esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Quantitative descriptions of scale threshold and scale domain
### Local variance method (LVM)  
In order to choose an appropriate scale for a particular application, [PERSON] and [PERSON] [61] proposed the local variance method, which is related to the relationship between the size of objects in a scene and the spatial resolution of sensors. The local variance uses the standard deviation as an indicator to reflect the mean value of the standard deviation of a moving window over the entire image. According to the graphs of local variance as a function of scale, the spatial structure of an image can be measured. The reasons are very simple. When the spatial resolution is considerably finer than the size of objects in the scene, most of the measurements in the image will be highly correlated with their neighbors and the local variance would be low. As the size of spatial resolution increases, it may approximate the size of objects, then the likelihood of neighbors being similar decreases and the values tend to be different from each other. It thus causes the local variance to increase. If the size of spatial resolution increases further, it would be greater than the size of the object, and the possibility of one single pixel containing many objects increases, then the local variance gradually starts decreasing. Therefore, the peak would appear when the size of spatial resolution matches the size of the objects. Multiple high local variance peaks may show that the scene has multiple scales of variation. For example, the local variance as a function of resolution for agricultural areas, may indicate two distinct scales of high variance, one related to the size of individual crop rows, and the other related to the size of the field [61]. With the help of analysis of local variance, the scale where the geographic phenomenon may occur can be found, and then the observational scale of study can be determined. Here, the local variance looks like the texture analysis in digital image processing [3]. The only difference is that the texture analysis method can use several indices, such as moments, min-max, entropy, and so on. The local variance method tries to find the \"scale of action\", however, there are certain limitations involved in the usage, which are acknowledged by the authors. One limitation is that it is unrealistic to assume an idealized square wave on the part of the sensor and the pixel value of a coarse resolution image is simply an average of finer resolution pixels within corresponding coarse pixels. The other limitation is that it is dependent on the global variance in the image and the values of local variance cannot be directly compared between different images. Therefore, the relevant improvements should be made around these limitations.
## 5 Conclusions  
This research holds a significant potential to forecast the demand for aviation spare parts, as it presents a new and more efficient way to forecast demand. The problem of accurately harnessing the uncertainties will prevail for a long time to come; however, an attempt is being made to keep the forecasting error to a minimum so that it can be effectively and practically utilized in the field of aviation. Ongoing work includes the application of the designed ANN model for the rest of the spare parts demand forecast. A comparative analysis of the forecast and actual demand values was carried out. Furthermore, this work also presents a detailed comparison of the proposed model with a few state-of-the-art models and observed that the proposed method's performance is superior to other methods. The current model can serve as a starting point for further advancements in forecasting the demand for aviation spare parts. This will increase the operational availability of aircraft and improve safety by reducing the number of flights with inoperative components.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No.** & **Model** & **MASE** \\ \hline
1 & Croston & 2.18 \\ \hline
2 & Holt-Winter & 1.07 \\ \hline
3 & Auto-ARIMA & 1.04 \\ \hline
4 & Random Forest & 1.15 \\ \hline
5 & XGBoost & 1.10 \\ \hline
6 & Auto-SVR & 1.06 \\ \hline
7 & MLP & 1.68 \\ \hline
8 & LSTM & 0.96 \\ \hline
9 & ANN & 0.821 \\ \hline
10 & SVM & 1.042 \\ \hline
11 & Adaptive univariate SVM [39] & 1.069 \\ \hline
12 & Propose approach & 0.613 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model comparison based on MASE.  
**Author Contributions:** Conceptualization, I.S. and A.S.; Data curation, A.S., J.A. and J.C.M.E.; Formal analysis, I.S. and J.A.; Funding acquisition, L.A.D.L.; Investigation, J.C.M.E. and E.B.T.; Methodology, J.A.; Project administration, L.A.D.L.; Resources, L.A.D.L.; Software, J.C.M.E. and E.B.T.; Supervision, I.A.; Validation, I.A.; Visualization, E.B.T.; Writing--original draft, I.S. and A.S.; Writing--review and editing, I.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the European University of the Atlantic.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Research on Double-Arc Cutting Tool Design and Cutting Performance
## 2 Design of DACT  
### Geometric Structure of DACT  
The working surface of the DACT can be regarded as a surface of revolution; the axial longitudinal section profile of the DACT is shown in Figure 1. The profile surface of the end part of the DACT is composed of two tangent surfaces: a spherical surface at the bottom part of the cutting tool, and a ring torus, which is tangent to both the spherical surface and the cylinder part. Meanwhile, the axial longitudinal-section contour curve is composed of two tangent arcs \(L_{e}\) and \(L_{r}\). The radius of the bottom spherical surface \(S_{e}\) is \(SR_{e}=3r\), the inner radius of the ring torus \(S_{r}\) is \(R_{r}=r\), and the radius of the cylinder part of the DACT is \(R_{c}=2r\). The two tangent arcs \(L_{e}\) and \(L_{r}\) can be expressed as follows:  
\[\frac{L_{e}:\left\{\begin{array}{l}x=3r\cos\theta_{e}\\ z=3r\sin\theta_{e}-\sqrt{3}r\end{array}\right.\quad\left(\theta_{e}\in\left[ \frac{\pi}{3},\frac{\pi}{2}\right]\right)\\ \hline L_{r}:\left\{\begin{array}{l}x=r\cos\theta_{r}+r\\ z=r\sin\theta_{r}\end{array}\right.\quad\left(\theta_{r}\in\left[0,\frac{\pi }{3}\right]\right)\end{array}\right. \tag{1}\]  
where \(\theta_{e}\) and \(\theta_{r}\) are curve parameters.  
