esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
## 5 Conclusions  
This study innovative proposed CA-RegNet model and employed six distinct advanced methodologies (RegNet, Res2 Net, ConvvNeXt, EfficientNet B7, Inception-RegNet, SE-RegNet) to segment diverse wetland types in Macau, using ResNet as a reference benchmark. The segmentation results were evaluated based on overall accuracy (OA), the Kappa coefficient, the mean accuracy (mAcc) and the mean intersection over union (mIoU) [50]. The findings consistently revealed the CA-RegNet model's superior performance relative to other models. Specifically, the CA-RegNet model achieved an OA of 0.9749, a Kappa coefficient of 0.9624, a mAcc of 0.9652 and a mIoU of 0.9517, all of which are commendable metrics. Detailed analysis of the segmented images further supports the assertion that the CA-RegNet model produces the most refined segmentation results. Moving forward, this research seeks to explore the potential applications of other deep learning models for image  
Figure 14: The Segmentation results of each model. (**a**) The original remote sensing image of Macau; (**b**) ResNet model segmentation result; (**c**) Res2 Net model segmentation result; (**d**) RegNet model segmentation result; (**e**) EfficientNet B7 model segmentation result; (**f**) ConvNeXt model segmentation result; (**g**) Inception-RegNet model segmentation result; (**h**) SE-RegNet model segmentation result.  
segmentation and data extraction related to Macau's wetlands, with the goal of enhancing the accuracy of wetland segmentation and classification.  
Conceptualization, C.L.; Methodology, C.L.; Software, C.L. and H.C.; Validation, C.L.; Formal analysis, C.L.; Investigation, C.L.; Resources, C.L. and H.C.; Data curation, C.L. and H.C.; Writing--original draft preparation, C.L.; Writing--review and editing, C.L. and X.T.; Visualization, C.L.; supervision, X.T. and H.C.; Project administration, C.L. and H.C.; Funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Department of Education of Guangdong Province \"Innovation and Strengthening Project\" Scientific Research Project. Grant number 2021 KTSCX176. And The APC was funded by it.  
Not applicable.  
The dataset of this study is Gaofen-2 remote sensing image data, because part of the data is related to the relevant confidentiality and privacy policy, therefore, the dataset of this study is not chosen to be made public.  
The authors thank [PERSON] for her help in the research.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Study Procedure  
A flowchart of the study procedure is shown in Figure 1. Participants were randomly allocated to 3 different mastication conditions: 4 g of raw carrot slices (CS), 4 g of raw carrot grated (CG), or a small piece (2 \(\times\) 2 cm) of parafilm (P) (Amcor, Oshkosh, WI, USA) as a control. The same carrots were used to slice or grate to avoid differences in the sensory characteristics of the two conditions due to differences in the carrots used. As such, on each experimental day, carrot slicing and grating were undertaken using the same batch of carrots. Participants were instructed to have their regular lunch and come to our laboratory 2 h 30 min later (between 16 and 17 h) without eating or drinking anything other than water after lunch.  
Figure 1: Flowchart with a timeline of the study procedure.  
Over three different non-consecutive days, the 15 participants were divided into groups of 5, with each of these 3 groups being submitted to each mastication condition in a random order. Each individual tested only one condition per day, ensuring that ultimately, all participants tested all 3 conditions. This study followed a full cross-over design where each participant experienced all conditions, acting as their own control.  
At each testing session, non-stimulated saliva samples were collected for 3 min before mastication (T0). Immediately after, a piece of carrots or parafilm was provided, and participants were asked to chew for 30 s. Carrot pieces were swallowed, and parafilm was spit out. Then, another 3 min period was dedicated for saliva collection (T1).
