esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
There have been similar practical efforts to assimilate a crop model with satellite images to improve the predictive performance of crop yields [11; 12; 13]. Meanwhile, the CIDS is unique, in that (1) it is formulated as a whole integrated program to project spatiotemporal variations in crop productivity, as well as (2) the CIDS requires simple input parameters and environmental variables utilizing remote sensing images. One of the critical issues to achieve advanced monitoring of crop productivity information using the system would be to determine a consistent representation of the information on crop canopy reflectance. In this regard, one should acquire stable predefined relationships between LAI and canopy reflectance values or VIs of interest for each crop, based on field experiments. It is essential to determine a reliable classification of crops of interest and to establish spatial variations in different planting dates for each crop well to project practical productivity, especially using coarse resolution satellite images. Dealing with these issues is beyond the scope of this study. It is also essential to extract the endmembers of the mixed pixels in the case of using the coarse resolution satellite images. In the case of the fine resolution satellite images, sparse changes of obtained images can be an issue for simulation, using the previous version of the GRAMI model [18]. The earlier GRAMI model required receipt of an even distribution of several input images during the crop-growing season to achieve dependable simulation outputs. This approach has been optimized in the current version of GRAMI based on the advanced empirical approach, using a predefined relationship between LAI and canopy reflectance values of wavebands or VIs of interest.
## 4 Result and Discussion
### GLONASS PPP with Identical Receivers or Mixed Receivers  
In this subsection, the impact of IFCBs on GLONASS PPP with identical receivers (identical receivers and antenna) and mixed receivers (identical receivers but different antenna) are demonstrated.  
Figure 4 depicts the clock difference series of the stations equipped with the identical receivers (CEBR-PT11 and AGGO-PT11) or mixed receivers (BRUX-PT11 and KIRU-PT11). For convenience, the clock difference series has deducted the mean values. We can see that Figure 4 illustrates the good performance of time transfer when considering IFCB parameters, especially for IFCB2 schemes. We further support this finding by showing more results. Figure 5 shows the STD of the clock differences from the selected stations equipped with identical receivers (top) (CEBR-PT11, KIRU-PT11, KOUR-PT11, REDU-PT11 and VILL-PT11) and mixed receivers (bottom) (AGGO-PT11, BRUX-PT11, ROAP-PT11, SYDN-PT11 and YAR2-PT11), for the IFCB0, IFCB1 and IFCB2 solutions, respectively. Meanwhile, the uncertainty (STD) improvement of the IFCB1 and IFCB2 solutions are described in Figure 6. It is noted that the centre node, PT11, is equipped with SEPT POLARX4 TR receiver and LEIAR25.R4 LEIT antenna. Taken together, we make three remarks. First, we can see that the STDs of the clock differences reach about 0.2 ns for the stations equipped with identical receivers or mixed receivers. Second, considering IFCB parameters, it is of interest to find an obvious improvement in the uncertainty (STD) of the GLONASS PPP-based time transfer. Compared with IF0, the percentage of improvement ranged from 3.0% to 49.0% for IFCB2. In addition, a similar characteristic is derived after considering the IFCBs for different stations equipped with identical receivers. Moreover, even though both ends of the time-links equipped with identical receivers (see in Figure 5 (top)), GLONASS PPP can reach better performance when considering the IFCBs. Third, similar to the solutions of stations equipped with the identical receivers, IFCB0 performs worst in the time transfer performance, while IFCB2 performs best. The uncertainty improvement is in the range of 0.96% to 59.4% and of 3.3% to 62.6% for the IFCB1 and IFCB2 solutions of the selected stations equipped with mixed receivers, respectively.  
Figure 3: Averaged bias between IFCB1 and IFCB2 for each station in DOY 277–297.  
Figure 4: The clock differences series of some stations equipped with the same firmware versions and antennas: CEBR-PT11 (**a**) and AGGO-PT11 (**b**) are equipped with a SEPT POLARX4 receiver and SEPCHOKE MC antenna; BRUX-PT11 (**c**) and KIRU-PT11 (**d**) are equipped with a SEPT POLARX4 TR receiver but different antennae.  
Figure 5: Distribution of the standard deviations (STDs) of the clock differences of some stations selected from the stations equipped with the identical receivers (**top**) and mixed receivers (**bottom**).
## 3 Performance of the Laplace Inversion Algorithm
### Model 2: Two-Layered Marine Geology Model  
The two algorithms were applied to a two-layered marine geology model, which included a seawater layer (4 S/m), and a seabed layer (0.5 S/m). The depth of the seawater was assumed to be 20 m. The source parameters remained the same as those in Model 1. As there is no analytic solution for the layered model, the voltage response was compared with a conventional solution obtained by the Hankel transform [25], which was operated in the frequency domain.  
