esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
GPC is an eco-environmentally friendly material which can be used as a replacement for cement concrete in civil engineering construction. The compressive strength is an important parameter for evaluation of the quality of this material. In this study, novel hybrid AI approaches named PSOANFIS and GAANFIS were proposed for quick prediction of the compressive strength of GPC using steel slag as both fine and coarse aggregates. A total of 210 samples were generated and tested in the laboratory to determine the parameters for modeling, such as the compressive strength as the output variable, and concentration of NaOH solution (10 M, 12 M, and 14 M), mass ratio of alkali activating solution to fly ash (AAS/FA) (0.4, 0.45, and 0.5), and mass ratio of solution Na\({}_{2}\)SiO\({}_{3}\) to solution NaOH (SS/SH) (2, 2.5, and 3) as the input parameters. Validation and comparison of the models was done using various error determination criteria, including MAE, RSME, and \(R\). The results show that the proposed models performed well for the prediction of the compressive strength of GPC, but the PSOANFIS model (MAE = 1.847 MPa, RMSE = 2.251 MPa, and \(R\) = 0.934) outperformed the GAANFIS model (MAE = 2.115 MPa, RMSE = 2.531 MPa, and \(R\) = 0.927). Thus, it can be reasonably concluded that the proposed hybrid AI approach of PSOANFIS is a promising method for the prediction of the compressive strength of GPC. This study would help in reducing the time and cost of construction, as well as in the improvement of the environment. However, a limitation of this study is that we considered only ingredients that combine to form geopolymer binders in predicting the compressive strength of GPC, and we did not consider the quality, quantity, or properties of aggregates which might affect the compressive strength of GPC. Thus, this is proposed to carry out a study of these factors in future modeling. In addition, a sensitivity analysis of different combinations of input data may also be carried out to test the importance of each input parameter for better modeling of the compressive strength of GPC.  
Conceptualization, D.V.D., H.-B.L. and B.T.P.; Methodology, D.V.D., H.-B.L. and B.T.P.; Software, B.T.P., H.-B.L.; Validation, D.V.D., S.H.T., and H.-B.L.; Formal Analysis, D.V.D. and S.H.T.; Investigation, D.V.D. and S.H.T.; Resources, D.V.D., S.H.T., H.-B.L. and B.T.P.; Data Curation, D.V.D. and S.H.T.; Writing--Original Draft Preparation, D.V.D., S.H.T., H.-B.L. and B.T.P.; Writing--Review & Editing, D.V.D., H.-B.L. and B.T.P.; Visualization, D.V.D., S.H.T., H.-B.L. and B.T.P.; Supervision, D.V.D.; Project Administration, D.V.D.; Funding Acquisition, D.V.D. This research received no external funding. The authors thank the University of Transport Technology (Hanoi, Vietnam) for providing the facility to carry out this research. The authors declare no conflict of interest.
## 3 Results and Discuss  
### Swelling Characteristics of Treated Soils  
The results of the free swell tests and confined swell tests for the soil samples are presented in Figures 2 and 3, respectively. These results reveal that as the cementation solution concentration increased, the free swell ratio of the soil samples decreased from 56% for S-0 to 34% for S-5, a reduction of 39.29%. Similarly, the unconfined swell ratio decreased from 16.38% for S-0 to 4.75% for S-5, a reduction of 71%.  
The free swell ratio and unconfined swell ratio both exhibited a decreasing trend, with a more rapid decline at lower cementation solution concentrations and a more gradual decline at higher concentrations. Specifically, for the soil samples S-0 to S-3 (cementation solution concentration less than 1 M), the free swell ratio and unconfined swell ratio decreased by 28.57% and 50.31%, respectively. In contrast, for the soil samples S-3 to S-5 (cementation solution concentration greater than 1 M), the decreases in the free swell ratio and confined swell ratio were only 10.72% and 20.69%, respectively.  
These results suggest that a cementation solution concentration of 1 M is effective in reducing the swelling potential of the expansive soil. The reduction in swelling characteristics can be attributed to the precipitation of calcium carbonate induced by the _Sporosarcina pasteurii_ bacteria and the cementation solution. The CaCO\({}_{3}\) precipitation fills the pore spaces and alters the internal particle structure of the expansive soil, effectively improving its water-induced swelling behavior.  
