Figure 10. In respect to the Class E passing-beam, the Class W passing-beam, designed for right-hand traffic only and a driving-beam. The score above the letters "E" and "W" indicates that these passing-beam classes are provided on that side of the system by more than this installation unit.
Figure 10. SLA Lifetime Forecast Figure 11: Workload Forecast Error Distribution Figure 10 shows the application of the workload forecast and the error distribution on a Wikipedia workload trace (Mituzas 2011). The blue line presents the observed workload process and the green line the forecast. For the first two hours of the forecast, the prediction error distribution is depicted by the box plots. Without a detailed analysis, we see that the workload forecast provides a good forecast of the process until the end of the SLA lifetime. Figure 11 depicts the workload forecast error distribution. The first peak represents the current workload level; the following curves show the prediction distribution of the forecast periods. For the near future the prediction is very accurate, though the accuracy decreases with the increasing forecast horizon. Both components (𝜔 and W˙ ) are later used by the dynamic SLA management model to derive efficient operation strategies online.
Figure 10. My account In the Tab “Password” you can change the login password (in compliance with international safety standards - Figure 11). Figure 11: Password edit
Examples of Figure 10 in a sentence
Figure 9 Device connected via USBAfter connection to Configurator Status window will be displayed (Figure 10 Configurator Status window).
Figure 10 Configurator Status windowVarious Status window tabs display information about GNSS, GSM, I/O, Maintenance and etc.
This is depicted in the scans provided at Figure 10 to 27 of the report.
See Figure 10 for the standard issuance releasability statements.
After launch software language can be changed by clicking in the right bottom corner (Figure 10 Language selection).
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Figure 10. The Phase-1 and Phase-2 latency vs. latency of One-phase pinpoint Gas consumptions. In Figure 11(a), we show the gas consump- tions of the main functions in our contract. Since we have applied the inline mechanism, the gas consumptions are calculated by dif- ∼ × ferential analysis. For the setup, the Agatha contract and the BOP library are deployed to Ethereum with 4.8 106 gas, which is one-time. For the normal case, the Submit (i.e., submitter’s on-chain ∼ commitment) costs 63,000 gas which corresponds to three ETH transfer transactions. For the pinpointing cost, we see that the gas consumed by BOPs is negligible compared to those of other func- tions. The maximal gas consumption for the whole pinpointing (i.e., Max total of VGG16) is about ∼86 ETH transfer transactions. Xxxxxx tree branches. We also study the impact of the number of the Xxxxxx tree branches on the performance, as shown in Fig- ure 11(b). Due to the space constraint, we only show the case of the MobileNet, which is similar to those of ResNet50 and VGG16. When the branch size varies from 2 to 64, we see a decrease in the number of interaction rounds and the Xxxxxx tree generating time, because the depth of Xxxxxx tree decreases. Interestingly, the gas consumption for pinpointing is minimal when the branch size is 32, which is caused by two effects: (1) The number of rounds decreases with a larger branch size, which reduces the total gas of Challenge and Response in Figure 11(a). (2) The increase of branch size leads
Figure 10. Indicative results (top-5 returned shots) for comparing RD-KSVM-iGSU with RD- KSVM, for four event classes are presented in Table 8.
Figure 10. The BIP Compiler tool-chain. The BIP framework is supported by a tool-chain including model-to-model transformations and code generators (see Figure 10). Installation and Usage Installation instructions can be found at xxxx://xxx-xxxxxxx.xxxx.xx/New-BIP-tools. html. The BIP compiler and engines are provided as an archive containing the binaries needed for executing the tool. The target platforms are GNU/Linux x86 based machines, however, the tool are known to work correctly on Mac OSX, and probably other Unix-based systems. The tool requires a Java VM (version 6 or above), a C++ compiler (preferably GCC) with the STL library, and the CMake build tool. More tool details and tool examples are available on the same page, a detailed BIP documentation is available at xxxx://xxx-xxxxxxx.xxxx.xx/TOOLS/DCS/bip/doc/ latest/html/index.html.
Figure 10. Mafic minerals map of the working area from the CRISM instrument, showing compositional variations of the primary mafic minerals. Red: olivine and mafic component of carbonates. Green: Low- calcium pyroxene. Blue: High-calcium pyroxene (basemap: CTX). Figure 11: Mafic minerals map focused on the fan delta area from the CRISM instrument, showing compositional variations of the primary mafic minerals. Red: olivine and mafic component of carbonates. Green: Low- calcium pyroxene. Blue: High-calcium pyroxene (basemap: HiRISE). MOLA The Mars Orbiter Laser Altimeter (MOLA) instrument is embarked on board the 1996 Mars Global Surveyor. This instrument was the first to provide information about the global altimetry and surface roughness of Mars, with a resolution up to 100 m/pixel (Xxxxx et al., 2001). These legacy data, available in their latest 2003 revision on the PDS (xxxx://xxx-xxxxxxxxxxx.xxxxx.xxx/missions/mgs/megdr.html) are not resolved enough for the PlanMap effort (~900 m/pixel in this area). Anyway, this deliverable provides an extract of the MOLA global altimetry cover (Fig. 12) as comparison and calibration reference for other altimetric data derived from indirect methods (e.g., HiRISE photogrammetric DEM), with elevations in meters. Figure 12: Digital Elevation Model of the working area (with horizontal resolution of ~900 m/px), obtained by laser altimetry from the MOLA instrument.
