esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter
## 2 GNSS and IMU Integrated Filter Design
### Error State Kalman filter
#### 2.2.3 Development of the Error State Model  
The discrete vector forms for all states, error states, IMU measurements, and noise are defined as follows:  
\[\begin{array}{l}\mathbf{x}_{k}=[\mathbf{x}_{k},\mathbf{v}_{k},\mathbf{q}_{k},\mathbf{a}_{bk}, \mathbf{\omega}_{bk}]^{T},\textit{ffi}\mathbf{x}_{k}=[\textit{ffi}\mathbf{p}_{k},\textit {ffi}\mathbf{w}_{k},\textit{ffi}\mathbf{a}_{bk},\textit{ffi}\mathbf{\omega}_{bk}]^{T},\\ \mathbf{u}_{mk}=[\mathbf{a}_{mk},\mathbf{\omega}_{mk}]^{T},\mathbf{w}_{k}=[\mathbf{w}_{bk},\mathbf{w} _{k},\mathbf{w}_{ak},\mathbf{w}_{lk}]^{T}\end{array} \tag{15}\]  
The recursive equation for all the error state is derived by combining Equations (5) and (7) in the form of  
\[\textit{ffi}\mathbf{x}_{k+1}=\textit{ffi}(\mathbf{x}_{k},\textit{ffi}\mathbf{x}_{k},\mathbf{u} _{mk},\mathbf{w}_{k})=F_{xk}(\mathbf{x}_{k},\mathbf{u}_{mk})+F_{wk}\mathbf{w}_{k}, \tag{16}\]where \(f_{\delta}(\cdot)\) represents the recursive function of the error state, the \(F_{\pm k}\) and \(F_{\pm k}\) are the Jacobian matrices corresponding to the error and noise states, respectively, which can be derived as  
\[\mathbf{F}_{\pm k}=\begin{bmatrix}\mathbf{I}&\mathbf{I}\mathbf{\Lambda}t&0&0&0\\ 0&\mathbf{I}&-\mathbf{R}_{k}^{T}(\mathbf{\omega}_{mk}-\mathbf{\omega}_{\mathbf{\eta}k})\mathbf{ \Delta}t&-\mathbf{R}_{k}\mathbf{\Delta}t&0\\ 0&0&\mathbf{R}_{k}^{T}(\mathbf{\omega}_{mk}-\mathbf{\omega}_{\mathbf{\eta}k})\mathbf{\Delta}t&0&- \mathbf{I}\mathbf{\Delta}t\\ 0&0&0&\mathbf{I}&0\\ 0&0&0&\mathbf{I}\end{bmatrix},\mathbf{F}_{\pm k}=\begin{bmatrix}0&0&0&0\\ \mathbf{I}&0&0&0\\ 0&\mathbf{I}&0&0\\ 0&0&\mathbf{I}&0\\ 0&0&0&\mathbf{I}\end{bmatrix}. \tag{17}\]
# Transform a Simple Sketch to a Chinese Painting by a Multiscale Deep Neural Network
## 5 Other Applications
### Image Colorization
#### 5.1.1 Comparisons with Other Approaches  
We compare against other methods in Figure 14. Our approach accurately colorized the ocean and beach, while the other two approaches biased tones towards either the ocean or the beach.  
Following the previous work [25], we conducted a user study by asking \"Does this image look natural?\" to evaluate the naturalness of the ground truth, as well as the results of our model and other approaches. The images are randomly selected and displayed one by one to the user. This research was done by five different users. For each type, we show approximately 100 images from 400 images in total. The users are instructed to use their intuition and feelings rather than spending too much time on the detail of the image. We can see the result in Table 6. For this, 93.80% of our method's images look natural, 96.60% of the ground truth. This strongly suggests that our model can create realistic colorizations.  
Figure 13: Example artificial colorization of satellite images: grayscale images (**left**), colorful images (**middle**) and ground truth (**right**).
# Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks
## 4 Experimental Results and Analysis  
In this section, we present a performance comparison of three clustering approaches: GWO-based clustering, ACO-based clustering (CACONET) and EALC. The metrics that we used to evaluate their performance are: number of clusters, cluster building time, cluster lifetime and energy consumption. The experiments are carried out in MATLAB with the different grid-VS-node setup. Parameters settings for simulation are shown in Table 1. We conducted ten experiments with each setup and computed their average results.
## 4 Computer Simulation Experiments  
To evaluate the performance of the ASTKF C/\(N_{0}\) estimation algorithm, simulation tests were conducted using the GNSS signal simulator and the vector software receiver. Figure 4 shows a block diagram of the simulation experiment's data generation and processing. In the tests, the Skydel SDX satellite navigation signal simulator was used to generate in real-time the in-phase/quadrature (I/Q)  
Figure 3: Summary of the adaptive strong tracking Kalman filter (ASTKF) carrier-to-noise ratio (\(C\)/\(N_{0}\)) estimation algorithm.  
samples representing the GPS baseband signals. Moreover, the SDX simulator can adjust the power of the simulation signal online [21]. Then, a universal software radio peripheral converted baseband I/Q samples to the GPS L1 C/A radio frequency (RF) signal. The RF front-end converted the simulated RF signal into a digital intermediate frequency (IF) signal and stored it on the computer. The signal simulation and acquisition equipment is shown in Figure 5. Table 2 presents the main parameters of the RF front end. The vector software receiver performed post-processing analysis on the digital IF signal and output the C/N\({}_{0}\) estimation results.
## 3 Results and Discussions
### SEM Analyses  
SEM analyses of the three solid catalysts were performed before and after the four cycles of pullulan oxidation in order to determine the morphological changes appearing during exposure of catalysts to the highly alkaline reaction medium. As can be seen from Figure 6, the overall integrity of the catalysts was maintained even after four reaction cycles. All of the catalysts show a spherical shape, with sizes varying from ~100 to 150 \(\upmu\)m (sample APS), ~500 \(\upmu\)m (sample AMPS), and ~65--85 \(\upmu\)m (sample BMPS), respectively.  
After their utilization in four reaction cycles, their shapes do not undergo any visible changes, the size remaining unaltered. A slight modification can be observed in the cases of AMPS and BMPS  
Figure 6: SEM microphotographs of the solid catalysts before (a), (b), (c) and after four cycles of pullulan oxidation reaction (a\({}^{\prime}\)), (b\({}^{\prime}\)), (c\({}^{\prime}\)).  
samples, their spherical shape becoming swollen in appearance, but without affecting their catalytic performance or destroying the initial shape and morphology.
## 5 Results and Discussions
### Time of Day Analysis of Cooling and Heating Operations
From Figure 9b, it can be seen that the heat utilization amount of Case ATES-C2-7 is smaller than that of Case WTES-C1-7, but it is secured to some extent, and the heat storage amount is suppressed compared to Case WTES-C1-7. Looking at the transition of the heat storage amount of Case ATES-C2-14, the stored heat is almost used up at 18:00. The heating operation on 23 January is shown in Figure 10. In the HEX2 heat storage tank side inlet/outlet temperature transition in Figure 10a, Case WTES-H1-45 can send hot water of about 45 \({}^{\circ}\)C to 42 \({}^{\circ}\)C from the heat storage tank. In Case ATES-H1-45, which is used in the same way as Case WTES-H1-45, the pumping temperature of the heat storage well drops to 42 \({}^{\circ}\)C after 11:00, and heat exchange is not possible from 11:00. As with  
Figure 9: Operation analysis on 9 August in summer. (**a**) Flow rate and temperature of Heat Exchanger 2. (**b**) Operational transition of heat storage and heat utilization. cooling operations, it is difficult to maintain a high temperature in the aquifer due to the influence of groundwater flowing in from the surroundings when water is pumped from the aquifer. Case ATES-H2-30 and Case ATES-H2-25 used for pre-heating operations are operating until 17:00. In Case ATES-H2-25, the temperature difference between the inlet and outlet on the HEX2 heat storage tank side has become smaller since 14:00. Case ATES-H2-25 specializes in lowering the temperature level for heat storage to 25 \({}^{\circ}\)C and supplying it for pre-heating. The HEX2 heat storage side inlet temperature is stable at 25 \({}^{\circ}\)C to 23 \({}^{\circ}\)C, and the temperature difference from the HEX2 heat storage side outlet temperature can be secured. The HEX2 heat storage side outlet temperature is about 22 \({}^{\circ}\)C. It can be seen that Case ATES-H1-45 in Figure 10b does not secure as much heat utilization as Case WTES-H1-45, even though it stores more heat than Case WTES-H1-45. The amount of heat stored in Case ATES-H2-25 is smaller than that of Case WTES-H1-45, but it is secured to some extent, and the amount of heat released is smaller than that of Case WTES-H1-45. In the transition of the heat storage amount of Case ATES-H2-30 and Case ATES-H2-25, the stored heat is almost used up at 17:00.
