esa-sceva/satcom-chunk-collection huggingface dataset:
# Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image +
## 2 PV Module Imaging System Using Drone Equipped with Dual Camera  
### Considerations for Acquiring an Effective Thermal Image Using a Drone  
Figure 1 illustrates the images captured from different altitudes. With a 100-m distance, we could obtain the full area of PV modules within one frame (Figure 1a,b), but due to low resolution in thermal camera, we could not obtain the explicit and proper images for thermal evaluation (Figure 1b). On the contrary, with a 10-m distance (Figure 1c,d), we could obtain the target image quality for thermal evaluation (Figure 1d). Therefore, it is necessary to place and keep the drone at a short distance, e.g., 10-m height.  
If we refer to Figure 2a,c, which were captured with the visible camera, both look acceptable for further processing. However, if we compare the images from thermal cameras, due to using different resolutions between the visible and thermal cameras, Figure 2b could not obtain the proper ROI for further processing, while Figure 2d shows an aligned image for thermal analysis. Thus, it is important to place the target ROI of a thermal camera based on image processing from a visible camera.  
Figure 3 shows the comparison of the acquired images with or without gimbal control during the drone's flight. If the gimbal is not controlled, the drone's driving changes irregularly due to the drone's GPS error, vibration, wind, and influence of the surrounding environment. Unstable drone driving has a direct effect on the camera image. However, in an ideal case, a uniform image may be obtained through gimbal control in a straight line. Obtaining a uniform image through gimbal control can help substantially in the next analysis process.  
Figure 1: Images from different altitudes: (**a**) image from visible camera at 100-m height; (**b**) image from thermal camera at 100-m height (low resolution); (**c**) image from visible camera at 10 m; (**d**) image from thermal camera at 10 m (low resolution).  
Figure 2: Comparison between proper and improper image acquisition: (**a**) visible image case 1; (**b**) thermal image case 1, improper image with ROI not centered; (**c**) visible image case 2; (**d**) thermal image case 2, proper image with ROI centered.  
Therefore, in this paper, we designed and implemented an image-based gimbal control system that is equipped with an independent dual camera mounted on the gimbal and performs image processing based on the acquired visible image to control the gimbal's roll and tilt so that the ROI of the acquired image is horizontal and placed in the center.
## 4 Conclusions  
In conclusion, we theoretically investigated the use of amorphous Ge\({}_{0.83}\)Si\({}_{0.17}\) to enable a low-loss small-footprint optical coupling between a Si\({}_{3}\)N\({}_{4}\) waveguide and a low-voltage Ge-based on Si optical modulator on bulk Si substrate. Despite a large refractive index and mode-size mismatch between Si\({}_{3}\)N\({}_{4}\) and the Ge-based modulator, the results showed that the coupling structure rendered a good coupling performance with a viable fabrication tolerance for large-scale advanced CMOS manufacturing on a Si wafer. From the 3D-FDTD simulation, the Si\({}_{3}\)N\({}_{4}\)-waveguide-integrated Ge-based optical modulators simultaneously provided usable values of ER and IL, which were comparable to the reported performance obtained from the SOI-integrated Ge-based optical modulator devices [3], for optical interconnect applications at the optical wavelength of 1.55 \(\upmu\)m with a compact footprint. Moreover, it should also be noted that the proposed coupling structure could be applicable with different Ge concentrations in amorphous GeSi for a wider range of operating wavelengths regarding optical integration between different Ge-based active devices and silicon nitride waveguides, which will be the subject of future investigation.  
Conceptualization, K.W. and P.C.; data curation, W.T.; formal analysis, W.T. and P.C.; investigation, W.T.; supervision, K.W. and P.C.; writing--original draft, P.C.; writing--review and editing, K.W.  
This research was funded by Kasetsart University Research and Development Institute (KURDI), and Thailand Research Fund and the Office of the Higher Education Commission (MRG6180286).  
W.T. acknowledges supports from the Institute for the Promotion of Teaching Science and Technology.  
The authors declare no conflict of interest.
