esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
This is in agreement with the result in [7] that the classification accuracy was improved from 77% to over 90% for window size from 100 to 600 ms. In contrast with the result found in [20], a window size of 100 ms gained a classification accuracy greater than 90%. A similar percentage was obtained for walking gait event detection with 313 ms window size, as discovered in [31]. Decreasing the window size might improve the classification performance by a lower processing time, but some windows would process without containing the desired event, as mentioned by [PERSON] et al. [8]. They also stated that if the window size increases, it may have a negative impact onthe real-time processing, and results in a higher computational load. It was reported that increasing window size from 250 ms to 300 ms and upwards provides no more than 1% average improvement in classification accuracy for each step-wise increment of the window length for all participant cases in [36]. Instead of aiming for higher classification accuracy, this study proved that the stationarity of EMG signals was higher within 200 ms window size, which may enhance the real-time processing in near future. Hence, a window size of 200 ms is suggested due to a compromise between feature bias (in short window sizes) and real-time constraints and signal stationarity (in long window sizes), and can be used for isotonic contractions. Even though the windowing techniques in pattern recognition and prosthesis control are an overlapped segmentation, an adjacent segmentation is widely adopted in determining the stationarity of EMG signals [21; 24; 25; 26]. It was reported there was no difference in classification accuracy in discriminating upper limb movements during isometric contractions [19]. Thus, the classification performance is assumed to be the same in isotonic conditions. Future work could explore this assumption, and the effect of overlapped and adjacent windowing techniques in the stationarity of EMG signals might be investigated.
## 3 Results
### Standardization of Climate Change Vulnerability Index
#### 3.1.1 Exposure to Climate Change
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sub-Watershed**} & \multicolumn{6}{c}{**SSP-126 Scenario**} \\ \cline{2-9}  & **Rx1 Day** & **CDD** & **CD5 Day** & **R10 mm** & **R50 mm** & **CD10 Day50 mm** & **EDI** \\ \hline Andong Dam & 18.30 & 13.37 & 87.44 & 27.38 & 35.11 & 44.51 & 29.82 \\ Imha Dam & 9.41 & 36.12 & 100.00 & 5.91 & 17.38 & 42.87 & 85.04 \\ Andong Dam Estuary & 0.00 & 88.06 & 12.05 & 0.00 & 32.67 & 17.05 & 91.12 \\ Naeseongcheon & 31.59 & 65.60 & 0.00 & 38.98 & 69.41 & 46.38 & 32.39 \\ Younggang & 3.84 & 49.01 & 8.69 & 42.46 & 74.73 & 27.11 & 92.18 \\ Byeongseong-cheon & 35.59 & 0.00 & 49.41 & 46.34 & 42.25 & 23.58 & 52.13 \\ Wicheno & 2.26 & 91.11 & 21.33 & 1.38 & 17.84 & 8.63 & 82.96 \\ Nakdong Gumi & 20.45 & 71.42 & 45.62 & 22.84 & 4.38 & 2.84 & 100.00 \\ Gamcheon & 45.52 & 3.69 & 65.24 & 41.35 & 17.10 & 16.07 & 46.33 \\ [PERSON] & 21.16 & 73.17 & 41.42 & 23.44 & 4.94 & 3.23 & 87.86 \\ [PERSON] & 8.08 & 55.09 & 18.91 & 39.92 & 60.17 & 23.57 & 94.40 \\ Geumhogang & 15.54 & 100.00 & 32.65 & 23.96 & 0.00 & 0.00 & 3.28 \\ Hoecheon & 55.17 & 70.50 & 33.52 & 60.30 & 61.95 & 42.68 & 26.72 \\ [PERSON] & 39.13 & 93.88 & 29.10 & 41.46 & 27.21 & 20.02 & 0.00 \\ Habcheon Dam & 49.46 & 42.03 & 45.02 & 72.31 & 75.63 & 48.96 & 16.47 \\ Hwanggang & 64.51 & 85.48 & 25.04 & 63.62 & 69.27 & 48.58 & 19.76 \\ [PERSON] & 62.04 & 87.09 & 23.56 & 62.21 & 65.06 & 50.29 & 22.05 \\ Namgang Dam & 97.18 & 45.36 & 35.44 & 100.00 & 94.06 & 65.44 & 77.73 \\ Nakgang & 64.24 & 68.82 & 30.95 & 98.44 & 100.00 & 79.81 & 78.29 \\ Nakdong Miryang & 53.92 & 87.82 & 11.61 & 57.56 & 50.61 & 57.09 & 33.78 \\ Miryanggang & 45.05 & 92.25 & 24.36 & 49.42 & 36.50 & 41.70 & 27.35 \\ Nakdong Estuary & 100.00 & 55.01 & 52.29 & 92.74 & 94.59 & 100.00 & 33.