esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
To solve the problems of difficult imaging and untimely acquisition of disaster information during flooding by traditional optical remote sensing means, this paper designs a time-series monitoring scheme for flooding by combining heterogeneous SAR images with the \"7-20\" rainstorm in Zhengzhou City as an example. By studying two types of heterogeneous SAR data, Sentinel-1A and GF-3, which were in transit during the \"7-20\" rainstorm in Zhengzhou City, the scheme successfully realized the dynamic monitoring and information extraction of flooding before and after the mega rainstorm by using the threshold segmentation method. Meanwhile, remote sensing and GIS data are fused to analyze the damage to urban roads under the influence of flooding, demonstrating the capability and application of obtaining disaster spatial and temporal information from multiple perspectives supported by multi-source SAR data. The results show that: (1) Guancheng District was the most affected area in the \"7-20\" rainstorm disaster in Zhengzhou City, and the total area of water bodies in this district increased by 33,402.2 m\({}^{2}\) by 20 July 2021. The recovery of the affected area in Erqi District was slow. As of 22 July 2021, the water body area in Erqi District has been reduced by 12,461.1 m\({}^{2}\) compared with that on July 20. This phenomenon is related to the dam failure of Guojiazui Reservoir in Erqi District. (2) In this rainstorm disaster, the total length of the road network in the main urban area of Zhengzhou was affected by 1324.63 km, of which the road network in Zhongyuan District was most seriously affected, reaching 349.17 km.  
This scheme adopts two kinds of heterogeneous SAR images to obtain detailed information on flood time sequence changes in the \"7-20\" rainstorm disaster in Zhengzhou City. It demonstrates the advantage of a joint observation scheme using heterogeneous radar image data in extracting timely and accurate information on surface flooded water bodies in rainstorms and flood disasters.  
Conceptualization, S.L.; methodology, G.J.; validation, J.L.; formal analysis, S.L.; investigation, J.L.; writing--original draft preparation, S.L.; writing--review and editing, G.J. and J.L.; visualization, J.L.; supervision, G.J.; funding acquisition, G.J. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China under grants 41474010 and 61401509, and the Natural Science Foundation of Henan Province under grant 182300410007.
## 2 Materials and Methods
### Sample Preparation  
We used an optically clear, medically approved silicone elastomer, NuSil MED6215, for all our samples, which is widely used for human implantation devices [31] and thus suits our development goal well. It consists of a Part A (refractive index \(n=1.409\), viscosity \(\eta=5.600\) cP) and a Part B to be combined in a ratio of 10:1 followed by a mixing and defoaming process. To ensure the homogeneity of the PDMS substrate, which is the key to high-quality waveguides, the mixing and defoaming were performed three consecutive times in a Thinky automatic mixer for about 3 min each time, followed by a final deaeration procedure in a vacuum desiccator. We cured the PDMS samples at room temperature over 48 h to ensure surface flatness and parallelism with the working plane. For the optical clarity of the PDMS substrate, the above preparation was performed in an ISO7 cleanroom.  
Figure 1: (**a**) Molecular structure of the divinylbenzene (DVB) monomer and Irgacure OXE02 and the polymerization of the monomer upon laser irradiation; (**b**) Absorption spectrum of 0.2 mM DVB and 0.5 mM Irgacure OXE02 in acetonitrile. Blue arrows indicate the absorption wavelength peak of the photoinitiator (PI) (337 nm) and the excitation wavelength of our system tuned to 680 nm.  
We prepared 1-mm thick pristine PDMS slabs of 1 \(\times\) 5 cm\({}^{2}\) surface area and immersed them in the monomer-PI solution for 144 h to load the monomer and PI molecules into the PDMS intermolecular space. This step was significantly longer than our previous work [24], because the PI molecule is much larger than the monomer and takes a much longer time to permeate the PDMS slab and reach the saturation level. Over the course of this infiltration, we measured the evolution of the weight gain of the PDMS slabs for different concentrations of the PI in the DVB monomer, which is shown in Figure 2. Although the weight gain does not change much after the first two hours, quality waveguides could only be fabricated after 144 h of immersion, which clearly reveals the effect of the larger PI molecule. In all three PI concentrations, the weight gain reaches its peak in the first one or two hours, which is an indication of the fast mobility of the small monomer DVB molecules. The weight gain slightly drops in the subsequent few hours possibly due to the leaching of certain small molecules originally presented in the PDMS since DVB is a good solvent. In the succeeding hours, the weight gain slowly increases again, suggesting the slow infiltration of the much larger PI molecules into the PDMS. During the infiltration, a certain number of the PI molecules may be blocked on the surface of the PDMS slab, causing a hindrance for further material infiltration and resulting in lower weight gain in the sample with higher PI concentration.
