**6. Conclusion**

The paravascular system is a branching structure that extensively connects the cells and vessels within the brain. The intricacy of the system and the challenges in performing studies have been a hurdle in exploring this system. However, an indepth analysis of brain fluid dynamics and its relationship to the cardiopulmonary mechanisms can provide a game changing pathway to the preventive and therapeutic measures of various pathophysiological brain disorders.


**Acknowledgement**

This work was supported in part by the Israeli Science Foundation (ISF), and by the Chief Scientist Office of the Israeli Ministry of Industry, Trade and Labor within "Tera Santa" consortium.

#### **7. References**

364 Optical Communication

**Author details**

Y. Ben Ezra

B.I. Lembrikov

*University, Israel*

D. Brodeski

consortium.

Avi Zadok, Ran Halifa

**Acknowledgement**

polarization of the optical wave has been calculated.

*Holon Institute of Technology (HIT), Holon, Israel; Optiway Integrated Solutions LTD, Rosh Haayin, Israel*

*Holon Institute of Technology (HIT), Holon, Israel*

*Optiway Integrated Solutions LTD, Rosh Haayin, Israel*

*School of Engineering and Institute for Nano-Technology and Advanced Materials, Bar-Ilan*

This work was supported in part by the Israeli Science Foundation (ISF), and by the Chief Scientist Office of the Israeli Ministry of Industry, Trade and Labor within "Tera Santa"

method in the modern optical systems combining the advantages of the coherent detection and OFDM modulation. However, the high data rate transmission is strongly influenced by the optical channel chromatic dispersion and PMD. As a result, the transmission system performance significantly deteriorates due to ISI and ICI. The dispersion influence can be mitigated by an appropriately chosen CP. Unfortunately, a long enough CP would decrease the system SE. The problem can be solved and the necessity of CP may be eliminated if a generic DFFT based CO-OFDM is replaced with a WPT-OFDM since WPs are localized both in time and frequency domains. The simulation results show that WPT-OFDM system provides a 500 km transmission distance at the FEC level of 2 2 10 without CP, with a small 5% GI. We proposed a novel hierarchical architecture of the WPT-OFDM system based on the separation of the low data rate and high data rate signal processing. The former ones are processed in the electrical domain, while the latter ones undergo the alloptical processing. The numerical simulations show that such an approach improves the WPT-OFDM system performance which is demonstrated by the constellation of a signal with QAM 16 modulation. The WPT-OFDM all-optical signal processing units can be implemented by using the passive SOI waveguide components. The passive components of this architecture are the wavelet filters realized by the SOI waveguide based MZIs. The change of the Si refraction index is realized by using the thermo-optic effect. We presented the numerical simulations of the Haar wavelet filters for all-optical signal processing based on such MZIs. The modal profile of a SOI waveguide in a single mode regime for each


4.2.3. ELM

The architecture of the ELM is the one previously used [23]: one input layer, one hidden layer, and one output layer. Activation function: first layer—ReLU; hidden layer— Sigmoid. Overall, the ELM model resulted (see Table 6) in a high accuracy and higher recall rates compared to Table 4, for any feature set. When compared to the SVM models, the *Base* model resulted in a lower recall rate (though a higher F1-score was achieved with the ELM model). On the other hand, the *Robust Base* resulted in a higher recall rate with the ELM model compared to the SVM model. Even though the *Robust Base* feature set had a low dimensional space, the three rates (i.e., accuracy, recall, and F1-score) were higher than those of the *Base* feature set. Using the sets that include the novel features increased these metrics while improving the robustness of the model at the same time.

**Table 6.** Model performance—ELM.


*3.2. Surface Integrity*

#### 3.2.1. Surface Roughness

Figure 5 displays the milled and burnished areas (4 mm × 4 mm) of each target surface in order to provide the qualitative effect of the burnishing operation under the stated operational and tribological conditions.

(**c**) AISI 316 burnished surface—270 N (**d**) S46500 burnished surface—270 N (**e**) S46500 burnished surface—470 N

**Figure 5.** Textured surfaces after milling and burnishing processes at different contact pressures.

It can be observed that the macro-texture conferred by surface milling was more prominent on the austenitic stainless steel than on the martensitic stainless steel. However, under the same burnishing conditions, the texture of the austenitic milled surface disappeared and was replaced by an imprint of the burnishing tool (Figure 5c), showing the extent of the contact area, as well as the magnification of the interaction between the ball and the surface. Regarding the surface finish obtained on the martensitic steel, a softened texture can be seen in comparison with the AISI 316 surface under the same conditions (270 N). It can also be observed that the peaks intensified as the load increased (470 N). Figure 6

summarizes the macro-texture parameters obtained after milling and ball burnishing in order to provide a quantitative description of the analyzed surface modifications.

**Figure 6.** Textured surfaces after milling and burnishing processes at different contact pressures.

Based on the height parameters Sa and Sq, a normal distribution of heights (Sa = 0.8 Sq) was evidenced under milling conditions for both materials. This observation was corroborated by the skewness values (Ssk ~ 0) under the same milling conditions. This tendency varied after ball burnishing. Thus, under the same load conditions, the statistical asymmetry after burnishing on AISI 316 stainless steel showed a mass distribution skewed to below the mean plane (Ssk > 0), while the surface of UNS S46500 stainless steel is skewed to above the main plane (Ssk < 0) in equal proportion. The load increment on the second one slightly varied under this condition. With regard to kurtosis (Sku ~ 3), it was observed that the austenitic surface became a non-abrupt platykurtic condition, whereas the martensitic surface responded with a better fit to a Gaussian distribution despite the load increase.

The S10z parameter provides a practical criterion for the statistical behavior of the height. It reveals that the five-point peak height and five-point pit height were 20% more prominent on the austenitic surface. However, this was reduced by 36% on martensitic stainless steel and only 20% on austenitic stainless steel after ball burnishing. This was not consistent with Figure 5c. Since the austenitic crystal lattice had a higher deformation capability (reflected in the COF value), a redistribution of the surface texture was evidenced. Thus, the new average roughness profile may have been displaced below the level of the initial valleys, leading to a loss of tolerance. On the other hand, increasing the pressure on the martensitic surface led to the generation of a pile-up and consequently an increase in the S10z parameter (~15%). This elucidates the marked differences in the surface roughness depending on the stainless-steel microstructure during the ball-burnishing process.

The directional properties quantified through the Str parameter were shown to be moderately isotropic (Str ~ 0.5) after milling for both surfaces. After ball burnishing, the austenitic surface became directionally anisotropic (Str < 0.3), whereas under the same conditions, the martensitic surface increased its isotropy. At a 470 N load, the martensitic surface became anisotropic.