Moreover, the significant decrease in swelling ratios, especially at lower cementation solution concentrations, indicates that the MICP treatment enhances the soil's resistance to volume changes upon wetting. This improvement is crucial for mitigating the adverse effects of swelling in expansive soils, such as uneven settlement and structural failures.
## 3 Identifying Promising Vacant Technologies
### Detailed Procedures  
#### 3.2.1 Identification of Vacant Technology Fields Using a GTM-Based Patent Map  
First, vacant technology fields can be identified through three substeps: collecting and preprocessing patent data, developing a patent map, and identifying vacant technology fields (Figure 2).  
Figure 1: Overall framework.  
Patent documents can be collected from various patent institutions; we collected patent data from the United States Patent and Trademark Office (USPTO) [51]. The patent documents contain various information such as abstract, title, and IPC classification for the specific technical fields.  
In this study, patent data preprocessing aims to generate a data set comprising a patent-IPC matrix and a patent-keyword matrix. In order to generate the patent-keyword matrix, textual information should be preferentially tokenized and then preprocessed by removing punctuation, abbreviations, and stop words [52; 53; 54; 55]. The refined textual data contains only the words that are essential to describe a patent document through the preprocessing of unstructured data text [56]. The patent-IPC matrix is developed based on the IPC in the patent document. IPC codes can categorize into specific technical fields [57], and patent documents must be assigned to one or more IPC codes [58]. The IPC code has a hierarchical structure including sections, classes, and subclasses. It is used by classifying it into IPC class and subclass for an accurate explanation about technologies. Therefore, in this study, we classified the IPC into subclass levels to clarify the explanation [38]. Patents with a higher than the average frequency of each subclass in the patent documents were employed to generate the patent-IPC matrix. Additionally, we develop the patent-keyword matrix base on the patent document's title and abstract. For developing the patent-keyword matrix, the TF-IDF method was applied to extract keywords. It is a method of extracting significant keywords by analyzing the frequency and TF-IDF weights of individual documents. The extracted significant keywords are used to make a keyword-patent matrix [36].  
Subsequently, we developed the GTM-based patent map using the patent-IPC matrix. GTM-based patent maps can be visualized onto a two-dimensional space, and patent vacuums can be detected in them. In order to develop a GTM-based patent map, the map size related to the number of grids should first be determined. If the map size is too small, the overall grids can be occupied, making it difficult to detect the patent vacuums. On the other hand, if the map size is too large, there will be too many patent vacuums to analyze because the map may be sparse. Prior to developing this map, we need to define the parameter of map size [37]. Therefore, we performed sensitivity experiments to determine the map size: (1) 4 \(\times\) 4, (2) 6 \(\times\) 6, (3) 8 \(\times\) 8, and (4) 10 \(\times\) 10.
# A 0.3 V, Rail-to-Rail, Ultralow-Power, Non-Tailed, Body-Driven, Sub-Threshold Amplifier
## 4 Amplifier Design and Simulation Results
### Sizing  
All the devices have been biased in the subthreshold region at \(|V_{gs}|=|V_{gs}|=150\) mV, and a dual supply of \(\pm 0.15\) V with \(V_{DD}-V_{SS}=2\,|V_{gs}|\) has been adopted. All the devices in the circuit have been sized with extremely long gates: this choice allows to minimize noise and increase the output resistance and the intrinsic gain of MOS devices as shown in [13]. The bias current of transistors in the input stage has been set to 5 nA as a tradeoff between power consumption and noise performance. For what concerns the second stage, simulations have shown that a design maximizing the CMRR according to (10) is very sensitive not only to mismatches but also to PVT variations; the stage has thus been  
Figure 4: Simplified small-signal equivalent circuit of the amplifier after applying Miller approximation for frequency response computation.  
optimized to provide good CMRR performance in the different PVT conditions by means of a design centering approach.  
To minimize power consumption, a 4 nA bias current has been adopted for the second stage. Even if this current is lower than the bias current of the first stage, the amplifier can still be efficiently compensated because the body driven input stage has a much lower transconductance than the second stage. A load capacitance \(C_{L}\) of 50 pF has been assumed and a compensation capacitor \(C_{c}\) of 1.2 pF has been added to provide an adequate phase margin. Table 1 reports sizing, bias current, and small signal parameters for all the devices.