As shown in Figure 3a,b, the voltage response obtained by the G-S algorithm deviated from the conventional solution, especially in later times. Moreover, the relative errors were larger for most of the observation times. By contrast, the Talbot algorithm worked well in all the observation times, as shown in Figure 3c,d, and the relative errors were below 1% in the majority of the observation times for all values of \(N\). Moreover, the Talbot algorithm rarely depended on \(N\). The G-S algorithm is often preferred in TEM land modeling owing to the minimum number of summation terms and rapid computation; however, in marine modeling, the skin effect of the seawater necessitates a long duration of observations. Thus, the response in late observation times is important for the investigation.  
Figure 3: Numerical results of the trapezoidal wave source for a two-layered model. (**a**) Voltage response of the G-S algorithm; (**b**) errors of the G-S algorithm relative to the conventional solution, where the dashed line indicates a relative error of 1%; (**c**) voltage response of the Talbot algorithm; (**d**) errors of the Talbot algorithm relative to the analytic solution, where the dashed line indicates a relative error of 1%.  
The following can be determined from the results above: (1) the G-S algorithm is not suitable for the trapezoidal wave source and also has poor performance on TEM applications for marine modeling, although it is simple to implement; and (2) the Talbot algorithm does not require many summation terms, and can yield an accurate response for the trapezoidal waveform regardless of where the voltage response originates from in the sea homogeneous half-space and sea-layered modeling. Therefore, the Talbot algorithm is more appropriate for computing the inverse Laplace transform in marine TEM investigations.
## 2 Materials and Methods
### Mg- or Zn-Doped GaN Films  
Once the Mg- or Zn-doped GaN powders were obtained, a tableting process was used to prepare the targets for the films' deposition. First, an agate mortar was used to finely grind the powders and were then lubricated with 0.5 mL of methanol to obtain a mixture. Afterward, the mixture was placed in a Blackhaw SP25B powder press to obtain the target (with a pressure of 10 ton/cm\({}^{2}\)). When the powders were compacted, the target was removed from the press to be individually sintered. The targets were sintered inside a conventional CVD furnace using an N\({}_{2}\) flow (150 sccm) at 900 \({}^{\circ}\)C for one hour to reduce the oxygen non-intentional impurities introduced with the methanol. The above process was repeated until the targets had the required hardness for the deposition by radio-frequency magnetron sputtering. Mg- or Zn-doped GaN films were deposited via radio-frequency magnetron sputtering at room temperature using an Intercovamex Sputtering System V1, (with a target size of 25.4 mm in diameter, and 5 mm in thick). A separation distance between the substrate and the target of 40 mm was applied. A chamber vacuum attained a pressure of 2 \(\times\) 10\({}^{-6}\) Torr before the layer growth. An N\({}_{2}\) flow was used during sputtering, as well as a RF power of 50 W and a gas pressure of 25 \(\times\) 10\({}^{-3}\) Torr were kept during the sputtering deposition. It also required a longer deposition time of 8 h to grow a thick layer [20,22]. Additionally, silicon (100) substrates were used to remove organic residues, along with the conventional cleaning of solvents and solutions. A diagram of this process of obtaining the Mg- or Zn-doped GaN films is shown in Figure 1a, while Figure 1b shows the targets prepared using Mg-doped, or Zn-doped GaN powders ([PERSON] et al. [17,19]).  
\begin{table}
\begin{tabular}{c c c} \hline
**Doping Element** & **Fusion** & **Homogenization** \\
**in Metallic Gallium** & **Temperature** (\({}^{\circ}\)**C**) & **Temperature** (\({}^{\circ}\)**C**) \\ \hline Zn & 440 & 460 \\ Mg & 670 & 690 \\ \hline \end{tabular}
\end{table}
Table 1: Fusion temperature, and homogenization temperature of the doping elements.
## 4 Experiments and Results
### Performance Measures  
To quantitatively assess the effectiveness of our method, we have used a couple of evaluation metrics, including Peak Signal-to-Noise Ratio (PSNR) [70] and Structural Similarity Index Matrix (SSIM) [71]. These metrics provide a clue to the quality, similarity, and error between the restored/enhanced images and the ground truth high-quality images.