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 3 Results
### Spatial Distribution of Forest Stand Age in Loess Plateau  
Figure 9 shows the spatial distribution map of forest stand age in the Loess Plateau produced using the RF model. The results showed that the age distribution of <20 years old and 20-40 years old accounted for 38.26% and 12.22%, respectively. Forests older than 100 years reached 24.01%, mainly distributed in the east and south of the Loess Plateau. The forests in the Loess Plateau are mainly young forests. Due to severe soil erosion problems in the region, the Chinese government initiated reforestation campaigns in the 1970s and implemented the largest historical afforestation project called \"Gain for Green\" in 1999 [1], which explains why the stand age distribution map has a higher proportion of young forests and verifies the validity of our results.  
Figure 8: Accuracy evaluation of forest stand age dataset from the Loess Plateau ((**a**) multiple linear regression algorithm; (**b**) random forest algorithm) (the gray dashed line is the 1:1 line, and the red line is the fitting line).
# 2D Gaze Estimation Based on Pupil-Glint Vector Using an Artificial Neural Network
## 3 Experimental System and Results  
### Experimental System  
In this study, we develop a wearable gaze tracking system composed of a helmet, a monitor, an array of four near-infrared light emitting diodes (NIR LEDs), and a microspur camera, as shown in Figure 3. The screen size of the monitor is 75 mm \(\times\) 50 mm. Considering the imaging distance is limited between 3 cm and 7 cm, a microspur camera is adopted to acquire the eye image. The image resolution is 640 \(\times\) 480 pixels (CCD sensor). As described in [75], when the wavelength of NIR LED is located within the range of 760 nm-1400 nm, the pupil absorbs nearly all the near-infrared light and the iris obviously reflects it. The wavelength of NIR LED employed in this paper is 850 nm and the power is less than 5 mw. The experimental system brings no harm to human eyes [76]. An NVIDIA Jetson TK1 embedded development board (Figure 4) is utilized for image acquiring and processing (NVIDIA: NVIDIA Corporation (Santa Clara, California, CA, USA). TK1: Tegra K1. Jetson TK1 is a code of embedded development board manufactured by NVIDIA Corporation).  
Figure 4: NVIDIA Jetson TK1 embedded development board.  
Figure 3: Wearable gaze tracking system.
# SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions
## 6 Convolutional Neural Network (CNN) Structure
### Dual-Stream CNN
#### 6.2.2 Late-Merge Dual-Stream CNN  
The difference between late-merge dual-stream CNN and early-merge dual-stream CNN is that late-merge dual-stream CNN merges immediately before the fully connected layer. Figure 11b shows the structure of a late-merge dual-stream CNN.  
The input data of the late-merge dual-stream CNN are converted into a \(32\times 32\times 32\) feature map through the \({}^{L}C1\) and \({}^{L}C2\) convolution layer. The feature map then goes through two inception modules (\({}^{L}IM_{A}\) and \({}^{L}IM_{B}\)) and two convolution layers (\({}^{L}C3\) and \({}^{L}C4\)). The \(32\times 32\times 64\) feature map which is output from \({}^{L}C4\), which passes through convolution layers \({}^{L}C5\), \({}^{L}C6\), and \({}^{L}C7\) and the max pooling layer \({}^{L}P1\), \({}^{L}P2\), and \({}^{L}P3\). Accordingly, the size of the feature map changes, in the order of \(15\times 15\times 128\), \(7\times 7\times 192\), and \(3\times 3\times 384\). In the last convolution layer \({}^{L}C7\), we transformed the feature map of each layer into a \(1\times 1\times 384\) feature map. After the last convolution layer, two \(1\times 1\times 384\) feature maps are merged to obtain a \(1\times 1\times 768\) feature map. The feature map output from the merge then proceeds to the fully connected layer.  
