**4. Effect of UWB Interference on the UMTS and CDMA-450 macrocell downlink performance**

To account for the UWB interference, an extra source of interference is added linearly to the UMTS and the CDMA-450 intra-system interference. The interference power is calculated by assuming the UWB source to be at different distances from the UMTS receiver (the mobile station). Therefore, the interference power generated by a UWB device, IUWB, is given by (in dBm):

$$I\_{\rm LINB} = P\_{\rm LINB} - L\_{\rm LINB}(d) + G\_{\rm LINTS} \tag{9}$$

UWB Coexistence with 3G and 4G Cellular Systems 321

(14)

(15)

(16)

**UWB**

In the frequency band used by CDMA-450, the UWB signal propagation loss in dB is

10 ( ) 25.7 20log ( ) 4 *UWB L d* ≈+ − *d*

*CDMA CDMA CDMA o CDMA UWB*

 = +

*CDMA*

*CDMA UWB*

*I I* = +

Fig. 1 represents the scenario of the studied WiMAX system. It shall be mentioned that the receiver is an indoor portable WiMAX. The UWB transmitter is also indoor within a distance

**WiMAX signal**

Let us study the case of 3.5 GHz WiMAX assuming that the WiMAX transmission power is 40 dBm/sector. Fig. 2 shows the WiMAX downlink modulation modes, as a function of distance between the WiMAX transmitter and receiver, for three different UWB power densities. It can be noticed that, without UWB interference, the WiMAX will have a range of 1481 m for the

,

*n*

**5. Results for a WiMAX system and a single UWB interferer**

of 0.5 to 5 m from the WiMAX receiver.

**WiMAX interference**

**Figure 1.** WiMAX studied scenario.

*I C*

*R I R II*

• RCDMA,o is the CDMA-450 macrocell initial range without the UWB interference. • RCDMA is the CDMA-450 macrocell range with the existence of the UWB interference. The normalized capacity of the CDMA-450 system Cn is given by (Ahmed et. al., 2008):

The normalized range is now given by (Ahmed et. al., 2008):

σ

1/

γ

calculated as:

where:

Where:

As the UWB devices are typically low power and short range devices the line-of-sight pathloss model is often most appropriate. Then the UWB signal propagation loss in dB is calculated as:

$$L\_{\rm LWB}(d) = \Im \theta.03 + \Im 0 \log\_{10}(d) - 4\sigma \tag{10}$$

The effect of the UWB interference is to reduce the UMTS macrocell range or/and the macrocell capacity.

The normalized range is given as (Ahmed et. al., 2008):

$$\frac{R\_{\rm LMTS}}{R\_{\rm LMTS,o}} = \left(\frac{I\_{\rm LMTS}}{I\_{\rm LMTS} + I\_{\rm LINB}}\right)^{1/\gamma} \tag{11}$$

The normalized capacity Cn is given as (Ahmed et. al., 2008):

$$\mathbf{C}\_{n} = \left(\frac{I\_{\rm LIMTS}}{I\_{\rm LIMTS} + I\_{\rm LIWN}}\right) \tag{12}$$

The interference power generated by a UWB device that affects the CDMA-450 receiver, IUWB, is given by (in dBm):

$$I\_{\rm LINB} = P\_{\rm LINB} - L\_{\rm LINB} \text{(d)} + G\_{\rm CDMA} \tag{13}$$

where:

In the frequency band used by CDMA-450, the UWB signal propagation loss in dB is calculated as:

$$L\_{\rm LWB}(d) = 25.7 + 20\log\_{10}(d) - 4\sigma \tag{14}$$

The normalized range is now given by (Ahmed et. al., 2008):

$$\frac{R\_{\text{CDMA}}}{R\_{\text{CDMA},o}} = \left(\frac{I\_{\text{CDMA}}}{I\_{\text{CDMA}} + I\_{\text{IDWB}}}\right)^{1/\gamma} \tag{15}$$

where:

320 Ultra Wideband – Current Status and Future Trends

**downlink performance**

dBm):

Where:

calculated as:

macrocell capacity.

IUWB, is given by (in dBm):

where:

**4. Effect of UWB Interference on the UMTS and CDMA-450 macrocell**

To account for the UWB interference, an extra source of interference is added linearly to the UMTS and the CDMA-450 intra-system interference. The interference power is calculated by assuming the UWB source to be at different distances from the UMTS receiver (the mobile station). Therefore, the interference power generated by a UWB device, IUWB, is given by (in

() *UWB UWB UWB UMTS I P L dG* =− + (9)

• LUWB(d) is the path-loss between the UWB device and the UMTS receiver which varies

As the UWB devices are typically low power and short range devices the line-of-sight pathloss model is often most appropriate. Then the UWB signal propagation loss in dB is

10 ( ) 39.03 20log ( ) 4 *UWB L d* ≈+ − *d*

,

*n*

*I C*

*R I R II*

*UMTS UMTS UMTS o UMTS UWB*

 = +

*UMTS*

*UMTS UWB*

*I I* = +

The interference power generated by a UWB device that affects the CDMA-450 receiver,

() *UWB UWB UWB CDMA I P L dG* =− + (13)

• LUWB(d) is the path-loss between the UWB device and the CDMA-450 receiver which

The effect of the UWB interference is to reduce the UMTS macrocell range or/and the

σ

1/

γ

(10)

(11)

(12)

• PUWB is the mean UWB EIRP in dBm in the UMTS band.

with the separation distance, d in m, and

The normalized range is given as (Ahmed et. al., 2008):

The normalized capacity Cn is given as (Ahmed et. al., 2008):

• PUWB is the UWB EIRP in dBm in the CDMA-450 band.

varies with the separation distance, d in m, and

• GCDMA is the CDMA-450 antenna gain .

• GUMTS is the UMTS antenna gain.

The normalized capacity of the CDMA-450 system Cn is given by (Ahmed et. al., 2008):

$$C\_n = \left(\frac{I\_{\text{CDMA}}}{I\_{\text{CDMA}} + I\_{\text{IDWB}}}\right) \tag{16}$$

## **5. Results for a WiMAX system and a single UWB interferer**

Fig. 1 represents the scenario of the studied WiMAX system. It shall be mentioned that the receiver is an indoor portable WiMAX. The UWB transmitter is also indoor within a distance of 0.5 to 5 m from the WiMAX receiver.

Let us study the case of 3.5 GHz WiMAX assuming that the WiMAX transmission power is 40 dBm/sector. Fig. 2 shows the WiMAX downlink modulation modes, as a function of distance between the WiMAX transmitter and receiver, for three different UWB power densities. It can be noticed that, without UWB interference, the WiMAX will have a range of 1481 m for the second modulation scheme. With a UWB power density of -88.5 dBm/MHz the range will be reduced by 2% and with a UWB power density of -41.3 dBm/MHz (recommended by FCC), the range will be only 213 m. Such a reduction drastically degrades the WiMAX performance.

UWB Coexistence with 3G and 4G Cellular Systems 323

 **PUWB/MHz = -41.3 dBm PUWB/MHz = -88.5 dBm**

 **PUWB/MHz = -41.3 dBm PUWB/MHz = -91.5 dBm**

 **No UWB**

 **No UWB**

**Figure 3.** 3.5 GHz WiMAX modulation modes for different UWB power densities with a 1 m distance between the UWB transmitter and the WiMAX receiver assuming a WiMAX transmitted power of 40 dBm/sector and that the WiMAX signal and interference are received through an open window.

200 400<PHONE_NUMBER> 1200 1400 1600 1800 2000

 **Distance Between WiMAX Tx. and WiMAX Rx. (m)**

 **WiMAX Modulation modes**

 **WiMAX Modulation modes**

**Figure 4.** 3.5 GHz WiMAX modulation modes for different UWB power densities with a 1 m distance between the UWB transmitter and the WiMAX receiver assuming a WiMAX transmitted power of 40

200 400<PHONE_NUMBER> 1200 1400

 **Distance Between WiMAX Tx. and WiMAX Rx. (m)**

dBm/sector.

0

 **Modulation mode**

 **Modulation mode**

**Figure 2.** 3.5 GHz WiMAX modulation modes for different UWB power densities with a 1 m distance between the UWB transmitter and the WiMAX receiver assuming a WiMAX transmitted power of 40 dBm/sector.

Let us consider now the case when the WiMAX signal and also the interference are received through an open window. Fig. 3 shows the WiMAX downlink modulation modes, again as a function of distance between the WiMAX transmitter and receiver, for three different UWB power densities. As can be seen, without UWB interference, the WiMAX will have a range of 1930 m for the second modulation scheme. With a UWB power density of -88.5 dBm/MHz the range will be reduced by 2%. And for a UWB power density of -41.3 dBm/MHz (recommended by FCC), the range will be 310 m. Again the WiMAX range performance is drastically degraded.

Let us now study the case presented in Fig. 2 but assuming this time that the maximum allowed WiMAX reduction range is 1%. Fig. 4 shows the WiMAX downlink modulation modes as a function of distance between the WiMAX transmitter and receiver for three different UWB power densities. It is clearly seen that, without UWB interference, the WiMAX will have a range of 1481 m for the second modulation scheme. The range will be reduced by 1% when the interfering UWB power density is -91.5 dBm/MHz.

Let us consider now the case when the WiMAX system operates in the 2.5 GHz band. Fig. 5 shows the WiMAX downlink modulation modes as a function of distance between the

dBm/sector.

drastically degraded.

0

1

2

3

4

5

 **Modulation mode**

6

7

8

9

10

second modulation scheme. With a UWB power density of -88.5 dBm/MHz the range will be reduced by 2% and with a UWB power density of -41.3 dBm/MHz (recommended by FCC), the range will be only 213 m. Such a reduction drastically degrades the WiMAX performance.

 **WiMAX Modulation modes**

 **PUWB/MHz = -41.3 dBm PUWB/MHz = -88.5 dBm**

 **No UWB**

**Figure 2.** 3.5 GHz WiMAX modulation modes for different UWB power densities with a 1 m distance between the UWB transmitter and the WiMAX receiver assuming a WiMAX transmitted power of 40

200 400<PHONE_NUMBER> 1200 1400

 **Distance Between WiMAX Tx. and WiMAX Rx. (m)**

Let us consider now the case when the WiMAX signal and also the interference are received through an open window. Fig. 3 shows the WiMAX downlink modulation modes, again as a function of distance between the WiMAX transmitter and receiver, for three different UWB power densities. As can be seen, without UWB interference, the WiMAX will have a range of 1930 m for the second modulation scheme. With a UWB power density of -88.5 dBm/MHz the range will be reduced by 2%. And for a UWB power density of -41.3 dBm/MHz (recommended by FCC), the range will be 310 m. Again the WiMAX range performance is

Let us now study the case presented in Fig. 2 but assuming this time that the maximum allowed WiMAX reduction range is 1%. Fig. 4 shows the WiMAX downlink modulation modes as a function of distance between the WiMAX transmitter and receiver for three different UWB power densities. It is clearly seen that, without UWB interference, the WiMAX will have a range of 1481 m for the second modulation scheme. The range will be

Let us consider now the case when the WiMAX system operates in the 2.5 GHz band. Fig. 5 shows the WiMAX downlink modulation modes as a function of distance between the

reduced by 1% when the interfering UWB power density is -91.5 dBm/MHz.

