esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Classification Results Using Proposed Novel HML Classifier
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Name of the Channel** & **PT** & **NT** & **TP** & **TN** & **FP** & **FN** & **F1** & **MCC** & **Sen** & **Spe** & **Acc** \\ \hline EMG1-EMG2 & 63 & 18 & 63 & 17 & 0 & 1 & 0.99 & 0.96 & 0.98 & 1.00 & 0.98 \\ ECG1-ECG2 & 65 & 16 & 63 & 15 & 2 & 1 & 0.97 & 0.88 & 0.98 & 0.88 & 0.96 \\
**C4-P4** & 63 & 11 & 62 & 10 & 1 & 1 & **0.98** & **0.89** & **0.98** & **0.90** & **0.97** \\ C4-A1 & 65 & 9 & 62 & 8 & 3 & 1 & 0.96 & 0.77 & 0.98 & 0.72 & 0.94 \\ \hline Mean & 64 & 13.5 & 62.5 & 12.5 & 1.5 & 1 & 0.97 & 0.87 & 0.98 & 0.65 & 0.96 \\ \(\pm\)SD & 1.15 & 4.20 & 0.57 & 4.20 & 1.29 & 0 & 0.01 & 0.07 & 0 & 0.37 & 0.01 \\ \hline \hline \end{tabular} Bold is the best model.  
\end{table} 
Table 4: Performance for the combination of subjects (bruxism and healthy) and sleep stages (w and REM) classification using the novel 20-fold cross-validation model of the HML classifier. Figure 8: Performance analysis of the sleep stages (w and REM) classification using the novel 20-fold HML classifier. (**A**) Comparison between all channels such as EMG1-EMG2, ECG1, ECG2, C4-P4, and C4-A1 channels of the sleep stages classification. In addition, (**B**) highest and mean performance of the sleep stages classification. The ECG1-ECG2 channel of the ECG signal has the highest accuracy (95%) in the sleep stages classification.
# Strain Relaxation Effect on the Peak Wavelength of Blue InGaN/GaN Multi-Quantum Well Micro-LEDs
## 3 Results and Discussion  
### InGaN/GaN Micro-Pillar Arrays PL Spectral Analysis  
Figure 3a is the PL peak wavelength distribution of the entire epitaxial wafer after epitaxial growth, which is recorded as the ORI (original wafer PL). The average wavelength of the PL spectrum is 463.04 nm and the standard deviation is 0.82 nm. It can be seen that the emission wavelengths generated by the epitaxial wafer are not completely consistent after epitaxial growth.  
Figure 3b shows the distribution of the PL peak wavelength obtained after micro-pillar ICP etching according to the layout shown in Figure 2, denoted as AFT (after etching PL). The obvious PL area outline proves that the etching was successful in dividing the wafer into the 18 smaller areas. The peak wavelength distribution characteristics inherited from the original wafer can also be seen in Figure 3b.  
The wavelength distribution inhomogeneity caused by the original wafer epitaxy affects the extraction of micro-pillar PL information. In order to suppress these influences, we subtracted the AFT PL from the ORI PL and filtered the peak wavelength information out of the micro-LED pillar area. Then, some abnormal values far from the average in the ORI minus AFT PL results caused by etching damage and wafer boundary epitaxy quality degradation are filtered based on the Chauvenet-criterion method [14]. The result is shown in Figure 3c and we can obtain the peak wavelength blueshift caused by the micro-LED pillar arrays. There is a wavelength shift of nearly 0-3 nm in the regions of  
Figure 2: Micro-pillar layout on the 4-inch wafer with diameters from 3 to 20 m.  
different micro-column sizes. It can be concluded that the smaller the column size, the greater the wavelength blueshift.  
To further illustrate micro-pillar arrays' peak wavelength shift, we employed a non-zero mean method to deal with the peak wavelength of each pillar region. As shown in Figure 3d, the wavelength blueshift gradually decreased from 3.1166 nm in the 3 \(\upmu\)m pillar size region to a minimum of 0.6634 nm in the 19 \(\upmu\)m pillar size region. We attribute these PL peak wavelength blueshifts to the InGaN/GaN multi-quantum well stress release at the side edge of the micro-pillar. As concluded in previous studies [10; 11; 12], the InGaN/GaN quantum well sidewalls' strain relaxation leads to the reduction of the piezoelectric polarization field in the quantum well. Affected by this, the equivalent InGaN/GaN band gap becomes larger and the emission wavelength is blueshifted.
