esa-sceva/satcom-chunk-collection huggingface dataset:
# A QR Code-Based Approach to Differentiating the Display of Augmented Reality Content
## 3 Demonstration of the Proposed Approach
### Reliability Evaluation  
Different mobile devices, scanning software, and environmental illumination may influence the accuracy of QR content decoding. Hence, we assess the feasibility of our system via three different smartphones (two in the Android system and one in the iOS system) and four types of scanning software under three different lighting conditions.  
Figure 4: The corners finding process of a QR tag [24]. (**a**) The position detection patterns of the QR tag. (**b**) The three-position detection patterns \(A_{0}\), \(B_{0}\), and \(C_{0}\). (**c**) The derived fourth corner \(D_{0}\).  
Figure 3: The results of the marked QR tag. (**a**) The original QR tag. (**b**) The marked QR tag. (**c**) The difference between (**a**) and (**b**).  
In the scanning tests, we evaluate QR codes displayed both on the screen and the printed paper using two QR tags generated by our method (see Figure 5). Table 1 shows the scanning results via two phones of the Android system (HTC: Desire 20 Pro and Samsung: Note 20), while Table 2 shows the results of the iOS system (iPhone 11 Pro MAX). We set three lighting conditions: the outdoor, the indoor with living room lights, and the indoor without artificial lights. The illuminance values captured in this test are lux = 305, 221, and 21, respectively. As shown, both two QR codes can be decoded by four types of scanning software under three sets of environmental illumination.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Mobile Device** & \multicolumn{3}{c}{**HTC**} & \multicolumn{3}{c}{**Samsung**} \\ \hline \multirow{2}{*}{Light Intensity (lux)} & Outdoor & \multicolumn{2}{c}{Indoor} & Outdoor & \multicolumn{2}{c}{Indoor} \\ \cline{2-7}  & 305 & 221 & 21 & 305 & 221 & 21 \\ \hline \multirow{2}{*}{Media Type of QR Code} & Print/ & Print/ & Print/ & Print/ & Print/ & Print/ \\  & Screen & Screen & Screen & Screen & Screen & Screen \\ \hline QR Code \& Barcode Scanner & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline QR Code Reader & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline QR Code Reader-Scanner App & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline Scandit Barcode Scanner Demo & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scanning tests of the Android system under different lighting conditions.  
Figure 5: Two QR tags generated by our method for the scanning test: (**a**) QR data content: Applied Sciences. QR version: 1, Error correction level: L, authentication length: 24 bits; (**b**) QR data content [26]: [[http://www.infocom.yzu.edu.tw/](http://www.infocom.yzu.edu.tw/)]([http://www.infocom.yzu.edu.tw/](http://www.infocom.yzu.edu.tw/)) (accessed on 10 December 2021). QR version: 4, Error correction level: H, authentication length: 256 bits.
## 4 Results and Discussion
### Two-Sensor Configurations
#### 4.3.4 Confusion Matrix Analysis for Two-Sensor 50%-Overlapping Configuration
In conclusion, overlapping data segmentation enhances model performance by preserving important temporal transitions. While LSTM, TCN, and Transformer exhibit strong classification capabilities, TCN emerges as the most consistent and accurate model, further affirming the importance of deep learning architectures designed for sequential data in human activity recognition applications. The deep learning models' capability to leverage both short-term and long-term dependencies enables them to handle the complexity introduced by overlapping segments effectively, making them suitable choices for capturing activity transitions. Further analysis of the comparative performance of these models across configurations will be presented in Section 4.5, where a holistic evaluation of their strengths and limitations will be discussed.
# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## 4 Discussions
### FE Comparisons with GFED 4.1s  
Both the FE of this study and the GFED 4.1s showed a good agreement on fluctuation patterns and their magnitude (Figure 10a). Higher GFED values than those of FE in general cases were potentially due to the difference in spatial resolution (GFED has a 25 km resolution and that of FE is 1 km). Another potential discrepancy was caused by the calculation of combustion completeness. GFED uses fixed values that are assigned to several regions globally [61]. In contrast, we used dynamic changes in the combustion ratio from daily FRP MODIS data. More fluctuations in C emissions from biomass burning were captured by FE in this study. As a result, the spatial distributions of FE in the study area were heterogeneous (Figure 11) compared to the GPP and \(R_{e}\). Fire emissions are usually dependent on fire ignition events and are episodic [14]. For these reasons, relatively lower values were produced than the GPP and \(R_{e}\) when described with the NBE (Figure 12). However, fires that destroy vegetation as potential carbon uptake sources and disturb the hydrological cycle through the subsidence of peatlands have the most impactful in changing the carbon balance of peatlands [62]. The FE in the Papua region was compared with GFED 4.1s (Figure A1) because the visual interpretation of the map in the national scale was difficult. The FE of this study and GFED 4.1s in Papua were less detected than those of the other regions (e.g., Kalimantan). Relatively small scales fires were more detected in this study than by GFED 4.1s.  
