**5. Acknowledgement**

We kindly acknowledge the support of the WWF in the execution of this study.

#### **6. References**

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Lee, D.H.; Steffes, M.; Jacobs, D.R. Jr. (2007). Positive associations of serum concentration of

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Ohta, S.; Ishizuka, D.; Nishimura, H.; Teruyuki, N.; Aozasa, O.; Shimidzu, Y.; Ochiai, F.;

Ohta, S.; Tokusawa, H.; Nakao, T.; Aozasa, O.; Myata, H.; Alaee, M. (2008). Global

Peters, R.J.B.; Beeltje, H.; van Delft, R.J. (2008). Xeno-estrogenic compounds in precipitation.

Rimkus, G.G.; Wolf, M. (1995). Nitro musk fragrances in biota from freshwater and marine

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Ribas-Fito, N.; Sala, M.; Kogevinas, M.; Sunyer, J. (2001). Polychlorinated biphenyl (PCBs)

(2008). Global DNA hypomethylation is associated with high serum-persistent organic pollutants in Greenlandic Inuit. *Environmental Health Perspective*, Vol.116,

and neurological development in children: a systematic review. *Journal of*

the market fishes from Japan. *Chemosphere*, Vol.73, (2008), pp. S31-S38. Peters, R.J.B. (2005). TNO report B&O-A R 2005/129, Man-made chemicals in maternal and

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neurobehavioral development: review of epidemiological studies*. International Journal of Occupational Medical and Environmental Health,* Vol.21, (2008), pp. 121-132.

Perfluorooctane sulfonate and related fluorochemicals in marine mammals, fish and birds from the Baltic and Mediterranean Sea. *Environmental Science and*

M.A.; Olivero, J.; Van Wouwe, N.; Yang, J.H.; Aldous, K.M. (2004). Perfluorooctanesulfonate and related fluorochenmicals in in human blood from several countries*. Environmental Science and Technology*, Vol.38, (2004), pp. 4489-

polychlorinated biphenyls and organochlorine pesticides with self-reported arthritis, especially rheumatoid type, in women*. Environmental Health Perspective,*

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**6**

 *Greece*

**Pesticide Residues in Bee Products**

Andreas Thrasyvoulou1 and Urania Menkissoglu-Spiroudi2

"The man has no divine right over the food. He must compete for this with weeds, diseases,

With the above quote, Grodner clearly reflects the situation in the area production and processing of food. More than 10.000 species of insects and mites, 1.500 species of fungi and 600 plant species have been identified as harmful to agriculture (Grodner, 1996; Pimentel et al., 2000). The production of plant and animal products requires the use of large quantities of chemicals (plant protection agents, veterinary drugs, fertilizers, etc.), which could lead to increased production and improved quality, as a consequence. The quality of the final product is usually reflected to particular visual parameters like color, size and general appearance. In the case of food, however, many questions are raising about their safety. The reason is the possible presence of chemical residues detected in the final product. Nowadays, consumer safety is a major priority for governments of developed countries and food safety is a criterion for the trading and prices on the market. Reports in media about alimentary scandals cause anxiety to consumers and turning a part of the market to organic products, which are considered free, or at least less contaminated by hazardous substances. In recent years, reports in media have grown at an alarming rate and any references to the consumer aimed at creating impressions, achieved by overemphasizing the disadvantages of chemical use and mainly problems related to environmental pollution and its impact on human health. In contrast, reports in media referring to the advantages of using chemicals are minimal to nonexistent. For example, the absence of appropriate chemical agents to combat rodents lead to the first epidemic of the bubonic plague disease and the death of 65.000.000 people. Moreover, the starvation in Ireland began due to the fungal disease *Phytophthora infestans*, which destroyed potatoes causing 1.000.000 deaths from 1845 to 1851 (Knutson et al., 1990). Besides health problems that preoccupied humans in the past, the economic impact on different groups of consumers will be significant in case of pesticides withdrawal. Specifically, the weekly expenses for food are expected to rise by 44% for consumers of low average income. The economies of countries with intensive agriculture will be stroke because of the decline in exports of grains and products like cotton. Finally, undesirable environmental effects are expected due to the increasing of cultivated land and the erosion problems that could be observed because of the limited growth of the root

**1. Introduction**

insects and other organisms" (Grodner, 1996).

Emmanouel Karazafiris1, Chrysoula Tananaki1,

*2 Pesticide Science Laboratory, Faculty of Agriculture,*

*Aristotle University of Thessaloniki,*

*Aristotle University of Thessaloniki,*

*1Laboratory of Apiculture-Sericulture, Faculty of Agriculture,*


Following international practice, EDR values are classified into four categories in terms of the intensity of turbulence. For convenience and in line with alerting purpose of low-level turbulence, EDRs in the following figures and context will be labeled or indicated as insignificant (LL), light (L), moderate (M), and severe (S) instead of its value. It is also worth mentioning that EDR values presented in this paper are derived from the spectrum width data after smoothing by using a 9 point median filter along the radar beams in order to suppress the fluctuations in the determination of spectrum width values. This kind of fluctuation is expected, for instance, to arise from the limited and finite number of data points in the digitization of the spectrum of the return signal.

The spectrum width errors are large in region of low SNR. Here we selected a case to demonstrate the importance of the SNR threshold in the quality control of EDR data. Around 21 UTC on 6 June 2008, the TDWR radar observed thunderstorms over HKIA. Without SNR threshold, estimated EDR suggested severe turbulence region (red color; Figure 9(a)) in the region about azimuth of 270o and centered at about 25 km. High spectrum widths (~4.5 m/s) are indeed measured in this region (see Figure 9(c)). But reflectivity (Figure 9(e)) and SNR (Figure 9(d)) are around -8 dBZ and 10 dB respectively. The relatively large spectrum widths in this region can be caused by incorrect noise power estimates (Fang et al., 2004). To avoid such biases, we use a SNR threshold of 20 dB as recommended by Fang et al., (2004).

Fig. 9. (a) EDR, (b) EDR with SNR> 20 dB, (c) spectrum width, (d) SNR, (e) reflectivity, and (f) Doppler velocity at elevation angle of 0.6o at 21:28 UTC on 6 June 2008. Range ring is 50 km and azimuths are every 30o.

Aviation Applications of Doppler Radars in the Alerting of Windshear and Turbulence 89

Fig. 10. EDR maps (a) at 21:32 UTC on 27 April 2006 and (b) 13:17 UTC on 13 June 2008. The

Another issue of the comparison is the contribution of mean wind shear to the measured spectrum width. For the estimation of EDR, the contribution of wind shear has to be extracted from the radar measured spectrum width. But for the comparison with aircraft measured EDR or even turbulence alert for aviation safety, wind shear might not need to be removed. For example if the aircraft experiences a sharp change in altitude, this may not be caused by isotropic turbulence but it is a measure of aircraft response to vertical shear of mean wind. As such, the aircraft estimated EDR based on vertical velocity may be slightly higher. Pilots and passengers in aircraft may also experience severe "turbulence", which is a

Scatterplots of median and maximum EDR along the 5 nm of flight paths estimated by aircraft and radar are shown in Figure 11. Two plots for each are shown; one in which mean wind shear contributions to the observed spectrum widths are removed and a second plot in

mountainous Lantau Island is located to the south of the radar scans.

combination of the effects of both turbulence and wind shear.

On the other hand, there are two small regions near the radar at the range of 6 km where EDR is also high. But in this region there is relatively strong horizontal shear of the radial wind component (Figure 9(f); green color identifies the wind has a component toward the radar and red color indicates wind is away from the radar). Furthermore, the reflectivity is about 10 dBZ and SNR is around 35 dB. Because this region is on the downwind side of Lantau Island, the ambient flow (green in Figure 9(f)) is blocked by the Island and back flow (red in Figure 9(f)) is induced. The wind shear contributions, computed using Eq. (8), have been removed from the calculation EDR presented in Figure 9(a). Thus the EDR should not be biased by strong shear of mean radial wind. Thunderstorm outflow may be another reason for the severe turbulence in this region. Because there is no strong horizontal shear of the Doppler velocity field in the region 270o and 25 km, we conclude that the large EDRs presented in in that region of Figure 9(a) are unrealistic. After a threshold SNR> 20 dB is applied, it can be seen that these large EDR values are removed (Figure 9(b)).

Using the Hong Kong TDWR observations in 2006 and 2008, many EDR maps were produced and examined. Here wind shear contribution has been removed from spectrum width measurements. Here the mean wind shears in horizontal and vertical directions are calculated by using mean radial velocity field smoothed by a 9 points median filter along the radar beam in the Eq.(8). Figure 10 shows two typical EDR maps during light rain at 21:32 UTC on 27 April 2006 (Figure 10(a)) and during a thunderstorm at 13:17 UTC on 13 June 2008 (Figure 10(b)). For most of the scanned area, EDR is low and turbulence is classified as insignificant or light (green and light blue). Small pockets of moderate and severe turbulence (yellow and red) are scattered in the scanned area. Near the Lantau Island, moderate and severe levels of turbulence are frequently observed in the cases we studied. The blockage of the Island on the ambient flow may be a reason for the occurrence of the turbulent airflow. Based on the numerical simulations, Clark et al. (1997) and Chan (2009) found that mechanical effect of a mountainous island is a source of the generation of the turbulence.

