esa-sceva/satcom-chunk-collection huggingface dataset:
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 3 Design Methodology, Layout and Circuit Simulation
### Layout and Circuit Simulation
#### 3.2.6 Dipole Antenna  
The layout of the implemented inductive coupling dipole antenna is shown in Figure 23. It was simulated using an FR4 substrate with a 1.6 mm thickness, a relative permittivity  
Figure 21: Power harvester post-layout simulation. (**a**) Parametric sweep for \(W_{1}\) and \(W_{2}\). (**b**) Impedance sweep. (**c**) Start-up simulation. (**d**) Power conversion efficiency.  
Figure 22: Post-layout simulation of the implemented ASK backscatter modulator.  
\(\epsilon_{r}=4.6\), and a loss-tangent of \(tan\delta=0.01\). The copper foil presents a thickness of 35 \(\upmu\)m and a conductivity of \(\sigma=5.7\times 10^{7}\) S/m. The dimensions in Figure 23 are shown in Table 8. The \(S_{X_{rh-l}}\) and \(S_{Y_{rh-l}}\) parameters indicate the separation distance between the radiating body and the inductive loop over the x-axis and the y-axis.  
Figure 24 shows the frequency response and radiation pattern of the antenna. Figure 24a depicts the S11 reflection coefficient response, where the impedance of the stimulus port was set to \(Z_{REC}=175.8\)\(\Omega-j236\)\(\Omega\) to quantify the matching value at a center frequency of 910 MHz. The S11 values at 860 MHz, 910 MHz, and 960 MHz are \(-5.33\) dB, \(-79.49\) dB, and \(-4.645\) dB, respectively. Figure 24b indicates that the antenna impedance is 149 \(\Omega+j450\)\(\Omega\) at 860 MHz, 175 \(\Omega+j236\)\(\Omega\) at 910 MHz, and 50 \(\Omega+j290\)\(\Omega\) at 960 MHz. Figure 24c draws the radiation pattern.  
Figure 24: Antenna simulation. (**a**) Antenna S11 parameter. (**b**) Antenna impedance. (**c**) Radiation pattern.  
Figure 23: Layout of the implemented antenna.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Element** & **Value** & **Element** & **Value** & **Element** & **Value** & **Element** & **Value** & **Element** & **Value** \\ \hline \(A_{d}\) & 79.4 mm & \(A_{i}\) & 30 mm & \(L_{d1}\) & 4 mm & \(L_{i}\) & 11 mm & \(S_{Y_{rh-i}}\) & 0.4 mm \\ \(A_{d1}\) & 7.7 mm & \(A_{i1}\) & 3 mm & \(L_{d2}\) & 5 mm & \(L_{i1}\) & 1 mm & & \\ \(A_{d2}\) & 21.7 mm & \(L_{d}\) & 14 mm & \(L_{d_{3}}\) & 1 mm & \(S_{X_{rh-l}}\) & 3 mm & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Parameters of the dipole antenna.
## 5 Experimental Results and Analysis  
Rolling bearings are mainly divided into two categories: ball bearings and cylindrical roller bearings. Therefore, two experimental signals reflect the artificial deep groove ball bearing fault and cylindrical roller bearing fault were used to investigate the proposed method, respectively. Both of the two experiments were conducted on the experiment rig exhibited in Figure 6. The vibration signals of the two experiments were collected with the sampling frequency of 12.8 kHz.
