esa-sceva/satcom-chunk-collection huggingface dataset:
# Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network
## 5 Conclusions  
For all-weather imaging, PolSAR images play an increasingly important role in emergency response and disaster relief. This study proposed a method based on superpixel methods and graph convolution to extract the water area in PolSAR images, which has three inherent advantages: (1) superpixel segmentation makes the classified object correspond to a real-world object, reducing the influence of noise in the classification; (2) graph convolution no longer considers each classification object separately but considers the spatial relationship between objects, which improves the accuracy of classification. Moreover, this paper emphatically explains three processing parts: (1) feature extraction, including the statistical values of the scattering matrix, texture value, covariance matrix elements, and LWR, and their correlation and importance are discussed; (2) samples--an auxiliary unsupervised method was proposed to deal with complex scenes; (3) the GNN model, including graph construction and node classification. A GraphSAGE framework was chosen.  
Comparison experiments were conducted in two study areas at both the superpixel level and the pixel level. The results show that superpixel-based methods achieved much better performance than pixel-based methods, proving the advantage of using superpixels in SAR images. Though the performances of the former can be improved using extra skills or advanced algorithms, superpixels are still preferred. Among the superpixel-based methods, the GNN classifier obtained the highest scores, especially the recall metrics, which were about 3-4 percent higher than XGBoost and RF. The experiments in this paper prove that our proposed method is suitable for finely mapping water bodies in complex areas, such as in the case of floods. The GNN uses an inductive learning framework to mine deeper spatial relationship information and can efficiently classify the nodes (superpixels). Compared with CNN methods, the proposed method does not require high-quality datasets and much computing power while maintaining high accuracy. The proposed method can be used for reference in extracting other ground features in SAR images.
## 3 Proposed Methodology
### Decoding the Customer Vector
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Problems** & _BKS_ & _Cost\({}_{\text{BLS-PSO}}\)_ & _gap_ & _AvgT_ & \(\mathbf{k_{2}}\) & \(\mathbf{k_{3}}\) \\ \hline B-n41-k6 & 829 & 829 & 0.00 & 3.93 & 0.2 & 0.3 \\ B-n43-k6 & 742 & 742 & 0.00 & 2.55 & 0.7 & 0.1 \\ B-n44-k7 & 909 & 909 & 0.00 & 3.16 & 0.0 & 0.3 \\ B-n45-k5 & 751 & 751 & 0.00 & 2.04 & 0.8 & 0.2 \\ B-n45-k6 & 672 & 672 & 0.00 & 4.95 & 0.5 & 0.5 \\ B-n50-k7 & 741 & 741 & 0.00 & 5.45 & 0.3 & 0.2 \\ B-n50-k8 & 1312 & 1312 & 0.00 & 5.53 & 0.0 & 0.0 \\ B-n51-k7 & 1032 & 1032 & 0.00 & 5.39 & 0.2 & 0.0 \\ B-n52-k7 & 747 & 747 & 0.00 & 6.18 & 0.1 & 0.6 \\ B-n56-k7 & 707 & 707 & 0.00 & 7.31 & 0.2 & 0.2 \\ B-n57-k7 & 1153 & 1153 & 0.00 & 8.12 & 0.0 & 0.8 \\ B-n57-k9 & 1598 & 1598 & 0.00 & 8.56 & 0.4 & 1.0 \\ B-n63-k10 & 1496 & 1496 & 0.00 & 9.13 & 0.0 & 0.6 \\ B-n64-k9 & 861 & 884 & 2.67 & 16.75 & 1.0 & 0.8 \\ B-n66-k9 & 1316 & 1322 & 0.46 & 15.52 & 1.0 & 0.9 \\ B-n67-k10 & 1032 & 1032 & 0.00 & 8.24 & 0.1 & 1.0 \\ B-n68-k9 & 1272 & 1272 & 0.00 & 6.19 & 0.0 & 0.5 \\ B-n78-k10 & 1221 & 1221 & 0.00 & 9.71 & 0.5 & 0.2 \\ \hline \multicolumn{2}{c}{Average} & & 0.14 & 6.13 & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._The proposed BLS-PSO algorithm has reached the best-known solutions for 84 instances out of the 106 Euclidean distance-based benchmark instances used in our experiments. BLS-PSO has an average gap of 0.07%, 0.14%, 0.31%, 0.26%, 3.45%, 0.2% and 0.64% to solve instances in Sets A, B, E, F, M, P and CMT, respectively. It requires an average CPU Time of 5.5 s, 6.13 s, 11.51 s, 25.59 s, 68.69 s, 4.72 s and 13.33 s to solve instances in Set A, B, E, F, M, P and CMT, respectively. Thus, BLS-PSO has been found to obtain the results equal to the best-known results for most of the existing benchmark problems within short computational time. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Problems** & **BKS** & \(\textit{Cost}_{\textit{BLS-PSO}}\) & _gap_ & _AvgT_ & \(\textit{k}_{2}\) & \(\textit{k}_{3}\)_ \\ \hline CMT1 & 524.61 & 524.61 & 0.00 & 2.19 & 0.6 & 0.2 \\ CMT2 & 835.26 & 835.26 & 0.00 & 8.44 & 0.4 & 0.8 \\ CMT3 & 826.14 & 826.14 & 0.00 & 10.58 & 0.6 & 1.0 \\ CMT4 & 1028.42 & 1042.8 & 1.4 & 11.82 & 0.9 & 0.0 \\ CMT5 & 1291.29 & 1324.01 & 2.53 & 16.37 & 0.9 & 0.2 \\ CMT6 & 555.43 & 555.43 & 0.00 & 9.11 & 0.4 & 0.4 \\ CMT7 & 909.68 & 909.68 & 0.00 & 7.23 & 0.0 & 0.9 \\ CMT8 & 865.95 & 870.03 & 0.47 & 19.41 & 1.0 & 0.1 \\ CMT9 & 1162.55 & 1177.14 & 1.25 & 25.21 & 0.0 & 1.0 \\ CMT10 & 1395.85 & 1436.84 & 2.93 & 31.04 & 0.6 & 0.7 \\ CMT11 & 1042.12 & 1042.12 & 0.00 & 8.58 & 1.0 & 0.8 \\ CMT12 & 819.56 & 819.56 & 0.00 & 10.08 & 0.1 & 0.1 \\ CMT13 & 1541.14 & 1546.36 & 0.34 & 15.54 & 0.2 & 0.7 \\ CMT14 & 866.37 & 866.37 & 0.00 & 11.07 & 0.0 & 0.4 \\ \hline \multicolumn{2}{c}{Average} & 0.64 & 13.33 & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance of BLS-PSO in solving the benchmark problems in Set CMT.
## 1 Introduction
Among the previous studies of collagen crosslinking, several have investigated the relationship between mechanical or thermal properties and the degree of crosslinking [25; 26]. Moreover, there were also some studies about the difference between non-crosslinked and crosslinked collagen effects on the cytocompatibility and also different kinds of crosslinking agents [10; 26; 27; 28; 29; 30]. However, whether there is a relationship between the degree of crosslinking and cytocompatibility remains to be confirmed. On the other hand, to our knowledge, few reports have focused on the hemostatic properties of electrospun collagen with different degrees of crosslinking. Consequently, in order to provide a basis for the crosslinking of electrospun collagen, whether there is diversity among electrospun collagen, crosslinked to varied degrees in terms of cytocompatibility and hemostasis, needs to be explored. Therefore, in this study, a small amount of polyethylene oxide (PEO) was added into solutions to facilitate electrospinning. As a water-soluble polymer with good biocompatibility and low toxicity, PEO is quite feasible and versatile for electrospinning. It can be added to aid electrospinning by increasing polymer chain entanglements and can be subsequently extracted from the nanofibers by incubating in water with only collagen remaining. An electrospun collagen/PEO membrane with varied degree of crosslinking was obtained by adjusting the time for crosslinking. Subsequently, how the degree of crosslinking impacts the properties of collagen electrospun nanofibers, mechanical performance, water stability, blood clotting capacity and cytocompatibility was evaluated. Finally, because its compositional and structural mimicry of the ECM and satisfactory performance in the hemostatic process, the wound dressing was predicted to be effective in promoting cell adhesion and proliferation and controlling bleeding as an ideal choice for the wound dressing.
# Automatic Brix Measurement for Watermelon Breeding
## 2 Materials and Methods
### Automatic Watermelon Brix Measurement Method  
#### 2.3.1 Testing Point Localization Based on Image Analysis  
In order to obtain the pixel coordinates of the center measurement point, as shown in Figure 3, we use the following equation to extract the pulp mask \(M\):  
\[M\ =\ Otsu(R\ -\ 1.25G) \tag{3}\]  
where \(R\) is the red channel of the image, \(G\) is the green channel, and \(Otsu\) is the Otsu method for binarization calculation [27].  