Clear air cases have been investigated as well, but we found that SNR of the Hong Kong TDWR is too low to provide reliable and meaningful EDR maps.

After the EDR maps were generated, EDR profiles along the flight paths can be compared with aircraft measured EDR. A total of 14 cases are selected to make the comparison. The aircraft EDRs are estimated based on the vertical wind measured by aircraft (Cornman et al., 2004).

Radar derived EDR profile is constructed by selecting the EDR in a resolution volume V6 closest to the flight path and at an elevation angle of 0.6o. There are still differences in the measurement heights between the aircraft and the radar beam for these two EDR datasets. Only a part of the flight path is covered by the radar beam. For example, aircraft approaching runway 25RA is in the radar beam only at the distance between 0.5 and 1.5 nm from the end of runway. From this point of view, EDRs estimated by aircraft and the radar would be compared within this distance interval. It should also be mentioned that radar estimated EDR is based on the spectrum width of the Doppler velocity, i.e. velocity in the radial direction along a radar beam. On the other hand, the aircraft estimated EDR is based on the vertical wind. As such, the two EDR datasets are derived from different components of the wind. Put aside errors in measurement, in order to have agreement turbulence must be isotropic.

Doppler Radar Observations – 88 Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications

On the other hand, there are two small regions near the radar at the range of 6 km where EDR is also high. But in this region there is relatively strong horizontal shear of the radial wind component (Figure 9(f); green color identifies the wind has a component toward the radar and red color indicates wind is away from the radar). Furthermore, the reflectivity is about 10 dBZ and SNR is around 35 dB. Because this region is on the downwind side of Lantau Island, the ambient flow (green in Figure 9(f)) is blocked by the Island and back flow (red in Figure 9(f)) is induced. The wind shear contributions, computed using Eq. (8), have been removed from the calculation EDR presented in Figure 9(a). Thus the EDR should not be biased by strong shear of mean radial wind. Thunderstorm outflow may be another reason for the severe turbulence in this region. Because there is no strong horizontal shear of the Doppler velocity field in the region 270o and 25 km, we conclude that the large EDRs presented in in that region of Figure 9(a) are unrealistic. After a threshold SNR> 20 dB is

Using the Hong Kong TDWR observations in 2006 and 2008, many EDR maps were produced and examined. Here wind shear contribution has been removed from spectrum width measurements. Here the mean wind shears in horizontal and vertical directions are calculated by using mean radial velocity field smoothed by a 9 points median filter along the radar beam in the Eq.(8). Figure 10 shows two typical EDR maps during light rain at 21:32 UTC on 27 April 2006 (Figure 10(a)) and during a thunderstorm at 13:17 UTC on 13 June 2008 (Figure 10(b)). For most of the scanned area, EDR is low and turbulence is classified as insignificant or light (green and light blue). Small pockets of moderate and severe turbulence (yellow and red) are scattered in the scanned area. Near the Lantau Island, moderate and severe levels of turbulence are frequently observed in the cases we studied. The blockage of the Island on the ambient flow may be a reason for the occurrence of the turbulent airflow. Based on the numerical simulations, Clark et al. (1997) and Chan (2009) found that mechanical effect of a mountainous island is a source of the generation of the

Clear air cases have been investigated as well, but we found that SNR of the Hong Kong

After the EDR maps were generated, EDR profiles along the flight paths can be compared with aircraft measured EDR. A total of 14 cases are selected to make the comparison. The aircraft EDRs are estimated based on the vertical wind measured by aircraft (Cornman et al., 2004).

Radar derived EDR profile is constructed by selecting the EDR in a resolution volume V6 closest to the flight path and at an elevation angle of 0.6o. There are still differences in the measurement heights between the aircraft and the radar beam for these two EDR datasets. Only a part of the flight path is covered by the radar beam. For example, aircraft approaching runway 25RA is in the radar beam only at the distance between 0.5 and 1.5 nm from the end of runway. From this point of view, EDRs estimated by aircraft and the radar would be compared within this distance interval. It should also be mentioned that radar estimated EDR is based on the spectrum width of the Doppler velocity, i.e. velocity in the radial direction along a radar beam. On the other hand, the aircraft estimated EDR is based on the vertical wind. As such, the two EDR datasets are derived from different components of the wind. Put aside errors in measurement, in order to have agreement turbulence must

TDWR is too low to provide reliable and meaningful EDR maps.

applied, it can be seen that these large EDR values are removed (Figure 9(b)).

turbulence.

be isotropic.

Fig. 10. EDR maps (a) at 21:32 UTC on 27 April 2006 and (b) 13:17 UTC on 13 June 2008. The mountainous Lantau Island is located to the south of the radar scans.

Another issue of the comparison is the contribution of mean wind shear to the measured spectrum width. For the estimation of EDR, the contribution of wind shear has to be extracted from the radar measured spectrum width. But for the comparison with aircraft measured EDR or even turbulence alert for aviation safety, wind shear might not need to be removed. For example if the aircraft experiences a sharp change in altitude, this may not be caused by isotropic turbulence but it is a measure of aircraft response to vertical shear of mean wind. As such, the aircraft estimated EDR based on vertical velocity may be slightly higher. Pilots and passengers in aircraft may also experience severe "turbulence", which is a combination of the effects of both turbulence and wind shear.

Scatterplots of median and maximum EDR along the 5 nm of flight paths estimated by aircraft and radar are shown in Figure 11. Two plots for each are shown; one in which mean wind shear contributions to the observed spectrum widths are removed and a second plot in

Aviation Applications of Doppler Radars in the Alerting of Windshear and Turbulence 91

turbulence only occurs at one radar gate at the distance of 0 nm, closest to the end of the runway. It is noted that at this location, the radar beam is higher than the flight path by

We have also compared aircraft and radar estimated EDR profiles including wind shear contribution along the aircraft flight path. For this case, aircraft B777 flew through a storm

Figure 12 shows the EDR estimated by aircraft and the radar along the flight path 25RA around 13:05 UTC on 19 April 2008. It is one of the two cases in which severe turbulence was encountered by the aircraft. Blue dots in Figure 13 represent the EDR estimated by the aircraft as it was landing at HKIA. Three peaks over 0.5 2/3 *m s* / , classified as severe turbulence, are recorded at distance of 0.77, 3.65, and 4.90 nm away from the runway end. EDR profiles estimated by using radar data at an elevation angle of 0.6o with the wind shear contribution included in the volume scans around 13:05 UTC are overlaid onto the aircraft estimated EDR in Figure 13. The radar estimated EDR profiles at 13:01, 13:05, and 13:09 UTC (brown dots, red squares, and green dots in Figure 13) matches well with aircraft EDR between distance of 0.5 and 1.5 nm, shaded in green color in Figure 13, where the aircraft was in a region common to the 0.6o radar beam. It means that radar and aircraft were

with maximum reflectivity of 42 dBZ and landed in clouds and light rain at HKIA.

measuring turbulence in approximately the same region at nearly the same time.

Fig. 12. (a) EDR, (b) spectrum width, (c) reflectivity factor, and (d) Doppler velocity at

The peaks of these 3 EDR profiles at 13:01, 13:05, and 13:09 UTC are in the green shaded interval and the maximum value is 0.48 2/3 *m s* / , just slightly smaller than 0.5 2/3 *m s* / . In order to find if there are higher EDR near the flight time (13:05 UTC), we examined the EDR for the two scans one minute before and after the passage of the aircraft at 13:05 UTC in the same volume scan at 13:05 UTC. The profiles are shown with light and dark purple dots in Figure 13. High EDRs with values of 0.69 and 0.76 2/3 *m s* / are found within the shaded

elevation angle of 0.6o at 13:05 UTC on 19 April 2008. Range ring is at 10 km.

about 160m.

which mean wind shear contribution has been retained. All median EDRs are smaller than 0.4 2/3 *m s* / (i.e., moderate or light turbulence). 13 of 14 median EDRs indicate turbulences are light. Based on maximum EDRs, two severe turbulent patches (EDR > 0.5 2/3 *m s* / ) are detected by both aircraft and radar with wind shear, but they are not on the same flight paths. With wind shear contribution, median and maximum radar EDRs evidently increase.

Fig. 11. Scatterplots of median and maximum EDR estimated by aircraft and radar along the 5 nm of flight paths for the selected 14 cases.