**Figure 3.** 3.5 GHz WiMAX modulation modes for different UWB power densities with a 1 m distance between the UWB transmitter and the WiMAX receiver assuming a WiMAX transmitted power of 40 dBm/sector and that the WiMAX signal and interference are received through an open window.

**Figure 4.** 3.5 GHz WiMAX modulation modes for different UWB power densities with a 1 m distance between the UWB transmitter and the WiMAX receiver assuming a WiMAX transmitted power of 40 dBm/sector.

UWB Coexistence with 3G and 4G Cellular Systems 325

 **PUWB/MHz = -41.3 dBm PUWB/MHz = -97.5 dBm**

 **No UWB**

**Figure 6.** 3.5 GHz WiMAX downlink modulation modes versus distance between the WiMAX

the WiMAX receiver and assuming a WiMAX transmitted power of 40 dBm/sector.

reduction, unless Detect and Avoid (DAA) techniques are implemented.

subcarriers with a 40 dB notch filter.

0

1

2

3

4

5

 **Modulation mode**

6

7

8

9

10

by 1%.

transmitter and receiver, for different UWB power densities from 4 UWB transmitters at 1 m distance to

200 400<PHONE_NUMBER> 1200 1400

 **WiMAX Modulation modes**

 **Distance Between WiMAX Tx. and WiMAX Rx. (m)**

A band rejection up to 56 dB is needed for the DS-CDMA UWB system, while for the MB-OFDM UWB system a 51 dB band rejection is needed and can be obtained by nulling 16

We study now the same scenario but for the 2.5 GHz WiMAX. Fig. 7 shows the WiMAX downlink modulation modes as a function of the distance between the WiMAX transmitter and receiver for three different UWB power densities. It can be noticed that for the second modulation scheme without UWB interference, the WiMAX will have a range of 1817 m. At a UWB power density of -100.7 dBm/MHz, WiMAX range will be reduced

In summary, from the results presented in Figures 2, 3, 4 and 5 it can be concluded that the power density of -41.3 dBm/MHz recommended by FCC, implies a very high range

Fig. 8 represents the DAA requirement for Multiband OFDM UWB (MB-OFDM UWB) system and the Direct Sequence CDMA system (DS-CDMA UWB), with activity factors

(fraction of the time they work at the 3.5 GHz band) of 32% and 100% respectively.

**Figure 5.** 2.5 GHz WiMAX modulation modes for different UWB power densities with a 1 m distance between the UWB transmitter and the WiMAX receiver assuming a WiMAX transmitted power of 40 dBm/sector.

WiMAX transmitter and receiver for three different UWB power densities. Notice that, without UWB interference, the WiMAX will have a range of 1817 m for the second modulation scheme. With a UWB power density of -94.7 dBm/MHz the range will be reduced by 1%. For a UWB power density of -51.3 dBm/MHz (recommended by FCC), the range will be 378 m and such a reduction represents a drastic degradation of the WiMAX performance. In this case an UWB with a power density of -91.5 dBm/MHz will reduce the WiMAX range by 2%.

#### **6. Results for a WiMAX system and multi UWB interferers**

We will consider now the case of multi-UWB transmitters, assuming the case that 4 UWB are located at a distance of 1m from the WiMAX receiver. Fig. 6 shows the WiMAX downlink modulation modes as a function of the distance between the WiMAX transmitter and receiver (WiMAX link length) for three different UWB power densities. It can be noticed that, without UWB interference, the WiMAX will have a range of 1481 m for the second modulation scheme. The range will be reduced by 1% when the UWB power density is higher than -97.5 dBm/MHz. In this case, an UWB power density of -94 dBm/MHz will reduce the WiMAX range by 2%.

dBm/sector.

WiMAX range by 2%.

0

1

2

3

4

5

 **Modulation mode**

6

7

8

9

10

reduce the WiMAX range by 2%.

**Figure 5.** 2.5 GHz WiMAX modulation modes for different UWB power densities with a 1 m distance between the UWB transmitter and the WiMAX receiver assuming a WiMAX transmitted power of 40

200 400<PHONE_NUMBER> 1200 1400 1600 1800 2000

 **Distance Between WiMAX Tx. and WiMAX Rx. (m)**

 **WiMAX Modulation modes**

 **PUWB/MHz = -51.3 dBm PUWB/MHz = -94.7 dBm**

 **No UWB**

WiMAX transmitter and receiver for three different UWB power densities. Notice that, without UWB interference, the WiMAX will have a range of 1817 m for the second modulation scheme. With a UWB power density of -94.7 dBm/MHz the range will be reduced by 1%. For a UWB power density of -51.3 dBm/MHz (recommended by FCC), the range will be 378 m and such a reduction represents a drastic degradation of the WiMAX performance. In this case an UWB with a power density of -91.5 dBm/MHz will reduce the

We will consider now the case of multi-UWB transmitters, assuming the case that 4 UWB are located at a distance of 1m from the WiMAX receiver. Fig. 6 shows the WiMAX downlink modulation modes as a function of the distance between the WiMAX transmitter and receiver (WiMAX link length) for three different UWB power densities. It can be noticed that, without UWB interference, the WiMAX will have a range of 1481 m for the second modulation scheme. The range will be reduced by 1% when the UWB power density is higher than -97.5 dBm/MHz. In this case, an UWB power density of -94 dBm/MHz will

**6. Results for a WiMAX system and multi UWB interferers**

**Figure 6.** 3.5 GHz WiMAX downlink modulation modes versus distance between the WiMAX transmitter and receiver, for different UWB power densities from 4 UWB transmitters at 1 m distance to the WiMAX receiver and assuming a WiMAX transmitted power of 40 dBm/sector.

A band rejection up to 56 dB is needed for the DS-CDMA UWB system, while for the MB-OFDM UWB system a 51 dB band rejection is needed and can be obtained by nulling 16 subcarriers with a 40 dB notch filter.

We study now the same scenario but for the 2.5 GHz WiMAX. Fig. 7 shows the WiMAX downlink modulation modes as a function of the distance between the WiMAX transmitter and receiver for three different UWB power densities. It can be noticed that for the second modulation scheme without UWB interference, the WiMAX will have a range of 1817 m. At a UWB power density of -100.7 dBm/MHz, WiMAX range will be reduced by 1%.

In summary, from the results presented in Figures 2, 3, 4 and 5 it can be concluded that the power density of -41.3 dBm/MHz recommended by FCC, implies a very high range reduction, unless Detect and Avoid (DAA) techniques are implemented.

Fig. 8 represents the DAA requirement for Multiband OFDM UWB (MB-OFDM UWB) system and the Direct Sequence CDMA system (DS-CDMA UWB), with activity factors (fraction of the time they work at the 3.5 GHz band) of 32% and 100% respectively.

UWB Coexistence with 3G and 4G Cellular Systems 327

**7. Results for UMTS or CDMA-450 systems and single UWB interferer**

power density (PUWB) of -51.3 dBm/MHz within the UMTS bandwidth.

Let us now study the coexistence of UWB systems with the UMTS (working at the 2 GHz band) and CDMA-450 systems. In the analysis we assume that the UWB data rate is higher than the UMTS or CDMA-450 chip rate. In this case, the UWB interference can be considered as a Gaussian noise. We address here the effect that the UWB system produces on the downlink of the UMTS and CDMA-450 systems. In Fig. 9, the UWB interference power on the UMTS downlink (i.e. interference as seen at the mobile) is plotted assuming a UWB

**Figure 9.** UWB interference as a function of the separation between the UWB transmitter and the UMTS

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

 **Seperation between the UMTS mobile and the UWB source (m)**

Lets us study now the case of voice service [Gp = 256 and (Eb/No)req = 6 dB] assuming an UMTS interference of -88 dBm (14 dB Rise-Over-Thermal ROT). Fig. 10 shows the downlink macrocell normalized range as a function of the separation between the UMTS mobile and the UWB transmitter for three different values of the propagation exponent γ. It can be noticed that the UWB signal creates a high interference (which reflects a macrocell normalized range reduction of 35.6%) when the separation is 1 m. For larger separation, the

Fig. 11 shows the downlink macrocell normalized capacity as a function of the separation between the UMTS mobile and the UWB transmitter. It can be noticed that the UWB signal creates a high interference (which reflects a macrocell normalized capacity reduction of 78.6%) when the separation is 1 m. For larger separation, the interference is lower and thus

interference is lower and thus the range reduction is also lower.

the normalized capacity reduction is also lower.

mobile (PUWB = -51.3 dBm/MHz).

 **UWB Interference (dBm)**

**Figure 7.** 2.5 GHz WiMAX modulation modes for different UWB power densities with a 1 m distance between the 4 UWB transmitters and the WiMAX receiver assuming a WiMAX transmitted power of 40 dBm/sector.

**Figure 8.** DAA requirements within the 3.5 GHz band.

### **7. Results for UMTS or CDMA-450 systems and single UWB interferer**

326 Ultra Wideband – Current Status and Future Trends

dBm/sector.

0

1

2

3

4

5

 **Modulation mode**

6

7

8

9

10

**Figure 8.** DAA requirements within the 3.5 GHz band.

 **EIRP (dBm/MHz)**

**Figure 7.** 2.5 GHz WiMAX modulation modes for different UWB power densities with a 1 m distance between the 4 UWB transmitters and the WiMAX receiver assuming a WiMAX transmitted power of 40

200 400<PHONE_NUMBER> 1200 1400 1600 1800 2000

 **Distance Between WiMAX Tx. and WiMAX Rx. (m)**

3.45 3.5 3.55 3.6

 **FCC Mask DS-CDMA UWB MB-OFDM UWB**

 **Frequency (GHz)**

 **WiMAX Modulation modes**

 **PUWB/MHz = -51.3 dBm PUWB/MHz = -100.7 dBm**

 **No UWB**

Let us now study the coexistence of UWB systems with the UMTS (working at the 2 GHz band) and CDMA-450 systems. In the analysis we assume that the UWB data rate is higher than the UMTS or CDMA-450 chip rate. In this case, the UWB interference can be considered as a Gaussian noise. We address here the effect that the UWB system produces on the downlink of the UMTS and CDMA-450 systems. In Fig. 9, the UWB interference power on the UMTS downlink (i.e. interference as seen at the mobile) is plotted assuming a UWB power density (PUWB) of -51.3 dBm/MHz within the UMTS bandwidth.

**Figure 9.** UWB interference as a function of the separation between the UWB transmitter and the UMTS mobile (PUWB = -51.3 dBm/MHz).

Lets us study now the case of voice service [Gp = 256 and (Eb/No)req = 6 dB] assuming an UMTS interference of -88 dBm (14 dB Rise-Over-Thermal ROT). Fig. 10 shows the downlink macrocell normalized range as a function of the separation between the UMTS mobile and the UWB transmitter for three different values of the propagation exponent γ. It can be noticed that the UWB signal creates a high interference (which reflects a macrocell normalized range reduction of 35.6%) when the separation is 1 m. For larger separation, the interference is lower and thus the range reduction is also lower.

Fig. 11 shows the downlink macrocell normalized capacity as a function of the separation between the UMTS mobile and the UWB transmitter. It can be noticed that the UWB signal creates a high interference (which reflects a macrocell normalized capacity reduction of 78.6%) when the separation is 1 m. For larger separation, the interference is lower and thus the normalized capacity reduction is also lower.