# All You Need Is a Few Dots to Label CT Images for Organ Segmentation
## 3 Materials and Methods
### Evaluation Metrics  
To evaluate the performance of our model, we compare it with two baseline models: UNet and DeepNet-V3. We benchmark the segmentation performance with two segmentation measures: dice score and Hausdorff distance. Given the predicted region \(P\) and ground truth region \(G\), the dice score is computed as follows:  
\[DICE=\frac{2(P\cap G)}{|P|+|G|} \tag{6}\]  
where \(|P|\) and \(|G|\) mean the cardinality of \(P\) and \(G\), respectively. The dice score indicates overlapped area between the segmentation prediction and ground truth, penalizing the missing prediction. In case the model prediction is more similar to ground truth in terms of segmentation size, the dice score is more highly evaluated. The Hausdorff distance is computed as follows:  
\[HD(P,G)=\text{max}(\max_{p\in P}\min_{g\in G}\parallel p-g\parallel,\max_{g \in G}\min_{p\in P}\parallel g-p\parallel) \tag{7}\]  
In the inner terms, the first term calculates the maximum distance to the minimum distance from the predicted pixel to the ground truth pixel, and the second term calculates the distance. As a result, the Hausdorff distance represents the maximum distance between two pixels and that each pixel belongs to the exclusive region, respectively.
# Making Optimal Location-Sizing Decisions for Deploying Hybrid Renewable Energy at B&Bs
## 3 Optimization Model
The following is the proposed MIP model  
\[\text{minimize }B*\text{CBT}+\sum_{l\in\text{T}}\sum_{l\in\text{L}}\sum_{d\in \text{DVC}_{l}}Q_{d,l}*\text{ENR}_{d,l}*\text{CST}_{d} \tag{1}\]  
Subject to  
\[E_{l,t}=\sum_{d\in\text{DVC}_{l}}Q_{d,l}*\text{ENR}_{d,l}\ \ \forall l\in\text{L},\ t \in\text{T} \tag{2}\]  
\[I_{t}=\sum_{l\in\text{L}}E_{l,t}-\text{REQ}_{t}-W_{t}\ \ t=1 \tag{3}\]  
\[I_{t}=I_{t-1}+\sum_{l\in\text{L}}E_{l,t}-\text{REQ}_{t}-W_{t}\ \ \forall t\geq 2 \tag{4}\]  
\[I_{t}\leq B*\text{BCL}\ \ \ \ \ \ \ \ \forall t\in\text{T} \tag{5}\]  
\[\sum_{d\in\text{DVC}_{l}}Q_{d,l}*\text{UNT}_{d}\leq\text{ARA}_{l}\ \ \ \ \forall l\in\text{L} \tag{6}\]  
\[B,\ I_{t},\ W_{t},\ E_{l,t}\geq 0,\ \ \ \ Q_{d,l}\geq 0\text{ and integer} \tag{7}\]  
The model's objective, Equation (1), is to minimize the total costs for the subject B&B, which includes the battery costs and REGD costs incurred over the planning horizon. The REGD costs are calculated by multiplying the RE generated by certain REGDs deployed at each available location \(l\) (that is, those REGDs contained in DVC\({}_{l}\)) with their corresponding energy generation costs (that is, CST\({}_{d}\)). Equation (2) measures the total RE generated from each location \(l\) during each planning period \(t\) (\(E_{l,t}\)). \(E_{l,t}\) is contributed by REGDs that the model decides to deploy at \(l\) (that is, \(Q_{d,l}\)). Therefore, if every \(Q_{d,l}\) has a zero value, \(E_{l,t}\) will also be zero. Equations (3) and (4) calculate the energy saved in the batteries at the end of each planning period (\(I_{t}\)), equaling the previous energy savings (\(I_{t-1}\)) increased by the RE produced in the current planning period and then decreased by the B&B's energy requirements in the same planning period. As there could be times when the total energy supply is more than the total energy consumption, \(W_{t}\) will capture that surplus energy when that happens. Equation (5) governs the volume of RE saved in the battery. Generally, this energy cannot exceed the battery's charge limit, which is approximately 90% of the battery's storage capacity [63]. Equation (6) governs the deployment of all REGDs suitable for each location \(l\), ensuring that they do not overuse the available area at \(l\). Finally, Equation (7) specifies the allowable range for each decision variable.
## 4 Simulations and Real Data Processing  
In this section, the proposed CCAE method is first evaluated with different simulation parameters. In Section 4.2, the performance of the CCAE method is compared with that of the ACCF method at different SNRs. Finally, the CCAE method is verified using real radar data.