## 5 Performance Analysis of Proposed Schemes: MRS-ST-BL and MRS-SD-BL
### Results
#### 5.3.2 Comparison of Average Number of Probes of Proposed Schemes with Others
From the comparison of Figure 7a-c, we can also see that the number of probes for all the schemes increase with the increase in social trust among users in the network (figures from left to right). Figure A5 illustrates similar trend in change of probe number with the increase in search radius for different social trust scenarios, when node density in network is low. From the above analysis, we can see that taking battery level of devices into consideration while selecting a relay, filters out socially trusted devices but with low battery power. It reduces the number of devices to be probed. This helps to select only those devices with high social trust and battery level above battery threshold, as candidate relay devices for D2D communications. As a result, our proposed scheme MRS-ST-BL enhances throughput of D2D communications.
## 3 Results and Discussion
### Cooling Circuit Energy Balance
The thermal efficiency of plate heat exchangers was assumed to be unity. Table 8 shows results from the energy balance, considering electrical energy production. The gross electrical power generated was 1039 kW, which translates into 9102 MWh/year, equivalent to 837,384 \(\upxi\)/year. This energy is completely consumed at the plant. The value of the resulting utilization factor is 43.4%, not reaching 50% of the installed capacity, which is too low to guarantee the installation reaches profitability limits. The plant installed capacity for producing electricity is much greater than that produced by the engines. The inclusion of thermal hydrolysis for enhancing sludge stabilization and reducing sludge volume modifies the global energy balance because steam is now needed along with the additional equipment operating at higher temperatures. This modification, in an already operating WWTP, results in enhanced performance of digestion, but it does not necessarily lead to a better energy balance given all the additional equipment needed  
Figure 7: Mass and energy balance of the engine cooling circuits at the operating point. for the extra units and the lower capacity for finding use of low-temperature streams. However, the extra digester capacity is advantageous due to the shorter time required for sludge stabilization. This improvement in reactor capacity allows the treatment of co-substrates with high methane potential, thus producing more biogas to be valorized in the existent engines. High-strength organics considered suitable co-substrates include waste cooking oil, residual glycerin, or cheese whey from the food industry [49; 50; 51; 52; 53; 54]. The organic fraction of municipal solid wastes has been extensively studied as co-substrate [55; 56; 57], but it has the risk of increasing the amount of inert material [58], deteriorating sludge agronomic quality.
## 2 Study Area and Datasets
### Cropland Data Layer  
The National Agricultural Statistics Service (NASS) of the US Department of Agriculture (USDA) provides a CDL map for crop type classifications at 30 m resolution for 2006-2009 and 56 m resolution for 2010-2015 [34]. The CDL is based on the cropland census and satellite images from the Landsat Thematic Mapper (TM) and Advanced Wide Field Sensor (AWIFS) [35]. The CDL product is updated yearly and used to produce a crop mask map [36; 37]. In this study, we extracted pixels that were recorded as corn or soybean area on the CDL map for masking the croplands.