UWB Coexistence with 3G and 4G Cellular Systems 329

γ **= 3.50** γ **= 3.75** γ **= 4.00**

Next let us study the data service case [Gp = 32 dB and (Eb/No)req = 5 dB] assuming an UMTS total interference of -92.5 dBm (9.5 dB Rise-Over-Thermal ROT), representing a highly loaded macrocell. Fig. 12 shows the downlink macrocell normalized range as a function of the separation between the UMTS mobile and the UWB transmitter for three different values of the propagation exponent s. It can be noticed that the UWB signal creates a high interference (which reflects a high macrocell normalized range reduction of 50.5%) when the separation is 1m. For larger separation, the interference is lower and thus the range reduction is also lower.

 **Data Service**

**Figure 12.** Effect of the UWB interference on the macrocell normalized range as a function of the separation between the UWB transmitter and the UMTS mobile (PUWB = -51.3 dBm/MHz).

the normalized capacity reduction is also lower.

0.4

0.5

0.6

0.7

 **Downlink macrocell normalized range**

0.8

0.9

1

Fig. 13 shows the downlink macrocell normalized capacity as a function of the separation between the UMTS mobile and the UWB transmitter. It can be noticed that the UWB signal creates a high interference (which reflects a high macrocell normalized capacity reduction of 91%) when the separation is 1 m. For larger separation, the interference is lower and thus

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

 **Seperation between the UMTS mobile and the UWB source (m)**

It is obvious that such reductions (in range and capacity) are unacceptable. Thus the EIRP

Let us consider now the data service case assuming a PUWB of -81.4 dBm/MHz. Fig. 14 shows the downlink macrocell normalized range as a function of the separation between the UMTS mobile and the UWB transmitter. It can be noticed that the UWB signal creates a high interference (which reflects a high macrocell normalized range reduction) when the separation is lower than 0.25 m. For larger separation, the interference is lower and at a

power density should be reduced to get an acceptable range and capacity reduction.

distance higher than 1m, the effect of the interference is quasi null.

**Figure 10.** Effect of the UWB interference on the macrocell range as a function of the separation between the UWB transmitter and the UMTS mobile (PUWB = -51.3dBm/MHz).

**Figure 11.** Effect of the UWB interference on the macrocell normalized capacity as a function of the separation between the UWB transmitter and the UMTS mobile (PUWB = -60 dBm/MHz).

Next let us study the data service case [Gp = 32 dB and (Eb/No)req = 5 dB] assuming an UMTS total interference of -92.5 dBm (9.5 dB Rise-Over-Thermal ROT), representing a highly loaded macrocell. Fig. 12 shows the downlink macrocell normalized range as a function of the separation between the UMTS mobile and the UWB transmitter for three different values of the propagation exponent s. It can be noticed that the UWB signal creates a high interference (which reflects a high macrocell normalized range reduction of 50.5%) when the separation is 1m. For larger separation, the interference is lower and thus the range reduction is also lower.

328 Ultra Wideband – Current Status and Future Trends

0.6

20

30

40

50

60

 **Downlink normalized capacity (%)**

70

80

90

100

0.65

0.7

0.75

0.8

 **Downlink macrocell normalized range**

0.85

0.9

0.95

1

**Figure 10.** Effect of the UWB interference on the macrocell range as a function of the separation

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

γ **= 3.50** γ **= 3.75** γ **= 4.00**

 **Voice Service**

 **Seperation between the UMTS mobile and the UWB source (m)**

 **Voice Service**

**Figure 11.** Effect of the UWB interference on the macrocell normalized capacity as a function of the

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

**Seperation between the UMTS mobile and the UWB source (m)**

separation between the UWB transmitter and the UMTS mobile (PUWB = -60 dBm/MHz).

between the UWB transmitter and the UMTS mobile (PUWB = -51.3dBm/MHz).

**Figure 12.** Effect of the UWB interference on the macrocell normalized range as a function of the separation between the UWB transmitter and the UMTS mobile (PUWB = -51.3 dBm/MHz).

Fig. 13 shows the downlink macrocell normalized capacity as a function of the separation between the UMTS mobile and the UWB transmitter. It can be noticed that the UWB signal creates a high interference (which reflects a high macrocell normalized capacity reduction of 91%) when the separation is 1 m. For larger separation, the interference is lower and thus the normalized capacity reduction is also lower.

It is obvious that such reductions (in range and capacity) are unacceptable. Thus the EIRP power density should be reduced to get an acceptable range and capacity reduction.

Let us consider now the data service case assuming a PUWB of -81.4 dBm/MHz. Fig. 14 shows the downlink macrocell normalized range as a function of the separation between the UMTS mobile and the UWB transmitter. It can be noticed that the UWB signal creates a high interference (which reflects a high macrocell normalized range reduction) when the separation is lower than 0.25 m. For larger separation, the interference is lower and at a distance higher than 1m, the effect of the interference is quasi null.

UWB Coexistence with 3G and 4G Cellular Systems 331

Fig. 15 shows the downlink macrocell capacity as a function of the separation between the UMTS mobile and the UWB transmitter. It can be noticed that the UWB signal creates a high interference (which reflects a high macrocell capacity reduction) when the separation is less than 0.4 m. For larger separation, the interference is lower and at a distance higher than 1.0

Next we study the case of data service (Gp = 32 dB and (Eb/No)req = 5 dB) of the CDMA-450 3X assuming that the CDMA-450 total interference of -92.5 dBm (9.5 dB Rise-Over-Thermal ROT) and UWB power density of -95 dBm/MHz. The frequency of operation is assumed to

Fig. 16 shows the CDMA-450 downlink macrocell normalized range as a function of the separation between the CDMA mobile and the UWB transmitter. It can be noticed that the UWB signal creates a low interference when the separation is 1m which reflects a

Fig. 17 shows the CDMA-450 downlink macrocell normalized capacity as a function of the separation between the CDMA-450 mobile and the UWB transmitter. It can be noticed that the UWB signal creates a low interference when the separation is 1m which reflects a

 **Data Service**

**Figure 15.** Effect of the UWB interference on the macrocell normalized capacity as a function of the

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

 **Seperation between the UMTS mobile and the UWB source (m)**

separation between the UWB transmitter and the UMTS mobile (PUWB = -81.4 dBm/MHz).

m, the effect of the interference is to reduce the cell capacity by 1%.

normalized range reduction of less than 0.3%.

normalized capacity reduction of 1%.

90

91

92

93

94

95

 **Downlink normalized capacity (%)**

96

97

98

99

100

be 450 MHz.

**Figure 13.** Effect of the UWB interference on the macrocell normalized capacity as a function of the separation between the UWB transmitter and the UMTS mobile (PUWB = -51.3 dBm/MHz).

**Figure 14.** Effect of the UWB interference on the macrocell range as a function of the separation between the UWB transmitter and the UMTS mobile (PUWB = -81.4 dBm/MHz).

Fig. 15 shows the downlink macrocell capacity as a function of the separation between the UMTS mobile and the UWB transmitter. It can be noticed that the UWB signal creates a high interference (which reflects a high macrocell capacity reduction) when the separation is less than 0.4 m. For larger separation, the interference is lower and at a distance higher than 1.0 m, the effect of the interference is to reduce the cell capacity by 1%.

330 Ultra Wideband – Current Status and Future Trends

0

0.9

0.91

0.92

0.93

0.94

0.95

 **Downlink macrocell normalized range**

0.96

0.97

0.98

0.99

1

50

 **Downlink normalized capacity (%)**

100

**Figure 13.** Effect of the UWB interference on the macrocell normalized capacity as a function of the

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

 **Data Service**

 **Seperation between the UMTS mobile and the UWB source (m)**

 **Data Service**

**Figure 14.** Effect of the UWB interference on the macrocell range as a function of the separation

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

γ **= 3.50** γ **= 3.75** γ **= 4.00**

 **Seperation between the UMTS mobile and the UWB source (m)**

between the UWB transmitter and the UMTS mobile (PUWB = -81.4 dBm/MHz).

separation between the UWB transmitter and the UMTS mobile (PUWB = -51.3 dBm/MHz).

Next we study the case of data service (Gp = 32 dB and (Eb/No)req = 5 dB) of the CDMA-450 3X assuming that the CDMA-450 total interference of -92.5 dBm (9.5 dB Rise-Over-Thermal ROT) and UWB power density of -95 dBm/MHz. The frequency of operation is assumed to be 450 MHz.

Fig. 16 shows the CDMA-450 downlink macrocell normalized range as a function of the separation between the CDMA mobile and the UWB transmitter. It can be noticed that the UWB signal creates a low interference when the separation is 1m which reflects a normalized range reduction of less than 0.3%.

Fig. 17 shows the CDMA-450 downlink macrocell normalized capacity as a function of the separation between the CDMA-450 mobile and the UWB transmitter. It can be noticed that the UWB signal creates a low interference when the separation is 1m which reflects a normalized capacity reduction of 1%.

**Figure 15.** Effect of the UWB interference on the macrocell normalized capacity as a function of the separation between the UWB transmitter and the UMTS mobile (PUWB = -81.4 dBm/MHz).

UWB Coexistence with 3G and 4G Cellular Systems 333

**8. Results for a UMTS or CDMA-450 systems and multi UWB interferers**

Then we study the case of multiple UWB transmitters with four UWB transmitters at a distance of 1m around the UMTS receiver. Fig. 18 shows the downlink macrocell normalized range as a function of the EIRP power density in dBm/MHz. It can be noticed that the cell

**Figure 18.** Range reduction as a function of the EIRP in (dBm/MHz) for multi UWB transmitters.

maximum allowed EIRP reduces to -101 dBm/MHz.

Fig. 19 shows the downlink macrocell normalized capacity as a function of the EIRP power density in dBm/MHz. It can be noticed that, for a capacity reduction of only 1%, EIRP should be -87.4 dBm/MHz. this represents a 6 dB reduction equal to [10log10(4)], where 4 is the number of the UWB sources. The conclusion is that, for the case of single UWB transmitter, the UMTS can easily tolerate the UWB interference when the UWB EIRP is lower than -81.4 dBm/MHz for 1m distance between the UWB transmitter and the UMTS mobile. For the multi UWB transmitter case, the UMTS can easily tolerate the UWB interference when the UWB EIRP is -87.4 dBm/MHz. When using a CDMA-450 system the

 **EIRP (dBm/MHz)**

Table 2 presents the maximum allowed EIRP for different frequency bands, for UWB activity factor of 100% and multi UWB transmitter scenario, for two different cases, (case A with 99.995% confidence and case B with 99% confidence respectively). Table 3 represents the maximum allowed EIRP for different frequency bands for UWB activity factor of 10%

and multi UWB transmitter scenario for the two previous cases A and B.

range reduction is always lower than 1%.

0

0.1

0.2

0.3

0.4

0.5

 **Range reduction (%)**

0.6

0.7

0.8

0.9

1

**Figure 16.** Effect of the UWB interference on the macrocell normalized range as a function of the separation between the UWB transmitter and the CDMA450 mobile (PUWB = -95 dBm/MHz).

**Figure 17.** Effect of the UWB interference on the macrocell normalized capacity as a function of the separation between the UWB transmitter and the CDMA450 mobile (PUWB = -95 dBm/MHz).

