The principle of WDM phase regeneration of DPSK signals is shown in Fig. 2. The main idea is to convert the WDM signals to a high-speed serial single wavelength channel, which is then straightforward to regenerate in a single PSA-based optical phase regenerator without unwanted mixing of many pumps and wavelength channels. After regeneration, the serial signal is simply converted back into a WDM signal. The conversion between WDM and serial formats is based on time-lens-based OFT.
Principle of WDM regeneration. The WDM signal is converted to a high-speed serial signal using OFT 1, which is then regenerated in a single optical phase regenerator. After regeneration, the serial signal is converted back to a WDM signal using OFT 2
Experimental demonstration of simultaneous regeneration of 8 and 16 WDM DPSK channels. a Experimental setup of 8 and 16 10 Gbit/s DPSK WDM regeneration. At the transmitter: 8- or 16-CW carriers are generated by individual CW lasers, and are DPSK modulated by in a MZM. The obtained WDM channels are data decorrelated in a 4-path decorrelator, and are then sent to the optical WDM regenerator. Phase noise is added using a phase modulator driven by broadband electrical noise. At the receiver: after WDM demultiplexing, the regenerated WDM channels are received by a pre-amplified DPSK receiver including a DI and balanced photo-detection. b WDM regenerator, a 100-ps DI is used convert all WDM DPSK channels (S1) to OOK signals, which are then converted to a 80-or 160-GBd serial signal by the first OFT (S2). The obtained serial OOK signal (upper S3) is coupled into the HNLF as an XPM pump to optically phase modulate the pulsed XPM probe (S4). The pulsed XPM probe is generated from carving the CW carrier S using a NPRL. A single PSA (S5) is employed for phase regeneration of the obtained serial DPSK signal. Finally, the regenerated serial signal is converted back to WDM signals by the second OFT (S6) using a 90-ps pump pulse (lower S3). c Spectrum of the input WDM channels (S1), 1st OFT output spectrum (S2), waveform of the 80 GBd OOK signal (upper S3), waveform of the 2nd OFT pump (lower S3), waveform of the coherent pulse (S4), output spectrum of the PSA (S5), 2nd OFT output spectrum (S6)
There were no standards for the validation and verification of tsunami numerical models before 2004 Indian Ocean tsunami. Even, number of numerical models has been used for inundation mapping effort, evaluation of critical structures, etc. without validation and verification. After 2004, NOAA Center for Tsunami Research (NCTR) established standards for the validation and verification of tsunami numerical models (Synolakis et al. 2008 Pure Appl. Geophys. 165, 2197-2228), which will be used evaluation of critical structures such as nuclear power plants against tsunami attack. NCTR presented analytical, experimental and field benchmark problems aimed to estimate maximum runup and accepted widely by the community. Recently, benchmark problems were suggested by the US National Tsunami Hazard Mitigation Program Mapping & Modeling Benchmarking Workshop: Tsunami Currents on February 9-10, 2015 at Portland, Oregon, USA ( ). These benchmark problems concentrated toward validation and verification of tsunami numerical models on tsunami currents. Three of the benchmark problems were: current measurement of the Japan 2011 tsunami in Hilo Harbor, Hawaii, USA and in Tauranga Harbor, New Zealand, and single long-period wave propagating onto a small-scale experimental model of the town of Seaside, Oregon, USA. These benchmark problems were implemented in the Community Modeling Interface for Tsunamis (ComMIT) (Titov et al. 2011 Pure Appl. Geophys. 168, 2121-2131), which is a user-friendly interface to the validated and verified Method of Splitting Tsunami (MOST) (Titov and Synolakis 1995 J. Waterw. Port Coastal Ocean Eng. 121, 308-316) model and is developed by NCTR. The modeling results are compared with the required benchmark data, providing good agreements and results are discussed. Acknowledgment: The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant
Since its inception in 1999, the EPO office of the MIT Center for Space Research (CSR) has fostered direct participation of local scientists in educational initiatives such as teachers workshops and public tours of the Chandra Operations and Control Center. The role played by the CSR EPO office has grown significantly, thanks to the award of a number of EPO grants associated with the Chandra and HETE missions. In the past year about one-third of the CSR research staff was involved in the office's EPO initiatives: more than 500 K-12 students, about half from underrepresented groups, were included in formal education programs and informal education events attracted an estimated 900 people. Today the mission of the CSR EPO office is focused in two areas: professional development for K-12 science teachers, and educational programs in out-of-school time. To be associated with major NASA research missions is beneficial to our mission in several respects, but provides also specific challenges. We present here some of the strategies and intiatives that we have undertaken to overcome those challenges.
When the demand for either a region of airspace or an airport approaches or exceeds the available capacity, miles-in-trail (MIT) restrictions are the most frequently issued traffic management initiatives (TMIs) that are used to mitigate these imbalances. Miles-intrail operations require aircraft in a traffic stream to meet a specific inter-aircraft separation in exchange for maintaining a safe and orderly flow within the stream. This stream of aircraft can be departing an airport, over a common fix, through a sector, on a specific route or arriving at an airport. This study begins by providing a high-level overview of the distribution and causes of arrival MIT restrictions for the top ten airports in the United States. This is followed by an in-depth analysis of the frequency, duration and cause of MIT restrictions impacting the Hartsfield-Jackson Atlanta International Airport (ATL) from 2009 through 2011. Then, machine-learning methods for predicting (1) situations in which MIT restrictions for ATL arrivals are implemented under low demand scenarios, and (2) days in which a large number of MIT restrictions are required to properly manage and control ATL arrivals are presented. More specifically, these predictions were accomplished by using an ensemble of decision trees with Bootstrap aggregation (BDT) and supervised machine learning was used to train the BDT binary classification models. The models were subsequently validated using data cross validation methods. When predicting the occurrence of arrival MIT restrictions under low demand situations, the model was able to achieve over all accuracy rates ranging from 84% to 90%, with false alarm ratios ranging from 10% to 15%. In the second set of studies designed to predict days on which a high number of MIT restrictions were required, overall accuracy rates of 80% were achieved with false alarm ratios of 20%. Overall, the predictions proposed by the model give better MIT usage information than what has been 153554b96e