## 3 Results
### Scenario S2\({}_{x}\)
\begin{table}
\begin{tabular}{c c c c c} \hline  & **ENU (Wh)** & **HNU (h)** & **ENS (Wh)** & **LOLP (\%)** \\ \hline D1 alone (S1) & 4,560,010.28 & 1828 & 131,561 & 2.65\% \\ D2 alone (S1) & 3,292,706.47 & 1585 & 82,274 & 2.46\% \\ D1 cooperative (S2\({}_{30}\)) & 4,292,284.49 & 1617 & 106,547 & 2.24\% \\ D2 cooperative (S2\({}_{30}\)) & 3,260,417.70 & 1519 & 66,109 & 2.09\% \\ S1 (global) & 7,852,716.75 & 1706 & 213,835 & 2.55\% \\ S2\({}_{30}\) (global) & 7,552,702.19 & 1568 & 172,656 & 2.16\% \\ \hline \multicolumn{5}{l}{S2\({}_{30}\) improvement compared to S1 (\%)} \\ \hline D1 & 5.87 & 11.52 & 19.01 & 15.59 \\ D2 & 0.98 & 4.18 & 19.65 & 15.22 \\ Average & 3.82 & 8.11 & 19.26 & 15.41 \\ \hline \end{tabular}
\end{table} 
Table 3: Scenario S1 vs. scenario S2\({}_{30}\). Therefore, the S2\({}_{30}\) scenario offers the most balanced results in terms of the improvements obtained by both dwellings. After applying the proposed method, the study was completed with a sensitivity analysis shown in Section 4.
## 5 Numerical Results of the SEIR Model
### United Kingdom
#### 5.7.1 Extended SEIR Model  
We calculate \(\beta_{E}\) as in the previous countries. Observe in Figure 10 the drop of the values at the closed lockdown stages (Blue background zone).  
In this case, we can also use the linear regression technique for the individual 64 different counties that constitute the UK. A series of histograms related to each stage can be seen at Figure 11 and their associated statistical data at Table 6. There, the mean value of the \(\beta_{E}\)s, their variance and the confident interval are displayed, as well as the \(p\)-value between histograms to show the notable impact of the strategies on the different stages.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Phase** & **Mean** & **Variance** & **CI 95\%** & \(p\) \\ \hline Lockdown & 0.08 & \(10\times 10^{-4}\) & (80.7, 82.5) \(\times 10^{-3}\) & \\ Indoor contact & 0.46 & \(12\times 10^{-4}\) & (452.6, 469.5) \(\times 10^{-3}\) & 0 \\ Second lockdown & 0.22 & \(134\times 10^{-4}\) & (192.0, 248.6) \(\times 10^{-3}\) & 0 \\ Indoor contact & 0.47 & \(80\times 10^{-4}\) & (452.0, 495.7) \(\times 10^{-3}\) & 0 \\ Third lockdown & 0.17 & \(69\times 10^{-4}\) & (144.8, 185.4) \(\times 10^{-3}\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Histogram for the mean values of \(\beta_{E}\) at the different stages of lockdown in the UK.  
Figure 10: Value of \(\beta_{E}\) for the different levels of lockdown. Blue will correspond to Stage 1, yellow to Stage 3 and white to Stage 4. A green dotted line corresponds to the value of new cases per 100,000 hab.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Date** & **Total Cases** & **I** & \(\frac{I_{i}}{I_{i-14}}\) & **Death** \\ \hline
23 March 2020 & 12,639 & 18 & 19.9 & 938 \\
1 June2020 & 257,702 & 37.9 & 0.7 & 38,294 \\
20 August 2020 & 325,468 & 22.37 & 1.4 & 41,494 \\
5 November 2020 & 1,183,510 & 464.9 & 1.3 & 49,271 \\
2 December 2020 & 1,696,375 & 317.5 & 0.7 & 61,050 \\
4 January 2021 & 2,880,767 & 1042.7 & 1.8 & 79,323 \\
12 February 2021 & 4,040,037 & 328.2 & 0.6 & 118,097 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Data for the UK: Total cases, Cases per 100,000 inhabitants in the last 14 days, rate of change of I in 14 days, Death related to COVID-19.