#### **8. Results for a UMTS or CDMA-450 systems and multi UWB interferers**

332 Ultra Wideband – Current Status and Future Trends

0.95

95 95.5 96 96.5 97 97.5 98 98.5 99 99.5 100

 **Downlink normalized capacity (%)**

0.955

0.96

0.965

0.97

0.975

 **Downlink macrocell normalized range**

0.98

0.985

0.99

0.995

1

**Figure 16.** Effect of the UWB interference on the macrocell normalized range as a function of the separation between the UWB transmitter and the CDMA450 mobile (PUWB = -95 dBm/MHz).

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

γ **= 3.50** γ **= 3.75** γ **= 4.00**

 **Data Service**

 **Seperation between the CDMA-450 mobile and the UWB source (m)**

 **Data Service**

**Figure 17.** Effect of the UWB interference on the macrocell normalized capacity as a function of the separation between the UWB transmitter and the CDMA450 mobile (PUWB = -95 dBm/MHz).

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

 **Seperation between the CDMA-450 mobile and the UWB source (m)**

Then we study the case of multiple UWB transmitters with four UWB transmitters at a distance of 1m around the UMTS receiver. Fig. 18 shows the downlink macrocell normalized range as a function of the EIRP power density in dBm/MHz. It can be noticed that the cell range reduction is always lower than 1%.

**Figure 18.** Range reduction as a function of the EIRP in (dBm/MHz) for multi UWB transmitters.

Fig. 19 shows the downlink macrocell normalized capacity as a function of the EIRP power density in dBm/MHz. It can be noticed that, for a capacity reduction of only 1%, EIRP should be -87.4 dBm/MHz. this represents a 6 dB reduction equal to [10log10(4)], where 4 is the number of the UWB sources. The conclusion is that, for the case of single UWB transmitter, the UMTS can easily tolerate the UWB interference when the UWB EIRP is lower than -81.4 dBm/MHz for 1m distance between the UWB transmitter and the UMTS mobile. For the multi UWB transmitter case, the UMTS can easily tolerate the UWB interference when the UWB EIRP is -87.4 dBm/MHz. When using a CDMA-450 system the maximum allowed EIRP reduces to -101 dBm/MHz.

Table 2 presents the maximum allowed EIRP for different frequency bands, for UWB activity factor of 100% and multi UWB transmitter scenario, for two different cases, (case A with 99.995% confidence and case B with 99% confidence respectively). Table 3 represents the maximum allowed EIRP for different frequency bands for UWB activity factor of 10% and multi UWB transmitter scenario for the two previous cases A and B.

It shall be mentioned that if the critical distance is reduced from the 1m already considered down to 0.5m, the maximum accepted UWB power densities should be decreased by 6 dB from the values given before.

UWB Coexistence with 3G and 4G Cellular Systems 335

The coexistence of UWB with 3G and 4G Cellular Systems has been studied in this chapter. In particular UMTS in the 2 GHz and in the 450 MHz (CDMA-450) frequency bands have been selected as examples of 3G cellular systems and the WiMAX system as example of 4G. The methodology used to account for the impact of UWB interference on the coverage range and capacity of the interfered systems has been explained in detail. Finally it has been

From the above given results we can conclude that the spectrum mask proposed by the FCC for indoor application (-51 dBm/MHz in the UMTS band and -41 dBm/MHz for the CDMA-450 band) is very high and cannot be tolerated by the mobile systems. From the results obtained we conclude that another spectrum mask with lower UWB power density has to be used.

[1] Ahmed B. T., Ramon M. C. & Ariet L. H., 2004, "Impact of Ultra Band (UWB) on Macrocell Downlink of DCS-1800 and GSM-900 Systems", Radioenginnering, Vol. 14,

[2] Ahmed B. T., Ramón M. C., 2008, "On the Impact of Ultra-Wideband (UWB) on Macrocell Downlink of UMTS and CDMA-450 Systems", IEEE Electromagnetic

[3] Ahmed B. T., Campos JL. M., Cruz J. C, 2010, "Impact of Ultra Wide Band emission on WiMAX systems at 2.5 and 3.5 GHz", Computer Networks, Vol. 54, pp.1573-1583. [4] Ciccoganini W., Durantini A., and Cassioli D., 2005, "Time domain propagation measurements of the UWB Indoor Channel Using PN-Sequence in the FCC-Compliant Band 3.6-6 GHz", IEEE trans. Antennas and Propagation, Vol. 53, No. 4, pp. 1542-1549. [5] Giuliano R., Mazzenga F., Vatalaro F., "On the interference between UMTS and UWB systems", pp: 339 – 343, IEEE Conference on Ultra Wideband Systems and

[6] Chiani M, and Giorgetti A, "Coexistence between UWB and Narrow-Band Wireless Communication Systems", Proceedings of the IEEE, Vol. 97, No. 2, February 2009, pp.

[7] Chóliz J, Hernández A, Alastruey I and Valdovinos A, "Coexistence and interworking between UMTS and UWB: a performance evaluation of a UMTS/UWB interoperability

platform", Telecomunication systems, Vol. 49, No. 4, pp. 409-420.

applied in a set of study cases in scenarios involving the 3G and 4G selected systems.

**9. Conclusions**

**Author details**

Ahmed Bazil Taha

Miguel Calvo Ramon

**10. References**

231-254.

No.1, pp. 51-55.

*Universidad Autonoma de Madrid, Spain*

*Universidad Politecnica de Madrid, Spain*

Compatibility, Vol. 5, No. 2, pp. 406-412.

Technologies, 2003 , 16-19 Nov. 2003.

**Figure 19.** Capacity reduction as a function of the EIRP in (dBm/MHz) for multi UWB transmitters.

**Table 2.** Maximum allowed EIRP for different frequency bands with UWB activity factor of 100%.

**Table 3.** Maximum allowed EIRP for different frequency bands with UWB activity factor of 10%.


*2.1.2. Particle size in the suspension*

Several works report the influence of the particle size in suspension on the homogeneity of the coating, its microstructure and morphology, and on the particle incorporation in the metal matrix [37,46,47]. The number of particles incorporated per area is associated with the selectivity related to the particle size in suspension, as observed for the NiP–SiC coatings, where the amount of SiC particles in the coating increased due to the decreased size of the SiC particles in the electrolyte [27,28]. However, it is not a consensus in the literature, as there are reports showing a reduction in the amount of codeposited particles as their size decreases [5, 6]. In aqueous ionic solution, the particles are easily bonded to each other due to the compres‐ sion of the diffusion double layer around them, originated by the high ionic strength. Although the stirring of the electrolytic bath may decrease the particle aggregation and favors their codeposition, this effect is more pronounced for particles of micrometric size, as the shear forces on the agglomerates generated by the stirring process decrease with the particle size [5].

The influence of SiC particle size (1 μm and 45–55 nm) in the microstructure and morphology of the nickel MMC coatings was evaluated [46]. The results showed that, whereas the pure nickel coating presented a pyramidal morphology, the addition of SiC nano- or microparticles produced a coating with a rough nodular surface. This new morphology was related to the presence of nickel grains surrounding the SiC particles, forming globular aggregates. It has been argued that the introduction of the ceramic particles disturbed the formation of the metallic matrix and decreased the grain size of the coating [46]. However, no differences were observed concerning the size of the particles.

The effect of microparticle concentration of SiC (5 and 0.3 μm) in the volumetric percentage of metal deposited on the nickel matrix was evaluated [26], and it was observed that there was an increase in the volumetric percentage of deposited SiC particles as their concentration in the bath increased, independent of the particle size. However, the greatest volumetric percentage of codeposited SiC particles was obtained using 5 μm particles, whereas the lowest percent‐ age was deposited using 0.3 μm particles. It was pointed out that a direct comparison of the volumetric percentage of codeposited particles with different sizes may lead to a misunder‐ standing because of the differences in the particle density on the composite coating [26].

The effect of SiC particle size (1.2, 8.0, 14.0, and 20.0 μm) on nickel composite coatings was investigated by Kim and Yoo [48], and it was verified that an increase in the size of the particle (until 14 μm) added to the electrolytic bath induced an increase in the content of the particles in coatings. This fact was explained by the adsorption of Ni2+ ions on the particles, resulting in a strong Coulomb force and an increase of the SiC particles codeposited. Above 14 μm, however, the particles presented a sedimentation tendency due to their weight, which decreased the suspension stability and the content of SiC particles codeposited in the metallic matrix [48].


**Modeling of Soil Erosion and Sediment Transport**

Editor

**Vlassios Hrissanthou**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editor* Vlassios Hrissanthou Democritus University of Thrace Greece

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Water* (ISSN 2073-4441) (available at: https://www.mdpi.com/journal/water/special issues/modeling soil erosion).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Article Number*, Page Range.

**ISBN 978-3-03943-002-4 (Hbk) ISBN 978-3-03943-003-1 (PDF)**

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**3.3 NO-induced muscle relaxation through Ca2+ signaling**

To fully understand how NO causes vasodilation, it is necessary to perceive the relationship between NO-cGMP pathway and [Ca2+]i. At rest, extracellular Ca2+ concentration is high (1–2 mM), while the cytosolic Ca2+ is over 1000 times lower (>1 μM) [40, 41]. In the endoplasmic reticulum (ER, or SR in cardiac muscles), Ca2+ concentration is also high (about 400 μM) [40]. NO modulates [Ca2+]i by controlling Ca2+ exchange mechanisms on both cell and SR membranes.


# **Systems Engineering Availability and Reliability**

Edited by

Katarzyna Antosz, Jose Machado, Dariusz Mazurkiewicz, Dario Antonelli and Filomena Soares

Printed Edition of the Special Issue Published in *Applied Sciences*

www.mdpi.com/journal/applsci


**Intelligent Transportation Related Complex Systems and Sensors**

Editors

**Kyandoghere Kyamakya Jean Chamberlain Chedjou Fadi Al-Machot Ahmad Haj Mosa Antoine Bagula**

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This is a reprint of articles from the Special Issue published online in the open access journal *Sensors* (ISSN 1424-8220) (available at: https://www.mdpi.com/journal/sensors/special issues/ Transportation Systems).

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**3. Simulation and Analysis of the Welding Process**

Welding deformation is the most important factor affecting welding quality. Welding deformation will lead to a manufacturing delay, economic cost, and reduced productivity. Excessive deformation may seriously damage manufacturing in extreme cases, leading to failure [25]. At the same time, high welding residual stresses in the weld can adversely affect the safety and performance of welded components [26,27]. In this study, Simufact Welding software is used to simulate the welding process of duckbill welding parts, and the influence of deformation and the stress of weldments under a single-sided single welding torch and bilateral symmetrical double welding torch, two welding forms, and two welding process parameters, is analyzed.

#### *3.1. Heat Source Model*

In welding simulation, a reasonable heat source model is very important for the accurate calculation of post-weld deformation and welding stress [28]. To realize the simulation calculation, the commonly used heat source models are the classical Gaussian distribution heat source model and the double ellipsoid heat source model [29,30]. The Gaussian model can obtain better calculation accuracy for planar high-energy beam welds in simulation calculations. The double ellipsoid heat source model is more close to the actual welding situation of a fillet weld, so this study chooses the double ellipsoid heat source model for calculation.