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Standardized indices of climate change exposure in scenario SSP-126. The Nakdong Estuary sub-watershed showed the highest Rx1 day values in scenarios SSP-125, 370, and 585. The Namgang Dam sub-watershed showed the highest value in the SSP-245 scenario. The sub-watershed that showed the lowest values were the same in scenarios SSP-245 and 585, with different values in other scenarios. The sub-watersheds associated with the Andong Dam Estuary, Geumhogang, and Wicheon showed the lowest
## 5 Discussion  
### The Cause of Landslide  
#### 5.1.1 Rainfall  
The Zhonghai Village landslide in Hanyuan County developed in the Xigeda Formation of the Neogene Upper Miocene. The bedrock of the landslide is mudstone, sandstone, and carbonaceous clay rock, with poor engineering properties of the rock mass. Its special interbedded mudstone and sandstone structure allows rainfall to fully infiltrate into groundwater. The landslide occurred about 40 h after rainfall, and rainfall data for approximately two weeks before the landslide disaster were collected from the local meteorological station, as shown in Figure 11. From August 7 th to August 19 th, there were 7 days with daily rainfall exceeding 10 mm, 4 days with daily rainfall exceeding 25 mm, and even heavy rainfall on August 16 th and 18 th, with rainfall reaching 70.6 mm and 76.2 mm, respectively. The cumulative rainfall in the two weeks before the landslide reached 263.6 mm. This short-term concentrated rainfall caused the groundwater level to rise sharply and caused soil moisture saturation, thereby reducing the shear strength of the soil and greatly increasing the instability of the slope. Rainfall seeps into the rocks through the upper layer of soil, gradually infiltrating the rock and soil mass. Once it exceeds the stability threshold, a landslide occurs. Therefore, the short-term heavy rainfall directly triggered the occurrence of the landslide.  
Figure 11: Rainfall map of approximately two weeks before Hanyuan landslide.  
Figure 10: LOS displacement of landslide time series.
# Phenolic Profiling of Five Different Australian Grown Apples
## 3 Results and Discussion  
### Phenolic Compound Estimation (TPC, TFC and TTC)  
The [PERSON]'s reagent method determined the total phenolic content in the apple extracts and were expressed as gallic acid equivalents (GAE/g fw) as shown in Table 1. Red Delicious apple showed the highest TPC with 121.78 \(\pm\) 3.45 mg GAE/g and significantly higher than other samples (\(p\) < 0.05). The total polyphenol content of five different varieties of apples were in the order of Red Delicious > Royal Gala > Fuji > Pink Lady > Smitten. According to the study of [PERSON] et al. [32], [PERSON] et al. [33] and [PERSON] et al. [34], Red Delicious had more phenolic content than Gala, Fuji and Pink Lady, which is consistent to the result of our study. [PERSON] et al. [34] reported that Fuji apple contains 14.7 \(\pm\) 0.4 mg (GAE)/g and [PERSON] et al. [32] study showed that Fuji has 489.59 \(\pm\) 4.21 mg (GAE)/g, the difference in the phenolic content might be due to the geographical location, soil nutrients, growth period and harvest season [35]. Additionally, due to the lack of research on Smitten apple variety, there is no valid data for Smitten for comparison.  
