esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Manual versus Semi-Automatic Segmentations of the Stenotic Carotid Artery Bifurcation
### Imaging of Carotid Artery Pathology and Its Clinical Relevance  
According to Class I, level A recommendation CT angiography (CTA) is suitable for extent and severity evaluation of extracranial carotid stenosis [1]. The advantages of CTA are wide accessibility in daily clinical routine, high spatial resolution, allowing delineation of arterial outlines, well-established imaging protocols, shorter scan times, ensuring reduced motion artefacts [3] and the capacity to accurately define plaque volume and subcomponents, especially when coupled with post-processing analysis techniques [4]. Furthermore, CTA-derived diameter stenosis measurements correlate well with catheter angiographic measurements [5]. The importance and efficacy of semi-automatic segmentation with CTA has been proven to be fundamental in the daily clinical routine in stenosis grading. Four advanced semi-automatic vessel analysis applications were tested in comparison by two expert neuroradiologists. They have a low-to-good sensitivity, good specificity and overall diagnostic accuracy, as well as high interobserver reproducibility in stenosis grading. It was, however, remarked that due to the false-negative classifications, which were caused by ulcerative plaques and observer variation in stenosis and reference measurements, automatic measurements should be checked by an experienced radiologist [6]. Moreover, CTA can be used to assess the volume of atherosclerotic plaques and detect ulcerations. Furthermore, CTA image analysis provides enough detail for morphological analysis and for calcium identification. However, it is difficult to reliably differentiate between soft plaque components because of an overlap in Hounsfield units (HU), as it is not an ideal approach to identify the fibrous cap and it overestimates the stenosis grade because of calcium deposits [7].
# Tissue Engineering Strategies for Retina Regeneration
## 4 Biomaterials and Scaffolds Used for Tissue Engineering
### Biohybrid Scaffolds  
Hybrid scaffolds have the advantage of combining the properties of both natural and synthetic nanofibers by incorporating both materials to make composite scaffolds. This combination approach is different from coating the synthetic scaffolds with natural materials like extracellular matrices or proteins. The combination approach allows tailoring scaffold properties of the synthetic component and gaining natural properties of proteoglycans, proteins, and glycosaminoglycans from the natural polymer. [PERSON] et al. [111] manufactured five blends of PLLA with PLGA to evaluate a variety of suitable scaffolds for RPE transplantation. The blend with a 25:75 (PLLA:PLGA) ratio was found to be the thinnest and most porous with minimal cell death [111]. A novel scaffold fabricated from Silk Fibroin (SF) and Poly(L-lactic acid-co-\(\varepsilon\)-caprolactone) (PLCL, 1:1) showed RPC growth, proliferation, and differentiation into photoreceptors [112]. A cationic chitosan-graft-poly/text/textepsilon-caprolactone)/polycaprolactone (CS-PCL/PCL,20/80) hybrid scaffold produced using electrospinning fabrication technique demonstrated great RPC proliferation [113]. Previous studies have demonstrated that PCL with laminin and PCL with chitosan electrospun nanofibers, can improve cell adhesion, proliferation, or differentiation and promote the expression of genes specific to photoreceptor cells or bipolar cells [96; 113; 114]. Issues related to reproducibility and batch variability while using natural polymers exist in the combination approach. Future studies should address these issues along with effective measures to control the biodegradability and immunogenicity of the by-products of combination scaffolds. A summary of different biomaterials used for retinal tissue engineering is included in Table 1.  
Figure 1: Histologic assessment of Mesh-supported submicron parylene C membranes (rMSPM)+ Vitronectin and California Project to Cure Blindness–Retinal Pigment Epithelium 1(rCPCB-RPE1) implants in Royal College of Surgeons (RCS) rats. Representative hematoxylin eosin (HE) staining images of rat retina after implantation. Implanted (**a**) parylene membrane (rMSPM+ Vitronectin) and (**b**) rCPCB-RPE1 in the subretinal space (large white arrow), surviving outer nuclear layer (ONL) (red arrows), and an area showing some cellular reaction (white stars). Relatively intact host retina, elevated and wavy inner nuclear layer (INL) and focal loss of INL cells can be observed in both (**a,b**). The choroidal layer that appears to be separated from the remaining retina is considered a histologic artifact. (**c**) Immunostaining of TRA-1-85/RPE65 shows implanted hESC-RPE cells (white arrowhead). (**d**) Rhodopsin immunostaining showing rhodopsin-positive phagosomes inside the implanted RPE65-positive hESC-RPE cells (small white arrow pointing to phagosomes) (reprinted with permission from [PERSON] et al., 2016).
