esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Aerosols significantly affect carbon dioxide (CO\({}_{2}\)) retrieval accuracy and precision by modifying the light path. Hyperspectral measurements in the near infrared and shortwave infrared (NIR/SWIR) bands from the generation of new greenhouse gas satellites (e.g., the Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite, TanSat) contain aerosol information for correction of scattering effects in the retrieval. Herein, a new approach is proposed for optimizing the aerosol model used in the TanSat CO\({}_{2}\) retrieval algorithm to reduce CO\({}_{2}\) uncertainties associated with aerosols. The weighting functions of hyperspectral observations with respect to elements in the state vector are simulated by a forward radiative transfer model. Using the optimal estimation method (OEM), the information content and each component of the CO\({}_{2}\) column-averaged dry-air mole fraction (XCO\({}_{2}\)) retrieval errors from the TanSat simulations are calculated for typical aerosols which are described by Aerosol Robotic Network (AERONET) inversion products at selected sites based on the _a priori_ and measurement assumptions. The results indicate that the size distribution parameters (r\({}_{\text{eff}}\), v\({}_{\text{eff}}\)), real refractive index coefficient of fine mode (a\({}_{\text{f}}\)) and fine mode fraction (fmf) dominate the interference errors, with each causing 0.2-0.8 ppm of XCO\({}_{2}\) errors. Given that only 4-7 degrees of freedom for signal (DFS) of aerosols can be obtained simultaneously and CO\({}_{2}\) information decreases as more aerosol parameters are retrieved, four to seven aerosol parameters are suggested as the most appropriate for inclusion in CO\({}_{2}\) retrieval. Focusing on only aerosol-induced XCO\({}_{2}\) errors, forward model parameter errors, rather than interference errors, are dominant. A comparison of these errors across different aerosol parameter combination groups reveals that fewer aerosol-induced XCO\({}_{2}\) errors are found when retrieving seven aerosol parameters. Therefore, the model selected as the optimal aerosol model includes aerosol optical depth (AOD), peak height of aerosol profile (H\({}_{\text{p}}\)), width of aerosol profile (H\({}_{\text{w}}\)), effective variance of fine mode aerosol (v\({}_{\text{eff}}\)), effective radius of coarse mode aerosol (r\({}_{\text{eff}}\)\({}^{c}\)), coefficient a of the real part of the refractive index for the fine mode and coarse mode (a\({}_{\text{f}}\) and a\({}_{\text{r}}\)\({}^{c}\)), with the lowest error of less than 1.7 ppm for all aerosol and surface types. For marine aerosols, only five parameters (AOD, H\({}_{\text{p}}\), H\({}_{\text{w}}\), r\({}_{\text{eff}}\)\({}^{c}\) and a\({}_{\text{r}}\)\({}^{c}\)) are recommended for the low aerosol information. This optimal aerosol model therefore offers a theoretical foundation for improving CO\({}_{2}\) retrieval precision from real TanSat observations in the future.  
TanSat; aerosol; DFS; error; CO\({}_{2}\) 20191110610.3390/rs110910610.3390/rs110910610.3390/rs110910610.3390/rs110910610.390/rs110910610.3390/rs110910610.
## 4 Results
### Performance Comparison of NARX and BP Neural Networks  
After completing the network training, the NARX network is converted to a closed-loop structure as shown in Figure 5. In this configuration, the latest temperature prediction values replace the actual temperature values obtained from monitoring for the next iterative calculation. At this stage, the network input vector consists of three items: water content, frost heave, and temperature prediction values. The model no longer uses past measured actual temperatures for iteration and continues with rolling predictions accordingly. Both Table 3 and Figure 10 compare the prediction results at 0.5 m below the subgrade surface.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **RMSE** & **MAE** & **R2** \\ \hline NARX network & 0.181083515 & 0.147216877 & 0.996078159 \\ BP neural network & 0.746974505 & 0.567286461 & 0.460336565 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of model prediction accuracy.  
Figure 9: Evolution of RMSE during model training.  
Figure 9(a) illustrates the comparison of values between the prediction from different models and the actual measured at 2.9 m depth under the subgrade surface. Overall, the outputs of the NARX network model are superior to those of the BP network. This improvement can be attributed to the feedback and delay mechanisms introduced in the NARX network model. These features provide the model with excellent memory functions and dynamic capabilities, resulting in better performance in predicting temperatures beneath seasonally frozen subgrades.