For future study, collecting more observations of carbon emission data during fire events is required for further validation. This study estimated FE based on the FRP of MODIS [46]; however, sub-daily scales of fire events might not be detectable. To estimate the total carbon emissions during one event from ignition to extinguishment, applications of geostationary satellite data are promising [46] to improve the FE due to their high temporal observation frequency.
## 2 State of the Art
Different methodologies can be found in the literature on how to process the vehicle accelerations to detect rail corrugation: the wavelet transform and the more conventional Fourier transform are the most common approaches; some publications opt for the Hilbert-Huang transform. In [18], the advantages and disadvantages of the wavelet, Fourier and Hilbert-Huang transforms are described and compared. In [19], the axle box accelerations, measured by an inspection car on a conventional line, are used to monitor rail corrugation by comparing the vehicle response with the rail surface roughness acquired by a trolley equipped with laser sensors. The rail roughness is detected in the wavelength range 40-250 mm, and the accelerations are filtered accordingly. A high correlation is observed between the standard deviation of accelerations and the standard deviation of rail roughness, calculated within windows of 25 m. The authors also investigate the accuracy achievable considering different amplitudes of the rail roughness. In [5], the same rail roughness is compared with the internal cabin noise level. By comparing the noise level in the cabin before and after grinding maintenance, it is found that the noise is significantly influenced by the rail surface defects. A high correlation between the internal noise level and the rail roughness is evaluated in terms of standard deviation. In [20], acceleration signals are acquired on a metro line and the wavelet technique is used to monitor the rail conditions. A comparison is made between the wavelet and Fourier approaches to show how better resolution can be achieved in the scalogram compared to the spectrogram. In [21], the track measurements are used to perform simulations and the resulting axle box accelerations are used to detect welds and corrugations. The analysis is performed comparing the accelerations with and without rail corrugation in terms of amplitude of the peaks observed in the PSD. In [22], the rail corrugation wavelength is evaluated analysing simulated axle box accelerations applying the ensemble empirical mode decomposition, then a support vector machine model is developed to estimate the rail corrugation depth. The effect of rail corrugation on vehicle response measurements has been widely investigated in the literature. However, a detection method capable of distinguishing rail corrugation from other effects such as wheel defects, vehicle modes, rail modes, etc. has not yet been developed.
# The Power System and Microgrid Protection--A Review
## 2 DG and Microgrid Challenges
### Recloser-Fuse Problems  
Reclosers and fuses are the main protective devices that are used to protect distribution systems against faults. Reclosers are utilized to clear temporary faults in the main feeders, while fuses are used to protect the system against permanent faults in lateral feeders. The presence of DGs may increase the fault current in distribution systems, so this presence may cause abnormality and miscoordination of reclosers and fuses [56; 57].  
DGs may affect the recloser performance in two ways: loss of coordination between the fuse and recloser or reducing recloser sensitivity. Moreover, the presence of a DG may influence the recloser pick-up sensitivity [58]. Figure 7 illustrates the design of DG and recloser placement in the distribution network.  
Figure 6: Sympathetic tripping of adjacent feeder in the presence of DG.
## 2 Mathematical Model
### Road Surface Modeling  
Road surface disturbances can be categorized into impulse disturbances and continuous vibrations. Impulse disturbances are caused by sudden protrusions or depressions on a flat road surface, lasting for a short time but with high intensity. Continuous vibrations arise from the ongoing variations along the length of a rough road surface [15]. In vehicle ride comfort studies, the focus is primarily on continuous excitations, and the road's roughness is typically measured based on the road's unevenness. The international standard ISO 8608 classifies road unevenness into eight grades based on PSD [16]. This study employs a filtered white noise technique for the modeling and examination of the road surface excitation's time domain representation. The road excitation model, as detailed in [17], is presented as follows:  
\[x_{r}=-2\pi f_{0}q(t)+2\pi\sqrt{G_{q}(n_{0})}uv(t) \tag{7}\]  
where \(G_{q}(n_{0})\) represents the road's power spectral density at the spatial frequency of \(n_{0}\) in m\({}^{3}\); \(n_{0}\) is the reference spatial frequency, typically \(n_{0}=0.1\) m\({}^{-1}\); \(x_{r}\) denotes the road surface displacement in meters; \(f_{0}\) is the lower cutoff frequency in Hz; and \(\omega(t)\) is a zero-mean, unit-variance Gaussian white noise sequence.  
