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Space V3.2



At least two geomagnetic field excursions (Mono Lake/Auckland and Laschamp) have been recorded at a number of globally distributed locations between 10 and 50 ka (e.g., Laj and Channell 2007; Laj et al. 2014; Nowaczyk et al. 2012, 2013; Singer 2014). Studies on high sedimentation rate cores across the Laschamp excursion (e.g., Nowaczyk et al. 2012) have revealed an ever more detailed picture of surface field changes. Furthermore, additional excursion-like behavior has been noted at distinctly different times, e.g., the Hilina Pali/Tianchi excursion (Coe et al. 1978; Singer 2014; Singer et al. 2014; Teanby et al. 2002), occurring at approximately 17 ka (Singer et al. 2014). A better documentation of excursions is integral to a fuller understanding of geodynamo processes (Amit et al. 2010; Olson et al. 2011; Wicht 2005); the interaction between the geomagnetic field, the paleomagnetosphere, and space climate during times of extreme geomagnetic change (Constable and Korte 2006; Stadelmann et al. 2010; Vogt et al. 2007; and the dramatic modulation of cosmogenic isotopes such as 10Be and 14C, with associated implications for dating (e.g., Muscheler et al. 2014). However, the physical origin of excursions is unclear, with multiple mechanisms proposed (see Amit et al. 2010). Although modeling of the Laschamp excursion has been attempted (Leonhardt et al. 2009), the time span was restrictive and the number of sediment records used for the modeling limited. The first step to understanding the evolution of the geomagnetic field over this time is the compilation and assessment of all available sediment records.




Space v3.2



Data from older publications are often not available in digital form; however, they can be digitized. These data are valuable for expanding the number of paleomagnetic measurements that can be used in global modeling of the geomagnetic field. However, caution must be exercised when using digitized data, as values cannot be precisely determined. Small uncertainties on digitized depth or age mean that although data can be from the same specimen or stratum, when parameters are digitized from different graphs (e.g., inclination and declination are shown on separate axes), it is not possible to unequivocally link data to the same specimen/stratum. This is common when data are closely spaced in depth or time or if two discrete specimens were measured at the same depth. To be conservative, each digitized datum is assigned a unique depth or age.


All etcd maintenance manages storage resources consumed by the etcd keyspace. Failure to adequately control the keyspace size is guarded by storage space quotas; if an etcd member runs low on space, a quota will trigger cluster-wide alarms which will put the system into a limited-operation maintenance mode. To avoid running out of space for writes to the keyspace, the etcd keyspace history must be compacted. Storage space itself may be reclaimed by defragmenting etcd members. Finally, periodic snapshot backups of etcd member state makes it possible to recover any unintended logical data loss or corruption caused by operational error.


Since etcd keeps an exact history of its keyspace, this history should be periodically compacted to avoid performance degradation and eventual storage space exhaustion. Compacting the keyspace history drops all information about keys superseded prior to a given keyspace revision. The space used by these keys then becomes available for additional writes to the keyspace.


v3.2.0 compactor runs every hour. Compactor only supports periodic compaction. Compactor continues to record latest revisions every 5-minute. For every hour, it uses the last revision that was fetched before compaction period, from the revision records that were collected every 5-minute. That is, for every hour, compactor discards historical data created before compaction period. The retention window of compaction period moves to next hour. For instance, when hourly writes are 100 and --auto-compaction-retention=10, v3.1 compacts revision 1000, 2000, and 3000 for every 10-hour, while v3.2.x, v3.3.0, v3.3.1, and v3.3.2 compact revision 1000, 1100, and 1200 for every 1-hour. If compaction succeeds or requested revision has already been compacted, it resets period timer and removes used compacted revision from historical revision records (e.g. start next revision collect and compaction from previously collected revisions). If compaction fails, it retries in 5 minutes.


After compacting the keyspace, the backend database may exhibit internal fragmentation. Any internal fragmentation is space that is free to use by the backend but still consumes storage space. Compacting old revisions internally fragments etcd by leaving gaps in backend database. Fragmented space is available for use by etcd but unavailable to the host filesystem. In other words, deleting application data does not reclaim the space on disk.


The metric etcd_mvcc_db_total_size_in_use_in_bytes indicates the actual database usage after a history compaction, while etcd_debugging_mvcc_db_total_size_in_bytes shows the database size including free space waiting for defragmentation. The latter increases only when the former is close to it, meaning when both of these metrics are close to the quota, a history compaction is required to avoid triggering the space quota.


The ARMAS iOS app supports six user groups with radiation exposure information. These groups include vehicle owners with an ARMAS FM7 instrument as well as pilots and crew, business flyers and high mileage frequent flyers who do not use the FM7. The interested public in space weather seeking information related to radiation exposure and the curious user not familiar with the radiation environment in an aircraft or space vehicle are also user groups. The ARMAS iOS app is a free download at the Apple App store and upgrades are managed within the app itself.


Space Environment Technologies (SET) is a global leader providing space weather mitigation instrumentation and cloud-based data applications to the U.S. government, academia, and the international commercial aerospace industry


After compacting the keyspace, the backend database may exhibit internal fragmentation. Any internal fragmentation is space that is free to use by the backend but still consumes storage space. The process of defragmentation releases this storage space back to the file system. Defragmentation is issued on a per-member so that cluster-wide latency spikes may be avoided.


We are using SafeOperation v3.2. Tool and base have been defined.When defining a safety zone in safety configuration - monitoring spaces, offered options to configure a space to monitor areOXYZ, AngABC of the origin of the monitoring space and minXmax, minYmax, minZmax size of the monitoring space.However because of the external axis, E1, KRC4 compares the defined monitoring space to $POS_ACT, which is wrt $BASE, and same coordinates can occur at two places along the external axis, which stops the robot at two different positions.


Okay... that doesn't make any sense. For a kinematically-integrated KL1500, $WORLD is rooted to the 0 position of the KL1500, not to the moving portion of the robot. So setting up Cartesian workspaces should be simple.


The LSRS measures formal learning spaces, defined as classrooms typically scheduled centrally and designed to accommodate all course participants for synchronous meetings. The information in the About section contains more detailed background and information on the LSRS project. In the Resources section, you will find articles, websites, and other resources relevant to the project.


Community members can add their LSRS v3 room scores to the corresponding learning space record in FLEXspace. V3 scores can be recorded in finer detail, including section scores. For more information, please visit the FLEXspace web site.


Version 3 shows significant improvements over the previous release. However, users are advised that the data contains anomalies and artifacts that will impede effectiveness for use in certain applications. The data are provided "as is," and neither NASA nor METI/Japan Space Systems (J-spacesystems) will be responsible for any damages resulting from use of the data.


The current version of Rosetta, v3.2, has been in development for the past two years. The original Rosetta software package was written primarily for ab initio protein folding [20] but quickly expanded to include an array of molecular modeling applications from protein docking to enzyme design. The new Rosetta software package [21] was written from the ground up with these diverse applications in mind. Essential components such as energy function calculators, protein structure objects, and chemical parameters were assembled into common software layers accessible to all protocols. Protocols such as side-chain packing, or energy minimization, were written with a modular object-oriented architecture that allows users and programmers to easily combine different molecular modeling objects and functions. Control objects were written to give users a generalized scheme from which to precisely specify the sampling strategy for a given protocol. Finally, user interfaces such as RosettaScripts,[22] PyRosetta [23], and a PyMol interface [24] were developed to provide unprecedented accessibility of the code. 041b061a72


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