esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing Selection Algorithm for Mobile Ad Hoc Networks Based on Neighbor Node Density
###### Abstract  
In the process of data transmission in mobile ad hoc networks, it is essential to establish optimal routes from source nodes to destination nodes. However, as network density increases, this process is often accompanied by a significant rise in network overhead. To address this issue, the ND-AODV (neighborhood density AODV) protocol has been introduced, which reduces the probability of transmitting control information in high-density node environments to mitigate network overhead. Nevertheless, this may come at the cost of reduced routing accuracy, potentially leading to unnecessary resource wastage in certain scenarios. Furthermore, ND-AODV does not comprehensively consider the location of the receiving nodes, which limits its ability to reduce network overhead effectively. To overcome these limitations, this paper introduces a novel routing approach, known as CND-AODV (common neighborhood density AODV). In comparison to ND-AODV, CND-AODV offers a more comprehensive solution to the challenges posed by high-density network environments. It intelligently processes control information based on the special positioning of the receiving nodes, thereby significantly reducing unnecessary network overhead. Through simulation experiments comparing performance metrics such as throughput, packet delivery rate, and latency, the results clearly indicate that CND-AODV substantially decreases network overhead, enhancing network performance. Compared to ND-AODV, this innovative routing approach exhibits significant advantages. It provides a more efficient and reliable solution for ad hoc networks in high-density environments.  
AODV; ND-AODV; CND-AODV; ad hoc; neighbor nodes; route discovery +
Footnote †: journal: _Sensors_  
1
Footnote 1: [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))
## 3 Results and Discussion
### Surface Morphology Analysis of [Ni\({}_{x}Zn_{1-x}\)]Fe\({}_{2}\)O\({}_{4}\)
Material magnetic measurements units (emu/g) represent magnetic susceptibility per weight. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature\({}^{\circ}\)C Mean Diameter** & **Composition/Method** & **Cu\({}_{0.2}\)Zn\({}_{0.5}\)Fe\({}_{2}\)O\({}_{4}\)** & **Ni\({}_{0.5}\)Zn\({}_{0.5}\)Fe\({}_{2}\)O\({}_{4}\)** \\ \hline
500 & 24.65 & 20 & 20.31 & 10 \\
600 & 30.36 & 25 & 37.77 & 20 \\
700 & 38.89 & 50 & 38.66 & 20 \\
800 & 50.50 & 70 & 59.05 & 30 \\
900 & 94.46 & 150 & 74.86 & 50 \\
1000 & \(-\) & \(-\) & 91.86 & 60 \\
1100 & \(-\) & \(-\) & 105.6 & 120 \\
1200 & \(-\) & \(-\) & 201.06 & 250 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The effect of sintering temperature on the mean diameter. The (M\({}_{\rm x}\)Zn\({}_{1-\rm x}\))Fe\({}_{2}\)O\({}_{4}\) (M=Cu, Ni) is a magnetic material, and TiO\({}_{2}\) is not. Therefore, the magnetic properties of the TiO\({}_{2}\)-coated magnetic material decreased due to the dilution effect. Table 4 shows the coercive force (Hc), saturated magnetization (Ms), and remanent magnetization (Mr) for [Ni\({}_{\rm x}\)Zn\({}_{1-\rm x}\)]Fe\({}_{2}\)O\({}_{4}\) at different proportions, cladded with TiO\({}_{2}\). It can be seen that, for the magnetic properties of the different systems, the Hc value did not exceed 200 Oe and the Mr value approached zero (soft ferrite had lower remanent magnetization). Therefore, all of the investigated ferrites were soft.
## 2 Materials and Methods
### Tolerance Analysis  
We conducted tolerance analysis through simulation to estimate the impact of possible errors on device performance. In the actual fabrication process of triplers and mixers, potential errors may occur from substrate processing, cavity machining, and circuit assembly. Among them, the semiconductor technology ensured that the substrate processing error was guaranteed within 0.5 \(\mu\)m, which had a negligible impact on the circuit, working at 0.34 THz. However, the tolerance of cavity machining and circuit assembly were generally about \(\pm\) 5 \(\mu\)m, which could not be ignored and would affect the performance of devices. Since the two types of error were relative, cavity machining error could be accumulated to the circuit assembly error, that is \(\pm\)10 \(\mu\)m. Therefore, it can be simplified to only perform tolerance analysis on the positioning deviation during substrate assembly. Taking the Cartesian coordinate system as a reference, the GaAs integrated circuit moved \(\pm\)10 \(\mu\)m in the x and y directions, as shown in Figure 9. The tolerance analysis simulation results and comparison with original simulation data are presented in Figure 10.  