# Numerical Simulation and Analysis of Heart-Aorta Fluid-Structure Interaction Based on S-ALE Method
## 3 Results and Discussion
### Effect of Changes in Blood Viscosity on the Aortic Hemodynamic Response
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Section**} & \multicolumn{2}{c}{**Peak Velocity**} & \multicolumn{2}{c}{**Peak Flow Rate**} & \multicolumn{2}{c}{**Peak WSS**} \\ \cline{2-7}  & \(\mathbf{r}\) & \(\mathbf{\varepsilon}\) & \(\mathbf{r}\) & \(\mathbf{\varepsilon}\) & \(\mathbf{r}\) & \(\mathbf{\varepsilon}\) \\ \hline
1 & 0.8746 & 0.0025 & 0.8810 & 0.0025 & 0.9999 & 0.1814 \\
2 & 0.6510 & 0.0013 & 0.6852 & 0.0013 & 0.9996 & 0.1840 \\
3 & \(-\)0.6318 & \(-\)0.0015 & \(-\)0.6581 & \(-\)0.0013 & 0.9997 & 0.1836 \\
4 & \(-\)0.6260 & \(-\)0.0010 & \(-\)0.6020 & \(-\)0.0010 & 0.9997 & 0.1858 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative analysis of the effects of viscosity coefficient on aortic hemodynamic parameters. in the velocity gradient. Therefore, the velocity gradient could be regarded as a constant. WSS was only related to the viscosity coefficient and exhibited a linear relationship. The peak WSS shown in Figure 10c confirms this conclusion. [PERSON] et al. [19] similarly analyzed blood viscosity factors. In their study, an increase in the blood viscosity coefficient led to a decrease in blood velocity. We believe that the primary reason for this phenomenon is that the resistance to blood flow at the outlet of the aorta had not been taken into account in their simulations, which did not correspond to the blood flow in real situations. Also, WSS was not considered in their study. Figure 10b presents the peak flow rate observed in sections 1-4 under different blood viscosity coefficients. The magnitude of flow rate was determined by the velocity and sectional area. However, there was no significant difference in the blood velocity through the sections for various viscosity coefficients, indicating that the sectional area did not change during blood flow. Therefore, it could be inferred that changes in blood viscosity had no effect on aortic deformation.
# Subsampling Algorithms for Irregularly Spaced Autoregressive Models
## 3 Full-Data Estimation for the IS-AR(1) Model
We find the value of \(\sigma\) given \(\phi^{(k)}\) by solving \(\dot{\ell}_{2}(\phi^{(k)},\sigma)=0\), which gives  
\[\sigma^{(k)}=\sqrt{\frac{1}{m}\sum_{i=1}^{m}\frac{(y_{j}-\phi^{d_{j}}y_{j-1})^ {2}{(1-\phi^{(k)}}^{2})}{1-\phi^{(k)}}}.\]  
Given \(\sigma^{(k)}\), we find the value of \(z\) in the \((k+1)\)-th step by implementing the Newton-Raphson algorithm, i.e., computing  
\[z^{(k+1),l+1}=z^{(k+1),l}-\frac{\sum_{j=1}^{m}\dot{\ell}_{z11}(\phi^{(k+1),l}, \sigma^{(k)};y_{j},y_{j-1},d_{j})}{\sum_{j=1}^{m}\dot{\ell}_{z11}(\phi^{(k+1), l},\sigma^{(k)};y_{j},y_{j-1},d_{j})}\]  
for \(l=0,1,2,\ldots\) until convergence, where \(\phi^{(k+1),0}=\phi^{(k)}\) and \(\phi^{(k+1),l}=\text{tanh}(z^{(k+1),l})\). We alternate this updating of the values of \(\sigma\) and \(z\) until the values become stable. We summarize the aforementioned estimation procedure in Algorithm 1. Here, \(\phi^{(0)}\) is an initial value, while \(\epsilon_{\text{NR}}\) and \(\epsilon_{\text{CA}}\) are error tolerances to determine convergence. For completeness, we provide below detailed expressions of the gradient vector and Hessian matrix of the per-observation log-likelihood:  
\[\dot{\ell}_{1}(\mathbf{\theta};y_{j},y_{j-1},d_{j}) =\frac{d_{j}\phi^{2d_{j}-1}}{1-\phi^{2d_{j}}}-\frac{\phi}{1-\phi ^{2}}+\frac{\phi\epsilon_{j}^{2}}{\sigma^{2}(1-\phi^{2d_{j}})}\] \[+\frac{1-\phi^{2}}{\sigma^{2}(1-\phi^{2d_{j}})^{2}}d_{j}\epsilon _{j}(\phi^{d  