## 6 Simulation Results
### First Stage: One Day Evaluation
#### 6.1.3 Exponential Moving Average (EMA) Method  
Simulation results for the EMA method are presented in Figure 13. All the simulations were made using a window size of 30. Although output and ESS powers are similar for each \(\alpha\), it is noted that the SOC reached 100% at t = 13:12 for \(\alpha=0.113\). Thus, the ESS stopped absorbing energy and PRR violations started to appear. A summary of the simulation results for the RS method is shown in Table 5. There are no violations with \(\alpha=0.123\) since it has the least PRR variance in all three tests.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement** & **W = 5** & **W = 10** & **W = 20** \\ \hline D(\%) & 0.0019 & 0.0052 & 0.0107 \\ \hline NC(80\%) & 0.1864 & 0.5201 & 1.0685 \\ \hline Violations (mins) & 64 & 0 & 0 \\ \hline SOC (end of the day) -SOC(ref) & \(-\)13.6058 & \(-\)27.1991 & \(-\)24.6659 \\ \hline \(\sigma^{2}(\text{PRR}_{\text{out}})\) & 25.3416 & 7.7530 & 2.6601 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of the simulation results for the SMA method.  
Figure 12: Simulation results for the SMA method. (**a**) One-day data results showing the total output power (\(\text{P}_{\text{out}}\)), the ESS output power (\(\text{P}_{\text{ess}}\)), the PRR at the output (\(\text{PRR}_{\text{out}}\)), and the state of charge (SOC). (**b**) Zoomed figures of the corresponding black squares.
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 4 Results and Discussion
### Cluster Lifetime  
The time between cluster formation and devastation represents the cluster lifecycle, significantly impacting network energy usage. The proposed method assigns CH positions to nodes with higher fitness values. Because a shorter cluster lifespan increases the control overhead due to CH regrouping and selection, the algorithm must initiate the CH selection process when node energy falls below a certain threshold level. Figure 15 depicts how cluster lifetime decreases as node density increases. Because of the mobility of the nodes, the network topology changes frequently, reducing the cluster's lifetime.
# High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario
## 3 Methods
### Reasoning Based on SWRL Rules
#### 3.1.1 Structure and Syntax of SWRL Rules  
SWRL is a type of language that describe rules in a semantic way. It is composed of OWLDL and OWLLite, which are the sub-languages of OWL. Besides, it follows the high-level abstract grammar of Horn rules and is one of the standards of W3C [35]. SWRL and OWL are highly integrated with each other, and rules in SWRL can be combined with knowledge in OWL. SWRL structure is mainly divided into four parts, namely _Imp_, _Atom_, _Variable_, and _Built-in_. The rules of SWRL consists of the part _Imp_, in which the rule is represented as the form _precondition_ (body) \(\rightarrow\)_conclusion_ (head). _Imp_ contains the head and body, in which head represents the conclusion of the inference rules, and body represents the precondition that the inference can be reached. The head and body are made up of the basic elements provided by _Atom_ or _Variable_. The following is a specific rule to analyze SWRL, and the rule is  
_Robot (?r) ~ hasBatteryQuantity (?r,?batteryquantity) ~ lowerThan (?batteryquantity, 10%) ~ needCharge(?r)_  
Figure 6: The relationships among three parts in Protégé.  
Rules are built with OWL classes, properties, instances, and data values. The above rule declares the class _Robot_ and _needCharge_ and captures the property _hasBatteryQuantity_ for inference. Using the function of the SWRL _Built_in_ component, the battery quantity of robot is compared with the minimum battery quantity. The rule asserts that, if a robot's battery is less than 10 percent, it indicates that the robot needs to be charged.
## 2 System Model
### MRC/MRT-SC with HD Scenario  
The secure MRC/MRT-SC protocol employs two phases. For MRC phase, the Alice transfers information to the Relay, and Relay receives a radio signals through MRC scheme based on HD operation. Thus, the instantaneous SNR at Relay can be represented as  
\[\gamma_{AR,1} =\frac{P_{5}}{\sigma_{R}^{2}}\|\mathbf{h}_{AR}\|^{2}, \tag{1}\]  
where \(\sigma_{R}^{2}\) denotes the noise variance at Relay, \(\mathbf{h}_{AR}\) is an \(N_{R}\times 1\) channel link vector Alice-Bob, and the transmit power of Alice is denoted by \(P_{S}\), which should be varied as [36]  
\[P_{S}=\min\left(\frac{Q}{|h_{AP}|^{2}},P_{t}\right), \tag{2}\]  
where \(Q\) and \(P_{t}\) are the interference temperature constraint and the maximum transmit power constraint at PU and Alice, respectively. \(h_{AP}\) is the channel coefficient for the Alice-PU link.  
Figure 1: System Model.  
Similarly, define the SNR at node Eve as \(\gamma_{AE,1}\), and \(\gamma_{AE,1}\) is given by  
\[\gamma_{AE,1}=\frac{P_{S}|h_{AE}|^{2}}{\sigma_{E}^{2}}, \tag{3}\]  
where \(\sigma_{E}^{2}\) denotes the noise variance at Eve, \(h_{AE}\) is the channel coefficient for Alice-Eve link.  
