Figure 25. Helper function: Member hash and Xxxx- script hash. The major changes from [13] are highlighted in yellow . The orange highlights components that are missing from prior works, which we believe is required to satisfy the UC functionality. Please see the proof for more detail. *derive-keys(G, G′, comSecret) 1 : 𝑠 ← HKDF.Extract(G.initSecret, comSecret)
Figure 25. The PE TFM reconstruction of FBH C (a), D (b) and E (c) and TFM reconstruction of FBH C (d), D (e) and E (f) with 0° incidence TRL The results presented above demonstrate that TRL-TFM improves SNR within the beam convergence region. As this beam intersection area is quite large and covers approximately 40mm, defects that fall into this region can all be detected with improved SNR compared to PE-TFM measurement. It can also be observed that in case of PE-TFM the noise is homogenous in all reconstruction plane (Fig. 25a-c). Meanwhile, in case of TRL- TFM the noise outside the beam convergence area is supressed, which demonstrates that the volume of material which contributes to the back-scattered noise is significantly reduced in case of TRL-TFM (Fig. 25d- f). Finally, the TRL-TFM set-up brings more flexibility for defect assessment. For example, using 0° incidence TRL wedges with matrix arrays it is possible to change the beam convergence depth dynamically by adjusting delay times of array elements. In this case, the beam can be dynamically focused at different depths of the material, while at the same time maintaining reduced backscatter volume. On the other hand, both linear and matrix arrays can be used with 0° incidence TRL wedges to steer the beam in frontal direction, achieving different incidence angles. As a result, one wedge could be used instead of a set of wedges for longitudinal wave focusing at particular depth and incidence angle.
Figure 25. A snapshot showing a subset of the Spice models for Resistance as extracted by Raphael for the AOI221 cell.
Examples of Figure 25 in a sentence
The collimation telescope forms at infinity the image of a polar co-ordinate system with a bright point at its centre (see Figure 25).
Figure 25 shows the corresponding DSE configuration file for the line follower experiments.
With 108 kt of waste and 480 mm of measured precipitation in 2014 [30], the distribution profiles are presented below in Figure 25 and Figure 26.
For the EnergyPLAN simulations hourly values are required and, therefore, the 15-minutes values from the production dataset are adapted to hourly ones and result in the Figure 21-Figure 23 and Figure 25- Figure 27.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 25: Hydro power production profile for river and dammed hydro in 2014 on Madeira [28] The waste incineration plant relies on the waste production of the inhabitants and visitors, but can operate to some extent according to the requirements of the electricity grid and the incineration plant.
More Definitions of Figure 25
Figure 25. Inter organizational data exchange flow
Figure 25. Accumulated EU28 land loss and migration over different time periods of 21st century. Error bars for RCP 2.6 and RCP 4.5 show the uncertainty range over all runs done for these RCPs. The lost land forces population who lived on that land to migrate. The land loss in the EU leads to cumulated migration of 0.1 (RCP2.6 with Adaptation) to 21.5 million people (RCP8.5 high end sea-level rise without adaptation). It has to be noted that in a rich region like the EU much of the population will be protected and thus forced migration will be lower than in other regions. On Country-level, beside the North- west Atlantic coast countries, Italy and Romania suffer big land losses (Figure 26). While in Italy these losses occur in the North-east and lead to significant migration. In Romania it is mainly the unpopulated Danube-delta that loses land under sea-level rise. Thus, the land loss there does not lead to significant migration. Romania thus has the highest land loss in the adaptation scenario, as this area will likely not be protected. The negative land loss under lower sea-level rise for Finland and Sweden is due to the isostatic rebound (Xxxxxxx,2015), which leads to raising land (as it is observed today). Only high rates of sea-level rise will overturn this effect.
Figure 25. Bayes network representation of learned causal linkages between LakeState model input and output variables.
Figure 25. A representative trace of a ‘premature expiratory cycling’ asynchronous event s Abbreviations: Chest RIP- chest wall respiratory inductance plethysmography, Abdo RIP- abdominal wall respiratory inductance plethysmography, Sum RIP- Sum of both the chest wall and abdominal inductance plethysmography bands, sEMGpara- second intercostal parasternal electromyography signal, Rectified RMS Signal- rectified root mean square of the parasternal electromyography signal.
Figure 25. One circuit modeled without LBDR extensions. Therefore the circuits can be modeled inside the partitions by proper configuring the LBDR bits. However, there are partitions that cannot guarantee any QoS circuit by definition. Figure 26 shows the case where there is no possibility to isolate the QoS traffic flow from other traffic flows. In this case, none of the solutions can set up a valid circuit.
Figure 25. Managing Licence Types Figure 26: Managing Technology Readiness The moderator can also edit the Business Services, as seen in Figure 27 and Figure 28. Figure 27: Moderator view for Business Services Figure 28: Editing a Business Service (as moderator) Again, the moderator changes the values shown in the lists of the Business Service form; Price Ranges (see Figure 29) and the Training Type that is used for training services (see Figure 30). Figure 29: Managing pricing for Business Services
Figure 25. GPFS storage at DKRZ Although data access on this system has been shown to be very performant there may be still some improvement possible. Possible changes are the algorithm used for metadata placement on disk (balance between redundancy and performance) and the size of the GPFS page pool. The baseline scalability tests at DKRZ are performed with two of the above mentioned GPFS servers: A virtual machine with 2 cores and 16 GB RAM and a physical node (blade) with 16 cores and 32 GB RAM The GPFS servers are connected with each other by a 10 Gigabit Ethernet (GE) backbone. The uplink to the internet is based on a 10 GE connection.