## 2 Materials and Methods
### Modeling Methods
#### 2.5.2 Partial Least Squares Regression (PLS)  
The PLS considers the correlation between data and incorporates principal component analysis to filter out spectral noise during the regression process. PLS generates latent variables that maximize the covariance between sensitive bands and sweetness variables. By prioritizing latent variables based on their contribution to the predictive quality of the regression model, PLS facilitates the selection of a simplified model without the risk of overfitting. In this study, the number of latent variables was selected on the basis of the standard error of leave-one-out cross-validation.
## 2 Introduction of Tri-Axis RINS  
### System Configuration  
As we can see from Figure 1, there are three orthogonal rotational gimbals inside the tri-axis RINS, which point to the azimuth, pitch, and rolling directions, respectively. On each side of each gimbal, a brushless DC motor and a high-precision angle encoder are installed to drive the gimbal to rotate and collect the rotational angles, denoted as \(\phi_{1},\phi_{2},\phi_{3}\). The combination of three gyros and three accelerometers is called the IMU, surrounded by the rotational gimbals and the relative circuit boards. The gyros and the accelerometers are placed approximately orthogonal to each other. The parameters of the relevant sensors are listed in Table 1. It should be explained that a slightly lower accuracy z-gyro can reduce the size of the tri-axis RINS because the higher the accuracy, the larger the fiber ring radius.  
The study in this manuscript focuses on the system in Figure 1, but the proposed rotation scheme is available for all types of tri-axis RINS with FOG.
# Surface-Wave Extraction Based on Morphological Diversity of Seismic Events
## 4 Examples  
### Synthetic Examples  
#### 4.1.1 Distortion of Surface-Wave Dispersive Energy Caused by Reflections  
The dispersive energy of surface waves can help us to pick dispersion curves. In addition, dispersion curves of surface waves can be inverted to obtain a near-surface S-wave velocity structure for PS-wave static corrections in seismic exploration [3; 4]. However, the dispersive energy of higher-mode surface waves may be influenced by reflections in X-component seismic data.  
Two layered earth models (Model 3 and Model 4) are given in Tables 3 and 4 to display the distortion of surface-wave dispersive energy caused by reflections. The layers of Model 3 are the first two layers of Model 4. A synthetic X-component shot gather (Figure 4a) of Model 3 is simulated using a staggered-grid finite-difference method [31] with an explosive source located at a 3-m depth. Another synthetic X-component shot gather (Figure 5a) of Model 4 is simulated using the same method and the same forward-simulation parameters. We simulate the records with 51 receivers evenly spaced (2 m) in line on the surface and the nearest offset of 40 m. As shown in Figures 4b and 5b, the two shot gathers are transformed into the f-v domain by high-resolution LRT.  
correspond to the first, second, and third higher modes. However, the events of the higher-mode surface waves in Figure 5a discontinuously overlap with the reflections in the two-way time of 0.35 s and 0.45 s. In addition, it is difficult to discern whether the dispersive energy, at frequencies of 25-33 Hz and apparent velocities of 470-530 m/s (energy circled in Figure 5b), corresponds to the second higher mode or the third higher mode. A comparison of Figures 4b and 5b demonstrates that reflections may disturb the dispersive energy of surface waves. What causes this phenomenon, \"mode kissing\", is the non-negligible effect of the reflections within this range of frequencies and velocities. The picked dispersion curves based on the amplitude and the continuity of dispersive energy are shown in Figure 5c, where the second higher mode of frequencies of 25-27 Hz is misidentified as the third higher mode. However, the surface-wave dispersive energy on Z-component seismic data is not severely influenced by the reflections from the deep reflectors according to [PERSON] et al. [19].  
Figure 4: (**a**) A synthetic X-component shot gather of Model 3 and (**b**) its image of dispersive energy in the f-y domain.  