The ISO road surface grade (from A to F) is defined by the \(G_{q}(n_{0})\) values shown in Table 2. In this paper, the geometric mean of the \(G_{q}(n_{0})\) columns in Table 2 is selected as the value of the \(G_{q}(n_{0})\) characteristic parameter for each road grade.
## 5 Results and Discussion
### Choice of GNSS Sample Locations  
To assess the accuracy of the GNSS shadow prediction methodology, all satellites visible to a Trimble R8 GNSS receiver were counted at various times and locations on campus. A number of representative points were selected to ensure shadowing of the receiver in at least one quadrant for every test. Figure 10a displays the selected locations for the validation tests. Locations such as an open space, a courtyard, a narrow access route between two buildings and four points adjacent to large obstructions to the N,S E and W were chosen. Figure 10b graphically displays the azimuth of the two dominant sources of shadow for each check point, numbered 1-10 and these coincide with the test locations marked in Figure 10a. An additional limiting factor when choosing the sampling-locations was vegetation. Because vegetation was not incorporated in this iteration of the methodology, sites with large amounts of vegetation were avoided when possible. The vegetation was predominantly located in the west of the campus as illustrated in Figure 10a.
## 3 Results  
This analysis includes 1995 patients from 11 different publications. With a standardized mean difference of \(-0.216\), patients with CKD have a lower kidney SWS than healthy controls. The number of cases with CKD, the mean kidney SWS in CKD, and the mean SD in CKD was compared to the same outcomes in the control group. The VTQ-detected shear wave velocity was substantially higher in healthy volunteers than in CKD patients, and it was related to eGFR. With a Q = 531.13, DF 10, \(p<000.1\), I\({}^{2}\) = 98.12% (95% CI for I\({}^{2}\) 97.52 to 98.57) the analysis shows an increased heterogeneity as expected (Figure 2).  
We also discovered a positive association in six studies that looked at the correlation coefficient between eGFR and kidney SWS with a pooled correlation coefficient of 0.383 (Z = 10.387, \(p<0.001\)). However, heterogeneity still rose among them with a D = 73.3, DF = 5, and I\({}^{2}\) (inconsistency) = 93.1 % (95% CI for I\({}^{2}\) 87.86 to 96.18). In total, 681 patients with CKD were included in this analysis (Figure 3).  
A pooled area under the ROC curve to predict chronic kidney disease was also performed. Seven studies including 589 patients with CKD provided measures for AUC and were therefore included in this analysis. Across the studies, the pooled area under the ROC curve for kidney SWS to predict chronic kidney disease was 0.831 (95% CI, \(p<0.001\)) with a statistically significant level of \(p=0.0001\), Q = 28.3, DF = 6, I\({}^{2}\) (inconsistency) = 78.82% (95% CI for I\({}^{2}\) 56.37 to 89.72) also preserving the inconsistency (Figure 4). A limitation of this pooled analysis is the different method of diagnosing CKD that has been used across the seven studies [18; 21; 22; 26; 29; 31; 34]: either the level of eGFR \(<60\) mL/min [18; 26; 29; 31], or the presence of fibrosis in the renal biopsy [21; 34].  
Figure 2: Forest plot for mean kidney SWS between 11 studies comparing CKD versus a control group.
# Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem
## 3 Methodology  
### GDNN Decision-Making Process  
As the solution construction process shows in Figure 4, we first update the features in the current state as input to the GDNN. The network interacts the input features and uses mask mechanism [29] to avoid infeasible tasks. The network outputs the probability of the tasks and selects the tasks. Then, the process will be repeated until the candidate task set is empty. Finally, we obtain the output solution.