# Hidden Markov Model-Based Dynamic Hard Shoulders Running Strategy in Hybrid Network Environments
## 3 Dynamic Hard Shoulder Running Strategy Based on Hidden Markov Model
### Control Process  
The HSR strategy proposed in this paper is shown in Figure 5. After obtaining the traffic state \(i\) of the road section in future \(t_{c}\) minutes, the Freeway Control Center, as shown in Figure 1, opens the roadside hard shoulder in advance to mitigate any impending future traffic breakdown. In addition, since the hard shoulder is primarily used as an emergency lane for vehicles with emergency needs, the strategy proposed in this paper will determine whether the hard shoulder needs to be closed after opening for \(T_{s}\) to avoid affecting the normal use of the hard shoulder. After opening the hard shoulder for \(T_{s}\), the future traffic state is predicted based on the observed traffic data, and if the road remains in a breakdown state in the next \(t_{c}\) minutes, keep opening the hard shoulder; otherwise, close the hard shoulder. The specific control steps are as follows:  
Step 1: Obtain \(K(t-t_{c})\), \(\rho_{CAV}(t-t_{c})\) as well as \(\pi(t-t_{c})\)\(t_{c}\) minutes ago and \(K(t)\), \(\rho_{CAV}(t)\) at the current moment;  
Step 2: Predict the traffic state for the next \(t_{c}\) minutes with the Viterbi algorithm and if a traffic breakdown occurs, open the hard shoulder; otherwise go to Step 3;  
Step 3: At this time, if the hard shoulder has been opened and the opening time is longer than \(T_{s}\), or if the hard shoulder has not been opened yet, skip to Step 4. Otherwise, continue to open the hard shoulder  
Step 4: Close the hard shoulder and start the next loop.
# Methane Gas Density Monitoring and Predicting Based on RFID Sensor Tag and CNN Algorithm
## 1 Introduction
Due to the harsh underground environment, the data measured by the passive wireless sensor contains miscellaneous noises, which makes it difficult to extract robust features from the raw signal by using traditional methods [26; 27; 28]. In recent years, deep learning technology with autonomous learning ability has been widely researched and has made surprising progress in feature learning [29; 30; 31; 32]. Deep belief networks (DBNs) [33], stacked denoising auto-encoders (SDAs) [32], and convolutional neural networks (CNNs) [34] are three widely employed deep learning approaches. CNN shows priority in feature extraction and data compression [35]. Therefore, in this paper CNN is employed for feature extraction. This paper presents a novel wireless sensor based on RFID technology for methane gas monitoring in the long term. It is a beneficial supplement to the current underground WSN monitoring system. Considering the environment interruption on data wireless transmission, CNN is adopted to extract robust and discriminative features from the raw signal.
## Results and Discussion  
Assignments of Metabolites by 1D Nuclear Magnetic Resonance (NMR) and 2D NMR Spectra in Microalgal Crude Extracts  
Previous studies on marine microalgae have reported an abundance of primary metabolites, including lipids, fatty acids, amino acids and simple sugars. However, there is limited data on secondary metabolites in microalgae. Assignment of peak signals in the \({}^{1}\)H NMR and 2D NMR spectra were based on previously reported literature [24; 25; 26; 27; 28] and by comparison with standard online databases (freely available), such as the Human Metabolome Database (HMDB) at [[http://www.hmdb.ca/](http://www.hmdb.ca/)]([http://www.hmdb.ca/](http://www.hmdb.ca/)), the Biological Magnetic Resonance (BMR) database at [[http://www.bmrb.wisc.edu/](http://www.bmrb.wisc.edu/)]([http://www.bmrb.wisc.edu/](http://www.bmrb.wisc.edu/)) and PubChemat [[https://pubchem.ncbi.nlm.nih.gov/](https://pubchem.ncbi.nlm.nih.gov/)]([https://pubchem.ncbi.nlm.nih.gov/](https://pubchem.ncbi.nlm.nih.gov/)). Figure 1 shows the representative \({}^{1}\)H NMR spectra of the different solvent extracts of _C. calcitrans_. The spectra showed the presence of several different classes of metabolites, including carotenoids, fatty acids, amino acids, organic acids, and sugars. The peak chemical shifts for the corresponding 29 identified metabolites are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Metabolites**} & \multicolumn{2}{c}{ \begin{tabular}{c} **1H NMR** \\ **(Multiplicity)** \\ \end{tabular}