The heat flux density expression of the front part of the double ellipsoid heat source is:

$$q\_f(x,y,z) = \frac{6\sqrt{3}f\_t q\_0}{abc\_f \pi \sqrt{\pi t}} \exp(-\frac{3x^2}{c\_f^2} - \frac{3y^2}{a^2} - \frac{3z^2}{b^2}).\tag{1}$$

The heat flux distribution expression of the second half of the double ellipsoid heat source is:

$$q\_b(x,y,z) = \frac{6\sqrt{3}f\_t q\_0}{abc\_b \pi \sqrt{\pi t}} \exp(-\frac{3x^2}{c\_b^2} - \frac{3y^2}{a^2} - \frac{3z^2}{b^2}).\tag{2}$$

In the formula: *a*, *b*, *cf*, and *cb* are oval shape parameters of the heat source; *q*0 is the heat input power, and *q*0 = *η*UI; and *ff*, *fb* are the heat flux distribution coefficients of the ellipsoid before and after the heat source, *ff* + *fb* = 2.

#### *3.2. Establishment of Welding Model*

The solid model of duckbill welded parts was established by SolidWorks, and then the model was imported into Hypermesh for hexahedral meshing. The number of finite element mesh nodes was 37,394, and the number of finite elements was 27,997. The divided model was imported into Simufact Welding for assembly and configuration, as shown in Figure 4. In this study, the weldment material is Q235, and the energy input per unit length of the weld (line energy) is calculated according to Equation (3).

$$Q = \eta \frac{IL}{v} \tag{3}$$

**Figure 4.** Meshing model of duckbill welding parts.

In the formula: *Q* is the line energy; *I* is the welding current; *U* is the welding voltage; *v* is the welding speed; and *η* is the welding thermal efficiency. As the weld of duckbill weldment is fillet weld, the welding heat is relatively concentrated. In this study, the welding thermal efficiency is taken as 0.8 in the simulation process [31].

#### *3.3. Welding Simulation Results and Analysis*

3.3.1. Effect of the Unilateral Single Welding Torch and Bilateral Symmetrical Double Welding Torch on Welding Deformation and Stress

Figure 5 shows the deformation of the duckbill welding parts under the single welding torch and the bilateral symmetrical double welding torch. By comparing and analyzing their total displacement cloud diagrams, the following conclusions were obtained: The area of deformation was larger under the condition of the single welding torch. This is because the two sides of the workpiece are uniformly heated and uniformly contracted at the same time by using the bilateral symmetrical double welding torch to reduce the distribution of welding deformation. The maximum displacement difference under the two conditions is 0.09 mm.

Figure 6 shows the equivalent stress diagram under the condition of the single welding torch and the bilateral symmetrical double welding torch. It can be seen from the figure that under the two conditions, the equivalent stress decreases rapidly from the center of the weld generation area, and then tends to be gentle until it is close to zero. A large stress is generated in the weld zone, which is one of the main reasons for the deformation of the static duckbill. After welding, the weldment is cooling, and the volume shrinkage around the weld is caused by the decrease in temperature. However, the weldment is constrained to prevent its shrinkage, so large tensile stress is generated in the weld area. Under both conditions, the maximum stress difference produced by the duckbill component is 7.28 MPa, but welding a duckbill component with a single torch takes more time than with a bilateral symmetrical double torch. Therefore, this study finally chose the welding method of the bilateral symmetrical double welding torch.

**Figure 6.** Equivalent stress diagram under the condition of the single welding torch and bilateral symmetrical double welding torch. (**a**) Single welding torch; (**b**) bilateral symmetrical double welding torch.

3.3.2. Effect of Welding Form on Welding Deformation and Stress

Figure 7, respectively, shows the use of continuous welding and spot welding under the two forms of total displacement cloud. From Figure 7, it can be seen that the displacement areas of the two were mainly distributed at the top of the static duckbill, and the deformation of the rest was relatively small. This is because the deformation of the fixed part is smaller than that of the free part. The position and deformation of the fixed part will be greatly limited under the action of the clamping device, so the thermal deformation

is reduced during the welding cycle. The maximum displacement of continuous welding is 0.98 mm, and that of spot welding is 0.26 mm. This is because in the weld, continuous welding, compared to spot welding, outputs greater thermal energy.

Figure 8 is the equivalent stress diagram of continuous welding and spot welding. It can be seen from Figure 8 that the stress distribution of spot welding is smaller than that of continuous welding, and the difference in their maximum stress value is 121.89 MPa. Their stress distribution is similar, the stress distribution appears to diffuse from the weld to the distance and then weaken, but it is obvious that the stress distribution of continuous welding is wider and wider. This study finally chose the welding form of spot welding.

**Figure 8.** Equivalent stress diagram under continuous welding and spot welding. (**a**) Continuous welding; (**b**) spot welding.

#### 3.3.3. Effect of Welding Process Parameters on Welding Deformation and Stress

Figure 9 is the total displacement diagram of the duckbill welded parts when the welding speed is 4 mm/s and 10 mm/s. It can be seen from the figure that the total displacement difference between the two welding speeds is 0.13 mm, but at the welding speed of 4 mm/s, the deformation area is relatively larger. This is because the deposition amount of the wire metal on the unit-length weld is inversely proportional to the welding speed, and the melting width is inversely proportional to the square of the welding speed. Therefore, when the welding speed increases, the energy decreases, the penetration depth and width decrease, and the deformation area is relatively reduced.

**Figure 9.** Total displacement diagram at the welding speed of 4 mm/s and 10 mm/s; (**a**) 4 mm/s; (**b**) 10 mm/s.

Figure 10 is the equivalent stress diagram under the two welding speeds of 4mm/s and 10 mm/s. As can be seen from the figure: 4 mm/s welding speed under the maximum equivalent stress is larger and the equivalent stress of a wider range of areas. Welding speed is directly related to the size of the welding productivity, and to obtain the maximum welding speed, should be on the premise of quality assurance as far as possible, according to the specific circumstances of the appropriate adjustment of welding speed, to ensure that the weld height and width are the same. In this study, the welding speed is finally selected as 10 mm/s.

**Figure 10.** Equivalent stress diagram at the welding speed of 4 mm/s and 10 mm/s; (**a**) 4 mm/s; (**b**) 10 mm/s.

#### **4. Design of Duckbill Welding Robot for Cotton Seeder**

### *4.1. Structure Composition and Working Principle*

The duckbill welding robot of the cotton planter is mainly composed of a girdle feeding mechanism, static duckbill feeding mechanism, hinge feeding mechanism, support table, welding fixture, welding actuator, and control system, as shown in Figure 11.

Working process: Firstly, the girdle feeding mechanism completes the girdle feeding, and then the hinge and the static duckbill feeding structure completes the feeding work in turn. After the three welding parts of the girdle, the hinge, and the static duckbill are all loaded, the workpiece enters the position to be welded, the clamping cylinder works to clamp the workpiece, and the welding actuator moves and performs welding. After the welding is completed, the welding platform is opened, and the weldment falls to the ground.

**Figure 11.** Structure diagram of duckbill welding robot for cotton seeder. 1. Girdle feeding mechanism; 2. static duckbill feeding mechanism; 3. hinge feeding mechanism; 4. support platform; 5. welding fixture; 6. welding actuator.

### *4.2. Design of Girdle Feeding Mechanism*

According to the analysis of the assembly requirements of the duckbill parts, the feeding mechanism needs to meet the following requirements: (1) the hinge and the girdle should be vertical; (2) the static duckbill and the hinge are symmetrically distributed in the transverse center when they are matched with the girdle; (3) the static duckbill should avoid shielding girdle under the mouth. According to the above assembly requirements and the structural parameters of duckbill welding parts, the feeding structure is designed. The feeding mechanism realizes the sequential feeding action of welded parts through the cooperation of an inductive proximity switch, electromagnet, and cylinder.

The structure size of the girdle feeding mechanism is 800 mm × 68 mm × 22 mm. It adopts a modular design and is installed on the support platform through the aluminum profile pillar. The working process is as follows: When the inductive proximity switch detects that there is a girdle in the storage chute, the electromagnet is energized and absorbs the second girdle, and the cylinder shrinks. The first girdle falls freely to the girdle waiting area due to gravity, and finally, the mini cylinder pushes the girdle into the welding area. The girdle feeding mechanism is shown in Figure 12.

**Figure 12.** Structure diagram of the girdle feeding mechanism. 1. Welding area; 2. girdle blanking waiting for area; 3. cylinder; 4. inductive proximity switch; 5. electromagnet; 6. storage chute; 7. girdle; 8. mini cylinder.

#### *4.3. Design of Static Duckbill and Hinge Feeding Mechanism*

To save space, the static duckbill feeding mechanism and the hinge feeding mechanism adopt an integrated design, and the assembly relationship of the parts is shown in Figure 13. The static duckbill and the hinge feeding mechanism are equipped with fixed plates to fix inductive proximity switches, electromagnets, and cylinders.

**Figure 13.** Structure diagram of static duckbill and hinge feeding mechanism. 1. Block cover; 2. cylinder; 3. inductive proximity switch; 4. electromagnet; 5. static duckbill; 6. arc feeding plate; 7. girdle; 8. connecting plate; 9. storage chute; 10. fixed plate 2.

The width of the storage chute of the hinge feeding mechanism is bent according to the dimensions of the hinge, and the bending angle is 90◦. To ensure that the hinge is perpendicular to the girdle during blanking, the lower end of the storage chute adopts a circular arc design, and its arc inner diameter is 100 mm. To prevent the hinge from sliding out of the arc guide rail when feeding, the guide bars are symmetrically distributed on both sides to guide and limit displacement. The verticality of the hinge is ensured by limiting the outer side of the guide bar and the arc guide rail. The guide bar is shown in the partially enlarged section view in Figure 13.

The main component of the static duckbill feeding mechanism is an arc feeding plate, and the arc feeding plate is connected with the hinge storage chute through a connecting plate. When the static duckbill is feeding, the contact with the feeding plate is strip contact, and the contact area is small, which greatly reduces the friction when sliding. When sliding, the static duckbill slides along the outer edge of the arc feeding plate. To prevent it from sliding out directly at the outer arc position, a block cover is placed at the lug of the hinge storage chute. The feeding accuracy of the static duckbill will directly affect the welding quality. Therefore, there are multiple through holes on the arc feeding plate and the connection plate, respectively, and the porous coordination ensures structural stability. The static duckbill and hinge feeding mechanism structure diagram is shown in Figure 13.

#### *4.4. Design of Welding Fixture*

As shown in Figure 14, the welding fixture is mainly composed of three parts: girdle clamping mechanism, hinge clamping mechanism, and static duckbill clamping mechanism. The girdle clamping device is positioned by a limit block and clamped by a girdle pusher. The girdle first slides down from the girdle storage chute to the girdle waiting area, and the girdle push plate sticks out. According to the four-point positioning principle, the transverse and longitudinal positioning and clamping of the girdle are completed.

**Figure 14.** Structure diagram of welding fixture. 1. Limit block; 2. limit block; 3. limit block; 4. welding workbench; 5. static duckbill clamping device; 6. girdle push plate; 7. static duckbill; 8. hinge; 9. hinge clamping device; 10. girdle; 11. limit block.