## 6 Concrete-Filled Steel Tubes under Combined Axial Force-Bending Moment
### Circular Composite Sections  
For the circular composite section, the relative strength ratio is given by (see Equations (1), (10) and (37))  
\[\xi=\frac{\left[\mathrm{D}^{2}-(\mathrm{D}-2t)^{2}\right]\mathrm{f}_{s}}{\left[ \left(\mathrm{D}-2t\right)^{2}\right]\mathrm{f}_{c}} \tag{40}\]  
For specific values of axial load, P, and relative strength ratio, \(\xi\), the corresponding bending moment of the P-M curve can be also defined from empirical Equation (41)  
\[\frac{M}{M_{pred}}=\frac{m_{1}+m_{2}ln(\xi)+m_{3}\left(\frac{P}{p_{pred}}\right) }{1+m_{4}ln(\xi)+m_{5}ln^{2}(\xi)+m_{6}\left(\frac{P}{p_{pred}}\right)+m_{7} \left(\frac{P}{p_{pred}}\right)^{2}} \tag{41}\]  
where, in this case, the m\({}_{1}\)--m\({}_{7}\) parameters are presented in Table 11.  
The applicability and accuracy of the proposed empirical Equation (39) for composite circular sections are shown in Figure 10. More specifically, this figure depicts the proposed axial force-bending moment interaction curves for various circular CFTs beam-columns in comparison to the experimental results that have been taken from previous studies. The corresponding data for these studies examined here, appear in Table 12. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Ref.** & **No.** & **L (mm)** & **B (mm)** & **t\({}_{\mathrm{f}}\) (mm)** & **H (mm)** & **t\({}_{\mathrm{w}}\) (mm)** & **f\({}_{\mathrm{y}}\) (MPa)** & **f\({}_{\mathrm{c}}\) (MPa)** & **P\({}_{\mathrm{exp}}\)** & **M\({}_{\mathrm{exp}}\)** \\  & I & 969 & 323 & 4.38 & 323 & 4.38 & 262 & 41.1 & 3306 & 201 \\
[43] & II & 969 & 323 & 4.38 & 323 & 4.38 & 262 & 41.1 & 1479 & 297 \\  & III & 954 & 318 & 6.36 & 318 & 6.36 & 618 & 41.1 & 4100 & 408 \\  & IV & 957 & 318 & 6.36 & 318 & 6.36 & 618 & 41.1 & 1967 & 593 \\ \hline  & V & 600 & 200 & 3.17 & 200 & 3.17 & 310 & 119 & 1049 & 136 \\  & VI & 600 & 200 & 3.17 & 200 & 3.17 & 310 & 119 & 2108 & 136 \\
[44] & VII & 600 & 200 & 3.17 & 200 & 3.17 & 310 & 119 & 2108 & 139 \\  & VIII & 600 & 200 & 3.09 & 200 & 3.09 & 781 & 119 & 1294 & 195 \\  & IX & 600 & 200 & 3.09 & 200 & 3.09 & 781 & 119 & 2569 & 143 \\ \hline
[25] & X & 630 & 210 & 5 & 210 & 5 & 750 & 32 & 3106 & 77.7 \\  & XI & 630 & 210 & 5 & 210 & 5 & 750 & 32 & 2617 & 130.9 \\ \hline
[27] & XII & 2416 & 220 & 5 & 220 & 5 & 761 & 20 & 2939 & 58.8 \\  & XIII & 2817 & 270 & 5 & 270 & 5 & 761 & 20 & 3062 & 76.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Data for rectangular concrete-filled steel tube beam-column members. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(m_{1}\) & \(m_{2}\) & \(m_{3}\) & \(m_{4}\) & \(m_{5}\) & \(m_{6}\) & \(m_{7}\) \\ \hline
0.954363 & 0.006437 & \(-0.946844\) & 0.050883 & \(-0.024529\) & \(-1.591625\) & 0.880543 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Parameters for the prediction of P-M curve–circular columns. Figure 10: _Cont._  
Figure 10: Cont.
## 3 Production of Plant Secondary Metabolites Using Natural Hosts
### Multigene Transformation of Plants  
Plant secondary metabolism involves the expression of multiple genes, and enhancing the co-expression of multiple genes is essential for further increasing the yield of secondary metabolites. The invention of large DNA fragment assembly technologies has made the construction of large fragment vectors more convenient. Some widely used techniques include Zinc Finger Nuclease and Homing Endonuclease-Mediated Assembly [139], ligation cycling reaction [140], Golden Gate cloning [141], Cre/loxP recombination [142], MultiRound Gateway recombination [143], Gibson Assembly [144], and yeast assembly [145]. However, due to the possibility of multi-gene silencing, rearrangement, or internal interactions in plants [146], successfully transforming vectors containing hundreds of thousands of base pairs into plant cells and achieving stable expression in plants, especially non-model plants, is one of the major challenges in plant metabolic engineering [147; 148]. The mainstream methods of introducing large DNA into plants include particle bombardment, polyethylene glycol-mediated protoplast transformation, and _Agrobacterium_-mediated transformation. The first two methods involve the induction of callus tissue or regeneration of the cell wall, which can be challenging for many plants. For the last method, _Agrobacterium_ cannot infect many medicinal plants or has very low efficiency [149]. Additionally, when co-expressing multiple genes, it is important to adjust the strength of gene promoters and carefully choose the insertion sites to prevent gene silencing [150]. Placing multiple pathway enzyme genes into artificial plant chromosomes can help avoid position effects associated with genome insertion, potentially achieving stable expression [151; 152]. However, this technology still faces challenges, such as the potential inability to form active centromeres and the risk of abnormal plant growth or death [153].