The dual-stream CNN reduced the weight of the network via the smaller input data size in comparison to single-input CNN. The actual solder region is smaller than \(32\times 32\) pixels. However, input data less than \(32\times 32\) pixels in size degrades the classification performance of CNN. Therefore, through experiment the optimal size of the input data was determined to be \(32\times 32\) pixels. In addition, unlike the chip component image used as the input of a single-input CNN, the solder region no longer includes the silk line background that is unnecessary for classifying defects. This further increases the accuracy of defect classification in a dual-stream CNN.
## 3 Results and Discussion
### Material Characterization
However, the atomic concentrations of those elements differed significantly between the results of those two techniques, which could be attributed to the fact that EDS focuses only on a relatively small area/particle of the material while XPS is employed to analyze a much larger, bulk quantity. In order to provide further insight regarding the chemical composition of N/S-Carbon QDs, high-resolution XPS analysis of the C1s, N1s, and O1s core levels was employed. From C1s, it was deduced that the material possesses high functionality on its surface including as C-N and C-O bonds as well as C-O-C and carboxyl groups (Figure 6b). In addition, the main carbon frame was revealed to be composed of C-C/C=C. The N1s spectrum (Figure 6c) demonstrated the amount and type of nitrogen dopants on the carbon structure. The highest amount of doping was found to be in the form of graphitic nitrogen (63.6%), and pyrrol (23.8%) and quaternary nitrogen (14.9%) also contributed to a lesser extent. Finally, the O1s spectra (Figure 6d) verified the findings from the C1s peak since both carboxyl (531.3 eV) and C-OH (532.1 eV) groups were found to be present. In addition, at 533.3 eV the fitted peak was attribute to epoxy and OH groups. Elemental analysis (Table 1) confirmed once again that N/S-Carbon QDs are mainly composed of carbon, which is doped with nitrogen and sulfur atoms. These findings are consistent with the results of EDS, ATR-FT-IR, and XPS. Interestingly, the data on carbon were found to be proportionally closer to that of EDS than that of XPS, while the opposite was revealed for nitrogen. These differences could be attributed to the fact that elemental analysis provides information regarding the atomic composition of the material as a whole (mass), while EDS and XPS are used to determine only the surface composition. Figure 5: ATR-FT-IR spectrum of N/S@Carbon QDs.
# Detecting Traffic Incidents Using Persistence Diagrams
## 2 Topological Data Analysis (TDA)
### Persistence Diagrams  
Persistence diagrams are computed based on the notion of a simplicial complex. It is natural to think of a simplicial complex in a Euclidean space, but a more abstract definition adequately serves our purposes.  
**Definition 1** (Abstract Simplicial Complex).: _A finite collection of sets \(A\) is called an abstract simplicial complex if \(A\) is closed under subset inclusion, i.e., if \(\beta\in A\) whenever \(\alpha\in A\) and \(\beta\subset\alpha\). Furthermore, we call \(\alpha\in A\) a combinatorial \(p\)-simplex if \(|\alpha|=p+1\)._  
In our consideration, each simplex is a subset of data points. There are several useful methods by which to construct simplicial complexes from data. We have chosen the Vietoris-Rips (VR) complex for its computational convenience.  
**Definition 2** (Vietoris-Rips Complex).: _Let \(Y\) be a finite subset of data points contained in some metric space \(X\). For every \(r\geq 0\), the Vietoris-Rips Complex of \(Y\) at scale \(r\) is defined as_  
\[VR_{r}(Y):=\{\alpha\subset Y:\text{diam}(\alpha)\leq 2r\}.\]  
We note that the VR complex is ascending in the sense that \(VR_{r_{0}}(Y)\subset VR_{r}(Y)\) for \(r_{0}\leq r\). Thus, as the radius \(r\) increases, we produce a filtration of simplicial complexes on top of the data points in \(Y\). This filtration yields a persistent homology that quantifies the apparent topological features for different dimensions. 0-, 1-, and 2-dimensional features refer to connected components, holes, and voids, respectively. Higher-dimensional features would be the analogues of these features. The persistence diagram quantifies the importance of these features in terms of their persistence over various radii. Persistence gives us a sequence of homology groups for each dimension of the ambient metric space containing the data. By identifying the radii in the sequence when the topological features appear and disappear we obtain a collection of birth and death times for each feature of each dimension.  