Comparing maximum intensity between aircraft and radar without wind shear, 8 of the 14 cases are in the same category. Seven of them are moderate turbulence. For 4 aircraft estimated light turbulence cases, the radar tends to overestimate them as moderate (3 cases) and severe (1 case) with wind shear contribution. After closer examination of the overestimation case at 07:17 UTC on 25 June 2008, it is found that the maximum severe Doppler Radar Observations – 90 Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications

which mean wind shear contribution has been retained. All median EDRs are smaller than 0.4 2/3 *m s* / (i.e., moderate or light turbulence). 13 of 14 median EDRs indicate turbulences are light. Based on maximum EDRs, two severe turbulent patches (EDR > 0.5 2/3 *m s* / ) are detected by both aircraft and radar with wind shear, but they are not on the same flight paths. With wind shear contribution, median and maximum radar EDRs evidently increase.

Fig. 11. Scatterplots of median and maximum EDR estimated by aircraft and radar along the

Comparing maximum intensity between aircraft and radar without wind shear, 8 of the 14 cases are in the same category. Seven of them are moderate turbulence. For 4 aircraft estimated light turbulence cases, the radar tends to overestimate them as moderate (3 cases) and severe (1 case) with wind shear contribution. After closer examination of the overestimation case at 07:17 UTC on 25 June 2008, it is found that the maximum severe

5 nm of flight paths for the selected 14 cases.

turbulence only occurs at one radar gate at the distance of 0 nm, closest to the end of the runway. It is noted that at this location, the radar beam is higher than the flight path by about 160m.

We have also compared aircraft and radar estimated EDR profiles including wind shear contribution along the aircraft flight path. For this case, aircraft B777 flew through a storm with maximum reflectivity of 42 dBZ and landed in clouds and light rain at HKIA.

Figure 12 shows the EDR estimated by aircraft and the radar along the flight path 25RA around 13:05 UTC on 19 April 2008. It is one of the two cases in which severe turbulence was encountered by the aircraft. Blue dots in Figure 13 represent the EDR estimated by the aircraft as it was landing at HKIA. Three peaks over 0.5 2/3 *m s* / , classified as severe turbulence, are recorded at distance of 0.77, 3.65, and 4.90 nm away from the runway end. EDR profiles estimated by using radar data at an elevation angle of 0.6o with the wind shear contribution included in the volume scans around 13:05 UTC are overlaid onto the aircraft estimated EDR in Figure 13. The radar estimated EDR profiles at 13:01, 13:05, and 13:09 UTC (brown dots, red squares, and green dots in Figure 13) matches well with aircraft EDR between distance of 0.5 and 1.5 nm, shaded in green color in Figure 13, where the aircraft was in a region common to the 0.6o radar beam. It means that radar and aircraft were measuring turbulence in approximately the same region at nearly the same time.

Fig. 12. (a) EDR, (b) spectrum width, (c) reflectivity factor, and (d) Doppler velocity at elevation angle of 0.6o at 13:05 UTC on 19 April 2008. Range ring is at 10 km.

The peaks of these 3 EDR profiles at 13:01, 13:05, and 13:09 UTC are in the green shaded interval and the maximum value is 0.48 2/3 *m s* / , just slightly smaller than 0.5 2/3 *m s* / . In order to find if there are higher EDR near the flight time (13:05 UTC), we examined the EDR for the two scans one minute before and after the passage of the aircraft at 13:05 UTC in the same volume scan at 13:05 UTC. The profiles are shown with light and dark purple dots in Figure 13. High EDRs with values of 0.69 and 0.76 2/3 *m s* / are found within the shaded

Aviation Applications of Doppler Radars in the Alerting of Windshear and Turbulence 93

This chapter discusses the aviation applications of TDWR. This radar issues microburst alerts which are crucial in the assurance of aviation safety. A typical case of microburst detection by TDWR in association with intense thunderstorms is described first in this chapter. Then the applications of TDWR in the alerting of windshear and turbulence are described. Windshear is alerted through the calculation of windshear hazard factor, which is a rather well established technology. On the other hand, the use of spectrum width data from the radar in the alerting of turbulence has a relatively shorter development history,

Study is underway in Hong Kong to use X-band radar in the alerting of windshear and turbulence on experimental basis at the Hong Kong International Airport. The use of longrange S band radar in the alerting of turbulence for enroute aircraft is also under study.

Brewster, K.A. and D.S. Zrnic, 1986: Comparison of eddy dissipation rate from spatial

Chan, P.W., 2009: Atmospheric turbulence in complex terrain: verifying numerical model

Clark, T.L., T. Keller, J. Coen, P. Neilley, H. Hsu, and W.D. Hall, 1997: Terrain-induced

Cornman, L. B., G. Meymaris, and M. Limber, 2004: An update on the FAA Aviation

Doviak, R. J., and D. S. Zrnic, 2006: Doppler radar and weather observations. Dover

Fang, M., R.J. Doviak, and Melnikov, 2004: Spectrum width measured by the WSR-88D

Gilbert, D., L.B. Cornman, A.R. Rodi, R.G. Frechlich, and R.K. Goodrich, 2004: Calculating

Haverdings, H., 2000: Updated specification of the WINDGRAD algorithm, NLR TR-2000-

spectra of Doppler velocities and Doppler spectrum widths. J. Atmos. Oceanic

results with observations by remote-sensing instruments. Meteorology and

turbulence over Lantau Island: 7 June 1994 Tropical Storm Russ case study. J.

Weather Research Program's in situ turbulence measurement and reporting system. Preprints, Eleventh Conf. on Aviation, Range, and Aerospace Meteorology,

Publications Inc., Mineola, New York, 562 pp. (except for the preface with links to online errata and supplements, this is an exact copy of the 1st and 2nd printing of

radar: Error sources and statistics of various weather phenomena. J. Atmos.

EDR from aircraft wind data during flight in and out of Juneau AK: Techniques and challenges associated with non-straight and level flight patterns. Preprints, 11th Conf. on Aviation, Range and Aerospace Meteorology. Hyannis, MA, Amer.

and the technology is under exploration in Hong Kong.

Atmospheric Physics, 103, 145–157.

Hyannis, MA, Amer. Meteor. Soc., P4.3.

the 1993 Academic Press edition).

Oceanic Technol., 21, 888-904.

Meteor. Soc., CD-ROM, 4.4.

63, National Aerospace Laboratory, 2000.

Atmos. Sci., 54, 1795-1814.

Such progress of these studies would be reported in the future.

**5. Conclusion**

**6. References**

Technol., 3, 440-452.

interval. This convinces us that the EDR peak is not caused by random error of radar measurements.

Fig. 13. EDR along the flight path estimated by the aircraft B777 (blue dots) at 13:05 UTC and by the TDWR radar at the time indicated in the legend on 19 April 2008. X axis is the distance between aircraft and the end of runway. The distance interval shaded by the green color indicates where the aircraft passes through the altitude interval observed with the 0.6o elevated beam.

It raises another question: the aircraft may contaminate the radar measurements of the atmospheric status, since the aircraft disturbs the atmosphere and changes the original atmospheric condition in the measurement region as it flies by. In addition, aircraft itself as a target embedded in other scatterers, such as raindrops, may contaminate the spectrum width measurements as well. Both of the two factors could affect spectrum width and EDR value.

It could also be seen that the radar EDR profiles do not match the two aircraft estimated EDR peaks at the distance of 3.65 and 4.90 nm. It might be caused by the spatial difference between the aircraft and the radar beams. The flight heights at the distance of 3.65 and 4.90 nm are higher than the radar beams by about 260 m and 400 m respectively.

Wind shear contribution to spectrum width measurement for this case has been examined. After removing wind shear contribution, the EDR peak at the distance of 0.69 nm is reduced from 0.48 to 0.46 2/3 *m s* / (not shown) at 13:05 UTC. It means that wind shear contribution is small in this region. Because wind shear of the large scale mean wind should be persistent over the 4 minute for entire volume scan, the EDR peaks without wind shear contribution at 13:04 and 13:06 UTC at the distance of 0.69 nm are reduced to 0.67 and 0.74 2/3 *m s* / respectively. It indicates severe turbulence that is matched with aircraft estimate at 13:05 UTC.

Note that the aircraft estimated EDR is considered as ground truth in the above analysis, but it also contains errors and requires significant QC effort, especially as airplane is climbing or descending (Gilbert et al., 2004).