## 1 Introduction
### Results
#### 2.3.1 Course of Experiments and Maximal Torsional Moments  
The maximum torsional moment achieved highlighted the differences between the two test specimen variants. Figure 7 shows the torsional moment curve as a function of the cross-sectional angle of twist measured at the load application point (Section A-A) for the three viable tests. The torsional moment was calculated as the measured force in the hydraulically coupled presses multiplied by their distance from one another. The displacement of the lower press (approx. 2-5 cm), as explained in Section 2.2, had to be added to the planned distance (300 cm) between the two presses.  
Figure 6: Test setup and measuring equipment: (**a**) View of the load application area (Section A–A). Hydraulic presses in red, including loading cells. (**b**) View of Sections B–B and C–C. Transducers placed on yellow wooden panels to measure the deflection of the webs.  
In the course of initially loading TS\({}_{\text{Ref}}\), a gap was observed between the test specimen and the steel girders at the load application point at a torsional moment of 420 kNm. This was due to the insufficient preload (150 kN/threaded rod). It was decided to unload the test specimen, increase the prestressing to 250 kN/threaded rod, and restart the test, explaining the division of the curves in Figure 7 into TS\({}_{\text{Ref}}\) first loading and TS\({}_{\text{Ref}}\) second loading. The initial crack within the specimen occurred at around 375 kNm. At an almost-constant load, a complete crack pattern formed at this point; thus, the transition to state II was complete. The load could then be further increased until a maximum value of 551 kNm. As a yield plateau was reached and no further load increase was possible yet the twist angle steadily increased, the test was stopped.  
Within the TS\({}_{\text{Joint\_1}}\) test, the initial crack and subsequent transition to state II occurred at around 380 kNm. In contrast to TS\({}_{\text{Ref}}\), no yield plateau occurred after further load increase. At around 580 kNm, the concrete began to spall due to compression in the area of the middle diaphragm, without any major load drop. A further increase in the load was considered possible; however, it was decided to stop the test at this point to prevent damage to the second test area. The test TS\({}_{\text{Joint\_2}}\) performed almost identically up to the maximum load of TS\({}_{\text{Joint\_1}}\), but without spalling. At a load of 678 kNm, sudden failure of the unreinforced vertical shear joint between the web and the diaphragm occurred.
## 1 Introduction
Then, the base stations apply the pre-coding scheme to the signals to be transmitted by using the received feedback information. As a result, the CoMP scheme proposed in this paper can improve the diversity gain of the mobile by using the signal with pre-coding scheme applying the feedback information [14; 15; 16]. The pre-coding scheme used in the proposed CoMP scheme is SPC. The SPC scheme is one of the pre-coding schemes. The SPC changes the channel relationship between the base station and the mobiles by using the CSI [17]. And then, the channel coefficients are increased by constructive superposition. In addition, the SPC changes the relationship of channels to destructive superposition. When a mobile user is located in the cell edge, the ICI is caused by the other signals transmitted from neighboring base station. If the mobile suffers from a transmitted signal from adjacent base station, the received signals have a large error and the performance degradation. But the interference signals can be suppressed through the destructive SPC scheme [18; 19]. The schemes for interference mitigation using the relays are proposed in [18; 19]. If a relay is used, an error propagation is present, and several pre-coding schemes must be performed to mitigate the interference. However, this paper proposes an interference mitigation scheme by performing only pre-coding without using the relay. This paper is organized as follows. Section 2 shows the system model of the proposed scheme. Section 3 describes the constructive and destructive SPC principle. Section 4 explains proposed scheme for the CoMP with pre-coding to improve the reliability of the HetNet system. Section 5 shows the simulation results by comparing the conventional and the proposed scheme. Finally, Section 6 presents our conclusion.