The hinge clamping mechanism is composed of a guide bar and a hinge push plate. The guide bar is close to the side wall of the storage chute, symmetrically distributed on both sides, and plays a guiding and limiting role to the hinge. The guide bar is shown in the partially enlarged section of Figure 13. After the hinge is loaded onto the welding platform, the hinge is pushed out to complete the positioning of the hinge.

The static duckbill clamping mechanism is mainly composed of a cylinder and clamp push plate. The arc feeding plate supports and guides the static duckbill. After the static duckbill slides down to the welding workbench, the clamp push plate is pushed out to complete the horizontal and vertical positioning of the static duckbill.

The bottom of the welding workbench is composed of two welding bottom plates and two cylinders. The welding workbench can open and close under the action of the cylinder.


*Article* **Temporal and Spatial Variation of Radon Concentrations in Environmental Water from Okinawa Island, Southwestern Part of Japan**

**Shunya Nakasone 1,\*, Akinobu Ishimine 2, Shuhei Shiroma 3, Natsumi Masuda 3, Kaori Nakamura 1, Yoshitaka Shiroma 4, Sohei Ooka 5, Masahiro Tanaka 6,7, Akemi Kato 8, Masahiro Hosoda 9, Naofumi Akata 10, Yumi Yasuoka 11 and Masahide Furukawa 1**

	- 3 Faculty of Science, University of the Ryukyus, Nishihara, Okinawa 903–0213, Japan<EMAIL_ADDRESS>(S.S<EMAIL_ADDRESS>(N.M.)

**Abstract:** In this study, to get a better understanding in characterizing groundwater and ensure its effective management, the radon concentrations in water samples were measured through Ryukyu limestone in southern Okinawa Island, Japan. Water samples were collected from a limestone cave (Gyokusendo cave, dropping water) and two springs (Ukinju and Komesu, spring water), and the radon concentrations were measured by liquid scintillation counters. The radon concentrations in the samples from the Gyokusendo cave, and Ukinju and Komesu springs were 10 ± 1.3 Bq L−1, 3.2 ± 1.0 Bq L−1, and 3.1 ± 1.1 Bq L−1, respectively. The radon concentrations showed a gradually increasing trend from summer to autumn and decreased during winter. The variation of radon concentrations in the dripping water sample from the Gyokusendo cave showed a lagged response to precipitation changes by approximately 2–3 months. The estimated radon concentrations in the dripping water sample were calculated with the measured radon concentrations from the dripping water obtained during the study period. Based on our results, groundwater in the Gyokusendo cave system was estimated to percolate through the Ryukyu limestone in 7–10 days, and the residence time of groundwater in the soil above Gyokusendo cave was estimated to be approximately 50–80 days. This work makes a valuable contribution to the understanding of groundwater processes in limestone aquifers, which is essential for ensuring groundwater sustainability.

**Keywords:** radon concentration; groundwater; residence time; limestone aquifer; Okinawa Island

**Citation:** Nakasone, S.; Ishimine, A.; Shiroma, S.; Masuda, N.; Nakamura, K.; Shiroma, Y.; Ooka, S.; Tanaka, M.; Kato, A.; Hosoda, M.; et al. Temporal and Spatial Variation of Radon Concentrations in Environmental Water from Okinawa Island, Southwestern Part of Japan. *IJERPH* **2021**, *18*, 998. https://doi.org/10.3390/ ijerph18030998

Academic Editor: Paul B. Tchounwou Received: 28 December 2020 Accepted: 20 January 2021 Published: 23 January 2021

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**Copyright:** © 2021 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/).

#### **1. Introduction**

Radon is a radioactive noble gas with an atomic number of 86. Although there are many isotopes of radon, only three occur in the natural environment: 222Rn, 220Rn (Thoron), and 219Rn (Actinon), with half-lives of 3.8 d, 55.6 s, and 3.9 s, respectively. 222Rn is the most frequently used radon isotope due to its relatively long half-life compared to thoron and actinon. Hereinafter, "radon" refers to 222Rn. Radon is a natural radionuclide belonging to the uranium (238U) decay series, with radium (226Ra) as a parent nuclide. The 238U decay series ends with 206Pb, a stable isotope of lead. Inhalation of radon and its decay products is believed to increase the risk of lung cancer [1]. Most radon in the environment is produced by the alpha decay of radium in the soil and rocks. Since radon is extremely soluble in water (e.g., 0.295 cm3 kg−1 at 15 ◦C), there may be an exchange and equilibrium between pore-water and pore-gas when the soil is unsaturated [2]. In addition, radon is chemically inert. Therefore, if it is dissipated from soil grains into pore water or voids, it can migrate relatively far from where it is produced by advection or diffusion [2]. Eventually, some of the radon dissipated in the pore water migrates to the groundwater and is dissolved in spring or hot spring water. Radon, which is widely distributed in the natural environment, is commonly used to study atmospheric transport processes and groundwater flow systems such as groundwater recharge, flow, and discharge [3,4].

Radon can be used as a hydrological tracer for estimating the residence time of groundwater under very special boundary conditions (e.g., in limestone areas or for submarine groundwater discharge in the coastal zone) [5–7]. In karst aquifers, researchers have analyzed radon concentrations to understand the recharge dynamics of water passing through the soil layer to the saturated zone [8]. Additionally, groundwater and soil radon concentrations are measured during irrigated and non-irrigated periods in paddy fields, and the difference in the concentration values between groundwater and surface water is used to evaluate surface water infiltration into the aquifer [9]. Radon can also be used for estimating the probability of geophysical risk events such as volcanic activity and earthquakes [10–12]. Pre-earthquake radon anomalies have been found in soil gases, groundwater, and spring water [13,14]. Radon serves as a useful geochemical tracer for studying the short-term environmental characteristics of cave systems, such as the flow of the atmospheric circulation. Variations in the air density and atmospheric radon concentrations inside and outside caves have been reported for the seasonal natural ventilation in the cave [15–17].

The storage capacity of a limestone layer alone is low due to it being highly permeable and porous. However, when a limestone layer overlies an impermeable layer in a nonconformant way, as is the case on Okinawa Island, the limestone serves as an aquifer. Limestones can serve as excellent natural aquifers and provide drinking water to 25% of the world's population [18]. Moreover, water in limestone aquifers is the only available drinking water in some regions and serves as a valuable water source for agriculture [18].

Okinawa Island, a Japanese island in the East China Sea, is characterized by short and steep river channels. Moreover, because its watershed area is small, the rainfall is discharged directly into the ocean. Okinawa Island experienced a relatively high annual precipitation of 2040 mm during the 1981–2010 period [19]. However, due to significant seasonal and interannual changes, the flow of rivers on the island is unstable and the amount of water available is limited. Therefore, it is difficult to secure stable water resources in Okinawa Island from river water alone and part of the water for agriculture and domestic use is provided by groundwater. To establish a stable supply and ensure the sustainable use of groundwater in the future, it is necessary to determine the groundwater characteristics, such as availability and residence time, in limestone aquifers. However, because of the heterogeneous permeability and complex hydrogeological structure of limestone, it is difficult to understand the behavior of groundwater from existing numerical models. For example, groundwater infiltration and residence time in limestone aquifers vary with each precipitation event.

Characteristics of local groundwater processes can be inferred from radon measurements. Shiroma et al. (2016) intermittently measured the radon concentration in dripping

water exuding from the ceiling of a cave and reported that it is a potential source of radon in the cave atmosphere [20]. Although other factors may be involved, by comparing radon concentrations and monthly precipitation, changes in radon concentrations were shown to correspond to precipitation changes after a delay of 60–90 days. Furthermore, groundwater was calculated to percolate through the Ryukyu limestone in 9 to 10 days. However, the radon concentration data were collected intermittently for only one year and only at one site.

The main objective of this study is as attempt to calculate the residence time of groundwater in limestone areas. The study area comprised a limestone cave and two springs in the southern part of Okinawa Island. The residence time of groundwater from the soil to the cave was calculated based on the relationship between the radon concentration of the dripping water from the cave ceiling and the monthly precipitation. Moreover, we sampled two springs in the study area to understand the spatial distribution of radon concentrations in the groundwater.

#### **2. Materials and Methods**

#### *2.1. Overview of the Study Site*

Okinawa Prefecture, located in southwestern Japan, is the only prefecture in the country with a subtropical climate (Figure 1A). Ryukyu limestone, which originates from coral reef sediments, formed approximately one million to 500,000 years ago [21]. Limestone is widely distributed, and there are numerous limestone caves in Okinawa. The southern part of Okinawa Island consists of the Neogene-Quaternary Shimajiri Group and the Ryukyu Group, with low-lying terrain below 200 m in elevation. The Shimajiri Group of the Neogene Pliocene is predominantly composed of mudstone and is prone to erosion, while the limestone Quaternary Pleistocene Ryukyu Group, which is predominantly composed of limestone, is more resistant to erosion and forms a flat-surfaced topography [22]. The Ryukyu limestone overlies the Shimajiri Group in a non-conformant way. The Ryukyu limestone is permeable, while the Shimajiri Group is impermeable [23]. Therefore, Ryukyu limestones serve as aquifers in Okinawa Island (i.e., Ryukyu aquifer).

**Figure 1.** Locations of the study area (**A**) and sampling sites (**B**). The white square shows the Automated Meteorological Data Acquisition System (AMeDAS) situated at Itokazu.

Okinawa Island has three major soil types: Shimajiri Mahji, Kunigami Mahji, and Jahgaru. Shimajiri Mahji is a dark red soil, which is widely distributed in the Ryukyu limestone distribution area [24], with a reddish brown to dark brown color that is neutral to slightly alkaline [25]. Dark red soil is derived from the weathering products of limestone and/or a high-background radiation area, mainly the southeastern part of China [25].

#### *2.2. Sample Collection*

Water samples were collected from Gyokusendo cave (26◦08 25N, 127◦44 57W, 46 m altitude, dripping water), Ukinju spring (26◦08 15 N, 127◦47 35 W, 6.5 m altitude, spring), and Komesu spring (26◦05 18N, 127◦42 04W, 3.2 m altitude, coastal spring), all located in the southern portion of Okinawa Island (Figure 1B). Water samples were collected from October 2016 to October 2020. Sample collection at Ukinju and Komesu started in April 2018 and May 2018, respectively. The collection period in the springs was delayed because it took time to find a location with a high flow rate in the coastal area. However, data for April to June 2020 are missing for each location due to the COVID-19 pandemic.

Gyokusendo cave is a limestone cave situated in a 120-m-thick body of Ryukyu limestone. Shimajiri Mahji soil is distributed on the surrounding ground surface. The total length of Gyokusendo cave reaches 5000 m, and approximately 800 m are open for tourism [26]. A concrete staircase with a height of approximately 30 m serves as an entrance to the main cave. A branch cave in an undisclosed area about 30 m away from the stalagmite named "Shoryu no Kane" was selected as the water sampling point (Figure 2). There are soda-straw stalactites (straws) at this site with a significantly high drip rate. Therefore, it was possible to collect 10 mL of dripping water in approximately 10 min. The Ryukyu limestone is exposed at Ukinju spring and Komesu spring, and Shimajiri Mahji is distributed in the surrounding ground surface at both sites. The Ukinju and Komesu springs are located near the coast, 170 m and 12 m from the coastline, respectively.