# An Obstacle-Tolerant Path Planning Algorithm for Mobile-Anchor-Node-Assisted Localization
## 2 Related Works
### Offline Path Planning Algorithm
The aforementioned offline path planning schemes assume an obstacle-free environment; however, only a few studies discuss the obstacle-present environment. Although Z-curve [8], perpendicular intersection (PI) scheme [16], and SCAN-based scheme [17] have been proposed as solutions for the obstacle-present problem, these schemes cannot guarantee that the unknown nodes in the area can receive three location-information from the MAN for localization. [PERSON] et al. proposed a path planning scheme called PI [16], where a MAN is installed on a rotating arm and continuously broadcasts location information to unknown nodes based on RSSI and the MAN rotation angle. PI uses perpendicular intersection geometric relationship to calculate the location of the unknown nodes. In this method, the MAN requires continuous broadcast location information, thereby resulting in the high energy consumption of the MAN and unknown nodes. In [17], a range-free localization scheme is used, whereas a new path planning scheme based on SCAN is proposed in [18]. In view of the obstacle blocking problem, [PERSON] et al. [17] propose the concept of using virtual keys, which can predict the location of the beacon point. SCAN-based schemes and PI exhibit identical drawbacks, and the MAN requires continuous broadcast location information. [PERSON] et al. proposed a path planning method called Z-curve [8] to construct a \"Z\"-shaped path trajectory. The Z-curve can solve the beacon point collinearity problem, and is more effective than SCAN, Hilbert, and CIRCLES in terms of localization accuracy and time costs.
## 4 Materials and Methods
### Generation of Recombinant RcTPS23 and In Vitro Enzyme Activity Assays  
The cDNA sequence of _RcTPS23_ was cloned into the _pET-32a_ vector by In-Fusion cloning using the forward primer 5\({}^{\prime}\)-GGCCTGATATCGGATCCATGTCATTCTTCAAGC-3\({}^{\prime}\) and the reverse primer 5\({}^{\prime}\)-CAAGCTTGTGCAGGACTCGCTAGCCATGATGATGGC-3\({}^{\prime}\) and transformed into _E. coli_ strain BL21 (DE3). Recombinant protein production was induced by adding 0.8 mM isopropyl-b-d-thiogalactopyranoside (IPTG) and incubating at 16 \({}^{\circ}\)C for 24 h. The _E. coli_ cells were sonicated and centrifuged to obtain the supernatant, and proteins were purification on a Ni Sepharose column. The purified proteins were collected and concentrated.  
In vitro enzyme activity assays of RcTPS23 recombinant protein were carried out using 50 \(\upmu\)g purified protein in 200 \(\upmu\)L of assay buffer [50 mM HEPES (pH 7.4), 25 mM MgCl\({}_{2}\), 5 mM DTT, 5% (\(v/v\)) glycerol, 50 \(\upmu\)M FPP or 50 \(\upmu\)M GPP]. The blank control contained the same buffer and substrates but no recombinant protein. The samples were incubated at 30 \({}^{\circ}\)C for 2 h, and the volatile products were absorbed on 100 \(\upmu\)PDMS fiber prior to GC-MS analysis. The PDMS fiber was transferred to the injection port (250 \({}^{\circ}\)C) of the GC-MS system and desorbed for 5 min. GC was performed using a GC system coupled with a mass spectrometer. For GC, an HB-5 MS column (5% phenyl methyl silox: 30 m \(\times\) 250 \(\upmu\)m i.d., 0.25 \(\upmu\)m) was used for all samples. The analytical conditions were as follows: The initial temperature was held at 50 \({}^{\circ}\)C for 1 min and raised to 210 \({}^{\circ}\)C for 2 min at 3 \({}^{\circ}\)C/min, maintained for 3 min, and increased to 230 \({}^{\circ}\)C at 15 \({}^{\circ}\)C/min. The analysis was conducted in split-less mode; helium was used as the carrier gas at a rate of 1.0 mL/min. The mass spectrometer was set as follows: 230 \({}^{\circ}\)C, electron-impact (EI) model with 70 eV electron power; 280 \({}^{\circ}\)C for the auxiliary temperature. The scan range was 80-500 \(m/z\). Refer to Section 4.3 for reagent specifications. The volatile products were identified and characterized by comparing mass spectra using AMDIS software (Automated Mass Spectral Deconvolution and Identification System, [[http://www.amdis.net/](http://www.amdis.net/)]([http://www.amdis.net/](http://www.amdis.net/)), (accessed on 10 May 2022)) and the NIST Mass Spectral Library ([[https://www.nist.gov/](https://www.nist.gov/)]([https://www.nist.gov/](https://www.nist.gov/)), (accessed on 10 May 2022)). The assay conditions were established based on previously validated methods for terpene synthase characterization [84; 85].