Then, the smallest circumscribed rectangle for the extracted pulp area is generated, and the center of the rectangle and the position, which is offset by 0.425 times the side length from the center of the rectangle along the short side direction, are set as the measurement points, as shown in Figure 3.  
Figure 2: Watermelon diameter measuring for automatic half-cutting.  
In order to drive the Brix measurement sensor using a 3-axis robotic arm, the pixel coordinate of the measurement points is converted into a camera coordinate. In the camera coordinate \(OXYZ\), as shown in Figure 1, since the laser rangefinder is perpendicular to the watermelon section, the \(z\) value of any point on the watermelon section can be easily obtained. The coordinates of any point on the watermelon section can be obtained through the pinhole imaging model [28].  
\[x = \frac{x_{pixel}\ -\ C_{x}}{f_{x}}\cdot z \tag{4}\] \[y = \frac{y_{pixel}\ -\ C_{y}}{f_{y}}\cdot z \tag{5}\]  
where \(x\) and \(y\) are the coordinate values, mm; \(x_{pixel}\) and \(y_{pixel}\) are the image coordinate values, pixel; \(C_{x}\) and \(C_{y}\), which are generated by the camera calibration, are the camera optical center coordinates, pixel; \(f_{x}\) and \(f_{y}\), which are also generated by the camera calibration, are the two components of the camera focal length, pixel; and \(z\) indicates the distance from each point in the image to the camera along the \(Z\) axis, namely the distance from the watermelon section obtained by the laser rangefinder to the camera.
## 5 Experimental Validation
### Optimizer Selection Experiment
#### 5.1.1 Oscillation Type Data Generation  
As shown in Figures 4-6, the step type data and oscillation type data come from the 1-filter-differential-pressure feature and 10 filter differential pressure feature of the oil dataset. Pulse data come from the gas dataset.  
As shown in Figure 4, the fluctuation trend of the data generated by using adagrad and Adam optimization methods is far from the original data; the data generated by the rmsprop optimization method is far from the original data in value range; however, the data generated by SGD and momentum optimization methods are more similar to the original data than the other three. Therefore, aiming at the change of oscillation characteristics, this paper chooses momentum as its optimizer.
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 4 Smart City De-Anonymization and Re-Identification Use Cases
### Social Networks  
Social network data can be integrated into smart cities to enhance various urban services and operations, such as traffic management, public safety and emergency response. For example, social media can be used to monitor real-time traffic conditions and adjust traffic lights, to collect information about road accidents and provide real-time alerts, or to facilitate communication between citizens and government agencies.  
However, this integration raises privacy concerns as personal information and location data from social media can be easily accessed, collected and analyzed. This can lead to the violation of citizens' privacy rights, as their personal information and location data can be used for unauthorized purposes, such as targeted advertising or political manipulation. Additionally, there is a risk of sensitive information being disclosed or hacked, leading to further privacy breaches. To address these privacy concerns, it is crucial to establish clear and stringent privacy policies and guidelines, as well as secure data management and protection systems.  
[PERSON] et al. [145] focused on analyzing privacy requirements offered by social networks through collecting data from 5000 users with different social network profiles, using image recognition techniques to retrieve personal data accessible through these networks. The aim is to raise awareness about the spread and management of social network data and highlight privacy issues by showing how easily users' data can be retrieved.
# Novel MIA-LSTM Deep Learning Hybrid Model with Data Preprocessing for Forecasting of PM2.5
## 5 Results & Discussions  
Since the concentration of a 2.5 micron-sized particulate matter depends on certain factors such as the concentration levels of other pollutants, for example, PM\({}_{10}\), CO, SO\({}_{2}\), NO\({}_{2}\), O\({}_{3}\), etc., and meteorological factors such as temperature, WSPM, rainfall DEWP, etc., the important factors were identified. Different regressor approaches for forecasting the pollutants, such as a random forest (RF) regressor, light gradient boosting machine (LGBM), gradient boosting (GB) regressor, and decision tree (DT) regressor are applied and discussed in the following section. State-of-the-art time series approaches, such as univariate LSTM, gated recurrent unit, 1D convolutional neural networks, multivariate LSTM, and proposed hybrid methods are also discussed.