**Figure 2.** Location of the sampling point in Gyokusendo cave (map from, Shiroma et al. 2016 [20]).

For sample collection, 20 mL high-performance glass vials (PerkinElmer, USA) containing 10 mL of high efficiency mineral oil scintillator (PerkinElmer, USA) were prepared. For each site, 5 samples were collected. The water samples were collected using a syringe with the needle removed. Water samples (12 mL) were collected directly from the straw formed in the cave ceiling in Gyokusendo cave. At the Ukinju and Komesu sites, the same sample amounts were collected near the spring outlets. The syringe was pointed upward, and the plunger was pressed carefully to remove air from the inside. A needle was then attached, and the tip of the needle was positioned just over the bottom of the glass vial, and 10 mL of sample was injected. The time elapsed from water sample injection to the closing of the lid of the glass vial was set as the water sampling time. After water sampling, the samples were shaken for 60 s and left in the dark for 4 h until radiative equilibrium was achieved to extract the radon from the sample water into the mineral oil scintillator.

Temperature and humidity data in the cave were measured using HOBO Pro v2 Loggers (U23–001, ONSET, USA). However, data for December 2016 and February 2017 were missing due equipment failure. Precipitation data outside the cave were obtained from the Automated Meteorological Data Acquisition System (AMeDAS) situated at Itokazu (Figure 1B). The pH and electrical conductivity (EC) of the water samples were measured using a pH meter (AS-712pH, HORIBA) and an EC meter (B-771COND, HORIBA). To verify the existence of saltwater inflow, the salinity (PSU; Practical Salinity Unit) was also measured at Ukinju and Komesu spring located in the coastal area using a digital salinity meter (HI96822, HANNA instruments).

#### *2.3. Analysis of Radon Concentration in Water*

The radon concentrations in water were measured for 60 min per sample using a liquid scintillation counter. The measurements were performed using a Quantulus 1220 (PerkinElmer, USA), at the National Institute for Fusion Science, from October 2016 to February 2019 and a Tri-Carb 2019TR (PerkinElmer, USA), at the University of the Ryukyus Center for Research Advancement and Collaboration, from March 2019 to October 2020. The radon concentration *C*R (Bq L−1) was calculated using the following equation:

$$\mathcal{C}\_{\rm R} = \frac{(A\_0 - B\_0) \exp\left(\lambda T\_p\right)}{f\_{\rm Rn} V} \tag{1}$$

where *A*0 is the counting rate (cps) of the water sample, *B*0 is the counting rate (cps) of the background sample, *λ* is the radon decay constant (day−1), *Tp* is the elapsed period (day), which was corrected for radioactive decay to between the sampling time and the middle measurement time, *f*Rn is the radon conversion factor of 3.24 cps Bq L−1 for the Quantulus 1220 and 4.5 cps Bq−1 for the Tri-Carb2019TR, and *V* is the sample volume (0.01 L) [27].

For measurements using the Quantulus 1220, *A*0 and *B*0 were calculated using the counting rate cps for 600–910 ch. Additionally, for measurements using the Tri-Carb2019TR, *A*0 and *B*0 were calculated using the counting rate cps for 0–1024 ch using the integral counting method [27–29]. The minimum detected radon concentration *MDC* (Bq L−1) was calculated using the following equation [30]:

$$N\_D = \ 4.65 \ \sqrt{\frac{B\_0}{t}} + \frac{2.71}{t} \tag{2}$$

$$\text{MDC} = \frac{N\_D}{f\_{\text{Ru}} V} \tag{3}$$

where *ND* is the minimum value of net count rate (cps), *t* is the counting time of the sample and the background (sec). The minimum detected radon concentrations by this method were 1.0 Bq L−1 for the Quantulus1220 and 0.9 Bq L−1 for the Tri-Carb2019TR [30].

Uncertainties were estimated for the following components: the counting rate (cps) of the water sample and background sample, the measurement time (s) of the water sample and background sample, the radon decay constant (s−1), the elapsed period (s), the radon conversion factor (cps Bq L−1), and the sample volume (L). Estimates were also made for each of these components, which were combined to give the expanded uncertainty [31]. By applying a coverage factor of *k =* 2, the expanded uncertainty was evaluated as 10%. In this study, radon concentrations are expressed as the arithmetic mean ± standard deviation of 5 samples.

#### **3. Results and Discussion**

#### *3.1. Physical Parameters and Meteorological Data*

The pH and EC of the water samples and the monthly precipitation data are shown in Figure 3. The pH and EC range of the samples from the Gyokusendo cave were 6.8–7.9, 0.76–1.52 mS cm−1. For the Ukinju spring, the pH and EC ranged from 6.7–8.2 and 0.64 and 0.99 mS cm−1. For the Komesu spring, the pH ranged between 6.5–8.1 and the EC measured between 0.55 and 0.96 mS cm−1.

**Figure 3.** Monthly variations for the October 2016–October 2020 period of pH (**A**), electrical conductivity (**B**), and precipitation (**C**) in the water samples, and temperature (**D**) in the Gyokusendo cave.

The results reveal no significant seasonal variation in pH and EC. The monthly precipitation ranged from 11.50–669.50 mm. The monthly precipitation tended to be higher during the rainy season (May-June) and the typhoon season (August-October) and lower in winter (December–February). The salinity of Ukinju spring and Komesu spring, located near the coast, was in the range of 0–1 PSU, and seawater contamination was not observed. The temperature inside the Gyokusendo cave is shown in Figure 3D. The temperature outside the cave showed a marked seasonal variation (high in summer, low in winter) of 15–27 ◦C [19], while inside the cave, the temperature varied marginally, ranging from 23–27 ◦C. The humidity in the cave was generally 100% throughout the measurement period.

#### *3.2. Temporal Variation of Radon Concentrations for the Water Samples*

Radon concentrations for the dripping water collected at the Gyokusendo cave ranged from 7.1–13 Bq L−1 (Figure 4), with an average of 10 ± 1.3 Bq L−1. The radon concentration for the dripping water collected at the same site between May 2013 and March 2014 was relatively consistent and ranged between 6.4 to 15 Bq L−1 [20]. The radon concentration for the dripping water increased gradually from summer (June–August) to autumn (September–November) and then decreased in winter (December–February).

**Figure 4.** Radon concentration variation for the Gyokusendo cave, Ukinju spring and Komesu spring water samples collected during the October 2016–October 2020 sampling period.

The monthly average radon concentrations of the dripping water for the entire sampling period were the highest (11 ± 0.8 Bq L−1) in October and the lowest (8.8 ± 1.2 Bq L−1) in February (Figure 5). These variations were similar to the seasonal variations in atmospheric radon concentrations in the Gyokusendo cave sample (high in summer and low in winter) observed between July 1990 and January 1993 [15–17]. The radon concentrations for the water collected at Ukinju spring and Komesu spring were 3.2 ± 1.0 Bq L−1 (1.1–6.1 Bq L−1) and 3.1 ± 1.1 Bq L−1 (1.1–5.8 Bq L−1) (Figure 4). There was a gradual increase in radon concentrations from summer to autumn. However, no clear seasonal variation was apparent (Figure 4).

**Figure 5.** Variation of radon concentrations for the dripping water sample from the Gyokusendo cave compared to the monthly precipitation obtained from AMeDAS (Itokazu) for the study period.

#### *3.3. Relationship between Radon Concentration for Dripping Water and Monthly Precipitation*

The temporal variation in the radon concentrations of the dripping water from the Gyokusendo cave and the precipitation recorded for the study period are shown in Figure 5. The monthly precipitation was approximately 500 mm during the rainy season, increased during the typhoon season, and decreased to approximately 100–150 mm during winter.

In May 2017, an anomalously high precipitation amount of 579 mm (equivalent to approximately 30% of the 2017 annual precipitation) was recorded, and the radon concentration of the dripping water in July of that year was 12 Bq L−1, the highest recorded in 2017. The precipitation from May to June 2018 was lower than usual, averaging a monthly amount of 300 mm. However, in September 2018, the radon concentration in the dripping water from the cave was exceptionally high, reaching 12 Bq L−1. In 2019, the highest amount of precipitation for the year (597 mm) was recorded in June, and the maximum radon concentration for the year was recorded in September (12 Bq L−1). More than 600 mm of precipitation was observed in June 2020, and maximum radon concentrations of 12 Bq L−1 and 13 Bq L−1 were recorded in July and October 2020. Conversely, a decreasing precipitation trend corresponded to a decrease in the radon concentration. Additionally, time series analysis (moving average) of the radon concentrations in dripping water and precipitation showed a correlation between the 2-month moving average (*R* = 0.50) and the 3-month moving average (*R* = 0.50) [32]. Therefore, variations in radon concentrations of the dripping water samples indicated that the radon concentration increased during

periods of heavy rainfall, although a delay of around two to three months after a monthly precipitation change was observed. This suggests that precipitation percolates at a rate of 60 to 90 d from the soil to the Ryukyu limestone situated above the Gyokusendo cave and reaches the cave as dripping water.

Radon concentrations in groundwater reflect the inherent 226Ra contents of the host rock or formation [33]. In areas where karst aquifers are distributed, estimated radon concentrations in the soil are higher than the radon concentrations in the groundwater. Therefore, the main source of radon in groundwater has been reported to be soil [8]. Furthermore, the concentrations of the 238U series in the Ryukyu limestone (8.6 Bq kg−1) were reported to be lower than those in Shimajiri red soil (86.0 Bq kg−1) [34], suggesting that the main source of radon in groundwater is the soil deposited in the upper part of the Gyokusendo cave. Additionally, the amount of soil water infiltrating into the aquifer increases with heavy precipitation [7]. In limestone areas, the residence time of soil water was noted to reflect the residence time in the limestone bodies [8]. The flow velocity is proportional to the flow rate and inversely proportional to the cross-sectional area of the channel. The flow velocity is the distance an object moves per unit time, the flow rate is the volume of fluid per unit time flowing past a point through the cross-sectional area. The channel is referring to solution openings through the limestone. The following equations was used to calculate the flow velocity:

$$\nu = \frac{Q}{A} \tag{4}$$

where *ν* is the flow velocity (m s−1), *Q* is the flow rate (m3 s−1), and *A* is the cross-sectional area of the channel (m2). It is assumed that the main source of radon in groundwater is the soil deposited in the upper part of the Gyokusendo cave and that the cross-sectional area of the channel does not change significantly. Therefore, radon in the dripping water is derived from radon in the soil pore water, which is governed by the soil 226Ra content and the residence time of groundwater in the Ryukyu limestone. Moreover, the amount of groundwater in the Ryukyu limestone depends on precipitation. High precipitation rates shorten the residence time of groundwater in the Ryukyu limestone by increasing infiltration into the soil, which accelerates the groundwater flow velocity. Low precipitation rates reduce infiltration into the soil, thus, decreasing the groundwater flow velocity and increasing the residence time of groundwater. Attenuation is constant (the half-life, 3.8 day) over time. Therefore, the longer the residence time, the lower the radon concentration and vice versa. In this study, the radon concentration trends correlated with the precipitation trends, suggesting that the soil may be the source of radon in the groundwater. Moreover, the variation in the radon concentration of the dripping water sample may have been due to the residence time of groundwater in the Ryukyu limestone.