The shape and trend of the simulation curves agreed with the original result after the substrate was shifted, though there were still certain impacts on the center frequency, bandwidth, output power, or conversion loss, mainly caused by impedance variations at RF/LO transition probes, which affected the matching condition at the diode. Additionally, the effect depended more on the \(R_{f}\) transition structure because the absolute size was smaller. However, the deterioration of the circuit performance due to the substrate assembly error was acceptable, indicating that the circuits had a good tolerance to errors caused by substrate assembly.  
Figure 8: Photograph of the assembled SHM block with the K-connector for the IF output (**left**). Picture of the fabricated THz GaAs monolithic integrated SHM circuit and assembly diagram in the split block with the enlarged view of diode chip (**right**).  
Figure 9: Tolerance analysis of (**a**) 0.34 THz tripler and (**b**) 0.34 THz SHM caused by a substrate assembly error. The substrate assembly position deviates from the original position \(\pm\)10 \(\mu\)m along the x and y directions, respectively.
# Ultra-Generalized Continuous Class F Power Amplifier with Finite Third-Harmonic Load Impedance
## 2 Analysis of the Proposed UCCF Mode
### Load Impedance Design Space of the Proposed UCCF Mode  
The drain voltage in the UCCF mode \(v_{d}\)[32] is the same as that in the continuous class F mode and can be calculated using  
\[v_{d}= V_{k}+(V_{dd}-V_{k})\bigg{(}1-\frac{2}{\sqrt{3}}\cos(\theta) \bigg{)}^{2}\bigg{(}1+\frac{1}{\sqrt{3}}\cos(\theta)\bigg{)} \tag{10}\] \[\times(1+\gamma\sin(\theta)),-1<\gamma<1\]  
where \(V_{dd}\) represents the drain bias voltage, \(V_{k}\) represents the knee voltage of the transistor,and \(\gamma\) is the correction factor. The load impedance design space can be obtained as follows:  
\[\left\{\begin{array}{l}Z_{f0}=\dfrac{1}{\sqrt{3}I_{1}}\cdot R_{opt}+j\dfrac{ \gamma}{2\cdot I_{1}}\cdot R_{opt}\\ \\ Z_{2f0}=-j\dfrac{7\cdot\gamma}{12\sqrt{3}\cdot I_{2}}\cdot R_{opt}\\ \\ Z_{3f0}=-\dfrac{1}{6\sqrt{3}I_{3}}\cdot R_{opt}\end{array}\right. \tag{11}\]  
where  
\[R_{opt}\] = \[\dfrac{2(V_{dd}-V_{k})}{I_{\max}} \tag{12}\]  
\(R_{opt}\) is the optimal fundamental load impedance, and \(Z_{f0}\), \(Z_{2f0}\), and \(Z_{3f0}\) are the fundamental, second-, and third-harmonic load impedances of the UCCF mode, respectively.  
Equation (11) implies that the broader range of \(I_{3}\) leads to a broader design space for the third-harmonic load impedance. The range of \(I_{3}\) is 0 to \(-0.047\) in the GCCF mode, 0 to \(-0.137\) in the PCC-CF\({}_{3}\) mode, and 0 to \(-0.179\) in the UCCF mode. The UCCF mode can produce smaller \(I_{3}\) values, indicating that compared to the other two modes, it offers a broader third-harmonic load impedance design space.  
As shown in Figure 6, the real part of the fundamental impedance \(Z_{f0}\) varies with \(pcr\) and \(\alpha\), and the imaginary part varies with \(\gamma\). The \(Z_{f0}\) and \(Z_{3f0}\) of the GCCF mode are highlighted in blue, whereas the expanded \(Z_{f0}\) and \(Z_{3f0}\) relative to the GCCF mode in the PCC-CF\({}_{3}\) mode are marked in green. Additionally, the expanded \(Z_{f0}\) and \(Z_{3f0}\) relative to the PCC-CF\({}_{3}\) mode in the UCCF mode are denoted in red. The real part of the second harmonic load impedance is zero, and the imaginary part varies with \(pcr\), \(\alpha\), and \(\gamma\). The \(Z_{2f0}\) of the UCCF mode remains consistent with the GCCF mode, while the \(Z_{2f0}\) of the GCCF mode is highlighted in blue. This shows that the UCCF mode expands not only the finite third-harmonic load impedance design space but also the fundamental and second harmonic load impedance design space.  