## 4 Theoretical Analysis of the Influence of AL on Coherent/Non-Coherent OOK Reception
### AL Research on the Coherent OOK Receiver
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SAL Power** & **PSD of SAL** & **Voltage Variance of SAL–LO** \\  & & **Beat Noise** \\ \hline
1.15 uW & \(4.64\times 10^{-18}\) W/Hz & \(2.70\times 10^{-3}\) V\({}^{2}\) \\
0.86 uW & \(3.48\times 10^{-18}\) W/Hz & \(2.00\times 10^{-3}\) V\({}^{2}\) \\
0.58 uW & \(2.32\times 10^{-18}\) W/Hz & \(1.30\times 10^{-3}\) V\({}^{2}\) \\
0.29 uW & \(1.16\times 10^{-18}\) W/Hz & \(6.71\times 10^{-4}\) V\({}^{2}\) \\
0.14 uW & \(5.80\times 10^{-19}\) W/Hz & \(3.36\times 10^{-4}\) V\({}^{2}\) \\
57.56 nW & \(2.32\times 10^{-19}\) W/Hz & \(1.34\times 10^{-4}\) V\({}^{2}\) \\
46.05 nW & \(1.86\times 10^{-19}\) W/Hz & \(1.07\times 10^{-4}\) V\({}^{2}\) \\
28.78 nW & \(1.16\times 10^{-19}\) W/Hz & \(6.71\times 10^{-5}\) V\({}^{2}\) \\
5.76 nW & \(2.32\times 10^{-20}\) W/Hz & \(1.34\times 10^{-5}\) V\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SAL power, PSD of SAL, and SAL–LO beat noise voltage variance (coherent).
## 3 Kinetic Analysis Methodology  
Methods for solid-gas reactions are either differential, based on the differential form of the reaction model, _f(\(\alpha\))_, or integral, based on the integral form \(g(\alpha)\)[47].The expression for \(g(\alpha)\) is the general expression of the integral form:  
\[g(\alpha)=\int_{0}^{\alpha}\frac{d\alpha}{f(\alpha)}=A\int_{0}^{t}exp\left( \frac{-E_{\alpha}}{RT}\right)h(P)dt \tag{4}\]  
where A is the pre-exponential factor (min\({}^{-1}\)), \(R\) is the universal gas constant (kJ mol\({}^{-1}\) K\({}^{-1}\)), \(T\) is the temperature (K), and _h(P)_ is the pressure dependence term (dimensionless). Non-isothermal experiments employ a heating rate (\(\beta\) in K min\({}^{-1}\)) which is constant with time, leading to Equations (5) and (6) [48].  
\[\frac{d\alpha}{dT}=\frac{A}{\beta}exp\left(\frac{-E_{\alpha}}{RT}\right)h(P)f(\alpha) \tag{5}\]  
\[g(\alpha)=\frac{A}{\beta}\int_{0}^{T}exp\left(\frac{-E_{\alpha}}{RT}\right)h(P )dT \tag{6}\]  
Equation (5) is the starting point for differential methods of kinetic analysis, such as the Friedman (isoconvorsional) method. This method calculates \(E_{\alpha}\) independently of a reaction model and requires multiple data sets [49]. It is referred to as a model-free technique as it avoids assumptions about a reaction mechanism and can identify multi-step reactions. This method is explained in detailed elsewhere [24; 30].  
Equation (6) is the starting equation for many integral methods of evaluating non-isothermal kinetic parameters with constant \(\beta\). One example is the Coats-Redfern integral approximation method, referred to as a model-fitting method of kinetic analysis.  