# An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering +
## 3 Materials and Methods
### Experiments  
We employed similar experiment settings in [4] with a swarm size of 20 and maximum iteration of 100, together with the following experiment settings. We previously obtained good results using similar datasets. Ten generations are run in the cluster-based MOEA/D framework, and each experiment is run thirty times. The data are scaled before applying clustering. To produce different solutions, we set the MOC algorithms to generate more than one solution. The final solution is selected by crowding distance. The number of clusters is set as the number of classes in the dataset.  
The evaluation metrics used are accuracy, F1 and Kappa for external evaluation, and QE, WSS and BSS for internal evaluation. Different works in data clustering use different measures, which make comparison of other work difficult. There appears to be a mixed opinion to use external metrics to evaluate clustering, while another school of thought is that the point of clustering is to generate labels to perform classification and aims to generate such labels. This depends on the objective of the users and it is not within our scope to discuss this matter. We present both types of criteria for relevant researchers.  
To evaluate the clustering performance and investigate in clustering behavior of CM-BBPSO, we conduct several analyses. We compare the CM-BBPSO with non-cluster based MOEA/D BPPSO and with BBPSO to study how the cluster-based MOEA/D approach has made a difference. Previously in [4], we observed that the application of MOEA/D in BBPSO algorithm improves the performance. By comparing with BBPSO clustering techniques, we can determine if the cluster-based MOEA/D approach further improves BBPSO and M-BBPSO.  
CM-BBPSO is then compared with other MOPSOs and MOO Fuzzy algorithms to evaluate its performance. In particular, M-Fuzzy is found to perform competitively to M-BBPSO in [4]. Previously, we were not able to obtain results on CMC, segment and vehicle datasets. Through this analysis here, we are re-evaluating by comparison with the said algorithms and the additional datasets, which usually give poor results, as was reported in [30]. We also compare CM-BBPSO results with the existing cluster-based MOO technique HT-MOC [20], MOC techniques MOFC-TMS [25], VAMOSA [31], GenClustMOO [32], MOAC [26], ES-NMPBFO [27] and BBPSO clustering technique CBPSO [24] based on commonly reported datasets and evaluation metrics.  
The following swarm sizes and maximum iterations (presented as ss-mi) are tested: 20-100, 30-100, 30-200 and 40-100. This is done to study whether am increase in the two parameters will improve clustering. The biplot visual examination allows us to evaluate CM-BBPSO performance in comparison with the ground truth, examining how well CM-BBPSO identifies overlapping clusters. With the analysis of the convergence plots of _pbest_, we can evaluate the stability of CM-BBPSO.
## 3 Methods
### Preliminary Extraction of Water Body  
In the first step, we first implement monthly mean composite using monthly Sentinel-1 images. The Sentinel-1 data covering China include two polarization modes: VV polarization and VH polarization. With reference to existing research, water bodies based on the two polarization modes have different thresholds [16]. In order to verify the feasibility of the threshold method, we divide land cover into seven land-cover types, referring to Copernicus Global Land Cover Layers--Collection 2 and high-resolution images of Google Earth: buildings, snow, forests, crops, sand dunes, grassland and water bodies [22]. Water bodies are further divided into freshwater lakes, saltwater lakes with high mineral content, reservoirs with complex geometry, rivers, water in high mountains, and periodically frozen water bodies. Based on Copernicus Global Land Cover Layers--Collection 2, the stratified random sampling approach is used to generate sample point locations, and then they are visually confirmed one by one based on the high-resolution images on Google Earth. Finally, there are totally 550 training sample locations for these 11 land-cover types across China. At each sample location, the backscatter coefficient set of the above land-cover type are extracted based on time series Sentinel-1 VH/VV polarized images. We select 6866 backscatter coefficient samples from VH images in 2018, 6820 backscatter coefficient samples from VV images in 2017 and then calculate the median, mean, upper and lower quartiles, and 1.5 interquartile range (IQR) of these backscatter coefficients (Figure 3). The results show that all of the water body subtypes have similar values and all of the non-water land-cover types, except for sand dunes, have good separability with water bodies. With all this, on the VV polarization image, the pixels with a backscattering coefficient value not greater than \(-\)15 dB are regarded as a body of water, and on the VH polarization image, the pixels with a backscattering coefficient value not greater than \(-\)23 dB are regarded as a body of water. These characteristics of water bodies are what past research studies have used [16; 23; 24; 25; 26]. These characteristics also allow us to detect water bodies with periodically frozen ice in winter using the above thresholds. When using GEE and Sentinel-1 archives, this effective approach is clearly operational and efficient for large-scale water mapping.  
