, evapotranspiration, population and per capita consumption, share of return flow from irrigation (used as drainage), and capacity of drinking water and wastewater treatment plants. In addition to the baseline representation of the system, the following scenarios have been built for further development and validation: A scenario for the decrease of Nile inflows to the area. As the water system of Egypt is highly centralized, the inflow to the upper river reach of the Nile segment pertaining to Rosetta will depend on future, national, water allocation policies, which will be influenced by: (a) population growth and land reclamation schemes upstream, (b) potential changes in t...
Figure 15. Cereal usage in EU-15 countries. Source: authors, based on FAOSTAT (2017).
Figure 15. How individuals moved through each compartment in a high-income country versus in a low-income country (base model). Individuals in high-income countries (solid lines) moved into the the infected class (red) at a much slower pace than in low-income countries (dashed lines). The case-fatality rate for high-income countries was 0.845% while in the low- income country it was 2.50%. Likewise, the TAR for the high-income country was 19.4% while the TAR in low-income countries was 72.1%.
Figure 15. The upper panel is the same as in Figure 13 and is shown here to help the comparison with the Qs as function of time shown in the lower panel. The dots in the lower panel indicate the mean value of the Qs obtained for each analysed event (St.Gallen site). The red line corresponds to the moving average.
Figure 15. The information cascade as appears in EUNOMIA’s Digital Companion (early engineering prototype visualisation).
Figure 15. [top left] Model for the numerical simulations. The model consists of a cap- (light grey on faults) and a reservoir rock mass (dark grey) that are cut by permeable faults. Injection of cold water is through an open hole section in the southern compartment (blue line) while production is conducted in the northern compartment (red line). [top right] Results of the simulation with poroelastic rock mass. Large areas of the southern fault become destabilised during injection (red), while the northern fault is stabilised by production (green). Stability on the faults is not aligned with pore pressure (isolines). The rock mass shows some increase in differential stresses at the injection well and close to the faults (red isobar). In the production compartment large volumes get depressurised as indicated by the blue isobar. [bottom left] Results from simulation with a non-homogeneous fracture containing rock mass highlight reduced areas of stability change on faults. [bottom right] The results of simulation of the influence of a hydraulic fracture treatment show comparable fault stability pattern compared to the homogeneous solution (top right) but with altered influence on rock mass. 22 Figure 16: Diagram of the de- and stabilized areas on the faults (left) and moment magnitude (right). 24 Key word list Fracture mechanics simulation, Fracture network evolution, Permeability changes, Upscaling of permeability, Hydraulic stimulation Definitions and acronyms Acronyms Definitions dfn distinct fracture network FEM finite element method XFEM extended finite element method
Figure 15. Interpreted section of sub-bottom profile 423, transecting Resource Area 1 (See Figure 8 for line location). The image shows the east end of the sub-bottom profile towards the right. Changes in depositional environments and unconformities are labeled and delineated. See text for detailed descriptions. Depth is reported in meters below sea surface with an assumed sound velocity of 1524 m s-1.