#### *3.4. Estimation of Residence Time Using Radon Decay in the Dripping Water*

A schematic of a vertical section of the Gyokusendo cave is shown in Figure 6. In the figure, A shows the boundary between the soil and the Ryukyu limestone, and B is a straw stalactite on the cave ceiling where the dripping water is collected. Precipitation percolates through the soil and limestone bodies above the cave and seeps into the cave from the ceiling as dripping water. Since radon is rarely present in precipitation, radon in groundwater is mainly supplied by the soil. The concentration of radon in water infiltrated into the ground increases with time due to the supply of radon produced by soil particles. However, as the concentration increases, the number of decaying radon atoms increases, and eventually, an equilibrium state is reached where supply and decay are equal. Therefore, radon in the soil water and 226Ra in the solid phase are in permanent equilibrium. Additionally, if radon remains in the soil long enough, the concentration of radon in the gas and liquid phases will reach the equilibrium state specific to each soil species. The following equations were used to quantify the radon concentration in soil water and the gas phase at gas-liquid equilibrium:

$$E\_{\rm w} = K\_T E\_{\rm a} \tag{5}$$

where *E*w is the radon concentration in soil water (kBq m−3), *KT* is the radon partition coefficient, and *E*a is the radon concentration in the gas phase (kBq m−3). The radon partition coefficient was calculated using the following equation [35]:

$$K\_T = \frac{9.12}{17.0 + T} \tag{6}$$

where *T* is the temperature (◦C), and the mean value of the temperature in the cave, 24.9 ◦C, was used.

**Figure 6.** Schematic of the Gyokusendo cave profile, where A is the boundary between the soil and the limestone, and B is the target straw stalactite.

According to the UNSCEAR 2000 (United Nation Scientific Committee on the Effects of Atomic Radiation 2000), the concentration of radon in soil pore gas *E*a (Bq m−3) can be calculated using the following equation [36]:

$$E\_{\mathfrak{a}} = \,^c\mathbb{C}\_{\mathbb{R}\mathfrak{a}} \, f \, \rho\_{\mathfrak{s}} \, \varepsilon^{-1} \left(1 - \varepsilon\right) \left[m(K\_T - 1) + 1\right]^{-1} \tag{7}$$

where *C*Ra is the concentration of 226Ra in the soil (Bq kg−1), *f* is the radon emanation coefficient, *ρ*s is the density of solid particles (kg m−3), *ε* is the porosity, and *m* is the water saturation. In this study, based on a previous report [37], the radon emanation coefficient for typical soil on Okinawa Island was set to 0.40, the concentration of 226Ra in soil was set to 106 Bq kg−1 dry, the solid particle density was set to 2697 kg m−3, and the porosity was set to 0.67. The water saturation was 0.95, which is representative of the UNSCEAR 2000 [36]. The radon partition coefficient *KT* was calculated using Equation (6) [35]. Since we could not directly measure the soil temperature, we used the temperature in the cave (24.9 ◦C) as approximation of the soil temperature. Radon concentrations (Bq m−3) in soil water were calculated using Equation (5). The above values were defined as the radon concentration in the groundwater at point A (Figure 6).

Because radon was not supplied from the Ryukyu limestone, we assumed that the radon concentration in the groundwater was attenuated by the residence time of the groundwater in the Ryukyu limestone. The estimated radon concentration in the dripping water sample *E*d, exuding from point B (Figure 6), was calculated using the estimated radon concentration in soil water from Equation (5) and the following equation.

$$E\_{\rm d} = E\_{\rm w} \exp(-\lambda t) \tag{8}$$

where *t* is the residence time (days) of groundwater in the Ryukyu limestone.

The estimated radon concentrations in the dripping water sample calculated from Equation (8) were compared with the measured radon concentrations in the dripping water obtained during the study period. The radon concentration in the soil pore gas calculated from Equation (7) was 215 kBq m−3. The radon concentration in the soil pore water calculated from Equation (5) was 46.8 kBq m−3. Radon concentrations for the dripping water sample obtained during the study period ranged from 7.1–13 kBq m−3 as shown in Figure 4. Based on the decay of radon (half-life: 3.8 d) from point A (estimated values) to point B (measured values) and the assumption that the residence time of soil and Ryukyu limestone is 60 to 90 d, the residence time of groundwater in the Ryukyu limestone was estimated to be 7 to 10 d. These results are generally consistent with previously reported values (9–10 d) [20]. Moreover, the residence time of groundwater in the soil deposited in the upper part of the Gyokusendo cave was estimated to be approximately 50 to 80 d.

The residence time of groundwater in the Ryukyu limestone at the Ukinju spring and Komesu spring sites was also calculated from Equation (8). For this calculation, we assumed that no radon was supplied from the Ryukyu limestone, the soil was uniformly deposited, and the radon concentration in the soil was in radiative equilibrium. The radon concentrations for the water samples from the Ukinju spring and Komesu spring sites ranged from 3.1–6.1 Bq L−1 and 1.1–5.8 Bq L−1, respectively. Based on the radon attenuation between the measured radon concentrations in the water samples and the estimated radon concentrations in the pore water, the residence times in the Ryukyu limestone at Ukinju spring and Komesu spring were estimated to be 12 to 21 d and 12 to 19 d, respectively. Nakaya et al. (2018) reported an average residence time in the Ryukyu limestone aquifer of 14–34 years based on the results of SF6 concentrations in the spring and well water around the Komesu spring site [38]. The residence time estimated in this study is very limited because it was calculated based on observations at only one point in the catchment. Although we were able to calculate the residence time, our results significantly differ from those of previous studies. In the future, it will be necessary to discuss the residence time after considering additional factors such as the frequency of collection, and number of sites, and depth of sampling sites.

#### **4. Conclusions**

Radon concentrations for dripping water and spring water samples were collected in the southern part of Okinawa Island from October 2016 to October 2020. The estimated radon concentrations in the dripping water sample were calculated with the measured radon concentrations from the dripping water obtained during the study period. Based on our results, we determined the following:

**Author Contributions:** Conceived and designed the survey: S.N., Y.S., N.A., M.F. Performed experiments: S.N., A.I., S.S., N.M., S.O., K.N., A.K. Analyzed the data: S.N., A.I., S.S., N.M. Wrote the paper: S.N. Acquired the funding: S.N., M.T., N.A., M.H., Y.S. Contributed to discussions: S.N., Y.S., N.A., Y.Y., M.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by JSPS KAKENHI Grant Number JP20J12397 and 18K14543, the NIFS budget NIFS20KLEA046 and NIFS18KNWA002.

**Acknowledgments:** The radon concentration analyses were performed at the National Institute for Fusion Science, University of the Ryukyus Center of Research Advancement and Collaboration.

**Conflicts of Interest:** The authors declare that they have no conflict of interest.

#### **References**


**2. Experimental Section**

The purpose of the performed experiments was to compare the structure and properties of Fe-Cr-Nb-B nanocrystalline surface layers deposited by manual metal arc (MMA) welding with a covered electrode 3.2 mm in diameter to previously used abrasion-resistant materials. HARDOX 400 steel was used as a reference material in the assessment of resistance to metal-mineral abrasion. The following materials were tested in the experiments:

Metallographic examinations of the deposited coatings were carried out on a Zeiss SteREO Discovery, and LEICA MEF4A optical microscope (Leica Microsystems, Cambridge, UK) in addition to a Zeiss Supra 35 (SEM) scanning electron microscope (Carl Zeiss GmbH, Jena, Germany). Metallographic images were taken of transverse sections with respect to weld deposition direction. The specimens were prepared by standard metallographic techniques and etched in Nital solution. Phase compositions of the investigated materials in addition to the size of the crystallographic grains of nanocrystalline microstructure were determined using an Xpert PRO X-ray diffractometer with step data logging, employing the filtered K@ X-rays. The chemical composition of precipitates was determined by energy-dispersive X-ray spectroscopy (EDS) with an EDAX detector. Hardness tests were conducted on a Zwick Roell ZHR hardness tester based on the Rockwell method, while cross-sectional hardness was measured using the Vickers method with a Future-Tech FM-700 tester (Future-Tech Corp., Kawasaki, Kanagawa Prefecture, Japan).


**Li Li 1, Shengxian Wang 1, Shanqing Zhang 1,\*, Ting Luo 2 and Ching-Chun Chang 3**

Received: 31 January 2020; Accepted: 21 February 2020; Published: 1 March 2020

**Abstract:** Robust reversible watermarking in an encrypted domain is a technique that preserves privacy and protects copyright for multimedia transmission in the cloud. In general, most models of buildings and medical organs are constructed by three-dimensional (3D) models. A 3D model shared through the internet can be easily modified by an unauthorized user, and in order to protect the security of 3D models, a robust reversible 3D models watermarking method based on homomorphic encryption is necessary. In the proposed method, a 3D model is divided into non-overlapping patches, and the vertex in each patch is encrypted by using the Paillier cryptosystem. On the cloud side, in order to utilize addition and multiplication homomorphism of the Paillier cryptosystem, three direction values of each patch are computed for constructing the corresponding histogram, which is shifted to embed watermark. For obtaining watermarking robustness, the robust interval is designed in the process of histogram shifting. The watermark can be extracted from the symmetrical direction histogram, and the original encrypted model can be restored by histogram shifting. Moreover, the process of watermark embedding and extraction are symmetric. Experimental results show that compared with the existing watermarking methods in encrypted 3D models, the quality of the decrypted model is improved. Moreover, the proposed method is robust to common attacks, such as translation, scaling, and Gaussian noise.

**Keywords:** three-dimensional models; cloud computing; histogram shifting; encrypted model; decrypted model


*Article* **Effect of K and Zn Application on Biometric and Physiological Parameters of Different Maize Genotypes**

**Hafiz Muhammad Ali Raza 1 , Muhammad Amjad Bashir 1,2 , Abdur Rehim 1,2,\*, Qurat-Ul-Ain Raza 1 , Kashif Ali Khan 2 , Muhammad Aon 1 , Muhammad Ijaz 2 , Shafeeq Ur Rahman 3 , Fiaz Ahmad 4 and Yucong Geng 5,\***

**Abstract:** Potassium (K) and zinc (Zn) are mineral nutrients required for adequate plant growth, enzyme activation, water retention and photosynthetic activities. However, Pakistani soils are alkaline and have serious problems regarding Zn deficiency. The current study aims at finding the nutrient–nutrient interaction of K and Zn to affect maize plants' (i) physiological processes and (ii) productivity. For this purpose, a pot experiment was conducted at the research area of the Department of Soil Science, Faculty of Agricultural Science and Technology, Bahauddin Zakariya University, Multan. Two maize genotypes, DK-6142 (hybrid) and Neelam (non-hybrid), were used with three K fertilizer doses, i.e., 0, 60 and 100 kg ha−1 in all possible combinations with three Zn fertilizer doses, i.e., 0, 16 and 24 kg ha−1 . The treatments were replicated under a completely randomized block design. The results elucidated that the combined application of K and Zn with K60 + Zn16 treatment significantly increased agronomic, productive, and physiological attributes. It has improved fresh biomass (89%), dry biomass (94%), membrane stability index (142%), relative water content (200%) and chlorophyll contents (191%) as compared to the control. Moreover, the mineral uptake of K and Zn was significantly improved with their maximum fertilization rate in hybrid genotype compared to non-hybrid and CK.

**Keywords:** potassium; zinc; maize; physiological attributes