#### **5. Conclusion**

Doppler Radar Observations – 92 Weather Radar, Wind Profiler, Ionospheric Radar, and Other Advanced Applications

interval. This convinces us that the EDR peak is not caused by random error of radar

Fig. 13. EDR along the flight path estimated by the aircraft B777 (blue dots) at 13:05 UTC and

It raises another question: the aircraft may contaminate the radar measurements of the atmospheric status, since the aircraft disturbs the atmosphere and changes the original atmospheric condition in the measurement region as it flies by. In addition, aircraft itself as a target embedded in other scatterers, such as raindrops, may contaminate the spectrum width measurements as well. Both of the two factors could affect spectrum width and EDR

It could also be seen that the radar EDR profiles do not match the two aircraft estimated EDR peaks at the distance of 3.65 and 4.90 nm. It might be caused by the spatial difference between the aircraft and the radar beams. The flight heights at the distance of 3.65 and 4.90

Wind shear contribution to spectrum width measurement for this case has been examined. After removing wind shear contribution, the EDR peak at the distance of 0.69 nm is reduced from 0.48 to 0.46 2/3 *m s* / (not shown) at 13:05 UTC. It means that wind shear contribution is small in this region. Because wind shear of the large scale mean wind should be persistent over the 4 minute for entire volume scan, the EDR peaks without wind shear contribution at 13:04 and 13:06 UTC at the distance of 0.69 nm are reduced to 0.67 and 0.74 2/3 *m s* / respectively. It indicates severe turbulence that is matched with aircraft estimate at 13:05

Note that the aircraft estimated EDR is considered as ground truth in the above analysis, but it also contains errors and requires significant QC effort, especially as airplane is climbing or

nm are higher than the radar beams by about 260 m and 400 m respectively.

by the TDWR radar at the time indicated in the legend on 19 April 2008. X axis is the distance between aircraft and the end of runway. The distance interval shaded by the green color indicates where the aircraft passes through the altitude interval observed with the 0.6o

measurements.

elevated beam.

value.

UTC.

descending (Gilbert et al., 2004).

This chapter discusses the aviation applications of TDWR. This radar issues microburst alerts which are crucial in the assurance of aviation safety. A typical case of microburst detection by TDWR in association with intense thunderstorms is described first in this chapter. Then the applications of TDWR in the alerting of windshear and turbulence are described. Windshear is alerted through the calculation of windshear hazard factor, which is a rather well established technology. On the other hand, the use of spectrum width data from the radar in the alerting of turbulence has a relatively shorter development history, and the technology is under exploration in Hong Kong.

Study is underway in Hong Kong to use X-band radar in the alerting of windshear and turbulence on experimental basis at the Hong Kong International Airport. The use of longrange S band radar in the alerting of turbulence for enroute aircraft is also under study. Such progress of these studies would be reported in the future.

#### **6. References**

**Part 2**

**Precipitation Estimation and Nowcasting**


*1.1. LED Lights in Closed Plant Production Systems*

Agriculture in 2050 will have to produce almost 50% more output to meet the demand for food supplies, presenting it with a crucial challenge in meeting the increase in demand [1]. Technological development and innovation can offer alternatives to ensure food security sustainably. The use of closed growth environments, such as greenhouses, plant factories, and vertical farms [2–6], represents a sustainable alternative for fresh food production. In closed plant production systems (CPPSs), several variables can be controlled and optimized, such as water, fertilizers, CO2 injection, and temperature, as well as the quantity and quality of light thus ensuring minimum greenhouse gas emissions [3]. CPPSs allow growing of any plant variety, no matter the season of the year. Artificial lighting plays an essential role in CPPSs, as it promotes growth by providing optimal conditions for

**Citation:** Montes Rivera, M.; Escalante-Garcia, N.; Dena-Aguilar, J.A.; Olvera-Gonzalez, E.; Vacas-Jacques, P. Feature Selection to Predict LED Light Energy Consumption with Specific Light Recipes in Closed Plant Production Systems. *Appl. Sci.* **2022**, *12*, 5901. https://doi.org/10.3390/ app12125901

Academic Editors: Luis Hernández-Callejo, Sergio Nesmachnow and Sara Gallardo Saavedra

Received: 3 May 2022 Accepted: 26 May 2022 Published: 9 June 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

plant development. LEDs are energy-efficient replacements that contribute to plant growth in agriculture. An outstanding advantage of LED lamps is their ability to operate with specific wavelengths (λ) that considerably reduce energy consumption. LEDs regularly generate continuous light. Likewise, they can radiate pulsed light (on/off in microseconds (μs)) with high power and low energy consumption at a specific frequency and duty cycle without upsetting the vegetative development of plants [4,7,8]. LED technology can produce different colors of light—that is, different qualities—called light recipes (different wavelength combinations). The wavelength combinations (red, blue, green, ultraviolet, and infrared) and the photosynthetic photon flux density (PPFD, given in μmol m−2 s−1) are the components that constitute the light recipes. Light recipes impact crop growth from branching to flowering; optimize the biomass; and increase the antioxidant capacity levels of calcium, potassium, magnesium, chlorophyll, iron, vitamins A, B, and E, and other substances [7–9]. Crop quality and productivity rely upon the time and the light quantity supplied to the plants.

CPPSs can offer several advantages (improved management control of all variables involved—temperature, CO2, radiation—and increased productivity, growth, and yield) and generate an impact on humanity. Nevertheless, it is a model with a high demand for electricity for the artificial radiation systems needed to enhance the developing plants. Environmental control (refrigeration), the air required to remove the heat produced, and artificial lighting account for approximately 32%, 11%, and 57% of the total energy demand, respectively [10]. Furthermore, according to Avgoustaki and Xydis [11], the artificial lighting system accounts for 80% of the electrical demand, since the overall operability of the CPPS accounts for 40% of the total energy consumption.

Innovative approaches, such as fluid dynamics, evolutionary algorithms [12,13], the derivative integral model, and derivative model [14–16], control the resources in CPPSs. Artificial neural networks predict weather conditions and energy consumption [13–15,17]. Other techniques predict energy consumption performance for plant production [18,19]. Finally, other techniques focus on in the optimization of resources and reducing energy demand in CPPSs [20,21].


2.2.4. Decision tree (DT) classifier

Pr Yj=di

124 Machine Learning - Advanced Techniques and Emerging Applications

2.2.3. Support vector machine (SVM) classifier

subject to dið Þ w Yi þ b ≥ 1 i ¼ 1, 2, …, M:

i¼1 αi di Yi and PM

i¼1 αidi Yi Yj

where μj

[13].

function as

w ¼ PM

b = dj � PM

function

probability.

� � ¼ 1

min w, b

L wð Þ¼ ; b; α k k w

zation problem that describes the hyper-plane can be written as

1 2 X M

0 @

i¼1

X M

j¼1

From expression (22), we can assess α and compute w using w ¼ PM

class Yð Þ¼ x sign X

minα

k k w 2 2 !

> 2 2 �X M

σj ffiffiffiffiffiffi 2π p e

� Y�μ ð Þj

2σj , Y1 < Y < Yk, σj > 0,

, where wk k2 ¼ wT w (20)

αið Þ dið Þ� w Yi þ b 1 , αi ≥ 0 (21)

A, αj ≥ 0 (22)

i¼1 αi di Yi. Then by

(23)

, σj are mean and variance of the set Y1;Y2; …;Yk ½ �: Eq. (19) means that Naïve Bayes

classifier will label the new Yx with the class label di that achieves the highest posterior

For a given training set of pairs Yi ð Þ ; di , i ¼ 1, 2…M , where Yi ∈R , and di ∈ ð Þ þ1; �1 , the minimum weight w and a constant b that maximize the margin between the positive and negative class (i.e., w Yi þ b ¼ �1 ) with respect to the hyper-plane equation w Yi þ b ¼ 0 can be estimated using support vector machine classifier by performing the following optimization

The solution of this quadratic optimization problem can be expressed using Lagrangian

where α ¼ ð Þ α1; α2;…; αM is the Lagrangian multipliers. IF we let L wð Þ¼ ; b; α 0 , we can get

i¼1 αidi ¼ 0 , and by substituting them into Eq. (21), the dual optimi-

M

1

i¼1 αj

� �, we classify the new instance Yx using the following classification

αjdjð Þþ Yi Yx b !

i¼1

didj Yi Yj

choosing αi > 0, from the vector of α ¼ ð Þ α1; α2;…; αM and calculating b from

M

i¼1

� �αiαj �X

For the training of a set of pairs of sensing decision Yi ð Þ ; di , i ¼ 1, 2, …, M , di ∈ ð Þ �1; 1 , the decision tree classifier creates a binary tree based on either impurity or node error splitting rule in order to split the training set into separate subset. Then, it repeats the splitting rule recursively for each subset until the leaf of the subset becomes pure. After that, it minimizes the error in each leaf by taking the majority vote of the training set in that leaf [14]. For classifying a new example Yx, DT classifier selects the leaf where the new Yx falls in and classifies the new Yx with the class label that occurs most frequently among that leaf.