The birth-death pairs as a multiset in \(\mathbb{R}^{2}\) make up the persistence diagram corresponding to \(Y\). The persistence diagrams in our algorithm only contain information on connected components, the zero-dimensional features. We were unable to find a meaningful use for persistence diagrams computed using higher-dimensional features.
## 3 UAV Applications in Climate Change Research
### Assessment of Climate Hazard Impacts and Emergency Response Coordination  
In the domain of climate hazard assessments, UAVs are emerging as an invaluable tool for rapidly assessing infrastructure damage and the environmental impacts following climate-related disasters. Following floods, storms, wildfires, and other events, UAVs provide rapid aerial-based assessments when manual assessments are restricted due to safety concerns [166; 167]. Visible and multispectral UAV sensors supply detailed visual evidence of the destruction critical for compiling robust post-disaster reports [168; 169]. UAVs also enable the scoping of disaster extents across neighborhoods and cities to allocate a response capacity. Another application is coordinating emergency response logistics using UAVs as communication relays and cargo transporters. UAVs can provide wireless connectivity to areas with destroyed infrastructures and monitor personnel deployments [170]. Using thermal and multispectral sensors, UAVs help locate victims during search-and-rescue operations, especially in inaccessible urban areas [171]. For cargo delivery, UAVs assist in transporting critical supplies, such as medical samples, water testing kits, radio equipment, and batteries, when ground transport is hindered post-disaster [172].  
Furthermore, UAVs can be utilized to assess flood events and their impacts on urban areas. By collecting high-resolution images and mapping the extent of flooding, UAVs provide valuable information for estimating direct tangible losses to residential properties and assessing the overall impact of floods. These data can support emergency response coordination, aid in resource allocation, and inform decision-making processes for future flood mitigation and adaptation measures [173]. Another critical value of deploying UAVs is providing real-time information on the behavior and impacts of natural hazards, such as wildfires, landslides, and coastal erosion. These data are crucial for emergency response coordination, enabling authorities to make informed decisions and perform actions in a timely manner to protect communities and critical infrastructure [174]. UAVs also aid impact attribution studies by providing baseline mapping to compare with post-disaster conditions. As the climate risks increase, UAVs are becoming integral to assessing hazards, coordinating responses, and building climate resilience.
## 1 Introduction  
Acute ischemic stroke continues to be a significant burden on worldwide health systems and remains one of the leading causes of disability and death. Intravenous atteplase administration is first-line reperfusion therapy with proven efficacy [1]. However, this therapy can only be given within 4.5 h after symptom onset, and this narrow therapeutic time window is one of the most well-recognized limitations of this treatment. Recent trials proved the efficacy of a mechanical thrombectomy over medical treatment for patients with acute ischemic stroke, with a balance of equivalent rates of adverse events [2; 3; 4; 5; 6; 7]. Good clinical outcomes were proven to be correlated with early vessel recanalization [8; 9]. Therefore, early recognition of ischemic stroke is very critical to improving management of this disease.  
[PERSON] et al. [10] reported that up to 22% of ischemic stroke cases were initially misdiagnosed even in academic teaching hospitals and a large community hospital. The failure to diagnose ischemic stroke in the emergency department is a missed opportunity for prompt treatment and acute interventions. Magnetic resonance diffusion-weighted imaging (MR-DWI) is another solution for characterizing ischemic brain tissues [11; 12]. The detection rate of hyperacute ischemic lesions was higher on MR-DWI than on plain computed tomography (CT; 76.9% vs. 30.0%) [13]. However, there is limited availability in broad clinical settings because of the need for longer scan times or high-end technology to achieve a fast scan protocol [14]. Therefore, non-contrast CT (NCCT) is always applied by most institutions as a first-line imaging modality for ischemic stroke, and extracting as much information as possible from NCCT may have exceptional benefits.  
