esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
## 2 ScanSAR Modes of GF-3 Satellite  
As an important technology based on stripmap SAR, ScanSAR has been applied in many spaceborne SAR systems. Figure 1 shows the working mechanism of spaceborne ScanSAR. Unlike the fixed antenna pointing of stripmap SAR, ScanSAR can obtain several times the range swath of stripmap SAR by switching radar beams within a preset angle range. The antenna scans from different subswaths in turn.  
The antenna spends a fixed time transmitting a set of pulses (i.e., a burst) and receiving echo data on each subswath [1]. Then, the radar beam is switched to the next subswath, and the above-mentioned process is repeated until all the subswaths are scanned once. A period of scanning is completed when the antenna returns to the point of the first subswath. The working mechanism of spaceborne ScanSAR shows that there is a time gap between two bursts in the same subswath, which means that the azimuth resolution of the ScanSAR image is sacrificed. Moreover, the wide-range swath is composed of multiple subswaths with independent echo data phases. Therefore, stitching the imaging results of each subswath into a smooth and seamless wide image is an effective approach.  
As the first C-band polarization high-resolution SAR satellite in China, GF-3 carries three advanced ScanSAR modes: NS, wide scanning (WS) and global observation (G). Table 1 shows the information of these modes [26]. The NS, WS and G modes all use the same antenna beam as the standard stripmap (S) mode, which covers an approximately 130 km swath. Its capability of wide swath observations plays a crucial role in sea surface information retrieval and sea monitoring.
## 5 Discussion
The results show that the Longformer model achieves better results than RoBERTa, which could be due to its handling of a larger number of tokens, causing RoBERTa to truncate some notes. However, it is evident that neither of the two fine-tuned models surpassed the results obtained with the word-embedding schemes. This outcome is support by [42], who found that the performance of binary classification for ICD-9 codes exhibited similarity when employing word-embedding schemes compared to transformer-based models. These results might stem from the fact that for particular tasks that do not require extensive context, such as text classification, the complexity of language models might not be necessary. In such cases, word-embeddings can provide more efficient solutions. Future work should be aimed at testing different large language models with a larger dataset in the fitting process, especially in those classes with fewer samples. According with the confusion matrix, the most false positives are present in the other causes (OC) class. The reason for this is that this group incorporates a large number of ICD-10 codes distributed in the different chapters. A similar situation occurs in the WS category, since all the ICD-10s that did not fit into the SMM grouping were aggregated here. Note that WS and OC are classes that comprise a large amount of ICD-10 codes. In contrast, more defined categories such as hypertensive disorders (HD), miscarriage complications (MC), and obstetric sepsis (OS) show a performance of around 80% of true positive rates. Taking in account that the objective is to detect groups with a high risk of SMM, this is a promising result. As shown in Table 5, the results suggest that our premise and model proposed can be used as screening tool for classes such as HD and MC. These classes exhibit the highest TPR. Nevertheless, it is worth noting that only the MC class displays higher values for both TPR and PPV. For instance, the performance of class HD, despite its strong TPR, has a low PPV. Even so, the model was effective at correctly identifying instances with performance above 85% in this class. This means that while it correctly identifies instances of the HD class, it also generates a notable number of false positives for this class, primarily from the without-SMM class (WS). This disparity arises from the substantial class imbalance, where a considerable number of samples belong to WS. As a result, even though the model's performance in these classes is suitable, there is a relatively higher count of false negatives in comparison to true positives. Regardless, in a potential future application of these models as screening tools for generating alerts related to maternal mortality risk, emphasizing TPR remains crucial. The issue of potential false positives can be addressed through a secondary evaluation conducted by a specialist.