4. Numerical analysis and discussions

### 4.1. Point-to-point QKD network

responsivity and noise equivalent power (NEP) of the receiver at 1550 nm are 4 K.V/W, 0.8 A/ W and 22 pW/pHz, respectively. For our analysis, we have kept the high-power laser at the receiver, that is, integral part of Bob in order to avoid any eavesdropping or hacking on the reference signal. That is why, it is termed as local local oscillator (LLO). The LLO photon level is considered as 1 108 photon per pulse. A phase noise cancelation (PNC) [25] based algorithm is implemented to minimize the excess noise as shown in Figure 2(c). The PNC stage has two square operators for in phase and quadrature operators of the light signal, and a digital DC cancelation stage, assisted by a down-converter. The comprehensive implementa-

As a first step, we investigated the coherent receiver to detect the m-PSK signals as it is well known that the specific modulation formats require a very particular optical signal-to-noise ratio (OSNR) in order to be detected at a bit error rate (BER) threshold. After the modulation stage, the 4-PSK and 8-PSK signals, back-to-back signals are detected at the coherent receiver and normalized signal-to-noise ratio (Eb/N0, the energy per bit to noise power spectral density ratio) is plotted against BER. The results are plotted in Figure 3(a). The BER threshold is set to be 3.8 103 (Q-factor of ≈ 8.6 dB), corresponding to a 7% overhead, that is, hard-decision forward error correction (HD-FEC). While soft-decision FEC (SD-FEC) level of BER 2.1 102 (Q-factor of ≈ 6.6 dB) can also be used corresponding to 20% overheard. From the results, we can depict that the minimum of 10 dB and 6 dB Eb/N0 values is required for the 8-PSK and 4- PSK signals at HD-FEC. While this limit can further be reduced to smaller values but at the cost

We have summarized the ADC requirements to detect the m-PSK signals. The results are as shown in Figure 3(b). The ADC resolution (bits) is analyzed with respect to the SNR penalty for 1- and 4 GBaud m-PSK modulated signals. From the results, it is clear that 6–8 bit ADC can be installed in the network to recover the noisy m-PSK signals at diverse baud rates while keeping the SNR penalty ≈ 1 dB. Despite the well-known fact that high-resolution ADC can

tion of the PNC module is described in Section 2.2 [25].

3.4. Characterization of coherent receiver

Table 2. Physical characteristics of the fiber at 1550 nm.

28 Telecommunication Networks - Trends and Developments

of 20% overheard in data rates, that is, SD-FEC.

Since the noise equivalent power (NEP) determines electronic noise of the coherent receiver and digital post processing unit, it is important to choose a TIA and ADC with lower NEP values for low aggregate electronic noise to shot noise ratio (ESR). Furthermore, as the NEP of the TIA is amplified by the TIA itself (gain amplifiers), it governs the total electronic noise. However, the ESR negligibly changes as the bandwidth of the detector is increased. This is because of the fact that both electronic and shot noise variances linearly increase with the bandwidth, so it is advantageous to use the receivers having 1–20 GHz bandwidth. Since, 20 GHz receivers are easily commercially available so we have modeled them for our investigations. Furthermore, the quantum channel includes the standard SMF and VOA to model the channel loss. The variance of the excess noise is largely due to the bias fluctuation of the I/Q modulator and timing jitter of the Bob, that is, receiver modules. It is estimated that the excess noise can be limited to be as small as 0.01 [25] below the zero key rate threshold. After optimizing the transmission model: (1) the corresponding power is approximately 70 dBm (approximately 7.8 106 photons per pulse) [26], (2) the detector efficiency is 60% and (3) reconciliation efficiency is 95%.

spaced system shows loss in SKR due to the impact of intersymbol interference between the

Recent Progress in the Quantum-to-the-Home Networks http://dx.doi.org/10.5772/intechopen.80396 31

The best available resource to mitigate the artifacts from the low-quality signal is to use raisedcosine filters for the pulse shaping at the transmitter. We can infer from the analysis that the quantum signals are compatible and ideal with classical optical add-drop multiplexers (OADMs) but the insertion loss from these modules can impact the SKR. A comparison of distance and secure key generation rate between CV-QKD using 20 GHz receiver and stateof-the-art DV-QKD systems based on T12 protocol [27, 28] is shown in Figure 5. The transmission distance of CV-QKD systems is limited than for DV-QKD demonstrations. However, comparative analysis of DV-QKD and CV-QKD shows that CV-QKD has the required performance to offer higher speed secure key transmission within an access network area (100 m to 50 km). Especially from 0 to 20 km range, that is, typical FTTH network, the SKR generated by using the traditional telecommunication components are 10s of magnitude higher than that of

Most of the efforts on the QKD system design and experimental demonstrations are limited to laboratory environments and point-to-point transmissions. While actual FTTH networks have in-line optical devices including but not limited to routers, switches, passive splitters, adddrop multiplexers, erbium-doped fiber amplifiers (EDFA), as envisioned in Figure 6(a). This restricts the deployment of QKD networks along with the classical data channels. However, in this chapter, we have investigated the compatibility of optical network components and their impact on the secure key rates. We have emulated the scenario of a typical quantum access

Figure 5. Performance comparison of CV-QKD vs. DV-QKD for access and metro networks.

adjacent neighboring channels.

DV systems.

4.2. QTTH network

network as shown in Figure 6(b).

Based on the abovementioned values, we extended our studies to calculate the secure key rates (SKR) at different transmission distances, that is, transmittance values. The input power is the same for every evaluation. Furthermore, SKR for both the 4-PSK and 8-PSK modulation formats under collective attack [22] are shown in Figure 4(a). The maximum of 100 Mbits/s SKR can be attained with this simple configuration by using the commercially available modules for transmittance (T) =1 for 4-PSK modulation. While SKR of 25 Mbits/s and 1 Mbit/s at T = 0.8 and 0.6, respectively. From the results, it can also be concluded that the maximum transmission distance for CV-QKD-based network is 60 km. Hence, it is recommended that this QKD protocol can effectively be used for access network, that is, QTTH. We have also investigated the performance of 8-PSK modulation and the results are plotted in Figure 4(a). The transmission performance is affected as compared to 4-PSK modulation, and this is due to the PNC algorithm that is executed to post-process the received quantum signal. This concept of generating seamless quantum keys can further be improved for multichannel networks that will help to generate high aggregate SKR via diversely multiplexing the neighboring quantum channels either by time, space, wavelength or polarization. In this chapter, we have multiplexed 12 WDM quantum channels to generate the aggregate SKR with the minimum channel spacing of 25- and 50 GHz. The WDM-QKD results, based on 4-PSK modulation, are shown as in Figure 4(b).

The results depict that the classical multiplexing techniques can efficiently be used to multiplex quantum signals without any degradation in the SKR. We have multiplexed the signals by using 25- and 50 GHz channel spacing. Also, aggregating secure key rate can reach upto 1.2 Gbits/s for a 12 WDM quantum system at T = 1. The importance of these results are due to the fact that next-generation PON services are already aiming at Gbits/s data rates, so QKD can match the data rates. The 50 GHz channel spaced system depicts insignificant performance degradation as compared to single wavelength transmission. However, the 25 GHz channel

Figure 4. Calculated QKD with respect to transmission distance for: (a) 4-PSK and 8-PSK modulation and (b) single channel (1-Ch) 4-PSK modulation, 12 channel WDM 4-PSK modulation with 25 and 50 GHz channel spacing. (Note: Simulations are performed by assuming 60% detector efficiency and 95% reconciliation efficiency).

spaced system shows loss in SKR due to the impact of intersymbol interference between the adjacent neighboring channels.

The best available resource to mitigate the artifacts from the low-quality signal is to use raisedcosine filters for the pulse shaping at the transmitter. We can infer from the analysis that the quantum signals are compatible and ideal with classical optical add-drop multiplexers (OADMs) but the insertion loss from these modules can impact the SKR. A comparison of distance and secure key generation rate between CV-QKD using 20 GHz receiver and stateof-the-art DV-QKD systems based on T12 protocol [27, 28] is shown in Figure 5. The transmission distance of CV-QKD systems is limited than for DV-QKD demonstrations. However, comparative analysis of DV-QKD and CV-QKD shows that CV-QKD has the required performance to offer higher speed secure key transmission within an access network area (100 m to 50 km). Especially from 0 to 20 km range, that is, typical FTTH network, the SKR generated by using the traditional telecommunication components are 10s of magnitude higher than that of DV systems.