Figure 6: Load impedance design space with the UCCF mode.
## 3 Results
### Rotation (Figure 5, Table 4)
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Resorption (mm)** & & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \multirow{3}{*}{Component} & \multirow{3}{*}{Tooth \%} & a & 20.10 & 30.51 & 40.55 & 50.22 & 59.57 & 61.98 & 64.16 & 66.13 & 67.92 \\  & & m & 30.15 & 37.92 & 45.39 & 52.61 & 59.57 & 61.98 & 64.16 & 66.13 & 67.92 \\  & & c & 40.21 & 47.27 & 54.07 & 60.63 & 66.96 & 67.25 & 67.49 & 67.72 & 67.92 \\  & & C & 80.39 & 80.79 & 81.17 & 81.54 & 81.89 & 82.76 & 83.53 & 84.23 & 84.87 \\  & & a & **14.76** & **24.51** & **33.90** & **42.97** & **51.71** & **55.11** & **58.17** & **60.94** & **63.47** \\  & & m & 14.76 & 24.51 & 33.90 & 42.97 & 51.71 & 55.11 & 58.17 & 60.94 & 63.47 \\  & & c & 35.91 & 42.77 & 49.39 & 55.77 & 61.93 & 62.38 & 62.77 & 63.14 & 63.47 \\  & & C & **18.13** & **18.90** & **19.64** & **20.36** & **21.05** & **20.58** & **20.15** & **19.76** & **19.40** \\ Bone \% & & a & **12.51** & **14.24** & **15.90** & **17.50** & **19.05** & **18.50** & **18.00** & **17.55** & **17.15** \\  & & m & 12.51 & 12.56 & 12.61 & 12.65 & 12.70 & 12.74 & 12.78 & 12.80 & 12.86 \\  & & c & **50.04** & **50.24** & **50.43** & **50.61** & **50.78** & **48.50** & **46.44** & **44.58** & **42.89** \\ NVB \% & NVB & **0.57** & **0.63** & **0.69** & **0.74** & **0.79** & **0.89** & **0.97** & **1.04** & **1.11** \\ Pulp \% & a & **0.05** & **0.05** & **0.06** & **0.06** & **0.07** & **0.08** & **0.08** & **0.09** & **0.09** \\  & & c & **0.05** & **0.05** & **0.06** & **0.06** & **0.07** & **0.08** & **0.08** & **0.09** & **0.09** \\ PDL \% & a & **1.00** & **1.14** & **1.28** & **1.41** & **1.54** & **1.70** & **1.85** & **1.98** & **2.10** \\  & & m & 1.00 & 1.71 & 1.77 & 1.99 & 2.30 & 2.24 & 2.19 & 2.14 & 2.10 \\  & & c & **1.74** & **2.19** & **2.62** & **3.04** & **3.00** & **4.13** & **3.00** & **4.13** & **4.42** & **4.70** \\ \hline \hline \end{tabular}  
* structure—stress displayed by the 3D model. Tooth + bracket, Tooth, Dentine, Bone, NVB, Pulp, PDL components —stress displayed by these components. a—apical third, m—middle third, c—c—cervical third, C—crown. Tooth + bracket %, Tooth %, Dentine %, Bone %, NVB %, Pulp %, PDL % components—% stress displayed by these components.  
\end{table} 
Table 3: \(Cont.\)to ischemic and resorptive risks in both middle and cervical third PDL and radicular dentine. The bracket absorption-dissipation ability was 18.73% in 0 mm, 17.63% in 1 mm, and 17.48% in 8 mm loss. The enamel absorption-dissipation ability was 60.09% in 0 mm, 60.09% in 1 mm, and 62.84% in 8 mm loss.