Flavonoids have attracted a lot of attention due to their strong antioxidant activity [36]. In TFC, Red Delicious apple had the highest flavonoid content of 101.23 \(\pm\) 3.75 mg QE/g and the lowest flavonoid content was present in Smitten. In a previous study, TFC of Red Delicious (98 mg QE/g) and Royal Gala (89 mg QE/g) were similar to that of our apple samples [37]. In another study, the values of total flavonoid content of Fuji apple (108 mg QE/g) was reported more than our value which may be due to the difference of varieties or solvent extraction ratio [38]. The TTC in our selected apples ranged between 4.65 \(\pm\) 0.03 to 2.17 \(\pm\) 0.05 mg CE/g. Fuji apple showed higher level of tannin content followed by Pink Lady, Smitten, Royal Gala and Red Delicious. Previously, the total tannin content of different varieties ranged from 0.75 mg CE/g to 14.79 mg CE/g, which is consistent with our results [39]. Overall, the variety of Red Delicious had the highest content of TPC and TFC and Fuji variety had a high content of TTC.
## 3 Optimal Energy Consumption Model in HWSNs
### System Model
#### 3.1.3 Definition 3  
If the node had not been cluster head (CH) for the last (\(1/p\)) rounds, the threshold \(T(s_{i})\) would be a random number between 0 and 1. If the random number is less than the threshold \(T\) (\(n\)), then the node will become the cluster head in the next round and send a broadcast message. \(T(s_{i})\) can be defined as follows:  
\[T(s_{i})=\left\{\begin{array}{cc}\dfrac{p}{1-p_{opt}(r\text{mod}\dfrac{1}{p} )}\times[\dfrac{E_{res}(i)}{E_{init}(i)}+(r_{s}div\dfrac{1}{p})(1-\dfrac{E_{ res}(i)}{E_{init}(i)})],if\ \ s_{i}\in G\\ 0,\hskip 113.811024 ptotherwise\end{array}\right. \tag{1}\]  
where \(r\) denotes the number of current round, and \(G\) is the set of nodes that are not being selected as the CH in recent \(r\) mod(\(1/p_{opt}\)) rounds, so each node has the opportunity to become the CH. The threshold function in Equation (1) is improved actually based on LEACH, and demonstrates the bootstrap if the node has not yet been elected as cluster head node in the current \(1/p\) rounds. Meanwhile, the probability value assignment for CH selection is conducted randomly to ensure that the is a suitable number of cluster heads in the network, thus to improve the coverage ratio of the whole network. However, the threshold function is focused on the application of heterogeneous wireless sensor networks [23]. For a heterogeneous network, the threshold function should be improved to be adaptive to the nodes with different initial energy.
## 5 Application to the Liquid Bulk Terminal in the Port of Cartagena (Spain) and Its Evaluation
### Dataset Description  
The results of this study are based on the data collected in the INTEGRA2 database for the 10 years between January 2011 and August 2020, describing the actual operations conducted in the port.  
The study focuses on the berths of the E010 dock, at which 1000 calls occurred during the period noted. E010 is a multi-client dock used for loading/unloading liquid bulk that, in general, cannot be handled at other docks in the port. Therefore, the simultaneous arrival of ships on this front is an unavoidable cause of congestion in Cartagena since only one of them can use the berth, while the rest must wait.  
From the initial 1000 calls, 960 are considered in the analysis because these calls do not involve other docks. The other 40 calls are assumed to be nonoptimizable, i.e., the vessel arrival times cannot be modified. Note that by restricting the view to one single dock, it is not allowing the simulated HADES allocation to exploit the distribution of vessels to different quays. This is because there are no historical records of the existing flexibility of arrivals during the last 10 years and, in general, such flexibility is infrequent in our use case. Therefore, only the benefits that arrival time optimization could bring about will be observed.  
The E010 dock has an average occupation of 30%, with an average call time of 45.28 h, of which an average of 20 h is for anchoring and the rest (25.26 h) is actual E010 occupation. Of these 20 h of anchoring, an average of 6.3 h is caused by congestion, with an estimated cost of 500,000 dollarsR per year in ship freight. To determine congestion anchoring, it is assumed that a vessel delay was not caused by congestion anchoring provided that its anchoring started when the E010 dock was idle.