# An End-to-End Video Steganography Network Based on a Coding Unit Mask
## 1 Introduction
It features a custom attention-based structure for generating content-adaptive arbitrary data and has two adversaries to critique the video quality for the robustness of the model. The other is DVMark [28], which includes a multiscale structure that can distribute watermarks across multiple spatial-temporal scales, and achieves robustness through a differential distortion layer. However, none of them consider the characteristics of video compression such as coding unit division, transformation, intra prediction, inter prediction, and quantization. In this paper, we introduce an end-to-end video steganography method based on the pyramid-like generation adversary neural network (PyraGAN). We hide messages of randomly generated 0 and 1 bits, and make sure that the container video frames are as close as possible to the cover video frames. To facilitate the visual quality of stego video, a novel solution is proposed. Specifically, a CU (coding units) mask, which is extracted from the CU partition mode of versatile video coding (VVC), is combined with a convolutional block attention mechanism. The experimental results show that the visual quality of the proposed method surpasses the state-of-the-art deep-learning based methods on the premise of ensuring an equivalent payload capacity. Proposed video steganography is different from image steganography, in that the cover looks like an image, but actually, it is an I-frame of a continuous video. Note that when a video of many consecutive frames is compressed by VVC, it is divided into groups of pictures (GOPs). Regardless of whether the GOP is based on IIIPs (intra-prediction encoding for each frame) or IPPP (intra-prediction encoding for the first frame, inter-prediction encoding for other frames), we hide messages in all decoded I-frames and then transmit the entire video. The main contributions of this paper are as follows:  
* We introduce a simple and light-weight end-to-end model architecture combined with GAN to achieve steganography for VVC videos. * While ensuring a relatively large payload capacity, the visual quality is improved based on the CU mask and convolutional block attention mechanism.
# Landslide Susceptibility Modeling Using a Deep Random Neural Network
## 3 Study Area and Data Used
### Landslide Inventory Map  
Landslide inventory records important information about landslides such as location, size, and type. Therefore, preparing a landslide inventory is essential. According to the extensive field surveys made by the Department of Nature Resources of Yunnan Province, 413 landslides are found in the Lushui area (Figure 4a). The data collection time is from 2014 to 2020. The dominant type of landslides is soil landslides, accounting for 90% of the total landslides. Among these landslides, the smallest, largest, and average areas are 80 m\({}^{2}\), 600,000 m\({}^{2}\), and 39,557 m\({}^{2}\), respectively. According to the landslide volume, 53% of landslides are classified as small landslides, followed by medium landslides (41.4%), large landslides (5.3%), and huge landslides (0.3%). These landslides are easily affected by rainfall and excavation activities.  
To conduct landslide modeling, it is suggested to adopt 70% of the total landslides (289 landslides) for training, and the remaining 30% landslides (124 landslides) are used for testing. To select reliable and representative non-landslide locations from landslide-free areas, the K-means clustering method was adopted [27]. The whole study area was converted to grid units with 30 m \(\times\) 30 m, and the non-landslide units were classified into 413 abstract categories through K-means clustering. The units closest to the center of each category were chosen as negative samples (Figure 4b).
# GPR Data Augmentation Methods by Incorporating Domain Knowledge
###### Abstract  
Deep learning has significantly improved the recognition efficiency and accuracy of ground-penetrating radar (GPR) images. A significant number of weight parameters need to be specified, which requires lots of labeled GPR images. However, obtaining the ground-truth subsurface distress labels is challenging as they are invisible. Data augmentation is a predominant method to expand the dataset. The traditional data augmentation methods, such as rotating, scaling, cropping, and flipping, would change the GPR signals' real features and cause the model's poor generalization ability. We proposed three GPR data augmentation methods (gain compensation, station spacing, and radar signal mapping) to overcome these challenges by incorporating domain knowledge. Then, the most state-of-the-art model YOLOv7 was applied to verify the effectiveness of these data augmentation methods. The results showed that the proposed data augmentation methods decrease loss function values when the training epochs grow. The performance of the deep learning model gradually became stable when the original datasets were augmented two times, four times, and eight times, proving that the augmented datasets can increase the robustness of the training model. The proposed data augmentation methods can be used to expand the datasets when the labeled training GPR images are insufficient.  