## 2. Mobility Models and Related Work
### Random Direction Mobility Models (RDM)  
This model is slightly different from RWM in the way that every mobile node in this model picks up a random direction in the beginning and starts moving until it reaches the end of the simulation area. Once it reaches the boundary, it halts for a specific time and then again starts its movement at an angle between 0 to 180 degrees from the halt point [16].
###### Abstract
On the other side, microgrid stability can be impacted by operational and infrastructural damages such as hackers, false data injection attacks, denial-of-service (DoS) attacks, and uncertainties. Therefore, microgrids need to be prepared to cover the attacks and guarantee the restoration of stable operation. The types of cyberattacks on microgrids are presented in Figure 3. A false data injection attack, for example, which refers to the situation in which an attacker deceives sensor measurements to introduce unnoticed flaws into the state variable and value calculations [15], can have disastrous effects, particularly in emergency situations and situations involving critical loads. Therefore, one effective way to increase microgrid resilience against cyberattacks is through resilient control. Resilience can be defined as the ability to sufficiently prepare for and withstand effectively uncommon, severe abuse events in order to successfully reduce their damaging effects and/or shorten the period of the disruption [16]. This review subscribes to the MiDiNA project as a part of the European Union-African Union partnership for renewable energy, 'LEAP-RE'. This review will shed light on the energy transition in North African countries and the resilient controls for improving power availability in microgrids. It seeks to draw attention to the advantages and drawbacks  
Figure 2: Advantages of Microgrids. of each type of control as well as discuss the specifications for resilient controls that are necessary for microgrids in the North African environment. This work is organized as follows: Section 2 introduces the energy transition in North Africa. Section 3 presents the structures of microgrids. Section 4 focuses on the review of microgrid designs and controls. Section 5 discusses the control methods applied to microgrids developed in the North African region. A conclusion summarizes this study.
## 4 Discussion
[32] purified a peptide (LWHTH) from the marine animal Styela clava, which inhibited ACE with an IC50 value of 16.42 +/ - 0.45 \(\upmu\)M. In particular, some marine shellfish-derived peptides were found to have ACE inhibitory activities, such as, oyster protein peptide VVYPWTTQRF (IC50 = 0.066 mM) [33], fresh water clam muscle protein peptide VKK (IC50 = 1.045 mM) [34], and pear oyster meat protein-derived peptides HLHT and GWA (0.458 and 0.109 mM, respectively) [12]. In this study, the in vitro activity assay showed that SCCGY, SSGAW, IQPEW, and PICQF exhibited strong inhibition of ACE, with IC50 values of 0.09883, 0.2529, 0.6098, and 0.4131 mM, respectively. Thus, the ACE inhibitory activities of the peptides (SCCGY, SSGAW, IQPEW, and PICQF) identified by the present computational strategy were moderate compared with previous reports. Generally, there are three main active-site pockets (S1, S2, and S1\({}^{\prime}\)) in the ACE enzyme. The residues Ala354, Glu384, and Tyr523 are included in the S1 pocket; the residues Gln281, His353, Lys511, His513, and Tyr520 are included in the S2 pocket; and the Glu162 residue is included in S1\({}^{\prime}\) pocket [35]. In the present study, SCCGY formed hydrogen bonds with Ala354 of S1 as well as Gln281 and His353 of S2 (Figure 3B). PICQF had hydrophobic contacts with Ala354 of S1 as well as His353 and His513 of S2 (Figure 4B). It was reported that Ala354 is an important residue of ACE, interacting with its inhibitor lisinopril [36]. This suggested the potential for ACE inhibition. At last, the limitation of this study is that the present results were obtained by in silico proteolysis; real gastrointestinal digestion is involved in sequential digestion by enzymatic proteolysis (e.g., pepsin, trypsin, pancreatin, and other enzymes), which is a very complicated process; thus, a more detailed study on sequential digestion of protein hydrolysates is necessary. On the other hand, even the strongest peptide SCCGY (IC50 = 0.09883 mM) was weaker than captopril (IC50 = 0.017 mM), which is known as a potent, competitive inhibitor of ACE [37]. It is worth pointing out that comparing these IC50 values is not suitable if they are not obtained with same method and under same conditions. Nonetheless, these peptides deserve further study in vivo to confirm their activities due to the fact that food-derived peptides should be safer than chemical agents.