## 3 Instrument and Simulation Assumptions  
### Observation Geometry Setting  
The instrument in this study is a pre-developed sensor with extra capacity to measure polarization at multi-angle observations in the UV band. We refer to it as Multi-Angle Polarization Measurements with Ultraviolet band (MAPMU). Microphysical and optical properties of aerosols are derived from multi-angle measurements in 11 spectral bands from 388 to 2140 nm with bands near 388, 490, 670, 865, 1370,1640, and 2140 nm performing polarimetric measurements in addition to radiance. The properties of MAPMU are listed in Table 1 and the fundamental simulation presumptions, such as the aerosol model, the surface model, and the error covariance matrix, are discussed in this part.  
Four multi-viewing observation geometries were taken into consideration with the combinations of various solar zenith angles (\(\theta_{\mathrm{0}}\)), viewing zenith angles (\(\theta_{\mathrm{v}}\)), and relative azimuth angles (\(\varphi\)) to represent the typical observations in various locations in order to obtain synthetic MAPMU data for the information content analysis, just as listed in Table 2. Here, summer low and summer high refer to observations at low and high solar zenith angles, respectively. Figure 1a shows the polar-plot observational geometries, in which the radius means the \(\theta_{\mathrm{v}}\) change from 0\({}^{\circ}\) to 60\({}^{\circ}\) with steps of 20\({}^{\circ}\), and the circle represents the change in \(\varphi\) from 0\({}^{\circ}\) to 360\({}^{\circ}\). Correspondingly, Figure 1b plots the scattering angles based on the observational geometries within the ranges of [137.76\({}^{\circ}\), 173.68\({}^{\circ}\)], [105.05\({}^{\circ}\), 121.30\({}^{\circ}\)], [80.28\({}^{\circ}\), 109.40\({}^{\circ}\)], and [104.04\({}^{\circ}\), 145.82\({}^{\circ}\)].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Spatial res. (m) & 500 (vertical resolution) & Multi-angle & 5 \\ Instrument FOV & \(\pm 45^{\circ}\) & Polarized angle & 0\({}^{\circ}\), 60\({}^{\circ}\),120\({}^{\circ}\) \\ Pol. Cal. Error & \(\leq\)2\% & Stokes parameters & I, Q, U \\ Rad. Cal. Error & \(\leq\)5\% & Band width (nm) & 20, 20, 20, 20, 10, 40, \\  & 388 (P), 443, 490 (P), & & 20, 40, 40, 40, 40 \\ Central & 670 (P), 555, 865 (P), & & \\ wavelength/nm & 910, 1045, 1370 (P), & & \\  & 1640 (P), 2140 (P) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic characteristics of MAPMU.  
Figure 1: Observation geometries (**a**) with its corresponding scatter angle (**b**) of MAPMU sensor adopted in the simulation. In panel (**a**), the different geometries with \(\varphi=0^{\circ}\) represent the corresponding positions of the sun for summer low, summer high, winter low, and winter high scenarios, respectively.
## 3 Herringbone Gear Machining Method Based on SSEDC
### Design of Fixture and On-Line Device for Spatial Symmetry Error Measurement  
To clamp the herringbone gear and ensure the accuracy and efficiency of herringbone gear machining, a general fixture for hydraulic drive self-clamping is designed; it is composed of a pull rod, core clamper, spring, collect chuck, positioning sleeve, fixture body, etc., as shown in Figure 9. When the hydraulic cylinder drives the pull rod downward, the pull rod drives the collect chuck to clamp the shaft diameter of the herringbone gear and ensures that the positioning surface of the herringbone gear is attached to the positioning plane of the fixture, in order to facilitate automatic clamping of the herringbone gear. Table 1 lists the basic parameters of the herringbone gear.  
Figure 8: Flow chart of actual herringbone gear machining process based on SSEDC.  
Figure 9: Schematic diagram of designed fixture and OMFSDD.  
Meanwhile, an on-line spatial symmetry error measuring device controlled by the program of an NC system is designed to measure points on surfaces of the machining teeth. The symmetry center plane can be calculated based on the proposed method. As shown in Figure 9, the on-line measuring device is composed of a Marposs T25 probe and two electric slides [28]. The horizontal and vertical electric slides allow multi-degree freedom of movement of the Marposs probe. On that basis, measurements of gears of different heights and sizes can be realized.  