subsurface distress; GPR; deep learning; data augmentation; domain knowledge +
Footnote †: journal: Applied  
doi:10.3390/app122110896  
Footnote †: journal: Applied
## 4 Results and Analyses
### The Effect of VTLN: Analysis by Age  
Experiment 3 showed that applying VTLN in addition to SP and SpecAug gave the best recognition performance on children's speech for all three languages; however, we also observed differences regarding these improvements based on the age groups of the child speakers for Dutch. We also saw differences for Set C1 and Set C2 for Mandarin, but we cannot disentangle the effects of younger age in Set C2 compared to Set C1 and the use of spontaneous speech in Set C2 compared to read speech in Set C1. Here, we further investigate the relationship between the effect of VTLN and the child's age for Dutch as this dataset has the largest age range and provides speaker age for most speakers, i.e., approximately 95% of the DC speaker group, 64% of the DT speaker group, and 100% of the NnT group. We use these data in our analyses. Given the overall better performance of the VTLN\({}_{\text{MultiV1}}\) model for all languages, we used this model for our analysis. Figure 3 shows the WER by speaker's age (in years) for the model trained with SP and SpecAug and no VTLN (blue); the model trained with SP, SpecAug, and VTLN\({}_{\text{MultiV1}}\) applied during training and testing (orange); and the model SP, SpecAug, and VTLN\({}_{\text{MultiV1}}\) only applied during testing (red), for the different speaker groups (DC, DT, and NnT) for read and HMI speech separately. From Figure 3, the following observations can be made:pronounced for HMI speech. This is potentially explained by the fact that the older a child/person is when learning a non-native language the less likely it is to achieve native proficiency [74], so the older speakers are likely to have stronger accents. The effect of VTLN seems to be highly similar for the different ages, which is similar to the findings for the Dutch teenagers: no particular age particularly benefits more from the application of VTLN.  
In summary, for native Dutch speakers, WERs decrease with age, especially in the younger age groups. Applying VTLN improves performance but is more effective for younger children, which can be explained by their higher-pitched voices, which could benefit more from vocal tract length normalization than the relatively less high-pitched voices of older children and teenagers. For non-native speakers, WERs are consistently higher and do not show a decrease but rather an increase with increasing age, with VTLN exhibiting no impact across ages. These results highlight the interplay of language proficiency and the effectiveness of different approaches at improving diverse children's speech recognition.
###### Abstract
Consequently, to understand the trend of gasoline consumption in the transportation sector, it is important to identify additional factors, other than price, that gas stations consider when determining orders. To analyze and predict the factors that determine gasoline orders at gas stations, monthly data related to the prices of crude oil and gasoline, economy, stocks, climate, environment, and policy from January 2008 to December 2022 were collected. As gasoline orders are collected monthly in accordance with the Domestic Petroleum and Alternative Fuel Business Act [10], there was a limitation in that only 180 months of data samples could be used. Outliers in small amounts of data samples risk degrading the performance of models. Sudden international issues, such as disputes, wars, and infectious diseases, lead to rapid fluctuations in gasoline orders, which can lead to outliers. However, to better understand gasoline orders, considering outliers is essential. A class imbalance occurs because outliers are generally fewer in number than normal data points. Therefore, it is necessary to augment data samples while considering their characteristics to address the class imbalance. By resolving the class imbalance in data samples, models with more stable performance can be developed. We proposed a method to group data samples into clusters using the K-means clustering algorithm [11] and then augment the datasets in the cluster through the Variational Auto-Encoder (VAE) [12]. The augmented datasets were used in our proposed linear regression model to identify the fluctuation factors in gasoline orders at gas stations. The main contributions are summarized as follows:  
* We proposed a gasoline order prediction model for gas stations using a linear regression model to understand the trend of gasoline consumption in South Korea. * We proposed a Variational Auto-Encoder (VAE) and K-means clustering algorithm to address data asymmetry. * We performed data augmentation on our model with the Variational Auto-Encoder (VAE) to implement a model with high accuracy and generalized performance. We grouped the datasets into clusters using the K-means clustering and then augmented each cluster's datasets with VAE to better reflect the characteristics of the data samples for augmentation. * We found significant independent variables that influence gasoline orders using the Variance Inflation Factor (VIF) and \(p\)-value. * We confirmed that linear regression is the most suitable method for the prediction of gasoline orders through modeling with various regression models. The structure of this paper is as follows: Section 2 describes related works. Section 3 describes a gasoline order prediction model using augmented datasets. Section 4 shows the results. Section 5 presents the discussions and conclusions.