Then the ring torus surface \(S_{r}\) and the bottom spherical surface \(S_{e}\) can be defined as follows:  
\[S_{r}=\left\{\left(r\cos\theta_{r}+r\right)\cos\varphi_{r},\left(r\cos\theta_{r }+r\right)\sin\varphi_{r},r\sin\theta_{r}\right\}\left(\theta_{r}\in\left[0, \frac{\pi}{3}\right],\varphi_{r}\in\left[0,2\pi\right]\right) \tag{2}\]  
\[S_{e}=\left\{3r\cos\theta_{e}\cos\varphi_{e},3r\cos\theta_{e}\sin\varphi_{e},3 r\sin\theta_{e}-\sqrt{3}r\right\}\left(\theta_{e}\in\left[\frac{\pi}{3}, \frac{\pi}{2}\right],\varphi_{e}\in\left[0,2\pi\right]\right) \tag{3}\]  
where \(\varphi_{e}\) and \(\varphi_{r}\) are surface parameters.  
Figure 1: Geometric schematic diagram of the axial longitudinal section profile of DACT.  
It is easy to obtain the cylindrical surface \(S_{c}\) as follows:  
\[S_{c}=\{2r\cos{\varphi_{c}},2r\sin{\varphi_{c}},u\}(u\in[-D_{c},0],\,\varphi_{c} \in[0,2\pi]) \tag{4}\]  
where \(D_{c}\) is the axial length of the circumferential cutting edge.
## 4 NFV and SFC Frameworks Taxonomy
### Resource Allocation and Service Orchestration Frameworks
#### 4.1.5 Open Baton Framework  
The Open Baton framework was proposed by [PERSON] et al. [72] as a framework for NFV service orchestration and management. The framework has the OpenStack cloud infrastructure as its underlying platform, which is compliant with the ETSI MANO architectural framework. The key component implemented by Open Baton is the multi-site service orchestration feature in heterogeneous network environments. Open Baton provides a management component for handling the life cycle of network functions, including the use of the JUJU [[http://openbaton.github.io/documentation/vnfm-juju/](http://openbaton.github.io/documentation/vnfm-juju/)]([http://openbaton.github.io/documentation/vnfm-juju/](http://openbaton.github.io/documentation/vnfm-juju/)) (accessed on 20 December 2021) virtual network manager for interoperability between diverse network functions.  
The framework provides support for diverse VIMS, which means there is no need to rewrite the components of the logic that is responsible for service orchestration. To speed up the NF instantiation time, drivers are provided for VIMS and VNFMs that support the deployment of containerized network functions. Scaling of network functions can be handled at runtime using the auto-scaling component [[https://github.com/openbaton/autoscaling-engine](https://github.com/openbaton/autoscaling-engine)]([https://github.com/openbaton/autoscaling-engine](https://github.com/openbaton/autoscaling-engine)) (accessed on 1 January 2022).  
Open Baton uses Zabbix [[http://openbaton.github.io/documentation/zabbix-plugin/](http://openbaton.github.io/documentation/zabbix-plugin/)]([http://openbaton.github.io/documentation/zabbix-plugin/](http://openbaton.github.io/documentation/zabbix-plugin/)) (accessed on 1 January 2022) to monitor network activities and network function status. The framework handles resource allocation by leveraging the concept of network slicing using SDN; thus, the extensibility and interoperability of Open Baton makes it ideal as an orchestration framework for heterogeneous network functions. A fault management module, together with a management dashboard, makes Open Baton a complete solution for heterogeneous NFV orchestration.