The detection principle and process of the device are as follows. The NC program controls the electric slides to turn the measuring head into the left-handed slot of the gear (as shown in Figure 9). After measuring the tooth centers of the up and down gears, an initial processing angle will be compensated and corrected according to the center deviation value to meet the requirements of symmetry.
## 3 Design and Analysis of the Birdcage RF coil
### Design Based on Analytical Solution
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Leg** & **End-Ring** \\ \hline Length (\(l\)) & 162 mm & 19.8 mm \\ Width (\(w\)) & 5 mm & 5 mm \\ Calculated Inductance (\(L_{cal}\)) & 151.4 nH & 10.2 nH \\ Effective Inductance (\(L_{\mathit{eff}}\)) & 147.5 nH & 12.9 nH \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Birdcage coil segments dimensions and inductances. \begin{table}
\begin{tabular}{c l c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Low Pass**} & \multicolumn{3}{c}{**High Pass**} & \multicolumn{3}{c}{**Band Pass**} \\  & & \(C_{I}\) & **\%Error** & \(C_{\sigma}\) & \(Error\) & \(C_{I}\) & \(Error\) \\ \hline \multirow{2}{*}{Proposed} & \(C_{cal}\) & 32.4 & \(-\)0.034 & 129.7 & \(-\)0.107 & 92.2 & \(-\)0.052 \\  & \(C_{\mathit{eff}}\) & 31.3 & \(-\)0.023 & 125.3 & \(-\)0.063 & 84 & 0.03 \\ \multirow{2}{*}{Conventional} & \(C_{cal}\) & 33.4 & \(-\)0.044 & 114 & 0.05 & 77.8 & 0.092 \\  & \(C_{\mathit{eff}}\) & 32.4 & \(-\)0.034 & 111 & 0.08 & 72.7 & 0.143 \\ \multirow{2}{*}{2D Simulation} & \(C_{cal}\) & 33.5 & \(-\)0.045 & 111 & 0.075 & 73 & 0.161 \\  & \(C_{\mathit{eff}}\) & 32.5 & \(-\)0.035 & 108 & 0.11 & 72.4 & 0.146 \\ \multirow{2}{*}{3D Simulation} & \(C_{\mathit{3D}}\) & 29.4 & \(-\)0.004 & 120 & \(-\)0.01 & 88 & \(-\)0.01 \\ \multirow{2}{*}{Measured} & \(C_{\mathit{mes}}\) & 29 & & 119 & & 87 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Lumped capacitor values in pF for 1.5 T birdcage coils. The comparison provided in above Tables 2 and 3 concludes that the numerical technique does only provide an approximate value of the lumped capacitor which is quite close to the actual value that is deployed in the prototype implementation. The measured capacitance value for the desired dominant resonance frequency is obtained by slightly tuning the numerically obtained capacitance. It is evident from the information provided in Tables 2 and 3 that in comparison to the conventional methods, the effective lumped capacitance obtained through the proposed method exhibits least numerical error and requires lesser tuning to meet the desired resonance condition. Furthermore, the proposed analytic method described in this paper is quite simple and it does not require the hit and try method for capacitance computation which is commonly adopted in conventioanl 3D EM simulations. The results obtained through such analytical technique can directly be used in the coil implementation engineering which also includes the impedance matching operation at the desired resonance frequency.
## 2 RISs and Their Integration with NTNs
### Application of RISs to NTN Communications
#### 2.5.2 Microelectromechanical Systems and Metasurfaces  
RISs or their various counterparts are the result of recent advancements in technology in microelectromechanical systems (MEMSs) and metasurfaces. These technologies have made it possible to repurpose passive reflections in real-time, significantly adjusting the inert transmission and reflection [78]. This development enables the creation of efficient, cost-effective wireless communication streams. Generally, RIS is a large EM metasurface composed of numerous, inexpensive static reflecting components. Each element can independently control the phase shift and frequency of incident signal, significantly boosting the power of the reflected signal in a desired direction, enabling fine-grained passive beamforming gains [79]. RISs are capable of handling the time-varying wireless channels  
Figure 3: RIS-assisted HAP system model facilitating communication between a LEO satellite and a user via dual signal paths.  
of high-mobility LEO satellites and of compensating for the significant path loss caused by vast transmission distances thanks to its real-time tunability [80; 81]. Moreover, unlike traditional active beamforming systems for satellite technology, RISs offer full-duplex, unobtrusive beamforming at minimal cost and power consumption, without the need for active transmitters or RF networks for signal processing or amplification [82].