# Photoelasticity as a Tool for Stress Analysis of Re-Entrant Auxetic Structures
## 5 Discussion and Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Point** & **FEM** & **EXP** & **Diff.** & **Point** & **FEM** & **EXP** & **Diff.** & **Point** & **FEM** & **EXP** & **Diff.** \\  & **(MPa)** & **(MPa)** & **(\%)** & **Point** & **(MPa)** & **(MPa)** & **(\%)** & **Point** & **(MPa)** & **(MPa)** & **(\%)** \\ \hline
1 & 28.34 & - & - & 11 & 9.52 & 9.65 & 1.3 & 21 & \(-\)1.21 & 1.29 & 1.3 \\
2 & 17.82 & - & - & 12 & 5.24 & 5.35 & 2.0 & 22 & \(-\)2.53 & 2.61 & 2.0 \\
3 & 9.49 & 9.64 & 1.5 & 13 & 0.29 & 0.32 & 9.2 & 23 & \(-\)10.17 & - & - \\
4 & 5.09 & 5.22 & 2.5 & 14 & \(-\)4.23 & 4.29 & 1.4 & 24 & \(-\)8.34 & 8.47 & 1.4 \\
5 & 2.00 & 2.07 & 3.4 & 15 & \(-\)11.40 & - & - & 25 & \(-\)5.20 & 5.34 & 0.7 \\
6 & 0.94 & 0.99 & 5.5 & 16 & \(-\)7.52 & 7.62 & 1.3 & 26 & \(-\)2.94 & 3.02 & 1.3 \\
7 & 29.26 & - & - & 17 & \(-\)5.64 & 5.75 & 1.9 & 27 & \(-\)0.68 & 0.72 & 1.9 \\
8 & 6.50 & 6.62 & 1.7 & 18 & \(-\)2.63 & 2.70 & 2.6 & 28 & \(-\)0.37 & 0.41 & 2.6 \\
9 & 28.88 & - & - & 19 & \(-\)1.72 & 1.79 & 3.8 & 29 & 1.81 & 1.90 & 3.8 \\
10 & 20.76 & - & - & 20 & \(-\)0.04 & 0.05 & 12.3 & 30 & 1.65 & 1.75 & 12.3 \\ \hline \hline \end{tabular} Color scheme corresponds to Figure 7.  
\end{table} 
Table 8: A comparison of the principal stresses from FEM and the experiment for Variant 2—asym metrical loading.
## 3 Data and Methodology  
In order to simulate the future urban LULC of BMR, this study implemented four major processes. The first process was the extraction of LULC data from the satellite images for several years. The second process was that of data input preparation for urban LULC modeling. This was the most important step to prepare the data set for each simulation period. In the third process, an analysis of the urban LULC change was taken into account to identify the major classes that had changed in each specific period. The last process was to model the urban LULC change using IDRISI Selva software packet version 17 developed by Clark Labs, Clark University [36]. The details of these four processes are explained in the following sections, and the overall operation of the methodology is shown in Figure 2.  
Figure 1: Study Area.