Figure 10 left: subset of the FEM grid of an OPV module, right: computed power dissipation density in the defective solar module
Figure 10 farmers income lagging behind salaries in the whole economy (source EC, 2017, p. 14)27 Figure 11: Trends on income (left) and costs (right) generated by the EU fleet (source Xxxxxx Xx Xxxxxxxx Xxxxxx et al., 2017) 28 Figure 12: Employment in agriculture since 1968 in Western Europe as a share of total employment. Source: (Pe’er et al., 2017, p. 89) 28 Figure 13: Average cereal yields for the EU-28 and 5 selected countries. Source: authors, based on FAOSTAT (2017). 32 Figure 14: Meat and milk production in today's EU-28 countries. Source: authors, based on FAOSTAT (2017). 33 Figure 15: Cereal usage in EU-15 countries. Source: authors, based on FAOSTAT (2017). 34 Figure 16: Average pulse yields for today's EU-28 and five selected countries. Source: authors, based on FAOSTAT (2017). 35 Figure 17: Commodity prices in the long run. Source: USDA, 2016 36 Figure 18: Pulse production and usage in EU-15 countries. Source: Authors’ calculations, based on FAOSTAT (2017). 39 Figure 19: Member States position in the debate of the reform of the CAP. Source: Clasper & Xxxxxxxx (2010) 43 Figure 20: Number of actors at each stage within European agribusiness chains. Source: Xxxxxxxx, 2002, as shown in Xxxxxxxx and Xxxxxxxxx, 2006 46 Figure 21: The four narratives in a snapshot (elaboration: authors) 52 Figure 22: Illustration of the narrative "International competition" 54 Figure 23: Illustration of the narrative "Europeanization" 59 Figure 24: Illustration of the narrative "Ecologization" 63 Figure 25: Illustration of the narrative "Dualization" 69 Table of tables Table 1: Utilized agricultural area (UAA) by size of holding, 1990, 2003, and 2007, selected countries in today's EU. Source: xxx xxx Xxxxx et al (2015). 20
Figure 10. Left: eigenfunction which vanishes at all vertices on a pumpkin chain consisting of 3 equilateral pumpkins. Right: eigenfunction corresponding to λ1 of the same pumpkin chain. D2 D Since G has only one pumpkin, λ (G) = π2 with an eigenfunction ψ(z) = sin πS(z) D for z ∈ G, and ψ vanishes at all vertices of G (here the only vertices of G are the two endpoints of the pumpkin). Define another function ψ˜ on G by ψ˜(z) := cos πS(z) . Clearly, ψ˜ is also an eigenfunction of λ1(G), but ψ˜(v0) = 1 and ψ˜(vD) = −1. (Figure 11) In conclusion, for any eigenfunction ψ associated with λ1(G) which vanishes at all vertices of G, we can find another eigenfunction ψ˜ associated with λ1(G) which does not vanish on at least one vertex of X. Xxxxx, we can always perform the averaging strategy to generate the eigenfunction ψ1 which only depends on the level of points in G. This implies that we only need to consider one-dimensional functions in the Rayleigh Figure 11: Eigenfunction corresponding to λ1 on a single pumpkin. There exists a sine eigenfunction which vanishes at endpoints, but also another cosine eigenfunction which does not vanish at endpoints. quotient with the weight function ρ(x) := #S—1(x) for x ∈ [0, D]. The weight function counts the number of edges at the given level. Therefore, the formula from Theorem 2.1 reduces to the following: (∫ r|u (x)| ρ(x) dx D 2 ∫ |u(x)| ρ(x) dx 1 λ (G) = inf 0 D 2 : u ∈ H1 ([0, D]) , D ∫ ) u(x)ρ(x) dx = 0 . 5 Application: An Estimate in terms of Diameter and Total Length By Lemma 4.2 and Lemma 4.3, to estimate the spectral gap of quantum graphs in terms of diameter and total length, it suffices to consider the one-dimensional Xxxxx-Liouville problem on pumpkin chains. Xxxxxxx et al. [8] give the following upper estimate.