## 4 Results
### Hypothesis Testing
#### 4.2.5 Summary of Hypothesis Testing  
The correlation matrix was calculated among the research variables using SPSS and all were statistically related at the \(p<0.001\) level as shown in Table 8.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Variable** & \(M\) & \(SD\) & \(n\) & \(SE_{M}\) & \(Min\) & \(Max\) & \(Skewness\) & \(Kurtosis\) \\ \hline Performance expectancy & 3.03 & 1.35 & 316 & 0.08 & 1.00 & 7.00 & 0.51 & \(-\)0.02 \\ Effort expectancy & 2.73 & 1.15 & 316 & 0.06 & 1.00 & 6.25 & 0.48 & \(-\)0.31 \\ Social influence & 3.95 & 1.59 & 316 & 0.09 & 1.00 & 7.00 & 0.14 & \(-\)0.60 \\ Facilitating conditions & 2.85 & 1.14 & 316 & 0.06 & 1.00 & 6.25 & 0.33 & \(-\)0.50 \\ Hedonic motivation & 3.32 & 1.31 & 316 & 0.07 & 1.00 & 6.33 & 0.12 & \(-\)0.40 \\ Price value & 3.61 & 1.29 & 316 & 0.07 & 1.00 & 6.67 & 0.18 & \(-\)0.41 \\ Habit & 4.83 & 1.57 & 316 & 0.09 & 1.00 & 7.00 & *0.33 & \(-\)0.68 \\ Behavioral intent to use & 3.77 & 1.59 & 316 & 0.09 & 1.00 & 7.00 & 0.23 & \(-\)0.46 \\ \hline \hline \end{tabular} Note. * Sample size was too small to calculate the statistic.  
\end{table}
Table 6: Summary statistics.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Scale** & **No. of Items** & \(\alpha\) & **Lower Bound** & **Upper Bound** \\ \hline Performance expectancy & 3 & 0.91 & 0.89 & 0.92 \\ Effort expectancy & 4 & 0.94 & 0.93 & 0.95 \\ Social influence & 3 & 0.96 & 0.96 & 0.97 \\ Facilitating conditions & 4 & 0.80 & 0.76 & 0.83 \\ Hedonic motivation & 3 & 0.94 & 0.93 & 0.96 \\ Price value & 3 & 0.94 & 0.93 & 0.95 \\ Habit & 3 & 0.91 & 0.89 & 0.93 \\ Behavior intent to use & 3 & 0.94 & 0.93 & 0.95 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Reliability results.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **R** & **Square** & **Adjusted R** & **Std. Error** & \multicolumn{5}{c}{**Change Statistics**} & \multicolumn{1}{c}{**Durbin-**} \\ \cline{4-9}  & **R** & **Square** & **of the** & **R Square** & **F Change** & **df1** & **df2** & **Sign. F** & **Watson** \\ \hline
1 & 0.867 * & 0.752 & 0.746 & 0.80212 & 0.752 & 133.502 & 7 & 308 & 0.000 & 1.689 \\ \hline  & & & & & & & & & & \\  & & & & & & & & & & & \\  & & & & & & & & & & & \\  &  
To understand whether two variables are correlated and the effect size, the researcher used the resulting correlation coefficients to accept or reject the null hypotheses as summarized in Table 9 and discussed in detail by research question and the associated hypotheses. The correlation coefficients are the effect size. Performance, social influence, facilitating conditions, hedonic motivation, price value, and habit were statistically significant in predicting older adults' behavioral intention to use IoT-enabled technologies in their homes.
# A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap
## Abbreviations  
GEO  
GEOmetric stitching  
CC  
Cross-Correlation stitching  
GOD  
Global Optimization of Differences stitching  
NR  
Naive Reflectance stitching  
EER  
Exhaustive Enumeration Reflectance stitching  
TSR  
Two-Step Reflectance stitching  
SEE  
Stitching Error Estimator  
SGL  
Stitching Gain Lift plot