## 3 Improved Point Cloud Comparison Method
### Data Analysis  
Taking one parameter as the variable and the other three as fixed parameters to analyze the calculation results [32], there were four cases in total:  
(1) Taking the interval of scanning points \(t\) as the variable  
Taking the average of the 27 sets of calculation results corresponding to a certain \(t\) value, and obtaining the relationship between \(t\) and \(n\): \(t\) and \(n\) have a positive correlation, that is, the smaller \(t\) is, the smaller \(n\) is, but \(t\) < 0.05 m will significantly increase the time consumed for scanning and deformation calculation, reducing the applicability of the model, as shown in Figure 6a.  
Figure 5: Test model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Parameter** & **Common Values** \\ \hline Interval of scanning points \(t\) & 0.05 m, 0.10 m, 0.20 m \\ Radius of the sphere used for normal estimation \(R\) & 0.05 m, 0.10 m, 0.20 m \\ Side length of the prism \(d\) & 0.10 m, 0.20 m, 0.30 m \\ Height of the prism \(H\) & 0.15 m, 0.30 m, 0.60 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Commonly used values of the parameters.  
(2) Taking the radius of the sphere used for normal estimation \(R\) as the variable  
Similarly, the relationship between \(R\) and \(n\) is as follows: \(R\) and \(n\) are inversely correlated, that is, the smaller \(R\) is, the larger \(n\) is, but \(R>0.20\) m will greatly increase the time consumed for deformation calculation, as shown in Figure 6b.  
(3) Taking the side length of the prism \(d\) as the variable  
Since the relationship between \(d\) and \(n\) is more complicated, this study performed an additional 15 groups of calculations at \(d\) = 0.025 m, \(d\) = 0.05 m, \(d\) = 0.15 m, \(d\) = 0.25 m, and \(d\) = 0.40 m, obtaining the following conclusions: \(n\) gradually decreases with the increase of \(d\); \(n\) takes the minimum value when \(d\) = 0.20 m; \(n\) increases with \(d\) when \(d\) > 0.20 m and when \(d\) > 0.30 m, \(n\) tends to be stable at about 0.03 m, as shown in Figure 6c.  
(4) Taking the height of the prism \(H\) as the variable  
Similarly, since the relationship between \(H\) and \(n\) is not obvious, this study performed an additional 15 groups of calculations at \(H\) = 0.10 m, \(H\) = 0.20 m, \(H\) = 0.40 m, \(H\) = 0.50 m, and \(H\) = 0.70 m, obtaining the following conclusions: \(n\) gradually decreases with the increase of \(H\); \(n\) takes the minimum value when \(H\) = 0.50 m; and \(n\) increases with the increase of \(H\) when \(H\) > 0.50 m, as shown in Figure 6d.  
To conclude, the optimal model parameters with the highest accuracy for slope deformation measurement are as follows: interval of scanning points \(t\) = 0.05 m, radius of the sphere used for normal estimation \(R\) = 0.20 m, side length of the prism \(d\) = 0.20 m, and height of the prism \(H\) = 0.50 m.  
Figure 6: Data analysis results: (**a**) relationship between \(t\) and \(n\), (**b**) relationship between \(R\) and \(n\), (**c**) relationship between \(d\) and \(n\), (**d**) relationship between \(H\) and \(n\).
# A Heterogeneous Robotic Swarm for Long-Term Monitoring of Marine Environments
## 2 Distributed Marine Monitoring System
### aPad  
The aPad, shown in Figure 6, is an overactuated Autonomous Surface Vehicle (ASV). The role of each of the five aPad units present in the subCULTron swarm is that of an energy and information sharing hub. They provide a connection to the terrestrial world--a bridge between the robotic ecosystem and a potential human observer and operator--as well as computational power. They also serve as anchors in the process of underwater localisation.  
Figure 5: aMussel buoyancy system. **Left** image shows aMussel with piston completely in, and **right** shows it with piston completely out.  
Figure 6: The artificial lily pad (aPad) autonomous surface platform, front view (**left**) and top view (**right**).  
Equipped with specially developed mechanical docking stations and inductive charging coils, each aPad has the ability to transport and charge up to four aMussels in parallel.  
Additionally, the aPads need to be able to adapt to a changing realistic marine environment (Figure 7), both fighting and exploiting the influence of phenomena such as water currents and wind. One of the energy-efficient aPad behaviours being explored in the system, adapting to changes in their environment even when in a mostly idle state (i.e., not actively performing tasks), is outlined in [13].