## 4 Results
### Snow Cover Analysis in the Varzob River Basin
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Month** & \multicolumn{3}{c}{**Anzob Snow Cover**} & \multicolumn{3}{c}{**Myakhura Snow Cover**} & \multicolumn{3}{c}{**Hushyori Snow Cover**} \\ \hline  & \(p\)**-Value \({}^{1}\)** & **Sen’s Slope** & **Trend** & \(p\)**-Value \({}^{1}\)** & **Sen’s Slope** & **Trend** & \(p\)**-Value \({}^{1}\)** & **Sen’s Slope** & **Trend** \\ \hline January & 0.600 & \(-\)0.003 & Decr. & 0.472 & \(-\)0.004 & Decr. & 0.277 & \(-\)0.007 & Decr. \\ February & 0.071 & \(-\)0.009 & Decr. & 0.593 & \(-\)0.003 & Decr. & 0.284 & \(-\)0.005 & Decr. \\ March & 0.872 & \(-\)0.001 & Decr. & 0.71 & \(-\)0.002 & Decr. & 0.333 & \(-\)0.006 & Incr. \\ April & 0.180 & 0.008 & Incr. & 0.520 & 0.003 & Incr. & 0.363 & \(-\)0.005 & Decr. \\ May & 0.821 & 0.002 & Incr. & 0.67 & 0.002 & Incr. & 0.701 & \(-\)0.001 & Decr. \\ June & 0.625 & \(-\)0.002 & Decr. & 0.647 & \(-\)0.002 & Decr. & 0.254 & \(-\)0.004 & Decr. \\ July & 0.613 & 0.002 & Incr. & 0.022 & 0.008 & Incr. & 0.863 & \(-\)0.001 & Decr. \\ August & 0.589 & 0.002 & Incr. & 0.277 & 0.003 & Incr. & 0.905 & 0.000 & Incr. \\ September & 0.011 & \(-\)0.010 & Decr. & 0.781 & \(-\)0.001 & Decr. & 0.249 & \(-\)0.004 & Decr. \\ October & 0.925 & 0.001 & Incr. & 0.6 & \(-\)0.003 & Decr. & 0.67 & \(-\)0.002 & Decr. \\ November & 0.722 & \(-\)0.002 & Decr. & 0.919 & 0.001 & Incr. & 0.032 & \(-\)0.011 & Decr. \\ December & 0.811 & \(-\)0.001 & Decr. & 0.647 & \(-\)0.002 & Decr. & 0.288 & \(-\)0.006 & Decr. \\ \hline \hline \end{tabular} \({}^{1}\) No significant trend at the 5.0% level. Decr.: decreasing, Incr.: increasing.  
\end{table} 
Table 6: Mann–Kendall’s test results estimations for snow cover area for the three stations: Anzob, Maykhura, and Hushyori in the Varzob River Basin in Tajikistan based on the MODIS data during the period 2001–2022. Figure 9: Mann–Kendall trend analysis \(p\)-value information based on the spatial-temporal analysis in the Varzob River Basin in Tajikistan from 2001 to 2022. Note: (**a**) January; (**b**) February; (**c**) March; (**d**) April; (**e**) May; (**f**) June; (**g**) July; (**h**) August; (**i**) September; (**j**); October; (**k**) November; and (**l**) December. which are considered the maximum and minimum snow cover zones during the period 2001-2022, respectively. In general, seasonal snow cover changes during the study period (2001-2022) indicated the largest area covered by snow in January, February, March, and December, and showed the smallest area of snow cover in June, July, and August. Figure 10 indicates that there appears to be a pattern in snow cover throughout the year, as well as across the 22 years from 2001 to 2022. There is maybe a connection between these patterns and variables including the temperature and precipitation patterns.
## 3 Results and Discussion
### Internal Porosity of Carbonate Sand  
Typical CT images presented in terms of three-dimensional and cross-sectional views for carbonate sand grains are shown in Figure 7. The image in the lower right corner was the three-dimensional picture of carbonate sand specimen, and that in the upper left corner was the cut image of the cross section (x-y section). In the upper right and lower left corners, they were the cut images of the vertical sections (x-z and y-z sections).  
Figure 6: Critical state lines of carbonate sand-fine mixtures with similar interfering void ratio in (**a**) \(p^{\prime}\)–\(q\) and (**b**) \(\epsilon\)–\(\ln p^{\prime}\) planes.  
Figure 7: Three-dimensional and cross-sectional views of carbonate sand grains.  