# Multi-Color Channels Based Group Sparse Model for Image Restoration
## 2 GSP Model
### Learning a Dictionary  
Sparse representation aims to obtain the sparsest representation of the original signal on the learned dictionary, and the signal is represented as a linear combination of a small number of dictionary atoms [32]. Therefore, in the framework of sparse representation, the construction of the dictionary is the key step of image restoration. However, in image restoration tasks, the original image \(x\) is unknown, and the original image group \(X_{i}\) cannot be obtained directly. The dictionary is only constructed according to the degraded image \(y\). The image \(y\) is first initialized using the bilinear interpolation algorithm. The following operations are similar to the procedures depicted in Section 2.1. Specifically, the estimated image group \(r_{i}\) corresponding to the original image group \(X_{i}\) is extracted from the initialized image \(y\). Then the singular value decomposition is performed on the estimation group \(r_{i}\) via the following Equation (6).  
\[r_{i}=U_{i}\underset{i}{\sum}V_{i}^{T}=\sum_{j=1}^{n_{1}}\gamma_{i,j}\Big{(}u_ {i,j}v_{i,j}^{T}\Big{)} \tag{6}\]  
Here, \(\gamma_{i}\) denotes \([\gamma_{i,1},\,\gamma_{i,2},\,\,\ldots,\,\gamma_{i,n1}]\), \(\sum_{i}\) denotes a diagonal matrix, \(\gamma_{i,j}\) denotes the main diagonal elements of \(\sum_{i}\), \(d\) represents each atom of the dictionary \(D_{i}\), and \(n_{1}\) is defined as the minimum of \(d\) and \(m\). \(u_{i,j}\) and \(v_{i,j}^{T}\) denotes the column vectors of the matrixes \(U_{i}\) and \(V_{i}^{T}\), respectively. Each atom \(d_{i\otimes j}\in R^{b\times m}\) of the adaptive dictionary \(D_{i}\) is defined as follows,  
\[d_{i\otimes j}=u_{i,j}v_{i,j}^{T},\,\,j=1,2,3,\ldots,n_{1} \tag{7}\]  
In this way, the adaptive dictionary \(D_{i}\), corresponding to each image group can be represented as follows:  
\[D_{i}=\big{[}d_{i\otimes 1},d_{i\otimes 2}\ldots,d_{i\otimes n_{1}}\big{]} \tag{8}\]  
The singular value decomposition is only needed to be performed on each group once to learn the adaptive dictionary corresponding to the image group. After the dictionary is learned, the sparse representation model is updated and then the \(L_{1}\) norm minimization is solved. Finally, the split Bregman iteration (SBI) algorithm [13] is used for iterative optimization to obtain the final restoration result.
# Shallow Subsurface Wavefield Data Interpolation Method Based on Transfer Learning
## 4 Discussion  
### Similarity Between Synthetic Data and Real Data  
The construction of the source domain dataset utilizes the concept of shared parameters in transfer learning, meaning that the source domain dataset theoretically includes or partially includes the features of the target domain dataset during simulation. Due to the unique characteristics of shallow subsurface structures, existing datasets do not fit the shallow structures studied in this research. Therefore, we used numerical simulations of known subsurface structures and source signals to create a large number of source domain datasets to train the interpolation network and then transferred the network model to the real dataset for further training.  
The numerical simulation is based on the elastic wave equation, which follows the same physical laws as the propagation of actual seismic waves. This ensures theoretical consistency of the simulated data in terms of dynamic features, such as first arrival travel times and reflection coefficient sequences. By constructing a velocity-density model similar to the target area, the simulated data could retain key characteristics of the actual stratigraphy.  
Synthetic seismic data cannot fully represent real seismic data. We used the Pearson correlation coefficient [31] to compare the degree of correlation between the simulated and denoised real waveforms under the same shot-receiver distance conditions. After comparing 100 pairs of signals, we found correlation coefficients ranging from 0.7 to 0.85, leading us to conclude that there is transferability between the source and target domains.