\[+\frac{(1-\phi^{2})d_{j}^{2}\phi^{2d_{j}-2}(2y_{j}e_{j}-y_{j-1}^{2}+y_ {j}^{2})}{\sigma^{2}(1-\phi^{2d_{j}})^{2}}\] \[+\frac{d_{j}e_{j}(y_{j-1}-\phi^{d_{j}}y_{j})[(d_{j}-1)\phi^{d_{j}- 2}-(d_{j}+1)\phi^{d_{j}}]}{\sigma^{2}(1-\phi^{2d_{j}})^{2}}\] \[-\frac{4(1-\phi^{2})d_{j}^{2}e_{j}^{2}\phi^{2d_{j}-2}}{\sigma^{2}( 1-\phi^{2d_{j}})^{3}},\] \[\breve{\ell}_{12}(\mathbf{\theta};y_{j},y_{j-1},d_{j}) =\frac{2d_{j}\phi^{d_{j-1}}(\phi^{d_{j}}y_{j}-y_{j-1})(1-\phi^{2} )}{\sigma^{3}(1-\phi^{2d_{j}})^{2}}-\frac{2e_{j}^{2}\phi}{\sigma^{3}(1-\phi^{2 d_{j}})},\] \[\breve{\ell}_{22}(\mathbf{\theta};y_{j},y_{j-1},d_{j}) =\frac{1}{\sigma^{2}}-\frac{3e_{j}^{2}(1-\phi^{2})}{\sigma^{4}(1- \phi^{2d_{j}})},\]  
where, \(e_{j}=y_{j}-\phi^{d_{j}}y_{j-1}\). ```
1. \(z\leftarrow\text{atanh}(\phi^{(0)})\)
2. \(\sigma\leftarrow\sqrt{\frac{1}{m}\sum_{i=1}^{m}\frac{(y_{j}-\phi^{d_{j}}y_{j-1} )^{2}(1-\phi^{2})}{1-\phi^{2d_{j}}}}\)
3. \(z_{o}\leftarrow\text{Inf}\)
4. while\(|z-z_{o}|>\epsilon_{\text{CA}}\)
5. \(z_{o}\gets z\)
6. \(\Delta_{\text{NR}}\leftarrow\text{Inf}\)
7. while\(|\Delta_{\text{NR}}|>\epsilon_{\text{NR}}\)
8. \(\Delta_{\text{NR}}\leftarrow\frac{\sum_{i=1}^{m}(1-\phi^{2})\ell_{1}(\phi\cdot \sigma y_{j}y_{j-1}-d_{j})}{\sum_{j=1}^{m}(1-\phi^{2})^{2}\ell_{11}(\phi\cdot \sigma y_{j}y_{j-1}-d_{j})-2(1-\phi^{2})\phi^{2}\ell_{1}(\phi\cdot\sigma y_{j} y_{j-1}-d_{j})}\)
9. \(z\gets z-\Delta_{\text{NR}}\)
10. end while
11. \(\phi\leftarrow\text{tanh}(z)\)
12. \(\sigma\leftarrow\sqrt{\frac{1}{m}\sum_{i=1}^{m}\frac{(y_{j}-\phi^{d_{j}}y_{j-1 })^{2}(1-\phi^{2})}{1-\phi^{2d_{j}}}}\)
```  
**Algorithm 1** Numerical optimization algorithm.
# Simulation of Gas Fracturing in Reservoirs Based on a Coupled Thermo-Hydro-Mechanical-Damage Model
## 4 Analysis of Numerical Simulation Results
### Impact of Parameter Variations on the Efficiency of Rock Extraction
This is because as the PL value increases, the efficiency of gas fracturing improves, leading to a higher degree of rock damage and an accelerated extraction efficiency of shale gas. Consequently, the gas content within the rock decreases. Figure 11: The shale gas content at different positions and times along the wellbore varies under different VL values. Figure 12: The relationship between shale gas content in the rock mass and different VL values at the same time. Based on numerical simulation analysis of shale gas content under varying permeability conditions, as shown in Figures 15 and 16, it is evident that the gas content decreases at a specific location with an increase in permeability. This is because a higher permeability indicates a greater number of interconnected pores within the sample. During gas fracturing, a significant amount of gas rushes into these pores, leading to an increase in pore pressure and subsequent expansion of the pores. This expansion results in more interconnected pathways for gas flow. However, it should be noted that samples with initially higher permeability exhibit even higher permeability after gas fracturing, which leads to increased gas leakage at the same location. As a result, the gas content gradually decreases. It is important to note that the decrease in gas content may not follow a linear trend, as other factors such as initial gas content, rock mechanical properties, and the type of fracturing fluid used can also affect the final outcome. Furthermore, studying the distribution of shale gas content under various permeability conditions provides valuable insights into the feasibility and optimization of shale gas extraction processes. Future research should aim to further explore the relationship between permeability and gas content, as well as  
Figure 14: Relationship between shale gas content of rock mass specimens at the same time under different PL. Figure 13: The shale gas content at different positions and times along the wellbore varies under different PL values. its implications for overall shale gas extraction efficiency. Additionally, efforts could be directed towards developing innovative techniques to enhance gas content and improve the accuracy of numerical simulations. These endeavors will contribute to the sustainable and efficient exploitation of shale gas resources.