For MRT-SC phase, the Relay transfers information to the Relay by applying MRT-SC scheme based on HD operation which is similar to analysis of the MRC phase to transmit signal. Therefore, the instantaneous SNR for the Bob can be represented as  
\[\gamma_{RB,1}=\frac{P_{R}\max_{i\in N_{B}}\left\|\mathbf{h}_{RBi}\right\|^{2}} {\sigma_{B}^{2}}, \tag{4}\]  
where \(\mathbf{h}_{RBi}\) refers to the \(N_{R}\times 1\) channel vector between Relay and the \(i\)-th receive antenna at Bob, \(\sigma_{B}^{2}\) refers to the noise variance at Bob, and the transmit power of Relay is denoted by \(P_{R}\), which must satisfy \(P_{R}=\min\left(\frac{Q}{|h_{RP}|^{2}},P_{t}\right)\). \(h_{RP}\) is the Rayleigh channel and obeys the exponential distribution [37].  
Similarly, define the SNR at node Eve as \(\gamma_{RE,1}\), and \(\gamma_{RE,1}\) is given by  
\[\gamma_{RE,1}{=}\frac{P_{R}|h_{RE}|^{2}}{\sigma_{E}^{2}}, \tag{5}\]  
in which \(h_{\text{RE}}\) is the channel coefficient for Relay-Eve link.
## 3 Improvement of the Retinex Algorithm
### Improvement in the Saturation Component S  
After the image's brightness is enhanced, the image's saturation will be low; therefore, to make the image more saturated in colour, the saturation component S of the image needs to be stretched. Due to the significant variation in the image under the mine, the degree of processing for the saturation is different, and an adaptive non-linear stretching algorithm is used for the saturation component, calculated as shown in Equation (16)  
\[S_{out}=\bigg{[}1+\frac{x}{y+z+1}\bigg{]}S_{in} \tag{16}\] \[\begin{cases}x=mean(R,G,B)\\ y=max(R,G,B)\\ z=min(R,G,B)\end{cases}\]  
where \(S_{out}\) represents the output image, \(S_{in}\) represents the input image, \(x\) is the mean value of RGB, \(y\) is the maximum value, and \(z\) is the minimum value.  
In summary, the BF-MSRCR algorithm consists mainly of the enhancement of the luminance component V and the adaptive stretching of the saturation component S so that the details of the image are preserved more fully during the enhancement process, without blurring the edges and without problems such as overexposure and haloing.
## 3 Results and Discussion
### Single Freplay Nonlinearity
The verification and calculation of unstable LCO in the time domain mainly use harmonic initial conditions to find the process of an approximately constant-amplitude vibration response. Figure 7a shows the time-domain response of the nondimensional LCO amplitude with only \(q_{\rm T0}\) input. The results show that when \(q_{\rm T0}/\delta_{\rm T}\) changes from 4.53 to 4.5305, the system response changes from convergence to LCO. This transition occurs around \(q_{\rm T0}/\delta_{\rm T}=7.5\), which is exactly the same as the nondimensional amplitude of the unstable LCO calculated by the DF method (Figure 5a, \(Q=48.0\) kPa). When only the \(q_{\rm T0}\) input is non-zero, the system response first transitions to an intermediate state. If the amplitude of this intermediate state is greater than the amplitude of the unstable LCO, the system response decays, otherwise, it increases to the stable LCO. Figure 7b shows the time-domain response with a harmonic initial condition corresponding to the unstable LCO at 48 kPa. The phase parameter \(\phi_{c}\) of the harmonic initial condition is set to 0. Since the harmonic initial condition causes the system state to fall within a small neighborhood near the theoretical LCO trajectory, it is necessary to slightly adjust the harmonic initial condition (the whole is multiplied by a number around 1.0) to observe the unstable LCO for a long enough time. Unlike Figure 7a, the system response initially enters an approximately constant-amplitude vibration process, which corresponds to the unstable LCO. Then as time goes on, the system response will decay or enter the stable LCO. The LCO amplitude is consistent with the result in Figure 5a. In general, for both LCO-i and LCO-ii, the initial conditions have a certain impact on the response of the system. With the introduction of harmonic initial conditions, the effects of initial conditions and unstable LCOs on the system response can be studied more clearly. Furthermore, we find that the unstable LCO is more like an obstacle. When the initial conditions are so small that the system cannot cross this obstacle, a system that is expected to enter the LCO or diverge would decay. Figure 6: Linear flutter dynamic pressure with the variation of bending and torsional stiffnesses. The LCO characteristics of the all-movable fin with bending or torsion freeplay nonlinearity in linear states (i) and (ii) are shown, respectively. So far, we have provided a comprehensive understanding of the LCO characteristics of an all-movable fin with a single freeplay nonlinearity and different bending and torsional stiffnesses. Two different types of LCO are proposed, and the influence of parameter variation on the LCO characteristics is investigated. From this perspective, one possible LCO suppression approach is to change the LCO characteristics from LCO-i to LCO-ii so that no LCO occurs below the linear flutter boundary. On the contrary, the appearance of freeplay nonlinearity will bring favorable influence. The specific implementation method may be to adjust the supported stiffness radio or add counterweights.