## 3 Methods  
The overall workflow of the apple orchards in the study area is shown in Figure 2. The specific steps include (1) sample selection; (2) improved spectral angle features; (3) input features; (4) experimental design; and (5) classification and accuracy evaluation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor Used** & **Bands** & **Descriptions** & **Resolution** \\ \hline \multirow{2}{*}{Sentinel-1} & VV & 5.405 GHz & 10 m \\  & VH & 5.405 GHz & 10 m \\ \hline \multirow{4}{*}{Sentinel-2} & Blue & 496.6 nm (S2A)/492.1 nm (S2B) & 10 m \\  & Green & 560 nm (S2A)/559 nm (S2B) & 10 m \\ \cline{1-1}  & Red & 664.5 nm (S2A)/665 nm (S2B) & 10 m \\ \cline{1-1}  & NIR & 835.1 nm (S2A)/833 nm (S2B) & 10 m \\ \cline{1-1}  & Red Edge 3 & 782.5 nm (S2A)/779.7 nm (S2B) & 20 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Remote sensing image waveform information.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Land Type** & **Training Samples/Pixels** & **Validation Samples/Pixels** \\ \hline
1 & Apple Orchards & 137 & 58 \\
2 & Croplands & 435 & 186 \\
3 & Artificial Surfaces & 95 & 41 \\
4 & Water Bodies & 60 & 25 \\
5 & Other Lands & 43 & 18 \\ \hline Total & & 770 & 328 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training and validation samples.  
Based on GEE, this study first conducted a visual interpretation of high-resolution images; determined five types of sample selection ranges, including apple orchards; and randomly generated 1098 sample points within the sample selection range, according to the area share of each location obtained from visual interpretation. The VV and VH bands in the monthly average Sentinel-1 images over 12 months were then calculated using a spectral angle mapper to generate the corresponding spectral angle features, SAM_VV and SAM_VH.  
To analyze the applicability of different features for apple orchard extraction in the Zhaotong City area, we designed 12 feature combinations based on the RGB band, red-edge feature, NDVI, texture feature, and improved spectral Angle feature. The reasons for selecting these features are that the RGB band can provide basic spectral information for classification, NDVI and red-edge features can better reflect the characteristics of vegetation, and texture features [26] can provide spatial information.  
Among them, NDVI is calculated directly from the Sentinel-2 image of October 2022, while texture feature needs to calculate the gray image of the Sentinel-2 image of October 2022. Then, the gray-level co-occurrence matrix (GLCM, window size: \(3\times 3\) pixels) was used to obtain four texture features: contrast (CON), entropy (ENT), angular second moment (ASM), and correlation (COR). These features are computed on the GEE platform.  
Finally, an RF classifier in GEE was used to extract the apple orchard distribution information, and the apple orchard extraction and overall accuracy of the different methods were evaluated.
###### Abstract  
Since wildfires have occurred frequently in recent years, accurate burned area mapping is required for wildfire severity assessment and burned land reconstruction. Satellite remote sensing is an effective technology that can provide valuable information for wildfire assessment. However, the common approaches based on using a single satellite image to promptly detect the burned areas have low accuracy and limited applicability. This paper develops a new burned area mapping method that surpasses the detection accuracy of previous methods, while still using a single Moderate Resolution Imaging Spectroradiometer (MODIS) sensor image. The key innovation is integrating optimal spectral indices and a neural network algorithm. We used the traditional empirical formula method, multi-threshold method and visual interpretation method to extract the sample sets of five typical types (burned area, vegetation, cloud, bare soil, and cloud shadow) from the MODIS data of several wildfires in the American states of Nevada, Washington and California in 2016. Afterward, the separability index M was adopted to assess the capacity of seven spectral bands and 13 spectral indices to distinguish the burned area from four unburned land cover types. Based on the separability analysis between the burned area and unburned areas, the spectral indices with an M value higher than 1.0 were employed to generate the training sample sets that were assessed to have an overall accuracy of 98.68% and Kappa coefficient of 97.46%. Finally, we utilized a back-propagation neural network (BPNN) to learn the spectral differences of different types from the training sample sets and obtain the output burned area map. The proposed method was applied to three wildfire cases in the American states of Idaho, Nevada and Oregon in 2017. A comparison of detection results between the new MODIS-based burned area map and the reference burned area map compiled from Landsat-8 Operational Land Imager (OLI) data indicates that the proposed method can effectively exploit the spectral characteristics of various land cover types. Also, this new method can achieve higher accuracy with the reduction of commission error (CE, >10%) and omission error (OE, >6%) compared to the traditional empirical formula method. The new burned area mapping method could help managers and the public perform more effective wildfire assessments and emergency management.  
MODIS; burned area; spectral indices; neural network 25 December 2018; Accepted: 2 February 2019; Published: 6 February 2019
# Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera
## 4 Experimental Results  
In this section, comprehensive experiments are presented and analyzed both quantitatively and qualitatively to evaluate the effectiveness and accuracy. The data include both rendered synthetic samples and real-world scenes captured by a lab-developed light field camera under a controlled studio lighting environment.