Figure 5: **(a)** A synthetic X-component shot gather of Model 4, **(b)** its image of dispersive energy in the f-v domain, and **(c)** dispersion curves picked from the dispersive energy, where the white dotted lines represent the theoretical dispersion curves.
# Fragments Inpainting for Tomb Murals using a Dual-Attention Mechanism GAN with Improved Generators
## 3 Methods
### Joint Segmented Loss Function  
#### 3.3.1 Joint Loss Function  
For the choice of the loss function, this paper uses adversarial loss, perceptual loss, style loss, and reconstruction loss. The loss function is normalized on this basis. Through the union of multiple loss functions, our primary loss function consists of the following components to constrain the model better. Let \(G\) be the generator, and \(D\) be the discriminator. \(I_{gt}\) denotes the actual image, \(E_{gt}\) is the complete edge map, and \(Y_{gt}\) is the grayscale image.  
For the reconstruction loss, this paper uses the L1 distance between \(I_{out}\) and \(Igt\) as the reconstruction loss, as in (8).  
\[\mathcal{L}_{rec}=\mathbb{E}\big{[}\big{\|}I_{out}-I_{gt}\big{\|}_{1}\big{]} \tag{8}\]  
Perceptual loss is used to evaluate the global structure of the image, where \(\phi_{i}\) denotes the activation map of the ith pooling layer of \(VGG-16\) for a given input image \(i\). In our implementation, \(pool1\), \(pool2\), and \(pool3\) were used, as in (9).  
\[\mathcal{L}_{pre}=\mathbb{E}\bigg{[}\sum_{i}\lVert\phi_{i}(\mathbf{I}_{out})-\phi_ {i}\big{(}\mathbf{I}_{gt}\big{)}\rVert_{1}\bigg{]} \tag{9}\]  
Figure 5: Mapping network structure improvement.  
Style loss was used to ensure style consistency, and style loss was used to calculate the L1 distance between feature maps, as in (10).  
\[\mathcal{L}_{\text{sty}}=\mathbb{E}\left[\sum_{i}\lVert\left(\psi_{i}(\mathbf{I}_{ out})-\psi_{i}(\mathbf{I}_{gt})\right)\rVert_{1}\right] \tag{10}\]  
The Gram matrix constructed from the activation map \(\phi_{i}\) is shown below (11).  
\[\psi_{i}=\phi_{i}^{T}\phi_{i} \tag{11}\]  
Adversarial loss, used to ensure the visual fidelity of the repaired image and the consistency of texture and structure, is defined as (12). Each parameter is explained as follows. \(\mathbf{Y}_{in}=\mathbf{Y}_{gt}\odot\mathbf{M}_{in}\) indicates a damaged grayscale image. \(\mathbf{E}_{in}=\mathbf{E}_{gt}\odot\mathbf{M}_{in}\) is the damaged edge map. \(\mathbf{I}_{out},\mathbf{E}_{out}=G(\mathbf{I}_{in},\mathbf{E}_{in},\mathbf{Y}_{in},\mathbf{M}_{in})\) indicates the output of the generator.  
\[\begin{array}{l}\mathcal{L}_{adv}=\min_{G}\max_{D}\mathbb{E}_{tgt,\mathbf{E}_{ gt}}\left[\log D\big{(}\mathbf{I}_{gt},\mathbf{E}_{gt}\big{)}\right]\\ +\mathbb{E}_{\mathbf{I}_{out},\mathbf{E}_{out}}\log[1-D(\mathbf{I}_{out},\mathbf{E}_{out})] \end{array} \tag{12}\]  
Joint loss function: the above losses are normalized into joint losses, as follows (13).  
\[\begin{array}{l}l_{\text{ss}}=&\frac{1}{N}\sum_{m=1}^{4}\sum_{i=1}^{N}(\lambda _{rec}\mathcal{L}_{rec}+\lambda_{pre}\mathcal{L}_{pre}\\ &+\lambda_{sty}\mathcal{L}_{sty}+\lambda_{adv}\mathcal{L}_{adv})\end{array} \tag{13}\]  
The specific parameters are as follows. \(\lambda_{\text{rec}}=10\), \(\lambda_{pre}=0.1\), \(\lambda_{sty}=250\), and \(\lambda_{adv}=0.1\), where \(N\) represents the total number of training graphs, and \(\lambda\) represents the weight coefficient of each loss function, respectively.
# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 2 Study Area and Data
### Data
#### 2.2.4 UAV and Other Data  
Tea plants exhibit significant differences from general field crops, making them difficult to distinguish from bushes, forests, orchards, etc., in low-resolution images. However, in high-resolution drone images, tea crops display distinct strip-like textural features, resembling fingerprints, making them easy to identify (Figure 5). Based on this, this study collected drone image data from Anji County in 2019, covering the main planting areas of tea gardens in northern Anji, with an area of approximately 150 km\({}^{2}\) and an image resolution of 0.1 m. These data provide important reference for subsequent sample selection and accuracy assessment. In addition, the study also involves other types of data, including photos obtained from field surveys, administrative boundary data, and phenological observation data.
## 2 Materials and Methods  
### General Considerations  
All chemicals were purchased from Sigma-Aldrich (St. Louis, MO, USA) and were used without any further purification. All solvents, such as toluene, tetrahydrofuran (THF), and ethanol, were dried by distillation from sodium diphenylketyl under dinitrogen and were stored over 5 A activated molecular sieves [42]. Spectrophotometric-grade dichloromethane (DCM) was used as received. All reactions were carried out under a nitrogen atmosphere. Commercial reagents were used without any further purification after purchasing from Sigma-Aldrich (bromobenzene, 4-bromobiphenyl, 2-bromonaphthalene, \(n\)-butyllithium solution (2.5 M in hexane, \(n\)-BuLi), potassium iodide (KI), sodium hypophosphite (NaPO\({}_{2}\)H\({}_{2}\)), acetic acid, 3-trifluoromethylomethylphenyllboronic acid, copper iodide (Cul), tetrakis(triphenylphosphine)palladium (Pd(PPh\({}_{3}\))\({}_{4}\)), potassium carbonate (K\({}_{2}\)CO\({}_{3}\))). The 2-bromo-9,10-arylanthracene precursors (**1a-3a**) were analogously prepared according to the reported procedures [38; 43]. Deuterated solvent (CDCl\({}_{3}\)) from Cambridge Isotope Laboratories (Tewksbury, MA, USA) was used after drying over activated molecular sieves (5 A). Nuclear Magnetic Resonance (NMR) spectra were recorded at ambient temperature on Bruker Avance 400 spectrometer (400.13 MHz for \({}^{1}\)H, 100.62 MHz for \({}^{13}\)C, and 376.5 MHz for \({}^{19}\)F) using standard parameters. Chemical shifts are given in ppm, and are referenced against external Me\({}_{4}\)Si (\({}^{1}\)H, \({}^{13}\)C) and CFCl\({}_{3}\) (\({}^{19}\)F). Elemental analyses were performed on an EA3000 (Eurovector, Pavia, Italy) in the Central Laboratory of Kangwon National University. The thermal properties of compound were investigated by TGA2940 system (TA Instrument, New Castle, DE, USA) and DSC2910 system (TA Instrument, New Castle, DE, USA) under a nitrogen atmosphere at a heating rate of 10 \({}^{\circ}\)C/min.  
UV/Vis absorption and PL spectra were recorded on a Jasco V-530 (Jasco, Easton, MD, USA) and a Spex Fluorog-3 Luminescence spectrophotometer (HORIBA, Edison, NJ, USA), respectively, in CH\({}_{2}\)Cl\({}_{2}\) solvent with a 1-cm quartz cuvette at ambient temperature. Cyclic voltammetry measurements were performed using an AUTOLAB/PGSTAT12 system (Artisan Technology Group, Champaign, IL, USA).