# Power Profiling of Smart Grid Users Using Dynamic Time Warping +
## 3 Research Methodology
### Research Approach  
This study follows a data-driven approach, leveraging power consumption data from the AMPds2 dataset. The methodology consists of the following steps:  
* Data collection and preprocessing: extracting appliance-level power consumption data from AMPds2.
* Feature extraction and profiling: using DTW to analyze consumption patterns and generate behavioral profiles.
* Evaluation and comparison: assessing the effectiveness of the model.
## 3 Results and Discussion
### Neutron Diffraction  
The typical neutron diffraction patterns of the mosaic samples 5 and 6 are shown in Figure 6. The neutron diffraction peaks corresponded to two dominant phases of mosaics fragments: calcite CaCO\({}_{3}\) and \(\alpha\)-quartz SiO\({}_{2}\). The complex shape of the background of the diffraction pattern of sample 5 indicates the presence of a large number of amorphous phases probably clays aluminosilicates phases. The calculated unit cell parameters of calcite are \(a\) = 4.978 (2) A, \(c\) = 17.064 (4) A and a = 4.974 (7) A, c = 17.026 (1) A for samples 5 and 6, respectively. A small difference in the c parameters of the unit cell of the calcite phases can be explained by the presence of a small amount of impurities in the rock samples of the studied mosaic fragments. If only a single calcite phase is observed for sample 6, then diffraction peaks corresponding to the quartz phase are also observed for sample 5. The lattice parameters of trigonal phase of quartz are \(a\) = 4.913 (1) A, \(b\) = 4.913 (1) A, \(c\) = 5.409 (2) A. The calculated ratio between calcite and quartz phases for sample 5 is 45:55.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Element** & **Sample 1** & **Sample 2** & **Sample 3** & **Sample 4** & **Sample 5** & **Sample 6** \\ \hline L\({}_{\rm x}\) & 87.01 & 86.61 & 84.97 & 85.63 & 78.61 & 89.88 \\ a\({}_{\rm x}\) & \(-\)0.39 & 0.62 & \(-\)0.97 & \(-\)0.57 & 2 & \(-\)1.08 \\ b\({}_{\rm x}\) & 12.11 & 7.4 & 5.70 & 7.37 & 9.61 & 7.20 \\ \(\Delta\)L\({}_{\rm x}\) & \(-\)11.86 & \(-\)0.4 & \(-\)2.04 & \(-\)1.38 & \(-\)20.27 & \(-\)8.99 \\ \(\Delta\)a\({}_{\rm x}\) & 1.92 & 2.94 & 1.34 & 1.75 & 4.31 & 1.23 \\ \(\Delta\)b\({}_{\rm x}\) & 1.65 & \(-\)3.05 & \(-\)4.75 & \(-\)3.08 & \(-\)0.87 & \(-\)3.25 \\ \(\Delta\)E\({}_{\rm x}\) & 12.13 & 0.84 & 12.62 & 1.58 & 20.71 & 9.64 \\ C\({}_{\rm x}\)1  & 12.11 & 7.42 & 5.78 & 7.39 & 9.81 & 7.28 \\ \(\Delta\)C\({}_{\rm x}\) & - & \(-\)4.69 & \(-\)6.33 & \(-\)4.73 & \(-\)2.29 & \(-\)4.82 \\ H  & 91.84 & 85.21 & 99.66 & 94.42 & 78.24 & 98.53 \\ \(\Delta\)H  & - & 6.63 & 7.82 & 2.58 & \(-\)13.6 & 6.69 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The chromatic parameters of the mosaic fragments.
## 4 Experiment and Security Analysis
### RS Steganalysis Analysis  
Steganalysis aims to detect any messages conveyed in a stego image. The statistical hypothesis of the well-known RS steganalysis method [(43)] is that in a typical image with no hidden message, the expected value of \(R_{m}\) equals that of \(R_{-m}\) (the regular groups with masks, \(m\) and -\(m\)), and the same is true for \(S_{m}\) and \(S_{-m}\) (the singular groups with masks \(m\) and -\(m\)).  