A two-dimensional cross-sectional CT image was scanned for each 5 \(\upmu\)m interval along the height of the container, as shown in Figure 8a. The figure clearly reveals that most carbonate sand grains contain a substantial number of internal pores. ImageJ was used to analyse the CT images. First, the original greyscale image was converted to a binary image as depicted in Figure 8b. In the binary image, the pore and solid are represented by black and white colour, respectively. A three-dimensional surface boundary of a single sand grain was identified based on a series of sequential two-dimensional cross-sectional images. The internal porosity \(n_{in}\) of each sand grain was estimated using the following equation:  
\[n_{in}=\frac{V_{in}}{V}=\frac{\int S_{in}dh}{\int Sdh}\approx\frac{\sum S_{in} \Delta h}{\sum S\Delta h}=\frac{\sum S_{in}}{\sum S} \tag{3}\]  
where \(V_{in}\) and \(V\) are volumes of the internal pores and sand grain, respectively; \(S_{in}\) and \(S\) are the areas of the internal pores and sand grain in one cross section, respectively; and \(dh\) and \(\Delta h\) are the spacing between neighbouring cross sections, which was 5 \(\upmu\)m in this study. \(S_{in}\) and \(S\) were estimated based on the amount of pixel points of the binary image, as shown in Figure 8b.  
In this study, a total of one hundred carbonate sand grains were analysed. The statistical results of the internal porosity are shown in Figure 9. It was found that the value of internal porosity ranged between 0 and 0.5. The statistical results conformed to the normal distribution with a mean value of 0.238. As a first approximation, 0.238 was used as the mean value of internal porosity for all tested carbonate sand in the subsequent analysis. The one hundred carbonate sand grains were randomly selected from the specimens of trivial tests, having a certain representativeness in terms of selection method. However, they were not selected proportionally according to the composition of carbonate sand. If the proportion of a certain type of carbonate sand is too high in the one hundred carbonate sand grains, it may affect the mean value of internal porosity.  
Figure 8: Typical CT image of a two-dimensional cross section of a carbonate sand specimen: (**a**) greyscale image; (**b**) binary image (black indicates void areas, and white represents solid areas).
## 3 Results
### BPA and PG Retention Studies by Selected MIPs and NIPs  
The retention capacities of the selected MIPs are presented in Figure 5. During the analysis, 50 mg of MIP or NIP was added to 15-mL Falcon tubes, followed by 5 mL of standard solution of BPA (3.12 mg/L) or PG (5.28 mg/L).  
In Figure 5, a significant difference in the retention capacity between MIPs and NIPs can be observed, which suggests the high efficiency of the imprinting process. MIP 2 for PG shows a retention rate 3.5 times higher than that of the control NIP 2, and 2.5 times higher in the case of BPA. Similar trend can be seen using MIP 3. The difference in the retention rate between MIP 3 and NIP 3 is 2.44 times higher for PG and 2.24 times higher for BPA.
(NIP 2) was 1.876 mg/g. Therefore, MIP 2 retained BPA 9.88 times more than NIP 2. For MIP 3, the highest value of Qmax was observed at 303 K (19.46 mg/g), while the Qmax of the control (NIP 3) was 2.584 mg/g. Hence, MIP 3 retained 7.53 times more BPA than NIP 3. In Table 6, the results from the linearised isotherm for PG can be seen. The highest value of Qmax was observed in MIP 2 at 308 K with value of 21.49 mg/g, while the Qmax for the control (NIP 2) was 2.593 mg/g. Thus, MIP 2 retained PG 8.28 times more than the control. Consequently, the highest value of Qmax for MIP 3 was observed at 303 K 17.63 mg/g, while the Qmax for the control (NIP 3) was 1.951 mg/g. MIP 3 retained PG 9.03 times more than NIP 3.
## 2 LDS-UFMC System Model  
In this section, we are going to define the major blocks of an LDS-UFMC system. The LDS-UFMC block diagram is shown in Figure 1. After coding and modulation, the symbols are multiplied with low density spreading sequences then modulated and transmitted simultaneously. Afterwards, spreaded data are shaped into a time-frequency grid. In this configuration, we address the pilots aided channel estimation. For simplicity, we propose to insert the pilots using the Comb Type arrangement. Then, this grid is UFMC modulated using an N-point Inverse Fast Fourier Transform (IFFT) and filtered by a L-length Dolph-Chebyshev filter. At the receiver side, the received signal is UFMC demodulated, then the pilots extraction is performed in order to estimate the channel using the Least Square (LS) method and linear interpolation. The equalizer block uses Zero-Forcing to eliminate the channel effect. The output of the equalizer is passed to the LDS detector where an iterative detection process based on MPA is performed to separate the users' symbols.