Model-fitting methods aim to determine the kinetic parameters of the reaction model integral by fitting obtained data to various known solid-state kinetic models and generally omit the pressure dependence term _h(P)_[47]. Kinetic models for reaction mechanisms can be categorized using different algebraic functions for \(f(\alpha)\) and \(g(\alpha)\), which can be found in [PERSON] et al. [30]. The general principle of model-fitting methods is to minimize the difference between the experimentally measured and calculated data for the given reaction rate expression [47].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Calcination** & & & **Carbonation** & \\ \hline \multirow{2}{*}{**Experiment**} & **Temp.** & \multirow{2}{*}{**Atmosphere**} & **Isotherm** & **Temp.** & \multirow{2}{*}{**Atmosphere**} & **Isotherm** \\  & (°C) & & **(min)** & & **(°C)** & \\ \hline \multirow{2}{*}{**Non-isothermal** **(mixed N\({}_{2}\)/CO\({}_{2}\))**} & \multirow{2}{*}{850} & \multirow{2}{*}{N\({}_{2}\)} & \multirow{2}{*}{0} & \multirow{2}{*}{–} & 75 v/v\% N\({}_{2}\) & \multirow{2}{*}{0} \\  & & & & & 25 v/v\% CO\({}_{2}\) & \\ \multirow{2}{*}{**Non-isothermal** **(100\% CO\({}_{2}\))**} & \multirow{2}{*}{1000} & \multirow{2}{*}{CO\({}_{2}\)} & \multirow{2}{*}{0} & \multirow{2}{*}{–} & CO\({}_{2}\) & 0 \\  & & & & & 25 v/v\% CO\({}_{2}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental conditions for kinetic analyses.
# A Robust Dual-Platform GMTI Method against Nonuniform Clutter
The former platform and the latter platform have the same subastral point tracks, which guarantee consistent scattering between the echoes from the former platform and the echoes from the latter platform. Therefore, the accurate CCM can be directly estimated from the echoes of the GMT zone based on the SAR image obtained by the former platform. Based on this fact, a new GMTI processing method is presented. Firstly, due to the errors in observation geometry between the two SAR images, there is a certain offset and distortion between the images, so it is necessary to register the images of the former and latter platforms. Following image registration, the next step is to calculate the CCM. In this paper, the calculation of the CCM is based on the echo from the CUT, which originated from the registered auxiliary image. To improve echo generation speeds, we used the fast echo simulation method based on range profile inverse transforming. After obtaining the accurate CCM, the detection threshold was set according to the estimated CCM and detection was executed accurately. Compared with the traditional CCM estimation method in STAP, using the nearby clutter cell instead of the CUT, this method can avoid CCM estimation mistakes in a nonuniform clutter environment. The clutter can be whitened and depressed more effectively. With the accurate threshold acquired from the CCM, the detection probability can be effectively improved under certain false-alarm criterion. The novelties and contributions of this paper are summarized as follows. * Directly calculate CCM with the range cell echoes at the location of the moving target that originated from the forward SAR images. * By estimating CCM more accurately, the target detection threshold will also be more accurate, making the detection results more reliable. This paper is organized as follows. Section 2 describes the dual-platform GMTI method against nonuniform clutter. In this section, the problem of STAP processing as well as existing problems in heterogeneous environments are also analyzed. Section 3 presents the simulation results, which prove the effectiveness of the proposed method. Discussions about the disadvantages of the proposed method and future research can be found in Section 4. Section 5 is the conclusion. Notation: Constants are denoted with nonbold, lower-case symbols; vectors and matrices are denoted by bold lower-case letters and bold upper-case letters, respectively. \(\mathbb{C}\) represents the complex field. The conjugation of a complex number is denoted by \([\cdot]^{*}\). The transpose, conjugate transpose, and inverse of a matrix are denoted by the superscripts \([\cdot]^{T}\), \([\cdot]^{H}\), and \([\cdot]^{-1}\), respectively. The Kronecker product of two matrices is represented by \(\otimes\). The expectation operator is denoted by \(E[\cdot]\). The absolute value of a scalar or the determinant of a square matrix is denoted by \(|\cdot|\).
# Dynamic Rating of Three-Core XLPE Submarine Cables for Offshore Wind Farms
## 2 Dynamic Cable Rating
### Determination of Cable Parameters
#### 2.2.1 Determination of Cable Losses and Loss Factors  
IEC 60287-1-1 [10] proposes a method to determine the conductor AC resistance \(R_{ac}\), dielectric losses \(W_{d}\), sheath loss factor \(\lambda_{1}\) and armour loss factor \(\lambda_{2}\). The conductor AC resistance \(R_{ac}\) is calculated in IEC 60287-1-1 [10] Clause 2.1. The calculation for a round stranded (extruded insulation) or round solid three-core XLPE SL-type cable can be summarised as follows.  