## 2 Methods  
### Spatial Frequency Domain Multiplexing Spectral Domain Optical Coherence Tomography (SDOCT) System  
Figure 1 depicts the schematic of the spatial frequency domain multiplexing SDOCT system. It comprises a super luminescent diode (SLD), an interferometer with two pivot-offset galvo scanners implemented in the two separate reference arms, a custom-built spectrometer, and a personal computer for signal processing and image display. Reference Arms I and II have different round-trip delays, which correspond to the shallow and deep regions of the sample, shown as \(Z_{A}\) and \(Z_{B}\) in Figure 1. Thus, two sets of the spectral interferogram corresponding to the depths \(Z_{A}\) and \(Z_{B}\) can be acquired. In the sample arm, the light beam just incidents upon the pivot of the GS0 for sample scanning. The light beams in the reference arms incident upon the GS1 and GS2 with the different offset distances \(s_{1}\) and \(s_{2}\) away from the pivots, respectively, as shown in Figure 1.  
When the galvo scanners in the sample arm and reference arms are driven simultaneously, the linear modulated phase will be added into the two-dimensional (2D) spectral interferograms. By ignoring the signals that do not contribute to the depth localization in the sample for simplicity, the 2D spectral interferogram detected at each wavenumber k and lateral position x can be expressed as  
\[\begin{array}{ll}I(x,k)&=2S(k){\int\sqrt{R(x,z)}}{\cos[2 nk(z-z_{A})+\Phi_{1} (x)+\phi_{A}(x,z)]}dz\\ &+2S(k){\int\sqrt{R(x,z)}}{\cos[2 nk(z-z_{B})+\Phi_{2}(x)+\phi_{B}(x,z)]}dz\\ \end{array} \tag{1}\]  
where \(k\) represents wavenumber and \(k=2\pi/\lambda\), \(z\) is the depth position coordinate of the scatters within the sample, \(n\) is the average refractive index of the sample, \(R\left(x,z\right)\) is the normalized intensity backscattered from the scatter at \(z\), \(S(k)\) is the spectral density of the light source, \(\Phi_{1}\left(x\right)\) and \(\Phi_{2}\left(x\right)\) represent the linear modulated phase induced by GS1 and GS2, and \(\phi_{A}(x,z)\) and \(\phi_{B}(x,z)\) are the phases that relate to the optical heterogeneity of the sample in the focus coherence volume. If the  
Figure 1: Schematic of the spatial frequency domain multiplexing spectral domain optical coherence tomography (SDOCT) system. SLD: super luminescent diode; FC: fiber coupler; NPBS: non-polarizing beam splitter; M: reference mirror; GS: galvanometer scanner; L: lens; SMP: sample; COL: collimator; PC: polarization controller; DG: diffraction grating; COMP: computer; CMOS: complementary metal oxide semiconductor.  
linear modulation phase \(\Phi_{1}\left(x\right)\) and \(\Phi_{2}\left(x\right)\) have different slopes about the lateral position, the spatial frequency contents of the 2D OCT spectral interferogram corresponding to the depths \(Z_{A}\) and \(Z_{B}\) will be shifted to the different center spatial frequencies away from the zero spatial frequency.
# Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar
## 4 Comparative Performance Analysis
### Evaluation of Doppler Shift Distortion of Range Profiles
#### 4.2.1 Peak Power Loss Ratio  
The attained PPLR results are shown in Figure 8. Because, except for the sums of the PACFs of the complementary sequences Golay A and B, all considered PRBSs from Table 1 theoretically yield the same PPLR versus \(f_{\text{D}}/\Delta f\) results as shown in Figure 8a, their measured PPLR values for all considered PRBSs were combined. The same was done for the combination of the complementary sequences Golay A and B, which yields the same PPLR profile regardless of the length \(N_{\text{PRBS}}\) of each of the complementary sequences. To account for variations in the measurements, both the mean PPLR (continuous line) and its standard deviation (shading in the background) are shown in Figure 8b. An analysis of the simulated results in Figure 8a and the measured results in Figure 8b show a close match, being the minor deviations explained by imperfect measurement calibration.  