Figure 11 shows the RS diagram for the RGB-channel of the dual stego images produced by our algorithm. The diagram depicts regular and singular groups with respect to different masks as functions of the embedding ratio from 1 to 100 %. As shown in the graph, \(R_{m}>S_{m}\), \(R_{-m}>S_{-m}\), \(R_{m}\cong R_{-m}\), and \(S_{m}\cong S_{-m}\) for all embedding ratios, indicating that the difference between \(R_{m}\) and \(R_{-m}\) exhibits no significant change as the number of embedded messages increases. In other words, the message embedding does not substantially change the number of regular or singular groups. These figures confirm that our proposed scheme is able to resist the RS steganalysis attack.
## 4 Experiments and Discussion
### Discussion  
#### 4.5.1 The Influence of Dynamic Factors  
Experiments show that regardless of the method used, considering dynamic factors is always beneficial to improve prediction accuracy. Dynamic information reflects changes in bus operation and is more time-sensitive than static information. Figure 4 shows the comparison of the true and predicted values of a sequence extracted in the test set. For a certain bus, the model prediction results  
Figure 5: A comparison of absolute errors when T takes different values. The absolute error predicted at T=5 is the smallest.  
Figure 6: The accumulation of prediction error with the increase of stations under offline conditions. The MAE of the prediction by SVM has a tendency to rise faster with an increase of the number of stations. However, the MAE of the prediction from other methods increases almost linearly with the increase of the number of stations. Two RNN-based methods have a smaller MAE and a slower MAE growth rate.  
using only static factors are more closely inclined to the historical average. When the actual situation is significantly different from the average of the previous observations, this static model will get unsatisfactory prediction results. Proper use of dynamic factors can alleviate this problem.
## 6 Evaluation  
The aim of the evaluation is threefold. Firstly, we aim to validate the objective function \(M\) we designed as a good measure that captures the tonal tension profile. Secondly, we aim to validate the use of GP with immune properties in the overall system as a good solution, comparing it with other proposals. Finally, we aim to determine the usability of ChordAIS-Gen through the users' experience.  
Firstly, we developed a prototype of our algorithm by using MATLAB tool. MATLAB was selected because it can be successfully utilized for bioinspired and evolutionary computation and also because it can be run on multiple platforms. We created a basic interface where the user can draw a curve for the tonal tension. Our specific proposal of GP was implemented in MATLAB version 2019a using standard libraries for common operations such as creating random numbers, plotting curves and calculating the fitness with the correlation values. Due to the specific features of the algorithm (which include AIS and GP properties), it was programmed entirely without any external library specifically designed for genetic programming.  
According to the theoretical description presented above, in our algorithm, the following parameters must be set:  
* _st_: suppression threshold
* _N_: initial population size
* _N_: number of clones per antibody
* _\(\beta\)_: range of the affinity proportional mutation
* _Q_m_: Probability of mutation
* _Q_c_: Probability of cross-over
* _d_: percentage of random population.  
Empirically, the parameters used were set as follows: _st_ = 0.001; \(N\) = 200; _N_c_ = 5; _\(\beta\)_ = 2.6; _Q_m_ = 0.6; _Q_e_ = 0.4; \(d\) = 10%.  
Note that the evaluation aims to demonstrate that \(M\) reflects the tonal tension of the chord progression. Therefore, we need to evaluate \(M\) in optimal conditions. That means we need to find the best values for weights \(k_{j}\) in Equation (5) by applying cross-validation. A previous work, [43] has demonstrated the independence of the hierarchical properties of the chord progressions, independently of the key. Additionally, we used cross-validation to calculate the weights \(k_{i}\) that best fit the scores resulting from a listening test with a total of 20 chord progressions. The final weights obtained were \(k_{1}=4.22\), \(k_{2}=2.13\), \(k_{3}=2.06\), \(k_{4}=3.76\).  
The evaluation remains a challenge in music generation with expert systems because of the inherent subjectivity of the final product [44]. Most works in the literature use a human expert [12; 23; 45] or a group of human listeners [11; 46; 47] to evaluate the results. They usually design a listening test and a comparative study between the present system and previous works, with successful results. Therefore, in this work, we will also perform a listening test, followed by statistical analysis and a comparative study between other proposals.