## 1 Introduction
The structure and composition of coal tar and its subfractions were analyzed by gas chromatography (GC), GC-MS, Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), inductively coupled plasma (ICP), and nuclear magnetic resonance (NMR) [28; 29; 30; 31; 32; 33; 34; 35; 36]. GC is a common technique for analyzing hydrocarbon mixtures due to its efficient separation ability [28]. However, most heteroatom compounds cannot be eluted from the GC column due to their poor volatility. [PERSON] et al. [29] characterized the molecular characterization of ketones in coal tar and its fractions by GC-MS. [PERSON] et al. [31] studied the differences in the organic groups before and after hydroprocessing by FT-IR. [PERSON] et al. [33] determined the distribution of 21 microelements in different distillate oils by inductively coupled plasma optical emission spectrometer (ICP-OES), and the distribution of elements in the distilled fractions was also investigated. [PERSON] et al. [36] used \({}^{1}\)H NMR and \({}^{13}\)C NMR to analyze the structure of low-temperature coal tar asphaltene and obtained the types and distributions of different hydrogen/carbon atoms. MLCT has a large molecular mass, and the boiling point range is also wide. First, MLCT is separated into several narrow fractions according to the boiling point of the fraction. After that, the composition and structure of MLCT, including the basic properties, hydrocarbon group composition, and structural group composition, are analyzed by advanced analytical means. In this study, true boiling distillation was used to separate MLCT into 11 narrow fractions. The composition and molecular structure of full-range fractions and narrow fractions were analyzed by GC-MS and \({}^{1}\)H NMR. The physicochemical properties and chemical composition of MLCT and narrow fractions were investigated at the macroscopic and molecular levels, respectively. The research results, the chemical composition of medium-low temperature coal tar was investigated in-depth from the molecular level, which will provide theory and data foundation for the utilization of medium-low temperature coal tar, including the technology selection, catalyst design, process development, and optimization. Meanwhile, the molecular level simulation optimization of the production process, the molecular level blending, and optimization of oils and refinement management of processing processes will be realized through the molecular level characterization of medium-low temperature coal tar.
# Role of Forster Resonance Energy Transfer in Graphene-Based Nanomaterials for Sensing
## 3 Application of Graphene-Based Nanomaterials in Sensing
### Glutathione Sensor  
Glutathione (GSH) is one of the best defenses against ROS, and plays ample roles in pathological situations [65]. The improper content of GSH at an intracellular level indicates that the human body contains various diseases, such as Alzheimer's disease, human immunodeficiency virus (HIV), acquired immune deficiency syndrome (AIDS), and diabetes. In addition, tumor cells contain more GSH discrepancy than normal cells, which could be utilized to identify cancer diagnostics. In 2020, [PERSON] et al. [66] developed a fluorescent nanosensor **G9** using the ultra-sonication method of mixing the GQDs-MnO\({}_{2}\) complexation in the presence of potassium permanganate under poly(allylamine hydrochloride) (PAH) via an in situ redox procedure (Figure 11). First, to verify the FRET process, a wide range of spectral overlapping was monitored between GQDs and MnO\({}_{2}\), and the fluorescence lifetime was also decreased after the binding of MnO\({}_{2}\) with GQDs. Upon the addition of GSH into the nanocomposite, the binding between GQDs and GSH decomposed the MnO\({}_{2}\) and enhanced the fluorescence. The LOD of **G9** was calculated to be 48 nM, and GSH detection was applied in bioimaging and human serum samples.  
Figure 10: Schematic illustration of the fabrication of GO-ssRNA QDs (**G8**) and their sensing mechanism. Reprinted from [64] with the permission from the Royal Society of Chemistry.  
[PERSON] et al. [67] reported a fluorescent turn-on assay for antioxidants using GQDs. In the absence of dopamine (DA), GQDs imposed a high fluorescent intensity, which was reduced with the addition of DA and polymerized to form a polydopamine (PDA) film. The PDA settled on the surface of the GQDs (**G10**), which resulted in quenching their fluorescence through the FRET process (Figure 12). **G10** was treated against a series of antioxidants, including GSH, AA, Cys, and HCys, and the fluorescence intensity of **G10** was restored. This process was confirmed through time-resolved fluorescent decay analysis. The LOD of **G10** with all four antioxidants was reported to be 2.4 nM, 1.5 nM, 4.2 nM, and 4.4 nM, respectively. The developed fluorescent assay **G10** was analyzed against rat brains for the detection of GSH, AA, Cys, and HCys antioxidants.  
Figure 11: Schematic diagrams of the preparation of the GQDs-MnO\({}_{2}\) nanocomposite (**G9**) and GSH responsiveness with the fluorescence off–on process. Reprinted from [66] with permission from Elsevier.  
Figure 12: Schematic diagrams for the detection of antioxidants using GQDs@PDA (**G10**). Reprinted from [67] with the permission from Elsevier.