## 4.2. QTTH network

optimizing the transmission model: (1) the corresponding power is approximately 70 dBm (approximately 7.8 106 photons per pulse) [26], (2) the detector efficiency is 60% and (3)

Based on the abovementioned values, we extended our studies to calculate the secure key rates (SKR) at different transmission distances, that is, transmittance values. The input power is the same for every evaluation. Furthermore, SKR for both the 4-PSK and 8-PSK modulation formats under collective attack [22] are shown in Figure 4(a). The maximum of 100 Mbits/s SKR can be attained with this simple configuration by using the commercially available modules for transmittance (T) =1 for 4-PSK modulation. While SKR of 25 Mbits/s and 1 Mbit/s at T = 0.8 and 0.6, respectively. From the results, it can also be concluded that the maximum transmission distance for CV-QKD-based network is 60 km. Hence, it is recommended that this QKD protocol can effectively be used for access network, that is, QTTH. We have also investigated the performance of 8-PSK modulation and the results are plotted in Figure 4(a). The transmission performance is affected as compared to 4-PSK modulation, and this is due to the PNC algorithm that is executed to post-process the received quantum signal. This concept of generating seamless quantum keys can further be improved for multichannel networks that will help to generate high aggregate SKR via diversely multiplexing the neighboring quantum channels either by time, space, wavelength or polarization. In this chapter, we have multiplexed 12 WDM quantum channels to generate the aggregate SKR with the minimum channel spacing of 25- and 50 GHz. The WDM-QKD results, based on 4-PSK modulation, are

The results depict that the classical multiplexing techniques can efficiently be used to multiplex quantum signals without any degradation in the SKR. We have multiplexed the signals by using 25- and 50 GHz channel spacing. Also, aggregating secure key rate can reach upto 1.2 Gbits/s for a 12 WDM quantum system at T = 1. The importance of these results are due to the fact that next-generation PON services are already aiming at Gbits/s data rates, so QKD can match the data rates. The 50 GHz channel spaced system depicts insignificant performance degradation as compared to single wavelength transmission. However, the 25 GHz channel

Figure 4. Calculated QKD with respect to transmission distance for: (a) 4-PSK and 8-PSK modulation and (b) single channel (1-Ch) 4-PSK modulation, 12 channel WDM 4-PSK modulation with 25 and 50 GHz channel spacing. (Note:

Simulations are performed by assuming 60% detector efficiency and 95% reconciliation efficiency).

reconciliation efficiency is 95%.

30 Telecommunication Networks - Trends and Developments

shown as in Figure 4(b).

Most of the efforts on the QKD system design and experimental demonstrations are limited to laboratory environments and point-to-point transmissions. While actual FTTH networks have in-line optical devices including but not limited to routers, switches, passive splitters, adddrop multiplexers, erbium-doped fiber amplifiers (EDFA), as envisioned in Figure 6(a). This restricts the deployment of QKD networks along with the classical data channels. However, in this chapter, we have investigated the compatibility of optical network components and their impact on the secure key rates. We have emulated the scenario of a typical quantum access network as shown in Figure 6(b).

Figure 5. Performance comparison of CV-QKD vs. DV-QKD for access and metro networks.

Figure 6. (a) Deployment of FTTH network with classical optical components; (b) downstream and upstream quantum access network and (c) hybrid classical-quantum traffic in access networks.

The optical line terminal (OLT) consists of a QKD transmitter, that is, in this chapter a m-PSK modulated transmitter is modeled. The optical distribution comprises as follows: (a) standard single mode fiber of 5 km length and (b) passive optical splitter with different split ratios. The commercially available splitters have insertion loss that is listed in Table 4.

4.3. Hybrid classical-quantum traffic in access networks

signals as broadband lasers are readily available commercially.

For the commercial compatibility of quantum signals with the existing optical networks, the wavelength and optimum power assignment to the signals are very much important. Different wavelength assignment [29–31] techniques have been investigated to avoid possible intersymbol interference between the classical and quantum signals. The best possible solution is to place the classical channels at 200 GHz channel spacing [31] in order to avoid any interference with the weakly powered quantum signals. Most importantly, we have implemented the concept of LLO, hence local oscillator signal is not generated from transmitter by using 90:10 coupler [18]. So apparently with LLO and 200 GHz channel spacing, there is no cross-talk among the hybrid classical quantum signals in the quantum channel. This is very much ideal for commercially available telecommunication components in the C-band (1530–1565 nm). Furthermore, with 200 GHz channel spacing, the classical channels can be encoded up to 400 Gbits/s line rate with advanced modulation formats, that is, dual-polarization m-QAM (m = 16, 32, 64 ….). But allimportant thing is, high data rate classical channels need sophisticated high bandwidth receivers that inherently have high electronic noise. For this reason, they are not suitable for quantum multiplexed signals as shown in Figure 6(c). As we are investigating a 20 GHz coherent receiver, so we have kept the data rate at 2.5 Gbits/s/polarization of quadrature phase-shift keying (QPSK) signals for classical data. The power of the classical data channels is optimized below 15 dBm. The quantum channel loss in this analysis corresponds to the 20 km of the optical fiber. The results for quantum signals at diverse wavelengths are depicted in Figure 8. The wavelength windows that are not occupied with the quantum channels are used for classical data transmission of QPSK signals. These signals are efficiently detected below the HD-FEC level. While the SKR of the quantum signals is 10 Mbits/s. We can conclude from the results the compatibility of quantum signals with the classical telecommunication components. Furthermore, L-band (1565– 1625 nm, extended DWDM band) can also be used to generate the hybrid classical-quantum

Recent Progress in the Quantum-to-the-Home Networks http://dx.doi.org/10.5772/intechopen.80396 33

Figure 7. Performance comparison of QTTH network with diverse passive split ratios as a function of achieved.

The variable splitting ratio is vital for the secure key rates as it will contribute to the attenuation and excess noise of the system. To test the simulation model under realistic conditions, we have also added 0.15 dB splicing loss for every connection with the passive optical splitter. The results are depicted in Figure 7 where we have plotted the SKR with respect to the splitting ratio of the system. It can be deduced from the graph that for a 1 2 splitting ratio, the SKR drops down to 10 Mbits/s per user while the SKR of 1 Mbits/s can be achieved with the splitting ratio of 1 4. Moreover, the classical telecommunication components can be used to design a seamless QTTH network and for short-range transmission as well as for data center applications it can perform better as compared to the much expensive DV-QKD systems [10].

Table 4. Summary of the average attenuation (dB) associated with the standard passive optical splitters.

Figure 7. Performance comparison of QTTH network with diverse passive split ratios as a function of achieved.

#### 4.3. Hybrid classical-quantum traffic in access networks

The optical line terminal (OLT) consists of a QKD transmitter, that is, in this chapter a m-PSK modulated transmitter is modeled. The optical distribution comprises as follows: (a) standard single mode fiber of 5 km length and (b) passive optical splitter with different split ratios. The

Figure 6. (a) Deployment of FTTH network with classical optical components; (b) downstream and upstream quantum

The variable splitting ratio is vital for the secure key rates as it will contribute to the attenuation and excess noise of the system. To test the simulation model under realistic conditions, we have also added 0.15 dB splicing loss for every connection with the passive optical splitter. The results are depicted in Figure 7 where we have plotted the SKR with respect to the splitting ratio of the system. It can be deduced from the graph that for a 1 2 splitting ratio, the SKR drops down to 10 Mbits/s per user while the SKR of 1 Mbits/s can be achieved with the splitting ratio of 1 4. Moreover, the classical telecommunication components can be used to design a seamless QTTH network and for short-range transmission as well as for data center applications it can perform better as compared to the much expensive DV-QKD systems [10].

commercially available splitters have insertion loss that is listed in Table 4.

Table 4. Summary of the average attenuation (dB) associated with the standard passive optical splitters.

access network and (c) hybrid classical-quantum traffic in access networks.

32 Telecommunication Networks - Trends and Developments

For the commercial compatibility of quantum signals with the existing optical networks, the wavelength and optimum power assignment to the signals are very much important. Different wavelength assignment [29–31] techniques have been investigated to avoid possible intersymbol interference between the classical and quantum signals. The best possible solution is to place the classical channels at 200 GHz channel spacing [31] in order to avoid any interference with the weakly powered quantum signals. Most importantly, we have implemented the concept of LLO, hence local oscillator signal is not generated from transmitter by using 90:10 coupler [18]. So apparently with LLO and 200 GHz channel spacing, there is no cross-talk among the hybrid classical quantum signals in the quantum channel. This is very much ideal for commercially available telecommunication components in the C-band (1530–1565 nm). Furthermore, with 200 GHz channel spacing, the classical channels can be encoded up to 400 Gbits/s line rate with advanced modulation formats, that is, dual-polarization m-QAM (m = 16, 32, 64 ….). But allimportant thing is, high data rate classical channels need sophisticated high bandwidth receivers that inherently have high electronic noise. For this reason, they are not suitable for quantum multiplexed signals as shown in Figure 6(c). As we are investigating a 20 GHz coherent receiver, so we have kept the data rate at 2.5 Gbits/s/polarization of quadrature phase-shift keying (QPSK) signals for classical data. The power of the classical data channels is optimized below 15 dBm. The quantum channel loss in this analysis corresponds to the 20 km of the optical fiber. The results for quantum signals at diverse wavelengths are depicted in Figure 8. The wavelength windows that are not occupied with the quantum channels are used for classical data transmission of QPSK signals. These signals are efficiently detected below the HD-FEC level. While the SKR of the quantum signals is 10 Mbits/s. We can conclude from the results the compatibility of quantum signals with the classical telecommunication components. Furthermore, L-band (1565– 1625 nm, extended DWDM band) can also be used to generate the hybrid classical-quantum signals as broadband lasers are readily available commercially.