## 3 Method
### Conversion from Malware to Images  
Although some previous work used malware images in rectangle shape with fixed width and variable height [26], instead we use square images in fix size \(256\times 256\) for batch integration purpose. In our implementation, malware as byte sequences will first be reshaped to square gray scale images. Then, up-sampling or down-sampling techniques will be applied on square images to fit the size of \(256\times 256\). This process is described in detail in Algorithm 1. Our method is kind of similar to the one in [44], and some information like tail bytes in malware will be dropped during the conversion which can degrade the performance. Thus, we suggest some alternatives of designing choice, such as using ceil operation in place of floor operation to get square image or using padding (0 or constant) to replace up-sampling operation. We have not tried these alternatives in our experiments but they worth considering if higher performance is indispensable.  
```
0:\(b\): Byte sequence of malware; \(l\): Length of byte sequence; \(w\): Expected width of square malware image
0: Square malware image \(x\)
1: Convert each byte in byte sequence to an integer ranges \([0,255]\): \(b_{i}\getsuint(b_{i}),i=1,2,3...l\)
2:\(h\leftarrow\lfloor\sqrt{l}\rfloor\)
3: Drop last \(l-h^{2}\) integers in sequence: \(b\leftarrow[b_{1},b_{2},...,b_{h^{2}}]\)
4: Reshape the integer sequence to a 2D square matrix: \(M\gets reshape(b,(h,h)),M\in R^{h\times h}\)
5: Convert 2D matrix to a gray scale image: \(x\leftarrow image(M)\)
6:if\(h<w\)then
7: Do up-sampling: \(x\gets upsample(x,(w,w)),x\in R^{w\times w}\)
8:else
9: Do down-sampling: \(x\gets downsample(x,(w,w)),x\in R^{w\times w}\)
10:return x
```  
**Algorithm 1** Conversion from malware to images
## 3 Integrating Plausible Rules with Ontologies
### Description Logic  
In the description logic \(ALC\), concepts are built using the set of constructors formed by negation, conjunction, disjunction, value restriction, and existential restriction, as Table 1 bears out [23]. Here, \(C\) and \(D\) represent concept descriptions, whilst \(r\) and \(s\) role names. In this study we used the extension of \(ALC\) with transitivity (\(R_{+}\)) on roles and role hierarchy (\(H\)), known as \(ALCR_{+}H\) or \(SH\). The syntax of \(SH\) is defined by the following grammar:  
\[C,D::=\top|A|\
eg C|C\sqcup D|\exists r.C|\forall r.C|r_{+}|r\sqsubseteq s \tag{1}\]  
where \(A\) represents an atomic concept and \(\top\) the top level concept.  
The semantics is defined based on an interpretation \(I=(\Delta^{I},\cdot^{I})\), where the domain \(\Delta^{I}\) of \(I\) contains a non-empty set of individuals, whilst the interpretation function \(\cdot^{I}\) maps each concept name \(C\) to a set of individuals \(C^{I}\in\Delta^{I}\) and each role \(r\) to a binary relation \(r^{I}\in\Delta^{I}\times\Delta^{I}\). The second column of Table 1 illustrates the extension of \(\cdot^{I}\) to arbitrary concepts.  
**Definition 2**: _A concept \(C\) is satisfiable if there exists an interpretation \(I\) such that \(C^{I}\
eq\emptyset\). The concept \(D\) subsumes the concept \(C\) (\(C\sqsubseteq D\)) if \(C^{I}\subseteq D^{I}\) for all interpretations \(I\)._  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Syntax** & **Semantics** \\ \hline \(\
eg C\) & \(\Delta^{I}\setminus C^{I}\) \\ \(C\sqcap D\) & \(C^{I}\cap D^{I}\) \\ \(C\sqcup D\) & \(C^{I}\cup D^{I}\) \\ \(\exists r.C\) & \(\{x\in\Delta^{I}|\exists y:(x,y)\in r^{I}\wedge y\in C^{I}\}\) \\ \(\forall r.C\) & \(\{x\in\Delta^{I}|\forall y:(x,y)\in r^{I}\to y\in C^{I}\}\) \\ \(r\sqsubseteq s\) & \(\{x\in\Delta^{I}|\forall y:(x,y)\in r^{I}\to(x,y)\in s^{I}\}\) \\ \(r_{+}\) & \(\{x\in\Delta^{I}|\forall y,z:(x,y)\in r^{I}\ and\ (y,z)\in r^{I}\to(x,z)\in r^{I}\}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Syntax and Semantics of \(SH\) concepts.  