## 3 Results
### The Breach at Sillon de Talbert
Similar to previous observations, adding GCPs in addition to camera information progressively improved the model quality mainly by reducing the horizontal bias to negligible levels (<0.01 m) from 5 GCPs, while the measurement precision remained relatively unaffected. Overall, the photogrammetric errors amounted to global precisions (RMSE3D) of approximately 0.05 m under optimum configurations. \begin{table}
\begin{tabular}{c c c} \hline \hline (a) Reference DEM: S-RTK-GCP & (b) S-GCP & (c) S-RTK \\ \hline ME = N.A. (0.000 m) & ME = N.A. (0.000 m) & ME = 0.363 m (N.A.) \\ SDE = N.A. (0.002 m) & SDE = N.A. (0.007 m) & SDE = 0.014 (N.A.) \\ \hline (d) S-RTK-1 GCP\({}_{1}\) & (e) S-RTK-1 GCP\({}_{2}\) & (f) S-RTK-3 GCP\({}_{1}\) \\ \hline ME = \(-0.019\) m (0.003 m) & ME = 0.002 m (0.002 m) & ME = \(-0.022\) m (0.001 m) \\ SDE = 0.011 (0.000 m) & SDE = 0.012 (0.000 m) & SDE = 0.010 (0.001 m) \\ \hline (g) S-RTK-3 GCP\({}_{2}\) & (h) S-RTK-5 GCP & (i) S-RTK-9 GCP \\ \hline ME = 0.016 m (0.001 m) & ME = \(-0.001\) m (0.000 m) & ME = 0.003 m (0.000 m) \\ SDE = 0.014 (0.001 m) & SDE = 0.013 (0.002 m) & SDE = 0.012 (0.002 m) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Vertical error evaluation for the different scenarios presented in Figure 3 using ground targets (GCPs and ChkPts) measured with RTK-GNSS. The values between brackets correspond to GCP-based error. \begin{table}
\begin{tabular}{c c c} \hline \hline (a) Reference DEM: S-RTK-GCP & (b) S-GCP & (c) S-RTK \\ \hline MEX = N.A. (\(-0.001\) m) & MEX = N.A. (0.000 m) & MEX = \(-0.022\) m (N.A.) \\ SDEX = N.A. (0.027 m) & SDEX = N.A. (0.026 m) & SDEX = 0.037 m (N.A.) \\ MEY = N.A. (0.001 m) & MEY = N.A. (0.000 m) & MEY = 0.039 m (N.A.) \\ SDEY = N.A. (0.029 m) & SDEV = N.A. (0.027 m) & SDEY = 0.053 m (N.A.) \\ \hline (d) S-RTK-1 GCP\({}_{1}\) & (e) S-RTK-1 GCP\({}_{2}\) & (f) S-RTK-3 GCP\({}_{1}\) \\ \hline MEX = \(-0.025\) m (0.025 m) & MEX = \(-0.019\) m (\(-0.029\) m) & MEX = \(-0.021\) m (\(-0.013\) m) \\ SDEX = 0.035 m (0.000 m) & SDEX = 0.038 m (0.000 m) & SDEX = 0.037 m (0.036 m) \\ MEY = 0.036 m (0.019 m) & MEY = 0.037 m (\(-0.014\) m) & MEY = 0.044 m (\(-0.007\) m) \\ SDEX = 0.053 m (0.000 m) & SDEY = 0.049 m (0.000 m) & SDEV = 0.048 m (0.028 m) \\ \hline (g) S-RTK-3 GCP\({}_{2}\) & (h) S-RTK-5 GCP & (i) S-RTK-9 GCP \\ \hline MEX = \(-0.019\) m (0.003 m) & MEX = \(-0.009\) m (0.003 m) & MEX = 0.003 m (\(-0.003\) m) \\ SDEX = 0.037 m (0.022 m) & SDEX = 0.039 m (0.018 m) & SDEX = 0.042 m (0.021 m) \\ MEY = 0.026 m (0.005 m) & MEY = 0.004 m (0.009 m) & MEY = \(-0.009\) m (0.004 m) \\ SDEX = 0.047 m (0.010 m) & SDEY = 0.047 m (0.016 m) & SDEV = 0.050 m (0.014 m) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Planimetric error (X easting and Y northing) evaluation for the different scenarios presented in Figure 3 using ground targets (GCPs and ChkPts) measured with RTK-GNSS. The values between brackets correspond to GCP-based error.