## 3 Results
### Reliability Assessment
#### 3.1.2 Inter-Rater Reliability  
To check inter-rater reliability, the results of measurements by two examiners with the LEMOCOT test from 45 patients on the same day (Table 2B), and from 22 patients on different days (Table 2C), were analyzed. Excellent intra-rater reliability was found under both conditions, with ICC (95% CI) = 0.94-0.99 (0.89-0.99), acceptable error level SEM: 1.39-3.20 targets, and MDC: 3.84-8.84 targets.
# A Bagged Ensemble Convolutional Neural Networks Approach to Recognize Insurance Claim Frauds
## 2 Related Work
In the financial sector, fraud can take many forms beside insurance fraud, such as credit card fraud, and money laundering. Convolutional neural networks (CNNs) have been used to detect fraudulent financial transactions in a number of studies. One example of using CNNs for fraud detection is a study by [PERSON] et al. [17]. In this study, the authors used a CNN to detect fraudulent credit card transactions in a dataset of credit card transactions. The CNN was trained on a dataset of both fraudulent and non-fraudulent transactions, and was able to accurately identify fraudulent transactions with high precision and recall out performing state-of-the-art methods. In [18], a CNN-based fraud detection model for online transactions is proposed. The model uses low dimensional, nonderivative transaction data as input and consists of a feature sequencing layer, four convolutional and pooling layers, and a fully connected layer. Experimental results show that the model outperforms the existing CNN for fraud detection. Most recently, in [19], authors proposed a combination of CNN and LSTM. The model was able to reduce the need of complex feature extraction processes that often rely on domain experts in traditional machine learning algorithms. In conclusion, the detection of fraud in insurance claims is a well-researched area. It appears that machine learning paradigms, particularly convolutional neural networks (CNNs), outperform traditional methods. It is important to note that the effectiveness of these methods for fraud detection depend on the quality of the training data and the specific details of the model architecture. Additionally, the imbalanced nature of data is a current challenge in the field. Consequently, in the work we propose a combination of a 1D Convolutional Neural Network and pre-trained CNN models enhanced with a bagged ensemble learning techniques to boost the detection effectiveness and achieve maximum accuracy. The main contributions of this article can be summarized as following:  
1. We used an analysis technique for cleaning and improving the quality of the chosen dataset. 2. we proposed a 1D Convolutional Neural Network along with the use of pre-trained CNN models. 3. We used a bagged ensemble learning based architecture to boost the model performance. 4. We assessed the performance of our proposed model using different paradigms and performance ratios.
# RoSe-Mix: Robust and Secure Deep Neural Network Watermarking in Black-Box Settings via Image Mixup
## 3 Proposed Method
### Security Analysis
#### 3.3.2 Mixer  
In contrast, the Mixer method requires the attacker to predict the correct mixing vectors, \(\lambda\) and \(\mu\), which are drawn from a Dirichlet distribution, but before this, the attacker must also guess the number of classes, \(k\), to mix from the total set of \(c\) possible classes.  
The probability of successfully guessing the correct number of classes \(k\) from \(c\) possible classes is \(\frac{1}{(k)}\), and the probability of predicting the mixing coefficients in \(\lambda\) and \(\mu\) within the tolerance \(\delta\), is determined by integrating the Dirichlet density function over the \(\delta\)-neighborhood around the actual values of \(\lambda\) and \(\mu\). This probability can be approximated by considering the space of possible values for \(\lambda\) and \(\mu\) as being divided into \(N^{k}\) discrete steps. Therefore, the probability of correctly predicting \(\lambda\) within this range is:  
\[P_{\text{values}}=\int_{|\lambda_{i}-\lambda_{i}|\leq\delta}f(\lambda;\mathbf{ \alpha})d\mathbf{\lambda}, \tag{8}\]  
where \(\lambda\) is the true vector, \(\mathbf{\lambda}\) the guessed vector, and \(f(\mathbf{\lambda};\mathbf{\alpha})\) is the Dirichlet density function. The same formula can be applied to \(\mu\). The overall probability of success for the attacker is the product of the probability of correctly guessing the number of classes and the probability of predicting the mixing coefficients:  
\[P_{\text{overall}}=\frac{1}{(k)}\times P_{\text{values}}. \tag{9}\]  
Now we transform these probabilities into work. The first step is to guess the positions of the \(k\) non-zero elements among the \(c\) possible positions. The number of possible combinations is given by the binomial coefficient \((k)\).  