The achieved results show a negligible PPLR of only \(-0.14\,\text{dB}\) at \(|f_{\text{D}}/\Delta f|=0.1\) for the greater set of PRBSs, i.e., all but the combination of the complementary sequences Golay A and B. At \(|f_{\text{D}}/\Delta f|=0.5\); however, a PPLR of around \(-4\,\text{dB}\) is observed, which causes a more significant reduction of the ideal processing gain of \(10\log_{10}(N_{\text{PRBS}})\,\text{dB}\) and ultimately reduces the SNR of target reflections in the range profile. The achieved results can, e.g., be illustrated by the lower normalized mainlobe levels at \(|f_{\text{D}}/\Delta f|=0.5\) compared to their original levels at \(f_{\text{D}}/\Delta f=0\) in the previously discussed Figures 6 and 7 for \(N_{\text{PRBS}}\approxeq 1024\).  
As for the sum of the PACFs of the complementary sequences Golay A and B, much more relevant PPLR degradation is observed, with worse values than \(-10\,\text{dB}\) for \(|f_{\text{D}}/\Delta f|\) between 0.2 and 0.3. The reason for the PPLR degradation is the Doppler shift-induced phase rotation \(\text{e}^{\text{j}2\pi f_{\text{D}}(2N_{\text{PRBS}}/F_{\text{s}})}=\text{e}^ {\text{j}\pi f(f_{\text{D}}/\Delta f)}\) between the evaluated Golay A and B sequences assuming the use of a CP of length \(N_{\text{CP}}^{\text{Golay}}=N_{\text{PRBS}}\), which results in a non-coherent accumulation of the PACF of the complementary sequences and, consequently, in both a processing gain and SNR reduction. It is worth highlighting that although using shorter CP lengths would yield less PPLR degradation and higher maximum unambiguous velocity, it would also reduce the maximum unambiguous range. For a more detailed description of the non-coherent accumulation effect, the reader is referred to the analysis in a previous study [23].
# Panchromatic and Multispectral Image Fusion Combining GHS, NSST, and PCA
###### Abstract
The conventional model of coupling method is shown in Figure 1a: (1) project MS into another vector space to separate the spectral information (MS_SIC) and spatial information (MS_SSC); (2) fuse MS_SSC and PAN using MRA-like methods to obtain the new spatial structure component (New-SSC); and (3) invert NEW-SSC and MS_SIC back to the original space to obtain the fused image. The addition of SSC reduces the information mismatch between PAN and MS, thus reducing the spectral distortion. However, the SSC is obtained directly from the MS, which lacks high-frequency spatial information, thus reducing the image's sharpness. Although the coupling method can overcome the spectral distortion of CS and the spatial distortion of MRA, its spatial information quality (sharpness) is inferior to that of CS, and the spectral information quality (color) is inferior to that of MRA. Therefore, it is of practical significance to optimize the coupling method to improve the quality of spatial information and the quality of spectral information of the fused images. The PCA technique can partially concentrate the spatial information shared by the bands in the first principal component through a linear transformation of the data. In this paper, a new fusion strategy is proposed, as shown in Figure 1b: (1) project MS into another vector  
Figure 1: Hybrid model of the CS method and MRA method. space to separate the spectral information (MS_SIC) and spatial information (MS_SSC); (2) combine PAN and MS for PCA transformation and use the first principal component (PC1) as the spatial component (PC1_SSC); (3) use the MRA-like method to fuse PC1_SSC and PAN to obtain NEW-SSC; and (4) invert NEW-SSC and MS_SIC back to the original space to obtain the fused image. The difference between the new and conventional modes lies in how the spatial structure components are obtained: the conventional mode obtains the spatial structure components directly from MS using color space transformations and others. In contrast, using PCA transformation, the new mode obtains the spatial structure components from PAN and MS. In the subsequent experiments, we selected the generalized intensity-hue-saturation (GIHS) algorithm from the CS methods and the NSST from the MRA class methods. In terms of fusion rules, this paper proposes a new low-frequency fusion rule using gradient-domain singular value decomposition (SVD) [12] and local structure descriptors to construct the weight coefficients, as well as a bootstrap filter [13], to guide the weights and to increase the spatial continuity of the weights; meanwhile, the high-frequency coefficients use local spatial frequencies to guide them.