Figure 2: Flow diagram of this study.
# Radiometric Identification of Signals by Matched Whitening Transform
## 5 Conclusions  
The problem addressed in this paper is the attribution of a signal to an unknown source. Previous approaches have been based on feature extraction, dimensionality reduction and some implementation of a minimum distance classifier. The approach here proposes the degree of whiteness of the transformed data as a signature for radiometric identification of the signal. It's a featureless approach that skips feature extraction by using the raw IQ data. This formulation demands minimal computational load compared to PCA or deep learning methods. There are two other features that make the algorithm stand out. One is using real data captured by satellite radios. The other is addressing carrier and phase recovery by reversing the embedded phase and frequency offsets as a preprocessing step. Algorithms that are tuned to the data assuming perfect carrier capture will fail in practice. This work can be extended in a number of ways, such as expanding the radio source database to military and commercial radar, wireless broadcasts, modeling time varying frequency offsets and a more broad comparison with the competing deep learning methods.  
Conceptualization, B.G.M. and A.L.; Software, A.L.; Writing and original draft, B.G.M.; Writing, review and editing, A.L. All authors have read and agreed to the published version of the manuscript. Funding for this research was partially provided by the RT Logic Corp. under award number A16-0008-001. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: The authors gratefully acknowledge the Funding support. Conflicts of Interest: The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Agilent** & **EBEM** & **Paradise** & **RTSim** & **USRP** \\ \hline Agilent & 17.5 & **27.5** & 17.5 & 5.0 & 10.0 \\ Viasat EBEM & 20.0 & **60.0** & 5.0 & 2.5 & 2.5 \\ Teledyne Paradise & 15.0 & 5.0 & **27.5** & 17.5 & 15.0 \\ KRATOS RTsim & 7.5 & **22.5** & 10.0 & 15.0 & 12.5 \\ USRP & **20.0** & 12.5 & 17.5 & 2.5 & 17.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Same as Table 3 @ SNR = 0 dB.
## 4 Results
### Signal to Noise Ratio Analysis
The SNR improvement index can be interpreted as a comparison of the level of a desired signal and the background noise. A larger value of the \(SNR_{i}\) implies a smaller level of the noise.
## 5 Conclusions  
This study fuses NTL data, resident population data, and floating population data to identify the spatial range of the PRD urban agglomeration from the dual perspectives of population distribution and population mobility. The results show that the spatial range identified by NTL_LS covers 6069.51 km\({}^{2}\), with an accuracy of 80.37% and a Kappa coefficient of 0.5225, whereas the spatial range identified by NTL_HM covers 90,338.47 km\({}^{2}\), with an accuracy of 89.17% and a Kappa coefficient of 0.7342. The accuracy and consistency of NTL_HM are significantly higher than those of NTL_LS. The NTL_HM-identified spatial range of the PRD is broader, particularly in the Guangzhou-Shenzhen corridor and its surrounding expansion areas, offering more comprehensive coverage of high-mobility zones and short-term economic activity hotspots. This suggests that incorporating floating population data into urban agglomeration identification not only improves the accuracy of spatial range identification but also better reflects the dynamic characteristics of population and economic activity in economically active regions like the PRD. This approach effectively addresses the limitations of resident population data in capturing economic and population dynamics.  
This study provides a new approach for urban agglomeration spatial range identification based on multi-source data integration by comparing the identification results of NTL_HM and NTL_LS data. By incorporating the dynamic characteristics of economic activity and population mobility, the identification method proposed in this study not only enhances the accuracy of spatial range identification but also offers more timely and dynamic reference information for regional planning and policymaking in complex urban agglomerations like the PRD urban agglomeration.  
**Author Contributions:** Conceptualization, Y.C., Q.L. and Z.Y.; methodology, Y.C.; software, Y.C. and Q.L.; validation, Y.C.; formal analysis, Y.C. and Z.Y.; resources, Y.C. and Q.L.; data curation, Y.C.; writing--original draft preparation, Y.C. and Z.Y.; visualization, Y.C.; funding acquisition, Y.C. and Z.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Youth Project of Guangzhou Academy of Social Sciences, grant number 24 QN004.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.