## 3 Results
### Determination of the k\({}_{d}\) Limits
\begin{table}
\begin{tabular}{c c c c c} \hline
**Site (Year)** & **Equation of the Best-Fit Curve** & **R\({}^{2}\)** & **k\({}_{\rm du}\)** & **k\({}_{\rm dl}\)** \\ \hline ATH (2000) & H\({}_{\rm bm}\) = 750.35 k\({}_{\rm d}\)\({}^{2}\) - 1911.13 k\({}_{\rm d}\) + 1160.42 & 0.99 & 0.79 & 0.26 \\ BOU (1999) & H\({}_{\rm bm}\) = 764.84 k\({}_{\rm d}\)\({}^{2}\) - 1906.61 k\({}_{\rm d}\) + 1145.82 & 0.99 & 0.79 & 0.26 \\ CAR (2018) & H\({}_{\rm bm}\) = 868.13 k\({}_{\rm d}\)\({}^{2}\) - 2035.91 k\({}_{\rm d}\) + 1172.93 & 0.99 & 0.77 & 0.26 \\ DAA (2017) & H\({}_{\rm bm}\) = 829.67 k\({}_{\rm d}\)\({}^{2}\) - 2009.26 k\({}_{\rm d}\) + 1182.98 & 0.99 & 0.78 & 0.26 \\ GAN (2015) & H\({}_{\rm bm}\) = 923.82 k\({}_{\rm d}\)\({}^{2}\) - 2150.63 k\({}_{\rm d}\) + 1230.29 & \(\approx\)1.00 & 0.77 & 0.26 \\ OHY (2018) & H\({}_{\rm bm}\) = 715.87 k\({}_{\rm d}\)\({}^{2}\) - 1879.32 k\({}_{\rm d}\) + 1165.29 & 0.99 & 0.80 & 0.27 \\ ILO (1999) & H\({}_{\rm bm}\) = 796.31 k\({}_{\rm d}\)\({}^{2}\) - 2007.88 k\({}_{\rm d}\) + 1208.37 & 0.99 & 0.79 & 0.26 \\ KIS (2020) & H\({}_{\rm bm}\) = 781.01 k\({}_{\rm d}\)\({}^{2}\) - 1935.32 k\({}_{\rm d}\) + 1155.12 & \(\approx\)1.00 & 0.78 & 0.26 \\ LER (2003) & H\({}_{\rm bm}\) = 930.30 k\({}_{\rm d}\)\({}^{2}\) - 2020.92 k\({}_{\rm d}\) + 1097.60 & 0.94 & 0.73 & 0.24 \\ LIN (2018) & H\({}_{\rm bm}\) = 779.37 k\({}_{\rm d}\)\({}^{2}\) - 1929.96 k\({}_{\rm d}\) + 1151.03 & 0.99 & 0.78 & 0.26 \\ PAY (2013) & H\({}_{\rm bm}\) = 715.85 k\({}_{\rm d}\)\({}^{2}\) - 1856.32 k\({}_{\rm d}\) + 1140.65 & 0.99 & 0.79 & 0.26 \\ REG (2003) & H\({}_{\rm bm}\) = 779.37 k\({}_{\rm d}\)\({}^{2}\) - 1929.96 k\({}_{\rm d}\) + 1151.03 & \(\approx\)1.00 & 0.78 & 0.26 \\ SOV (2002) & H\({}_{\rm bm}\) = 919.21 k\({}_{\rm d}\)\({}^{2}\) - 2192.18 k\({}_{\rm d}\) + 1270.02 & 0.99 & 0.78 & 0.26 \\ SON (2018) & H\({}_{\rm bm}\) = 704.02 k\({}_{\rm d}\)\({}^{2}\) - 1833.67 k\({}_{\rm d}\) + 1129.45 & 0.99 & 0.79 & 0.26 \\ \hline \end{tabular}
\end{table} 
Table 2: Second-order polynomial expression for the function H\({}_{\rm bm}\) vs. k\({}_{\rm d}\) with R\({}^{2}\), k\({}_{\rm du}\), and k\({}_{\rm dl}\) values. All values have been rounded to the second decimal digit. The year in parenthesis in column 1 denotes the period of the downloaded solar radiation measurements from the BSRN sites. The criterion for the year selection was to be outside the PV-GIS TMY period of 2004–2015, if possible. Figure 10: Determination of the k\({}_{\rm du}\) (red line) and k\({}_{\rm dl}\) (blue line) values for the (**a**) SON and (**b**) SOV sites.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 2 Types of Quadcopters
### Commercial Quadcopters
#### 2.1.5 DJI Phantom 4  
The DJI Phantom 4, shown in Figure 7, includes a three-axis gimbal for stable and high-resolution aerial images ideal for agricultural mapping and monitoring. Additionally, its obstacle avoidance sensor enhances flight safety, reduces crash risks, and supports advanced agricultural applications. The Phantom 4 has been used in numerous research studies to capture high-resolution aerial images for various agricultural applications, including irrigation [67; 68; 69; 70], mapping and monitoring [71; 72; 73; 74; 75], and plant health assessment [76; 77; 78; 79; 80]. For example, the DJI Phantom 4 was used in [71] to capture multi-scale digital elevation models of an agricultural field at the foothills of the Middle Atlas Mountains in Morocco, aiming at managing and monitoring various agricultural parameters.