frequency (RF) signals are modulated via an electro-optical I/Q modulator, where RF frequency is kept at 2 GHz. The modulation variance (VA) of the generated quantum signal is optimized by a tunable optical attenuator (TOA). As it is a hybrid classical quantum network, therefore classical 10 Gbits/s QPSK channels are multiplexed at 1531.2, 1571.4, 1591.1 and 1611.2 nm wavelengths. All the classical data channels are optimized at 0.5 mW input power. Whereas, multiplexers and de-multiplexers have 45 dB of isolation between the two adjacent channels, 80 dB isolation between nonadjacent channels and 0.85 dB of insertion loss at 1550 nm. The quantum channel comprises pure silica core fiber (PSCF) with different transmission lengths (maximum = 35 km) and the physical parameters of the fiber under test are enlisted in Section 3.2. The system performance of QKD network is also compared with the SSMF fiber in terms of secure key rates and transmission distance, while Alice and Bob

Recent Progress in the Quantum-to-the-Home Networks http://dx.doi.org/10.5772/intechopen.80396 35

The coherent receiver (Bob) consists of a 90 optical hybrid, a high optical power handling balanced photo-diodes with 20 GHz bandwidth and a real-time oscilloscope with a 100 GSa/s sample rate and 50 GHz analog bandwidth. We have kept the high power, narrow line width local oscillator at the receiver. It is termed as local local oscillator (LLO). The mean LLO photon level is 1 108 photon per pulse. The line width of the LLO is <10 kHz. After the system calibration at room temperature, the detector efficiency is measured as 0.6, while the electrical noise Vel is 0.85 (in shot noise units). The shot noise variance No is determined with sufficient LLO power to set the balanced detector in the linear detection regime. Shot noise calibration can be performed by shutting down all sources of incoming light or by ceasing the signal optical port on Bob side. The measured No for our setup is ≈ 17 0 mV2. The output signal is processed by the off-line digital signal processing module comprises phase noise cancelation (PNC) algorithm as depicted in Figure 9. The PNC stage has two square operators for in phase and quadrature operators, one addition operator and a digital DC cancelation block assisted by a down-converter. While all the secure key rate measurements are concluded with reconciliation efficiency of 90% for diverse modulation variances and transmission distances [30].

The extended experimental set-up for multiuser optically switched QKD network is shown in Figure 10. A MEMS-based 2 2 switch is incorporated after the quantum channel and demultiplexing, to implement inserted- and by-pass operations. The insertion loss of the switch is measured as 0.8 dB, while the cross talk (XT) is 52 dB, that is, negligible. In a bypass state, the input and output ports are connected to each other and in inserted state, the input and drop ports are connected to each other. In order to recover the classical 10 Gbits/s signals, we have used a standard coherent receiver with built-in DSP module of finite-impulse response filters (FIR) to compensate chromatic dispersion (CD). The forward error correction

We evaluated the average SKRs for diverse modulation variances and transmission distances. While for this investigation, we use two types of quantum channel, that is, SSMF and ultra-low loss PSCF. The results are as shown in Figure 11(a) and (b), respectively. Modulation variance is considered as 0.2, 0.3 and 0.4, while the length of quantum channel is considered upto 35 km

architectures are the same in both the cases.

(FEC) threshold is kept at 3.8 103 BER (bit error ratio).

5.2. Results and discussions

Figure 8. Optimum system performance and wavelength assignment for hybrid classical-quantum transmission.


**Shyh-Kuang Ueng**

Department of Computer Science & Engineering, National Taiwan Ocean University, Keelung 202, Taiwan<EMAIL_ADDRESS>
Received: 26 June 2020; Accepted: 25 September 2020; Published: 29 September 2020

**Abstract:** Wind turbine towers produce significant scatterings when illuminated by radars. Their reflectivity a ffects air tra ffic control, military surveillance, vessel tracking, and weather data sensing processes. Reducing the radar cross-section (RCS) of wind turbines is an essential task when building wind farms. It has been proved that round and bumpy structures can scatter radar waves and reduce the RCS of a reflector. Other research showed that taper towers generate smaller radar returns than cylindrical towers. In this research, we combine both strategies to devise a more e ffective method for designing wind turbine towers in the hope that their RCS can be further reduced. The test results reveal that the proposed method out-performs current reshaping methods. Wind turbine towers possessing taper shapes and periodic surface bumps deflect incident electromagnetic waves to insignificant directions. Thus, radar returns in the back-scattering directions decrease. Other experiments also verify that the proposed method maintains its e ffectiveness for radar waves with varying frequencies and polarization.

**Keywords:** RCS reduction; wind turbines; reshaping methods


**4.2 Land use change**

Satellite Landsat data was used to retrieve land use change around Sanya bay. Sanya River and the island between the two rivers was chose as land use change indexes (Yang 2008). From the satellite images, area of eastern and western Sanya Rivers reduced in 1999 compared with that in 1991. However the area of island between the two rivers enlarged more than 30%. The decreased river area, correspondently decreased the ecological wet land along the Sanya River, reduced the area for waste water cleaning. Seashore land use change was also retrieved with remote sensing data. From remote sensing data, the shape of costal line at the northeast of Lu Huitou peninsular changed greatly. In situ observation proved that buildings were constructed just along the coastal line. In situ observation also found that coast at the middle north of Lu Huitou peninsular was constructed as sea bath and sea diving area.

As water with high concentration of nutrients in Sanya River flow into Sanya Bay, it diffused by current and wave. The effluent plume streaming out of the Sanya river into the Sanya Bay flows straight for only a short distance before it is washed westward by the longshore current (Yang 2008). Sanya Bay is an open bay, the flows-in water only stay in a short time. Landsat TM data of 1991 was used to detect the diffuse pattern in Sanya Bay. From the satellite images, the water plume of Sanya River mainly distributed along the south of Sanya Bay. The reason is when tidal water rushed into Sanya Bay it splits and flows back from the south of Sanya Bay. In Sanya Bay, Seagrass mainly distributed along the north coast of Lu Huitou peninsular, the plume of Sanya River affected seagrass distribution in

**4. Impacts of environmental factors for seagrass distribution**

Fig. 13. The diffuse pattern of flows-in water in Sanya Bay (Yang 2008).

Satellite Landsat data was used to retrieve land use change around Sanya bay. Sanya River and the island between the two rivers was chose as land use change indexes (Yang 2008). From the satellite images, area of eastern and western Sanya Rivers reduced in 1999 compared with that in 1991. However the area of island between the two rivers enlarged more than 30%. The decreased river area, correspondently decreased the ecological wet land along the Sanya River, reduced the area for waste water cleaning. Seashore land use change was also retrieved with remote sensing data. From remote sensing data, the shape of costal line at the northeast of Lu Huitou peninsular changed greatly. In situ observation proved that buildings were constructed just along the coastal line. In situ observation also found that coast at the middle north of Lu Huitou peninsular was constructed as sea bath and sea

**4.1 Effluent diffusion in Sanya Bay**

great extent.

**4.2 Land use change**

diving area.

Fig. 14. Landsat TM data of Land use change around Sanya Bay in 1999 compared with that of in 1991 (Yang 2008).

Seagrass Distribution in China with Satellite Remote Sensing 91

remote sensing to quantitative mapping of sparse seagrass species is still in its early

An optical model was proposed to simulate the radiation transfer in multi-layer, nonhomogenous, heterogeneous, natural media. This algorithm enables us to simulate radiation fields in a wide range of variations of optical characteristics of these layers and to analyze the mechanisms of the formation of the radiation characteristics inside and outside the layers, as well as to estimate any contribution of each region. Based on the algorithm, we appropriately removed the distorting influence of the water column on the remotely sensed signal to retrieve an estimate of the reflectance of seagrass. Implementation of the method was found to be effective for improving the accuracy of coastal habitat maps and essential for deriving empirical relationships between remotely sensed data and features of interest in the marine environment. Retrieved bottom reflectance was then used to study the optical characteristics of seagrass. Through spectrum analysis it was found that the wavelengths for the discrimination and mapping of seagrass meadows of Sanya Bay, South China Sea lay between 500-630 nm as well as 680-710 nm. An appropriate hyper spectral band set for the remote sensing of seagrass should include narrow bands (maximum 5-10 nm bandwidth) centered around 555, 650, 675 and 700 nm. If satellite images were used, the effect of atmosphere should be taken into account. Though the blue band is more easily affected by atmosphere, the accurate surface reflectance could be acquired with the development of the theory and models in atmosphere correction. The relationship between seagrass leaf area index (LAI) and hyperspectra is very important when satellite remote sensing data is

Seagrass distribution in Xincun Bay spanning 15 years (1991-2006) was retrieved with satellite remote sensing. From the seagrass detection results, the resolution of satellite remote sensing image is very important for seagrass detection, so QuickBird data was more suitable for seagrass detection than Landsat TM and CBERS, especially when the seagrass distribution area was relatively small. Results in the paper proved that five classes can be classified clearly with QuickBird; however, only seagrass distribution contours can be

Though the accuracy of seagrass detection with satellite remote sensing can be affected by many factors, seagrass in Xincun Bay can be detected clearly for the sediment there was sand. Compared with satellite remote sensing data in 1991, the seagrass distribution area was reduced gradually and large areas of seagrass had disappeared by 2006. Human activities and extreme natural disasters were the main reasons for seagrass reduction, especially land use changes in recent years. The effect of land use change on seagrass distribution can be concluded as following: seagrass distribution in Sanya area conversely correlated with land use change, the more area of land use change the less coverage of seagrass distribution. Mainly because of land use change changed the water quality and

Except for hydrodynamic effect, distribution of seagrass was also affected by the following factors: (1) Sediments, in the area close to the bank, were sand and frequently affected by hydrodynamics, on which seagrass cannot grow flourish. (2) Human activities, such as aquaculture, digging clam worm and ship sailing, also affected seagrass growth in shallow waters; (3)Transparency was also an important factor for seagrass growth. Clear water

development.

applied for detecting seagrass distribution.

detected with Landsat TM and CBERS data.

sediment type.