For the number of trials to guess the values, once the positions are guessed, the attacker needs to guess the values of the \(k\) non-zero elements within the specified tolerance \(\delta\). The Dirichlet distribution generates continuous values, so an exact match is nearly impossible, but within a tolerance \(\delta\), we can estimate the number of trials as follows.  
If we assume that \(\lambda\) and \(\mu\) are discretized into \(N\) steps, where \(\delta=1/N\) represents the tolerance within which the attacker must guess the correct coefficients, the work is equal to \(N^{k}\). Finally, the total work to find \(\lambda\) for the Mixer method is given by:  
\[W_{\lambda}=\binom{c}{k}\times N^{k}, \tag{10}\]  
where \((k)\) is the binomial coefficient that represents the number of ways to choose \(k\) classes from \(\mathcal{C}\), and \(N^{k}\) represents the number of possible combinations for the coefficients in \(\lambda\) within the tolerance \(\delta\). The same work can be calculated for \(\mu\):  
\[W_{\mu}=\binom{c}{k}\times N^{k}. \tag{11}\]
# Assessment of Negative Gingival Recession: A Critical Component of Periodontal Diagnosis
## 6 Clinical Assessment Approaches  
### Manual Instrumentation  
CAL is determined by taking into account the probing depth and GR measurements. In the site where a negative GR exists, the distance from the gingival margin to the CEJ is subtracted from the probing depth to determine the CAL. Whereas at sites with positive GR (gingival margin located apical to the CEJ), CAL is determined by adding the PD with the positive GR value [2]. Locating the CEJ in the presence of negative GR can be particularly challenging, and there is a degree of guesswork involved in this indirect clinical approach that is subject to measurement errors and variations between examiners. CEJ location in such an approach is determined based on crown anatomy (crown length and ratio of length/width), curvature of the CEJ, and visibility of the adjacent CEJ. In the literature, measurement errors in the range of 30-50% and 20-40% have been reported for CAL and pocket depth measures using the indirect approach, respectively [72, 73, 74, 75].  
Alternatively, the clinician can directly \"reflect\" the papilla by the periodontal probe to visualize or \"feel\" for the CEJ to assess the amount of negative recession. However, in some scenarios, the CEJ may not be visible or may lack a clear demarcation, leading to visual or tactile errors [57]. On the proximal surfaces, the partial vertical course of the CEJ further increases the difficulty in locating the CEJ [76]. In addition, it is usually not feasible in daily clinical practice to complete a periodontal chart by direct approach due to its time-consuming nature. Vision-enhancement methods, e.g., the use of the operating microscope, equipped with high magnification (10-25\(\times\)) and co-axial illumination, could assist in identifying the CEJ for better negative-recession determination, but the availability of this tool for clinical use is currently limited (Figure 2). Another \"direct\" way to detect true attachment loss is to use the periodontal probe tip running along the tooth crown in an apical direction until the bottom of pocket is reached. Attachment loss is measured by a mental note made of the extent of sliding movement withdrawn from the pocket bottom until the CEJ is detected [77]. The reproducibility of this approach is limited, mainly from the variation in individual tactile perception of CEJ. From a measurement-error point of view, direct measurements are less error-prone than the indirect approach. However, the intra- and inter-examiner variabilities are still very high and both these approaches can be time-consuming [78].
# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 1 Introduction
Compared to traditional machine learning methods, the use of deep learning models to assist clinical image segmentation in the field of skin lesion area segmentation has received considerable attention. Both traditional skin lesion image segmentation algorithms and currently popular deep learning segmentation algorithms aim to achieve fast and accurate segmentation of skin lesion images. Although these methods perform well in skin lesion image processing, they still have many drawbacks. Most existing skin cancer image segmentation models suffer from insufficient feature extraction, inadequate preservation of edge information in the results, and other issues. In the consecutive convolution process, the model may learn more irrelevant features, ultimately leading to poor segmentation performance. Figure 1: Some dermoscopy images. Therefore, extracting low-level and high-level semantic features through networks is equally important for achieving automated skin lesion image segmentation. By doing so, irrelevant or redundant features are avoided during the feature extraction process, and more edge information is retained in the segmentation results. In conclusion, deep learning algorithms for skin lesion image segmentation are more effective than traditional segmentation algorithms. However, further research and improvement of existing models are needed to improve their robustness and accuracy while suppressing overfitting. According to the current research status, we have proposed a skin lesion segmentation model based on DenseASPP, attention mechanism, and residual learning. The proposed model only adds a few parameters and computational operations, but can highlight the regions of interest related to skin lesion segmentation. We have also conducted experimental evaluations on two datasets, and the results show that the proposed model has advanced performance in skin lesion segmentation. The paper's main contributions are summarized below:  
1. We propose a novel encoder module that utilizes the cross-residual (CR) module to extract features. This module is able to extract deep semantic information to avoid gradient vanishing during network training. 2. The ECA module is introduced in the feature extraction process of the encoder. This module effectively captures inter-channel interaction information so that the network can learn more lesion characteristics. 3. The bottleneck layer structure is modified. The redesigned DenseASPP is applied to the bottleneck layer to extend the receptive field via cascading expansion convolution and include more information in the feature map, leading to more exact segmentation of lesions in different sizes. 4. We propose the REDAUNet network model based on the UNet3+ network. This model is an \"end-to-end\" architecture and provides better performance for skin lesion segmentation on both the ISIC2017 and ISIC2018 datasets.
## 2 Materials and Methods
### Methods
#### 2.3.2 Evaluation of the Cloud Effect  
For the Sentinel-2 data, the percentage of no cloud cover (Pclear) was defined as  
\[P_{clur}=\frac{N_{clur}}{N_{clur}+N_{cloudabor}+N_{cloudshadow}}, \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Formulation** & **Reference** \\ \hline EVI & \(\frac{2.5\times(B8-B4)}{B8+6\times(B4-75\times B2+1}\) & [74] \\ NDVI & \(\frac{B8-B4}{B8-B4}\) & [75] \\ NIRv & \(\frac{B8-B4}{B8-B4}\) \(\times\) B8 & [76] \\ CI red edge (CIr) & \(\frac{B8}{B8-B4}\) \(\times\) & [39; 43] \\ CI green (CIg) & \(\frac{B8}{B8-B5}\) & [39; 43] \\ MTCI & \(\frac{B8-B8}{B8-B4}\) & [47] \\ NDRE1 & \(\frac{B8-B8}{B8-B5}\) & [36] \\ NDRE2 & \(\frac{B8-B8}{B8+B5}\) & [77] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vegetation indices derived from the Sentinel-2 multispectral instrument (MSI) sensor. The terms B2 to B8 are the abbreviations of band reflectance from the Sentinel-2 MSI sensor. The center wavelength of each band is as follows: B2 (490 nm), B3 (560 nm), B4 (665 nm), B5 (705 nm), B6 (740 nm), B7 (783 nm), B8 (842 nm). EVI—enhanced vegetation index; NDVI—normalized difference vegetation index; NIRv—near-infrared reflectance of terrestrial vegetation; CI—chlorophyll index; MTCI—medium-resolution imaging spectrometer (MERIS) terrestrial chlorophyll index; NDRE—normal deviation index of the red edge.  
where \(N_{clear}\) is the number of pixels with no cloud cover, \(N_{cloudovor}\) is the number of pixels with cloud cover, and \(N_{cloudshadow}\) is the number of pixels with cloud shadow masks. Here, we defined the effective images as the data in the spatial image window with \(P_{clear}\) values \(>\)0.7.  
Cloud cover affects the diffuse scattering of light in the sky; thus, it changes the light intensity and influences the carbon assimilation rate for photosynthesis [78]. Here, we applied the concept of potential PAR (\(PAR_{pot}\)), which was the maximal incident PAR during the 16-day interval [79]. Therefore, the daily cloud effect at the site was defined as  
\[\text{CSI}=\frac{PAR_{in}}{PAR_{pot}}. \tag{2}\]  
When the clear sky index (CSI) is approximately 1, clouds have little effect on the incident PAR.