# Bit Streaming Processing Algorithms for Intelligent Hardware Converters
## 2 Materials and Methods
### Process of Calculating of the Bit-Stream Multiplication-Division Function  
The multiplication-division function is often used in the implementation of functional conversions; using this function as an example, we can show the stream calculation peculiarities. A block diagram of the multiplication-division operation process is shown in Figure 1b.  
The process of calculating multiplication-division operation is periodic, and its period T depends on the digit capacity of the processed codes.  
To implement the streaming mode of conversion, the input codes N\({}_{1}\), N\({}_{2}\), N\({}_{3}\) (blocks 1, 2, 3) and initial undefined output code N\({}_{\mathrm{out}}\) (block 13) are converted into bit streams. These threads form the positive and negative branches of the calculation process.  
In each branch of the process, pulse-frequency sweep (PFS) of codes N\({}_{1}\), N\({}_{\mathrm{out}}\) (blocks 4, 7) and pulse-width sweep (PWS) of codes N\({}_{2}\), N\({}_{3}\) (blocks 5, 6) are executed in parallel. In the process under consideration, two streaming multiplications SM are organized (blocks 8, 9). Both of these perform multiplication of the PFM signal stream by PWM signals. That is, both SM blocks generate only the P2 streams (see Figure 1a).  
At the output of block 8 there appears a bit stream. The number of pulses on the output of the block during the device operation period is defined as \(\rm{N_{out1}}=\rm{N_{1}N_{2}}\) (block 10). The bit stream on the output of block 9 is the result of the following operation \(\rm{N_{out2}}=\rm{N_{3}N_{out}}\) (block 12).  
The resulting bit streams are combined and grouped (block 11) to get the difference \(\rm{R}=\rm{N_{1}N_{2}}-\rm{N_{3}N_{out}}\), which is accumulated and converted into the output code \(\rm{N_{out}}\) (block 13).  
Due to the use of negative feedback, the process branches come to an equilibrium state. In this state, the intensity of streams based on codes \(\rm{N_{out1}}\) and \(\rm{N_{out2}}\) is equal, and hence \(\rm{R}=0\). Using equality \(\rm{R}=\rm{N_{1}N_{2}}-\rm{N_{3}N_{out}}=0\), we obtain  
\[\rm{N_{out}}=\frac{\rm{N_{1}N_{2}}}{\rm{N_{3}}}. \tag{1}\]  
The multiplication-division operation is implemented as a tracking process, and the result of the computation is generated during the balancing of the system. The process constantly tries to maintain an equilibrium state and restores to its state after short-term failures occur.
# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 4 Experiments and Discussions
### Experimental Results  
#### 4.4.1 Target Recognition Accuracy  
As described in Section 4.2, the incremental learning experiments on the MSTAR dataset start with a pre-trained model on a portion of the old classes data. The incremental process can be divided into 2-phases, 5-phases, and 10-phases increments depending on the number of new target classes each time. 5-phases increment means the process of initial training on 2 classes of old data, followed by increments of 2 classes of new data each time. The rest of the incremental phases are similar to the above process.  
Table 2 shows the comparison of our method with existing methods. All tables show the average incremental accuracy and standard deviation of each method at different incremental stages. It can be seen that our method has a smaller standard deviation and less forgetting of old samples as new samples arrive. Figure 4 shows the accuracy differences between the proposed method and the comparison methods. The proposed method achieves superior performance at different incremental phases. The accuracy improvement is about 6% to 8%. It solves well the imbalance problem between old and new target classes, the bias of the fully connected layer for the old and new tasks.  