**Definition 3**: _An \(ABox\) is a finite set of concept assertions \(a:C\) or role assertions \((a,b):r\), where \(C\) represents a concept, \(r\) a role, and \(a\) and \(b\) are two instances. Usually, the unique name assumption holds within the same \(ABox\). A \(TBox\) is a finite set of terminological axioms of the form \(C\equiv D\) or \(C\sqsubseteq D\)._  
**Example 2**: _Consider the domain \(\Delta=\{s_{1},0.1^{\circ}C\}\). Let the terminology:_  
\[\begin{array}{l}TBox=\{Sensor\sqsubseteq\exists hasAccuracy.MeasureUnit, WirelessSensor\sqsubseteq Sensor\}\\ ABox=\{s_{1}:WirelessSensor,0.1^{\circ}C:MeasureUnit,(s_{1},0.1^{\circ}C): hasAccuracy\}\end{array}\]  
The interpretation function treats the individual \(s_{1}\) as a wireless sensor with the measurement accuracy of 0.1 \({}^{\circ}\)C, where the value is interpreted as an instance of the class _MeasureUnit_. Based on the second axiom in the _TBox_, the subsumption reasoning service of the DL derives \(s_{1}\) as an instance of the _Sensor_ concept.
## 2 Materials and Methods
### Measurements  
Small-angle X-ray scattering (SAXS) measurements were performed with an FR-E (Rigaku Corp.) equipped with a 2D imaging plate (R-AXIS IV, Rigaku Corp, Tokyo, Japan). The X-ray source and camera length were a Cu K\(\alpha\) beam (0.154 nm) and 300 mm, respectively. Grazing incident angle SAXS (GI-SAXS) measurements were also achieved with this instrument. Precise tilt control for this measurement was achieved with an ATS-C316-EM (Chuo Precision Industrial Co., Ltd., Tokyo, Japan).  
Gel permeation chromatography measurements were achieved with a DS-4 Shodex system equipped with KF-803L, KF-804L and KF-805 columns. The detection was achieved with a UV absorption (UV-41, Shodex) or a reflective index detector (RI-101, Shodex).  
The thermal transition properties of the polymers were evaluated by differential scanning calorimetry (DSC) using a TA 200 (TA Instruments).  
The UV-visible absorption spectra were found with an Agilent 8453 spectrometer (Agilent Technologies) with a light source of a deuterium/tungsten lamp.  
Contact angle measurements were made with a CA-XP (Kyowa Interface Science Co., Ltd., Niiza, Japan). Water droplets of 3 \(\upmu\)L were placed onto the polymer films at room temperature. The measurements were made after 5 s of dropping.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Polymer** & \(M_{\rm n}\) & \(M_{\rm w}/M_{\rm n}\) & **Thermal Transitions (\({}^{\circ}\)C)** \\ \hline Paz & \(2.2\times 10^{4}\) & 1.2 & \(g\)-43-SmC-100-SmA-120-\(i\)12 \\ P(Az-CB) & \(1.2\times 10^{4}\) & 1.1 & \(g\)-34-SmA-113-\(i\)1 \\ PBMA & \(1.9\times 10^{4}\) & 1.1 & \(T_{\rm g}\): 20 \\ P4 VP & \(6.5\times 10^{4}\) & 1.1 & \(T_{\rm g}\): 140 \\ PVP & \(1.0\times 10^{6.3}\) & No datum & \(T_{\rm g}\): 150 \\ \hline \hline \end{tabular} \({}^{\dagger}\)\(g\) = glass, SmC = smectic C, SmA = smectic A and \(i\) = isotropic. \({}^{2}\)[19]. \({}^{3}\) According to the manufacturer.  
\end{table}
Table 1: Molecular mass data of the polymers.  
The topographical surface profiles for the evaluation of the film thickness were obtained with white light interference microscopy using a BE-S501 (Nikon Corp, Tokyo, Japan).  
Polarized optical microscopic observations were performed with a BX-51 (Olympus Corp, Tokyo, Japan).