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3 results for “Telecommunication Network”
MODELING OF TELECOMMUNICATION NETWORKS BASED ON FUZZY LOGIC TECHNOLOGY
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Telecommunication Networks as Knowledge Graph Research Datasets
<p><strong>Telecommunication Networks as Knowledge Graph Research Datasets.</strong></p> <p>All the datasets were created using the PC with following parameters: Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz/16.00 GB RAM 1TB SSD</p> <p>All the software which is used for the datasets creation is available here: https://github.com/kulikovia/TN_KG_research<strong> </strong></p> <p>The datasets and supplementary files description of follow:</p> <table> <tbody> <tr> <td> <p><strong>#</strong></p> </td> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>1.</p> </td> <td> <p>Computational_complexity_v5_ (ENG).pdf</p> </td> <td> <p>SPARQL performance tests report</p> </td> </tr> <tr> <td> <p>2.</p> </td> <td> <p>Synthesis_performance_tests_results_v1.pdf</p> </td> <td> <p>Inductive and deductive synthesis performance tests report</p> </td> </tr> <tr> <td>3.</td> <td>Computational_complexity_Parallel_v2_ (ENG).pdf</td> <td>Comparision of SPARQL performanse using multi-level KG structure approach and execution using distributed RDF storege </td> </tr> <tr> <td>4.</td> <td>Synthesis_additional_experiments_v4.pdf</td> <td>Additional Inductive and deductive synthesis performance tests report (with different elements distribution by levels)</td> </tr> <tr> <td> <p>5.</p> </td> <td> <p>Dataset_10k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 10k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>6.</p> </td> <td> <p>Dataset_10k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 10k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>7.</p> </td> <td> <p>Dataset_1k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 1k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>8.</p> </td> <td> <p>Dataset_1k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 1k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>9.</p> </td> <td> <p>Dataset_200k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 200k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>10.</p> </td> <td> <p>Dataset_200k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 200k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>11.</p> </td> <td> <p>Dataset_500k_hierarchical.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 500k hierarchical model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>12.</p> </td> <td> <p>Dataset_500k_one-level.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: Input data for 500k one-level model synthesis (CSV)</p> </td> </tr> <tr> <td> <p>13.</p> </td> <td> <p>Synthesis_Hierarchical_RDF-XML.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: RDF/XML datasets for hierarchical models (1k, 10k, 200k, 500k)</p> </td> </tr> <tr> <td> <p>14.</p> </td> <td> <p>Synthesis_Linear_RDF-XML.zip</p> </td> <td> <p>Inductive and deductive synthesis performance tests: RDF/XML datasets for hierarchical models (1k, 10k, 200k, 500k)</p> </td> </tr> <tr> <td> <p>15.</p> </td> <td> <p>Hierarchy_model_results_Exp_10M.zip</p> </td> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, exponential distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> <tr> <td>16.</td> <td>Hierarchy_model_results_Exp_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M, 3-5-levels, exponential distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>17.</td> <td>Hierarchy_model_results_Exp_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, exponential distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>18.</td> <td>Hierarchy_model_results_Linear_10M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, linear distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>19.</td> <td>Hierarchy_model_results_Linear_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M, 3-5-levels, linear distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>20.</td> <td>Hierarchy_model_results_Linear_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, linear distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>21.</td> <td>Hierarchy_model_results_Quadro_10M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>22.</td> <td>Hierarchy_model_results_Quadro_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M, 3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>23.</td> <td>Hierarchy_model_results_Quadro_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, quadratic distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>24.</td> <td>Hierarchy_model_results_Uniform_10M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 10M, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>25.</td> <td>Hierarchy_model_results_Uniform_15M.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 15M, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td>26.</td> <td>Hierarchy_model_results_Uniform_200k.zip</td> <td> <table> <tbody> <tr> <td> <p>SPARQL performance tests: Datasets for 200k, 3-5-levels, uniform distributed model with connections between source models on levels 2 and 3 in RDF/XML format</p> </td> </tr> </tbody> </table> </td> </tr> <tr> <td> <p>27.</p> </td> <td> <p>Linear_model_resuts.zip</p> </td> <td> <p>SPARQL performance tests: Datasets for 200k, 10M, 15M, one-level model in RDF/XML format</p> </td> </tr> </tbody> </table>
Data set for "Stability limitations of optical frequency transfer in telecommunication DWDM networks"
<p>Here we share the relevant data of the manuscript “Stability limitations of optical frequency transfer in telecommunication DWDM networks”.</p> <p>The files:</p> <ul> <li>“fig_2a_total.txt”</li> <li>“fig_2a_diff.txt”</li> </ul> <p>contains respectively the timeseries data of phase fluctuations of total delay and differential delay between two fibers in 50 km-long soil-deployed cable. Same results for 110 km-long aerial cable are in files:</p> <ul> <li>“fig_2b_total.txt”</li> <li>“fig_2b_diff.txt”</li> </ul> <p>The files:</p> <ul> <li>“fig_3_aerial_MDEV_points.txt”</li> <li>“fig_3_soil_MDEV_points.txt”</li> </ul> <p>contains respectively points of modify Allan deviation of the differential delay fluctuations in 110 km-long aerial and 70 km-long soil-deployed cable. For short averaging the stability is limited by the noise of time interval counter (MTC 108). Before MDEV calculations the differential delay fluctuations was divided by factor of two, as a half of these fluctuations is present at the output of a stabilized system.</p> <p>The files:</p> <ul> <li>“fig_4_soil_MDEV_points_CW-total.txt”</li> <li>“fig_4_soil_MDEV_points_CW-diff.txt”</li> </ul> <p>contains respectively points of modify Allan deviation of the total and differential delay fluctuations in 70km soil-deployed cable, measured with the unmodulated coherent optical carrier.</p> <p>The files:</p> <ul> <li>“fig_7a_EDFA_phase.txt” and “fig_7a_EDFA_temp.txt”</li> <li>“fig_7b_Raman_phase.txt” and “fig_7b_Raman_temp.txt”</li> <li>“fig_7c_ROADM_phase.txt” and “fig_7c_ROADM_temp.txt”</li> </ul> <p>contains the timeseries data of simultaneous measurement of phase and temperature changes in individual cards (modules) of the DWDM system: EDFA amplifier, card containing the Raman module, as well as ROADM (Reconfigurable Optical Add Drop Multiplexer) module.</p> <p>Time series data of the fluctuations of the total and differential propagation delay of the signal transfer in the unmodified DWDM system (over 1500 km long route) include files:</p> <ul> <li>“fig_9_total_phase_fluct_part1”, “fig_9_total_phase_fluct_part2”, “fig_9_total_phase_fluct_part1” </li> <li>“fig_9_diff_phase_fluct_part1”, “fig_9_diff_phase_fluct_part2”, “fig_9_diff_phase_fluct_part3”</li> </ul> <p>The files:</p> <ul> <li>“fig_10_mdev_acusticNOISE.txt”</li> <li>“fig_10_mdev_air-conditioning_ON.txt”</li> <li>“fig_10_mdev_air-conditioning_MODIFIED.txt”</li> </ul> <p>contains points of modify Allan deviation that shows respectively: the influence of acoustic noise, stability of the frequency transfer in DWDM network in the case of sub-optimal (asymmetric) cooling within a single ILA node, while the last file shows the stability of the transfer after optimizing the airflow within this node.</p> <ul> </ul>
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