# Spatiotemporal Image Fusion in Remote Sensing
## 3 Synthesis: Challenges and Opportunities
### Methods to Increase Spatiotemporal-Spectral Resolution of Images  
Most image fusion methods described in this paper are dedicated to increasing the spatiotemporal resolution of input images. Only a few methods enable the fusion of spatiotemporal and spectral information [55; 104; 105]. [PERSON] et al. [104] described a Bayesian method to generate synthetic satellite images with high spectral, temporal and spectral resolution. [PERSON] et al. [105] developed a unified framework for spatiotemporal-spectral fusion based on maximum posteriori theory. The framework was successfully tested on QuickBird, ETM+, MODIS, Hyperspectral Digital Imagery Collection Experiment (HYDICE) and SPOT-5 images [55].
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.18. Fog Computing  
Multi-access Edge Computing (MEC) is related to the Fog Computing (FC) paradigm, which was presented by Cisco in 2012 and stretches cloud computing capabilities to the edge of the network to build a highly virtualized platform that delivers computing, storage, and networking services between end devices and traditional data centers [34]. Low-latency communication is a key property of FC, which makes it more suitable for 5G communications. Data must be centralized in some applications, which may necessitate the use of cloud computing. This \"cloud model\" can be extended to edge fog networking, which is a system-level horizontal architecture that distributes resources and services everywhere along the Cloud-to-Things continuum. Fog and Cloud fusion can provide device and service consolidation, as well as optimization of tenant and virtualized service life-cycle management, data policy management enhancements, and application and data management integration [25]. IoT services can also benefit from location awareness and geographical distribution features of fog computing [35].
## 3 Currently Operational UAS Systems for SIF Retrieval  
### \"Piccolo-Doppio\"--A Dual-Field of View (FOV) Dual Spectrometer System  
Remote sensing protocols for accurate SIF retrieval, based on high spectral resolution data at the oxygen absorption features, require the sunlight irradiance (downwelling) and surface energy emissions (upwelling) being simultaneously measured. These measurements generally mismatch in time in a range of multiple seconds, thus causing uncertainties in the SIF estimation. To overcome this, [PERSON] et al. [34] split the fore optic path into a QEPro spectroradiometer (Ocean Optics, Dunedin, Florida, USA) aiming at a dual FOV system as proposed by [41]. The sensor measures in the 650-800 nm spectral range at a resolution of 0.15 nm, with a full width at half maximum (FWHM) between 0.31 and 0.35 nm. The SNR, dynamic range, and integration times are: 1000:1, \(8.5\times 10^{4}\):1, and up to 60 min, respectively. The detector has 1048 \(\times\) 64 pixels (two-dimensional) with columns summed to give increased dynamic range (Table 1). The system, named Piccolo-Doppio, is capable to perform upwelling and downwelling (cosine error \(<\)2%) measurements almost simultaneously (\(\approx\)50 ms difference between each measurement), diminishing reported uncertainties in field readings [42]. Moreover, the capability to work with two spectroradiometers (with a double bifurcated fiber optic assembly) differentiates the Piccolo-Doppio from some other UAS-based methods, since it allows synchronized Visible and NIR (VNIR; 400-950 nm) and the SIF (640-800 nm) measurements using the same fore optic for upwelling and the same fore optic for downwelling readings. Thus, any VNIR index, e.g., the photochemical reflectance index (PRI), can be derived together with SIF. As a drawback, light transmittance is reduced in one channel (either upwelling or downwelling) as fibers of two different diameters have been used which increases integration times and reduces SNR of the channel with the smaller diameter fiber. The use of one spectrometer, rather than two with independent optical paths, is necessary (i) to avoid the interpolation of one wavelength scale to the other (which might lead to loose detail in particular across the O\({}_{2}\) absorption features); and (ii) to ensure the radiometric calibration is constant for both up and downwelling measurements.
# Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides
## 4 Results
### Assessment Levels of Historical Landslide Events  
Figure 8 and Table 5 show the susceptibility assessment levels at which historical landslide events occurred from 2010 to 2016.  
It can be clearly seen in Table 5 that the combined number of historical landslide events designated as levels III and IV is 106, 140, and 154, respectively, in 1992, 2000, and 2009. The RNA for level IV in 2009 is \(5.44\times 10^{-3}\)/km\({}^{2}\), which is the largest among the RNA values over the three years. The most ideal assessment of landslides would be most landslides occurring in level IV areas. There is a significant positive correlation between the RNA and the accuracy of the assessment. Therefore, the results of the susceptibility assessment using the DEM for 2009 are more accurate than the results using the DEM for 1992 and 2000.  
It is apparent from Table 5 that the small change in elevation over time has a great influence on the susceptibility assessment of landslides. However, in previous research, the survey data are not explained or the data are significantly different from the assessment time.