It can be also observed from Figure 4 and Table 2 that the more incremental phases there are, the more significant forgetting and accuracy decreasing. Namely, more incremental phases imply more forgetting. The proposed method significantly reduces forgetting compared with other methods. In the 10-phase increments with 1 class per phase, as  
Figure 4: Target incremental recognition accuracy at different incremental phases and different classes per phase. (**a**) 2 phases, 5 classes per phase. (**b**) 5 phases, 2 classes per phase. (**c**) 10 phases, 1 class per phase.  
shown in Figure 4c), the model by the proposed methods shows more significant differences from the other models, degrading slowly throughout the run against forgetting (\"gradual forgetting\"). In contrast, the other methods show a significant performance degradation at the beginning of the run (\"catastrophic forgetting\"). It is worth noting that the accuracy becomes better due to the mixture of bias correction methods and class separation loss. They solve the problem of biased output of the model and identification of confusion very well.
# Electric Stimulation of Astaxanthin Biosynthesis in _Haematococcus pluvialis_
###### Abstract  
The green microalga _Haematococcus pluvialis_ accumulates astaxanthin, a potent antioxidant pigment, as a defense mechanism against environmental stresses. In this study, we investigated the technical feasibility of a stress-based method for inducing astaxanthin biosynthesis in _H. pluvialis_ using electric stimulation in a two-chamber bioelectrochemical system. When a cathodic (reduction) current of 3 mA (voltage: 2 V) was applied to _H. pluvialis_ cells for two days, considerable lysis and breakage of algal cells were observed, possibly owing to the formation of excess reactive oxygen species at the cathode. Conversely, in the absence of cell breakage, the application of anodic (oxidation) current effectively stimulated astaxanthin biosynthesis at a voltage range of 2-6 V, whereas the same could not be induced in the untreated control. At an optimal voltage of 4 V (anodic current: 30 mA), the astaxanthin content in the cells electro-treated for 2 h was 36.9% higher than that in untreated cells. Our findings suggest that electric treatment can be used to improve astaxanthin production in _H. pluvialis_ culture if bioelectrochemical parameters, such as electric strength and duration, are regulated properly.  
microalgae; _Haematococcus pluvialis_; astaxanthin; electric treatment; stimulation; stress
###### Abstract  
Continuous and stable precision satellite clock offsets are an important guarantee for real-time precise point positioning (PPP). However, in real-time PPP, the estimation of a satellite clock is often interrupted for various reasons such as network fluctuations, which leads to a long time for clocks to converge again. Typically, code biases are assumed to stay constant over time in clock estimation according to the current literature. In this contribution, it is shown that this assumption reduces the convergence speed of estimation, and the satellite clocks are still unstable for several hours after convergence. For this reason, we study the influence of different code bias extraction schemes, that is, taking code biases as constants, extracting satellite code biases (SCBs), extracting receiver code biases (RCBs) and simultaneously extracting SCBs and RCBs, on satellite clock estimation. Results show that, the time-varying SCBs are the main factors leading to the instability of satellite clocks, and considering SCBs in the estimation can significantly accelerate the filter convergence and improve the stability of clocks. Then, the products generated by introducing SCBs in the clock estimation based on undifferenced observations are applied to PPP experiments. Compared with the original undifferenced model, clocks estimated using the new method can significantly accelerate the convergence speed of PPP and improve the positioning accuracy, which illustrates that our estimated clocks are effective and superior.  
precise point positioning; satellite code bias; satellite clock offsets; undifferenced observations 2021  
11 June 2021  
1
## 5 Parameters Influencing Impedance Frequency Response  
To extend our study, we wanted to understand the influence of some parameters on the impedance characteristics obtained using the proposed strategy. The three parameters investigated are the machine operating temperature, the inverter switching frequency, and the DC bus voltage amplitude. Findings are discussed below.
# IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
## 3 Performance Indicator of IPCB Geometry Configuration
In such cases, they cannot continue emitting navigation signals (detailed discussion in Section 5.1), which will decrease the number of available IPBs in the constellation. Once the number of available IPBs in the constellation drops below 4, the GDOP of the IPCB is defined as infinity in this paper. The geometry performance of an IPCB at time \(t\) can be assessed by the average GDOP of multiple users distributed in the service area, as described in Equation (8) [37]. \[GDOP(t)=\frac{1}{n_{u}}\sum_{i=1}^{n_{u}}GDOP(i,t) \tag{8}\]  
It can be concluded that the objective of IPCB geometry configuration planning is to obtain the minimal \(GDOP(t)\) for the whole service duration. However, this approach encounters difficulties when dealing with infinite values. To avoid such difficulties, this paper defines the performance indicator as Equation (9). \[F=\frac{1}{n_{t}}\sum_{t=1}^{n_{t}}\frac{1}{GDOP(t)} \tag{9}\]  
In Equation (9), \(n_{t}\) represents the expected service duration of the IPCB. The ultimate objective of IPCB geometry configuration planning is to maximize \(F\) defined in Equation (9).
# MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation
## 2 Related Work
### Loss Function  
We employed the DiceBCE Loss \(L(\cdot)\) to optimize the model training process, ensuring improved performance in the polyp segmentation task. DiceBCE Loss is a combination of Dice Loss and Binary Cross Entropy Loss. This loss function helps the model to better capture complex boundary areas while ensuring pixel-level classification accuracy.  
For each different scale of the output feature map \(\mathit{pred}_{i}\), we compute its loss with respect to the label \(\mathit{mask}_{i}\), which is mathematically expressed as:  
\[\mathit{loss}_{i}=L(\mathit{pred}_{i},\mathit{mask}_{i}) \tag{4}\]  
where \(\mathit{mask}_{i}\) is the labeled map obtained by sampling operation of ground truth.  
We compute the difference between the edge prediction map and the true edge map by separate edge loss function. The formula is:  
\[\mathit{loss}_{e}=L\Big{(}\mathit{Edge}_{\mathit{pred}},\mathit{Edge}_{gt} \Big{)} \tag{5}\]  
where \(\mathit{loss}_{e}\) represents the loss between the edge prediction map \(\mathit{Edge}_{\mathit{pred}}\) output by the EFE module and the ground truth of the edge \(\mathit{Edge}_{gt}\).  
The final total loss is the weighted sum of the sum of the individual scale losses and the edge loss, which is expressed as:  
\[\mathit{loss}_{total}=\sum_{i=1}^{5}\mathit{loss}_{i}+\mathit{loss}_{e} \tag{6}\]  
This total loss \(\mathit{loss}_{total}\) is the optimization objective of the model, which aims to improve the accuracy of the multi-scale segmentation effect and edge details by optimizing this loss function.
# Hash Indexing-Based Image Matching for 3D Reconstruction
## 1 Introduction
After a deep investigation into the topic of image matching, we found that hash indexing is an efficient approach to feature descriptor matching. As a result, in this paper, we propose the design of a HashMatch based on hash indexing for image matching in 3D reconstruction. The HashMatch consists of two modules. First, constructing a big hash table for located feature points via Haar wavelet feature transform [23]. Second, indexing a potential candidate by using a short hash code for the query point. The HashMatch can significantly reduce computational costs on image matching when dealing with big image datasets. According to our experiments, the HashMatch has not only proved to be extremely fast but also has the desired matching precision and is very suitable for the processing of large-scale 3D reconstruction. In summary, the main contributions of this work are summarized as follows:  
* We propose the use of hash indexing to replace the original brute force matching for image matching and present a HashMatch for 3D reconstruction. * We propose the use of the Haar wavelet feature transformation to construct a big and robust hash table for the located feature points for feature descriptor indexing. * We have designed a parallel architecture for hash matching to speed up the image matching process and save computing time. Additionally, a systematic experiment conducted on several benchmarking datasets is provided to assess the HashMatch against the state-of-the-art methods. The rest of this paper is organized as follows: After a brief introduction in Section 1, related work about image matching is presented in Section 2. In Section 3, we present the HashMatch in detail. In Section 4, a systematic experiment was conducted on several benchmarking datasets to prove the effectiveness of the HashMatch. Finally, the conclusion and remarking comments are presented in Section 5.