# A Hierarchical Control System for Autonomous Driving towards Urban Challenges
## 1 Introduction
After solving this optimization problem, we have both spatial and temporal parameters for the control. Recently, the deep learning approaches can be employed to manage lateral control directly [23] or aid the controller [24]. Nevertheless, the computational cost, complexity, and less robustness are the main drawbacks of the method. The classical controller as the PID method is the most popular control strategy which operates adequately in various applications of process control. However, distributed noises such as road conditions, friction forces are hard to handle by the PID controller. Moreover, the Model Predictive Control (MPC) [20; 25] is a state-of-the-art solution for cruise control due to its accuracy. However, the implementation of MPC is complex and offers a computationally-expensive solution. The authors of [20; 26] designed a unique Scheduled Feedforward PI (SFF-PI) controller for both acceleration and brake. We propose two SFF-PI controllers for the longitudinal control. The proposed architecture efficiently manipulates both acceleration and brake. Figure 1: A general framework of an autonomous driving system which is composed of the perception and environment mapping, motion planning, and controller. This paper is highlighted with substantial contributions as follows:  
1. We successfully develop and implement a hierarchical control system for an autonomous vehicle platform. The vehicle can manipulate the whole mission autonomously within a proving ground. 2. The motion planning with the decision-making mechanism using two-stage FSM for the SDCs is proposed. Besides, we solve an efficient LPP by using a nonlinear optimization technique and a real-time Hybrid A* algorithm. 3. The adaptive-pure pursuit algorithm for path tracking and a new SFF-PI architecture for longitudinal control are implemented successfully in urban environments. The remainder of this paper addresses the design of a hierarchical control system for a self-driving vehicle of Chungbuk National University called Clothoid. Section 2 describes the study on motion planning with decision making using two-level FSM. Next, the local path planning generating a comfortable trajectory by solving nonlinear constraints based-optimization is presented. The local path performs practically an obstacle avoidance algorithm with the occupancy grid map using real-time hybrid A*. Then, the control strategies that use an adaptive-pure pursuit for lateral control and two SFF-PI controllers for longitudinal control are exhibited. Finally, experimental results in the K-city proving ground are described in Sections 3 and 4 provides the conclusion of the paper.
## 2 Characteristic of the CB System
For improving the CB's function, it is necessary to determine the contrast between coarse particles and fine grains, make sure to always maintain the soil around the CB in an unsaturated state, and reduce the amount of water infiltrating from the surface layer [32; 39; 40]. The horizontal distance from the top of the slope to the breakthrough point is defined as the diversion length (L\({}_{\text{D}}\)) of the CB system (i.e., an indicator of its water-shielding performance) (see Figure 1) [41]. When applying the CB system to water-shielding or to the control of infiltrated water, L\({}_{\text{D}}\) is an important design parameter, which determines the size and structure of the CB system. The size of L\({}_{\text{D}}\) is related to the slope angle of the interface (as a structural factor), to the water retention characteristics of sand and gravel (as a material factor), and to the infiltration rate (as an external factor). Empirical equations for estimating the diversion length under the lateral flow (i.e., drained) condition and considering these influencing factors have been proposed by [PERSON] [4], [PERSON] [42], and [PERSON] et al. [28]. Among these, the equation of [PERSON] et al. [28], based on [PERSON]'s equation, is considered to have a particularly good accuracy [43; 44]. In this study, we compared the relationship between the estimated and measured L\({}_{\text{D}}\) values. First, the estimated values explained later were derived using Equation (2) proposed by [PERSON] et al. [28], based on modified [PERSON]'s equation. It should be noted that in this equation, the layer thickness does not affect the diversion length:  
\[\text{L}_{\text{D}}=\tan\Phi\cdot\left\{\frac{1}{b}(\frac{1}{\kappa}-1)+\frac{ 1}{\kappa}(h_{ae}-h_{ex})\right\} \tag{2}\]  
where \(b\) was derived from the equation of unsaturated hydraulic conductivity and from the relationship \(k=k_{sat}\cdot\exp(b\psi_{m})\) (with \(k_{sat}\) being the saturated hydraulic conductivity and \(\psi_{m}\) the suction (\(h\), cmH\({}_{2}\)O)). Moreover, \(\Phi\) is the slope of the interface, \(\kappa\) was derived from the relationship \(\kappa=q_{v}/k_{sat}\), (with \(q_{v}\) being the flux of water entering the soil), \(h_{ae}\) is the air entry value (AEV) of sand, and \(h_{ex}\) is the air expulsion value of gravel.