\[R_{ac}=R_{dc}(1+y_{s}+y_{p})\qquad[\Omega] \tag{1}\]  
\[y_{s}=\begin{cases}\dfrac{x_{s}^{4}}{192+0.8\cdot x_{s}^{4}},&\text{if }\;0<x_{s}\leq 2.8\\ -0.136-0.0177\cdot x_{s}+0.0563\cdot x_{2}^{2},&\text{if }\;2.8<x_{s}\leq 3.8\\ 0.354\cdot x_{s}-0.733,&\text{if }x_{s}>3.8\end{cases} \tag{2}\]  
\[x_{s}^{2}=\dfrac{8\cdot\pi\cdot f}{R_{dc}}\cdot 10^{-7} \tag{3}\]  
\[y_{p}=\dfrac{x_{p}^{4}}{192+0.8\cdot x_{p}^{4}}\cdot\left(\dfrac{D_{c}}{d_{cc} }\right)^{2}\cdot\left[0.312\cdot\left(\dfrac{D_{c}}{d_{cc}}\right)^{2}+ \dfrac{1.18}{\dfrac{x_{p}^{4}}{192+0.8\cdot x_{p}^{4}}+0.27}\right] \tag{4}\]  
\[x_{p}^{2}=\dfrac{8\cdot\pi\cdot f}{R_{dc}}\cdot 10^{-7} \tag{5}\]  
Figure 5: Electrical analogy for cables.  
Figure 6: Thermal analogy for cables.  
The dielectric loss is small and can be neglected for some voltage levels, as described in [10]. The dielectric loss is calculated in IEC 60287-1-1 [10] Clause 2.2. The calculation for a cable with XLPE insulation and a voltage level greater than 30 kV nominal voltage, can be summarised as follows:  
\[W_{d}=\omega\cdot\frac{2.5}{18\cdot\ln\frac{D_{i}}{d_{c}}}\cdot 10^{-9}\cdot U _{p}^{2}\cdot 0.001\qquad[\mathrm{W/m}] \tag{6}\]  
The sheath loss factor \(\lambda_{1}\) is calculated in IEC 60287-1-1 [10] Clause 2.3 and 2.3.10, and the calculation for a three-core SL-type cable is as follows:  
\[\lambda_{1}=\frac{R_{s}}{R_{ac}}\cdot\frac{1.5}{1+\left(\frac{R_ {s}}{2\cdot\omega\cdot 10^{-7}\cdot\ln\left(\frac{2\cdot d_{cc}}{d_{ms}} \right)^{2}}\right)} \tag{7}\]  
The armour loss factor is calculated in [10] Clause 2.4 and 2.4.2.5 as summarized below for a three-core SL-type cable:  
\[\lambda_{2}=1.23\cdot\frac{R_{a}}{R_{ac}}\cdot\left(\frac{2\cdot d_{co}}{D_ {a}}\right)^{2}\cdot\frac{1}{\left(\frac{2.77\cdot R_{a}\cdot 10^{6}}{ \omega}\right)^{2}+1}\cdot\left(1-\frac{R_{ac}}{R_{s}}\cdot\frac{\lambda_{1} ^{\prime}}{1.5}\right) \tag{8}\]  
The IEC method for calculating the armour loss of three-core SL-type cables has been questioned by cable production companies [12] and researchers [13]. The article [13] presented different methods to calculate a much more accurate armour loss, resulting in up to half the loss calculated by IEC's suggested method. To further optimise the method of dynamic cable rating, one of the suggested alternative methods could be applied instead of the IEC approach.
# Bidirectional Sliding Window for Boundary Recognition of Pavement Construction Area Using GPS-RTK
## 3 Experiments
### Recognition of Pavement Construction Area Boundary  
The six types of polygon construction joints shown in Figure 1 can be divided into straight polygons (Figure 1a-c) and curved polygons (Figure 1d-f). This section introduces the method of identifying polygon boundaries from these two perspectives.  
Figure 8: Global Positioning System—Real Time Kinematic (GPS-RTK) measurement error in the Gaussian plane coordinate system.  
Figure 9: Histogram and normal distribution curve of the GPS-RTK measurement error.