## 3 Results
### Attribution Analysis of WUEc Changes in Xinjiang
#### 3.3.2 Analysis of Dominant Factors in WUEc Changes  
Based on the SHAP values of meteorological and vegetation factors influencing WUEc in Xinjiang, we created a ranked map showing each factor's contribution to WUEc and a spatial distribution map of its dominant regions (Figure 8). The importance index and the percentage of each factor's dominant region are also summarized in Table 2. The results show that T is the primary factor influencing the change in WUEc in Xinjiang, with an importance index of 0.15 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\), much higher than that of the other influencing factors. T is the dominant factor driving changes in WUEc across 45.7% of the study area, primarily concentrated in Hetian, Aksu, Changji, Urumqi, and Kelamayi. LAI ranks second in the importance of all influencing factors, with an importance index of 0.1 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\) for WUEc in Xinjiang. Its dominant influencing area is concentrated in the northern regions of Aksu and Yili, covering 17.6% of the study area. The importance index of Pre on WUEc in Xinjiang is 0.09 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\), with its dominant area accounting for 17.4% of the study area, primarily in the southern part of Aksu and the northern part of Yili. The importance index of VPD was slightly smaller than that of precipitation, at 0.08 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\). It is the dominant factor driving changes in WUEc across 14.7% of the study area, mainly concentrated in the southern region of Yili. The effect of EVI on WUEc in Xinjiang was not significant, with an importance index of 0.05 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\). It was the dominant factor driving changes in WUEc in only 4.3% of the cro  
Figure 7: Spatial distribution patterns of SHAP values for vegetation factors: (**a**) SHAP value distribution for the Enhanced Vegetation Index (EVI), and (**b**) SHAP value distribution for the Leaf Area Index (LAI). Positive SHAP values indicate a positive effect on WUEc, while negative values indicate a negative effect.
# GRNN: Graph-Retraining Neural Network for Semi-Supervised Node Classification
## 4 GRNN: Graph-Retraining Neural Network
### The Benchmark Neural Network  
Since GCN has been demonstrated to be powerful and effective in the semi-supervised node classification of a graph, the benchmark neural network adopts its weight between connected nodes in graph learning. In addition, the benchmark neural network uses the dropout of the edges and nodes to improve the robustness of the model [34]. Specifically, given the node features \(X=\{x_{0},x_{1},\ldots,x_{n}\}\), the message aggregation from distant \(j\) to source \(i\) can be expressed as follows:  
\[h_{i}^{(0)} = u_{i}(\alpha)x_{i} \tag{4}\] \[\widetilde{h}_{i}^{(k+1)} = \sum_{j\in\mathcal{N}_{i}}v_{j}(\beta)e_{ij}^{(k)}(h_{j}^{(k)}+h_ {i}^{(0)}\widetilde{W}^{(k)})W^{(k)} \tag{5}\]  
where \(e_{ij}^{(k)}=1/\sqrt{d_{i}d_{j}}\) is a nonzero element of the normalized adjacency matrix, and \(\widetilde{W}^{(k)}\in R^{p\times p_{k+1}}\) is the learnable weight matrix of the \(k\)-th graph neural hidden layer. Specifically, \(u_{i}(\alpha)=0\) when \(\alpha\leq\gamma_{i}\), and \(u_{i}(\alpha)=1\) when \(\alpha>\gamma_{i}\) (where \(\alpha\in[0,1]\) and \(\gamma_{i}\) isthe random value from 0 to 1). \(v_{i}(\beta)=0\) when \(\beta\leq\theta_{i}\), and \(u_{i}(\beta)=1\) when \(\beta>\theta_{i}\) (where \(\beta\in[0,1]\) and \(\theta_{i}\) is the random value from 0 to 1).  
After obtaining the graph-aggregation features, the benchmark neural network uses a concatenation operation to improve stability:  
\[h_{i}^{(k+1)}=\sigma(\|_{c=1}^{C}[\widetilde{h}_{i}^{(k+1)}]) \tag{6}\]  
where \(\|\) is the \(C\) head concatenation operation, \(\sigma\) is a nonlinear activation function, and \(\sigma\) is assumed to be an exponential linear unit here.  
Specifically, the benchmark neural network averages the multi-head output and then normalizes it in the last output layer as follows:  
\[h_{i}^{(k+1)}=softmax(\frac{1}{C}\sum_{c=1}^{C}\widetilde{h}_{i}^{(k+1)}) \tag{7}\]  
Finally, the loss function of the benchmark neural network is a cross-entropy error over the training set:  
\[\mathcal{L}=-\sum_{l\in L_{t}}\sum_{r=1}^{q}Y_{lr}\text{lng}(X)_{lr} \tag{8}\]  
where \(g(\centerdot)\) is the benchmark neural network.  
In summary, the benchmark neural network is different in the transition matrix of the message passing from GAT, and it is different in the multi-head operation and the dropout of edges and nodes from GCN.