Data Structure Sample Clauses

Data Structure. In order to store the data extracted from the set of packets, we use different data structures. We will here expose the main structures. We will first expose the different structures generated by Process_Network_Data to store the addresses from the sets of packets. The second part of this section will address the data generated by Find_Anomalies to describe the anomalies found. 1. List of addresses The struct address_properties is used to store the statistics for a network/mask for the three metrics (#packets, #bytes and #SYN) used for the detection step of our ADS. There are three instances of this structure in the structure address_list. struct address_properties { int mean; uint64_t variance; uint32_t curr; uint32_t ant; }; typedef struct address_properties address_properties_t; Here is a detail of all these variables: int mean: mean for this particular variable over all slots uint64_t variance: variance for this particular variable over all slots uint32_t curr: temp variable for calculating P (this would be Xi+1) uint32_t ant: temp variable for calculating P (this would be Xi) This structure is used inside address_list to store the address and the mask for the network. typedef struct{ struct in_addr ip; uint32_t mask; } address_t; This structure is used to store the IP address from the sets of captured packets. struct address_list { address_t address; int *deviation; unsigned int *pktcount; // per slot struct address_properties *properties; struct anomaly_list *anomalies; struct address_list *next_octect; struct address_list *parent; struct address_list *next; }; typedef struct address_list address_list_t; Here is a detail of all these variables: address_t address: address of the current address (including mask cf. address_t). int *deviation: holds the original threshold (sigma*K) for each variable (PKT, SYN, BYTES). unsigned int *pktcount: struct address_properties *properties: statistics of the current address struct anomaly_list *anomalies: list of anomalies in this address struct address_list *next_octect: pointer to the next level in IP network (e.g. we are at 140.93.0.0 and this points on 140.93.4.0). struct address_list *parent: pointer to the previous level in IP network (e.g. we are at 140.93.0.0 and this points on 140.0.0.0). struct address_list *next: pointer to the next IP network inside the same IP level (e.g. we are at 140.93.0.0 and this points on 140.94.0.0). 2. List of anomalies This structure is used to store the anomalies for a speci...
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Data Structure. In SILAC experiments, isotopically-labeled amino acids are used to ‘pair’ peptide signals arising from two or more samples. The hierarchical structure of the resultant data can be outlined as follows. Let F 1, . . . , F n denote the data vectors originating from n gel fractions that are analyzed in an experiment. At the gel fraction level, the structure of the data can be described as follows. Assuming that we observe ni individual proteins in gel fraction i, we have F i = {Pri1, . . . , Xxxxx}; i = ı, . . . , n, where Pri denotes the jth protein observed from the ith gel fraction. Prij in turn nij represents the set of nij individual peptides {Ppij, . . . , Ppij
Data Structure i. Unless specifically directed otherwise, all data shall be structured according to the current version of the Spatial Data Standards (SDSFIE) in use by NAVFAC. A copy of the current data model implementation (specific GIS layers and any required associated tables) shall be provided to the contractor. ii. If new data is being created and the corresponding data structure does NOT yet exist, the contractor shall provide the GRC with a data dictionary identifying all of the SDSFIE Entity Types, attributes, and/or domain values associated with the new feature(s), the geographic area(s) covered by the data and Spatial extent information prior to the creation/editing of GIS data. Acceptable formats: MS Excel or MS Word. Adobe PDF is not an acceptable format. New non-SDS compliant attributes (meeting SDSFIE criteria) will require precise schema definitions.
Data Structure. For the results of the cost revenue assessment in section 3, a full value approach is used. That is, total revenues and costs are calculated for the business case in order to present a benchmark measure of financial feasibility for setting and operating longline mussel farming at sea. Financial sensitivity analysis in section 5.1 is presented in incremental values (e.g. ratios or percentages) against this benchmark measure to illustrate the deviations from the base case from changes in underlying financial assumptions.
Data Structure. A full value approach is used for the cost revenue assessment in section 3. That is, total revenues and costs for the business case in order to present a benchmark measure of financial feasibility for setting and operating a Recirculating Aquaculture System (RAS) to culture Seabream on top of floating modules ate sea. Financial sensitivity analysis in section 0 is presented in incremental values (e.g. ratios or percentages) against this benchmark to illustrate the deviations from the base case from changes in the underlying assumptions. 10 FAO (2020), Indicative factors for converting product weight to live weight for a selection of major fishery commodities, Coordinating Working Party on Fishery Statistics (CWP), Handbook of Fishery Statistics, available online: xxxx://xxx.xxx.xxx/cwp-on-fishery-statistics/handbook/capture-fisheries-statistics/conversion-factors/en/
Data Structure. It is necessary to develop data structure for the System to provide World Bank reporting standards. Reports and other information for the World Bank should be generated into special reports using data analysis specified for each transaction. S+А

Related to Data Structure

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