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 3 The Proposed Scheme
### Double Hiding  
The image owner performs image encryption and data embedding on the preprocessed image.
## 2 Data Description
### Training and Testing Data
The dust frequency map derived based on the testing data in Figure 2d is in overall good agreement with that based on the training data in Figure 2c, although it is noisier due to the small sampling rate. Overall, two points are evident from Figure 2: First, the statistics and geographical distributions of the training and testing data are almost identical, which means there are no sampling bias generated by our method of splitting the data. Second, even though the CALIOP operational retrieval is considered as the state-of-the-art method to detect dust, it still faces some challenges. Inevitably, our algorithms that are trained based on CALIOP retrievals will also face these challenges as explained in Section 4. Figure 2: (a,b) Distribution of clear sky and dust over land and ocean, for both training and testing data. (c,d) distribution of dust for training and testing data. Spatial distribution is averaged with 5 \(\times\) 5 latitude, longitude grid box.
## 7 Tests and Results
### Comparison with a Classic Lamp Posts  
The absence of mains near the isle prevented us to making a direct comparison with a classical technology lamp post as for _i.e._, in [3], so we designed this comparison following two ways: in the first our lamp post (called L1) is directly compared with another (called L2), similar to the first, but without the presence sensor control, so it was always switched on during the night; in the second, we realize a simulation considering the L2 supplied by mains (this lamp post is called L3). The results are reported in Table 6.  
We performed this test in February 2014 and both the lamp posts used a 18 W LED technology, 1550 lumen, 84 lm/W luminous flux, powered by a solar panel and a battery. For L3 we assumed the lamp post was supplied by mains through a 12 V AC/DC converter.  
\begin{table}
\begin{tabular}{c c c c} \hline
**UL Modulation** & **Packet Loss** & **CINR (dB)** & **AvgDistance from BS (km)** \\ \hline
16-QAM3/4 & 0\% & 23 & 4.5 \\
16-QAM1/2 & 0\% & 15 & 7.3 \\ QPSK3/4 & 0\% & 13 & 10.1 \\ QPSK1/2 & 0\% & 9 & 12.9 \\ \hline \end{tabular}
\end{table}
Table 5: WiMAX uplink modulation in LOS.  
Considering fixed the cost of the kWh of power (in Italy \(\approx\)0.2 \(\upcelsius\)[40]) and that the first and second lamp posts have no power consumption, to find the break-even we used the following formula [3]:  
\[PC_{{}_{Z}}-PC_{{}_{Y}}+\sum_{i=1}^{x}\left(kWh_{{}_{Zi}}-kWh_{{}_{Ti}}\right) \cdot C_{kWh_{{}_{Ti}}}=0 \tag{1}\]  
where PC is the plant cost difference between different lamp posts and it is a fixed cost; kWh is the number of kilowatt per hour used by the specific lamp post day by day; C\({}_{kWh}\) is the cost of kWh; it can change during a long period. The term x is unknown and represents the activity days necessary to reach the breakeven between the two different choices of lamp posts. As imaginable the first solution is always more convenient than the second, while the first becomes more convenient than the third after 56 months.  
For the coordinator lamppost we need to add the cost of the WiMAX apparatus (80 \(\upcelsius\)), while the Internet subscription cost is not considered because it is included in a WiMAX country public service. Moreover, there is a higher cost of the PV panel and of the battery equal to 80 \(\upcelsius\). Distributing this last cost between the five lampposts of the isle the breakeven is reached after 83 months. Obviously if the number of the lampposts of the isle is higher, the break-even will be reached earlier.