#### **4.3 Tendency of water chemical indexes by land use change in Sanya Bay**

Water quality of Sanya River and Sanya Bay, such as water transparency and water quality indexes, was provided by Sanya ecological field station and references. In situ water chemical data of inorganic nitrogen, phosphorus and chlorophyll a concentration was used for validation and correction of the results from satellite remote sensing data. Results showed that water quality of Sanya River degraded in 2002 compared with that in 1991(Fig.15); water quality in Sanya bay near the Sanya River mouth was also degraded. However, water quality in other part of Sanya bay changed little. Perhaps Sanya bay is an open bay, where water exchange rate is relatively high.

Fig. 15. Waste water discharge in Sanya Bay in 1991 and 2002 (Yang 2008).

#### **4.4 Relationship between land use change and seagrass distribution**

Seagrass distribution in Sanya area conversely correlated with land use change, the more area of land use change the less coverage of seagrass distribution. This mainly because distribution of seagrass was confined by the following factors: (1) Sediments, in the area close to the bank, were sand and frequently affected by hydrodynamics, on which seagrass cannot grow flourish. (2) Human activities, such as aquaculture, digging clam worm and ship sailing, also affected seagrass growth in shallow waters; (3)Transparency was also an important factor for seagrass growth. Clear water mainly distributed in the southwest of Sanya Bay, which provided the suitable condition for large area continuous seagrass distributed in the area.

### **5. Discussions and conclusions**

As described in our investigation, much is known about the photophysiology of seagrass, while much is still required for us to effectively manage this important yet diminishing resource. New coastal ocean remote sensing techniques permit benthic habitats to be explored with higher resolution than ever before, however, the application of ocean color

Water quality of Sanya River and Sanya Bay, such as water transparency and water quality indexes, was provided by Sanya ecological field station and references. In situ water chemical data of inorganic nitrogen, phosphorus and chlorophyll a concentration was used for validation and correction of the results from satellite remote sensing data. Results showed that water quality of Sanya River degraded in 2002 compared with that in 1991(Fig.15); water quality in Sanya bay near the Sanya River mouth was also degraded. However, water quality in other part of Sanya bay changed little. Perhaps Sanya bay is an

> 1991 2002 Year

Seagrass distribution in Sanya area conversely correlated with land use change, the more area of land use change the less coverage of seagrass distribution. This mainly because distribution of seagrass was confined by the following factors: (1) Sediments, in the area close to the bank, were sand and frequently affected by hydrodynamics, on which seagrass cannot grow flourish. (2) Human activities, such as aquaculture, digging clam worm and ship sailing, also affected seagrass growth in shallow waters; (3)Transparency was also an important factor for seagrass growth. Clear water mainly distributed in the southwest of Sanya Bay, which provided the suitable condition for large area continuous seagrass

As described in our investigation, much is known about the photophysiology of seagrass, while much is still required for us to effectively manage this important yet diminishing resource. New coastal ocean remote sensing techniques permit benthic habitats to be explored with higher resolution than ever before, however, the application of ocean color

**4.3 Tendency of water chemical indexes by land use change in Sanya Bay**

open bay, where water exchange rate is relatively high.

3000

0

Fig. 15. Waste water discharge in Sanya Bay in 1991 and 2002 (Yang 2008).

**4.4 Relationship between land use change and seagrass distribution**

500

1000

1500

Waste water discharge

distributed in the area.

**5. Discussions and conclusions**

(ten thousands ton)

2000

2500

remote sensing to quantitative mapping of sparse seagrass species is still in its early development.

An optical model was proposed to simulate the radiation transfer in multi-layer, nonhomogenous, heterogeneous, natural media. This algorithm enables us to simulate radiation fields in a wide range of variations of optical characteristics of these layers and to analyze the mechanisms of the formation of the radiation characteristics inside and outside the layers, as well as to estimate any contribution of each region. Based on the algorithm, we appropriately removed the distorting influence of the water column on the remotely sensed signal to retrieve an estimate of the reflectance of seagrass. Implementation of the method was found to be effective for improving the accuracy of coastal habitat maps and essential for deriving empirical relationships between remotely sensed data and features of interest in the marine environment. Retrieved bottom reflectance was then used to study the optical characteristics of seagrass. Through spectrum analysis it was found that the wavelengths for the discrimination and mapping of seagrass meadows of Sanya Bay, South China Sea lay between 500-630 nm as well as 680-710 nm. An appropriate hyper spectral band set for the remote sensing of seagrass should include narrow bands (maximum 5-10 nm bandwidth) centered around 555, 650, 675 and 700 nm. If satellite images were used, the effect of atmosphere should be taken into account. Though the blue band is more easily affected by atmosphere, the accurate surface reflectance could be acquired with the development of the theory and models in atmosphere correction. The relationship between seagrass leaf area index (LAI) and hyperspectra is very important when satellite remote sensing data is applied for detecting seagrass distribution.

Seagrass distribution in Xincun Bay spanning 15 years (1991-2006) was retrieved with satellite remote sensing. From the seagrass detection results, the resolution of satellite remote sensing image is very important for seagrass detection, so QuickBird data was more suitable for seagrass detection than Landsat TM and CBERS, especially when the seagrass distribution area was relatively small. Results in the paper proved that five classes can be classified clearly with QuickBird; however, only seagrass distribution contours can be detected with Landsat TM and CBERS data.

Though the accuracy of seagrass detection with satellite remote sensing can be affected by many factors, seagrass in Xincun Bay can be detected clearly for the sediment there was sand. Compared with satellite remote sensing data in 1991, the seagrass distribution area was reduced gradually and large areas of seagrass had disappeared by 2006. Human activities and extreme natural disasters were the main reasons for seagrass reduction, especially land use changes in recent years. The effect of land use change on seagrass distribution can be concluded as following: seagrass distribution in Sanya area conversely correlated with land use change, the more area of land use change the less coverage of seagrass distribution. Mainly because of land use change changed the water quality and sediment type.

Except for hydrodynamic effect, distribution of seagrass was also affected by the following factors: (1) Sediments, in the area close to the bank, were sand and frequently affected by hydrodynamics, on which seagrass cannot grow flourish. (2) Human activities, such as aquaculture, digging clam worm and ship sailing, also affected seagrass growth in shallow waters; (3)Transparency was also an important factor for seagrass growth. Clear water

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#### **6. Acknowledgment**

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**5**

*2Geologist, Perugia*

*Italy*

**The Use of Remote Sensed Data and GIS**

Thematic maps in the Earth Sciences are an essential tool for the representation, analysis and visualization of geological processes. Among the large variety of thematic maps, geomorphological maps are particularly useful in understanding natural phenomena

Geomorphological maps report the erosion and depositional relief landforms, including submarine ones, highlighting the morphographic and morphometric characters and interpreting the endogenous and exogenous morphological processes, both past or present, that produce and shape the topographic relief. In this kind of maps, the chronological sequence is also reported, distinguishing between active and inactive landforms. The geomorphological mapping, in addition to its scientific value, is the necessary starting point of different studies such applied geology and environmental protection investigations for

A major problem with geomorphological information is that it is extremely complex to be

associated with human activities (Dramis & Bisci, 1998 and references within).

In particular, the reproduced information can be summarized as follows:

**1. Introduction**

socio-economic improvement.

represented due to the huge amount of data.

**to Produce a Digital Geomorphological**

**Map of a Test Area in Central Italy**

Laura Melelli1, Lucilia Gregori1 and Luisa Mancinelli2 *1Department of Earth Sciences, University of Perugia, Perugia*


**Author details**

Juan Wu1 , Wai Keung Leung2 \* and Weibin Sun1 \*

1 Department of Periodontology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China

2 Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China

\*Address all correspondence to<EMAIL_ADDRESS>and<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Periodontal Disease Associated with Genetic Disorders DOI: http://dx.doi.org/10.5772/intechopen.97497*