With NCCT, different tissues have different appearance properties. Interpreting the heterogeneities and irregularities of tissues is relevant processing for tissue characterization. Descriptions of tissue patterns depend on a textural analysis. With the development of image processing, tiny pixel-wise correlations existing between tissues can be quantified and combined in an artificial intelligence classifier to achieve a computer-aided diagnosis (CAD) system [15; 16]. The CAD system can analyze textural patterns which might not be readily recognized by human beings and provide diagnostic suggestions. In this study, a Ranklet transformation was proposed to enhance local contrast by relatively ranking pixel values. Through a sophisticated integration of numerous textural features extracted from the Ranklet-transformed images, the probability of brain ischemia was calculated. The resulting CAD suggestion facilitates a clinical ischemic stroke diagnosis with rapid NCCT scanning in a more reliable way.
# An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks
## 6 Evaluation
### Limitations of Current Work  
There are some limitations of our current work. Firstly, limitations of our simulation. We assume that the mobile devices of users have sufficient computing resources to complete calculations and communications, and the factors such as communication delays among devices are not considered as well. Secondly, limitations of our SCTB algorithm. In our paper, we assume that the kinds of service category are distinct and semantically dissimilar, which means they have no semantic relationship. Therefore, how to apply our approach to semantically similar scenarios more practically is still a challenge. However, our simulations prove that our algorithm is useful for the clustering process. Besides, the results match to theoretical development which was presented between Sections 3 and 6. Testing those algorithms (i.e., PLAM, LLB, and our SCTB) in a real environment is still a challenge.  
Figure 14: The simulation results of SCTB algorithm under different scenarios.  
Figure 15: The simulation results for Scenario-4.  
That is because there are many uncertain factors, such as the strength of the network, different user habits, and different social conventions may cause different results.
# Synthetical Modal Parameters Identification Method of Damped Oscillation Signals in Power System
## 2 Synthetical Modal Parameters Identification (SMPI) Method  
This paper focuses on the theory and methods of SMPI proposed for modal parameter identification of DLFO in power systems. According to previous research, the SSI method is weak in identifying amplitude and phase angle of non-stationary signals [5], and the computation of damping ratio is inaccurate by the Prony approach [12]. Besides sensitivity to noise, estimation of model order is a difficulty for SSI and Prony. Under-estimating model order would lead to the omission of dominant modes of the original signal, while over-estimation of the model order would bring in fictitious modes, especially with the amount of noise.  
Accordingly, it is impracticable to utilize a single algorithm to obtain the full modal parameter of DLFO. Addressing this problem, this paper has presented a comprehensive method, i.e., the SMPI method, based on the parameter matching approach which fully makes use of the strengths of SSI approach and Prony method, to find out full modal parameters. In addition, SMPI can handily avoid the difficulty of estimating model orders. The described synthetical modal parameters identification method is shown in Figure 1.  
As Figure 1 shows, in the developed SMPI method, the raw signals are initially denoising by EMD. And the filtered signals are put into modal identification with Prony and SSI, respectively. Prony calculates natural frequencies, amplitudes, and phase angles of the signals, while SSI extracts frequencies and damping ratios. In respect of the same natural frequencies, all true modal parameters of DLFO, involving natural frequencies, damping ratios, amplitudes, and phase angles, are matched finally. The detailed implementations of EMD, SSI, and Prony approaches will be presented in the following parts.
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 3 Requirements of Internet of Drones (IoD)
### Communication Requirements
#### 3.1.5 Positioning of High-Precision  
For numerous drone applications, positioning is critical. In several drone applications, vertical positioning also is important, in addition to traditional positioning on the horizontal plane. The requirement for positioning accuracy will increase from tens of meters to sub-meters with the drone applications' development. Fifty meters positioning precision is adequate for regular monitoring activities. Applications such as agricultural land mapping and automated loading involve high precision positioning at the sub-meter [19].