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 6 Conclusions  
In this paper, we presented the first continuous GPS data obtained at two stations drilled into rock outcrops within the Southern Patagonian Icefield. These stations operated between 2015 and 2020 yielding a vertical uplift of 36.55 \(\pm\) 2.58 mm a\({}^{-1}\), the highest rate of crustal deformation continuously recorded in Patagonia. This crustal deformation is a response to glacial ice adjustment that has taken place in the region due to strong and fast ice was detected in this work by the comparison of ICESat-2 and SRTM data in the ablation area between 2000 and 2019, resulting in a median annual ice thinning of \(-\)3.36 \(\pm\) 0.01 m a\({}^{-1}\) in the ablation zone, and \(-\)0.94 \(\pm\) 0.01 m a\({}^{-1}\) in the accumulation zone. The main results of this study are similar in magnitude, trend, and signs compared to previously reported uplift rates mainly obtained by episodic surveys carried out in the region. The results of lake seasonal components with respect to GPS stations showed annual variations that may be linked to the hydrological cycle of the region, resulting in correlation coefficients exceeding 62% between GBGR-CS and O'Higgins/Chico lake. The correlations were smaller when comparing combined GPS data with lake Viedma data. This study illustrates the magnitude of the glacier changes taking place in Patagonia using geomatics techniques, and deserves further studies for a more detailed understanding of the complex interplay between climate change, glacier dynamics, the hydrological cycle, and crustal deformation.  
**Author Contributions:** [PERSON] carried out the conceptualization, design, writing--original manuscript preparation, methodology; [PERSON] installed the GPS stations, collected data, writing and editing; M.D.: GPS station design, field data measurements, GPS processing, analysis; P.V.: ICESat data processing; M.C.: processing of seasonal data analysis; E.L.: GPS station design, field data measurements, analysis of the manuscript; M.G.: GPS and seasonal methodology, evaluation, and formal analysis of the manuscript; [PERSON] reviewed the article, and acquired funding. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research has been funded by the Argentina's National Science and Technology Agency (PICT 1995-2013 and PICTO 050-2016). [PERSON] was funded by FONDECYT 1171832 ANIDA, Chile.  
**Data Availability Statement:** The study did not report any data.  
**Acknowledgments:** We appreciate the collaboration in the field of the Andean Geomatics Lab members. We thank the Los Glaciers National Park for permits to explore the study area. We thank the collaboration of those who helped install and operate the GPS stations, including [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] among many others. The collaboration of DGA and CECs is also appreciated.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Comparing End-to-End Machine Learning Methods for Spectra Classification
## 2 Related Work
### Convolutional-Based Approach  
Convolutional neural network (CNN) [21; 22; 23; 27] is one of the most commonly established deep learning models in time series data analysis. It can easily and effectively capture features in multi-scale by different convolution kernels and pooling operations. Many powerful CNN-based models have been proposed and have achieved great success. Among them, the authors of [29] applied a combination of 1D and 2D CNN to multisource multi-temporal satellite imagery for crop classification. It explored the features across spectral and spatial dimensions but did not consider temporal information. Meanwhile, Temporal Convolutional Neural Network (TempCNN) [35] where convolutions are applied in temporal dimension, is also well-explored. In [35], an exhaustive study of one-dimensional TempCNNs for the Classification of Satellite Image Time Series was conducted, their experimental results show that TempCNNs are superior to RNNs in terms of SITS classification. The Conv1D-based classification model designed in [36] further proved the effectiveness of TempCNNs in processing the temporal dimension of time series classification. Residual Networks (ResNets) [37] are another common backbone architecture in computer vision and are also adapted to time series classification [5; 27; 38], the residual connections or shortcut [37] in the model numerically add higher-level features to the forward propagated representation [5] and help to propagate the gradient back through the network, so very deep models can be trained [37]. Based on ResNet, Multiscale architectures [38] can process time series at multiple scales and achieve feature fusion by concentrating each stream. The Fully Convolutional Network (FCN) [39] which can yield a fixed-length feature for classification has also been well-exploited and has been shown to achieve the state-of-the-art performance for end-to-end time series classification [17]. In addition, dilated convolutions are also widely used to improve the feature resolution [40; 41]. In scientific research, the enormous potential of CNN-based methods has also been demonstrated in spectroscopy and diffraction technologies, including bacterial classification from Raman spectra [42], classification of edible oils using low field nuclear magnetic resonance [43], and spectra classification in the field of Neutron and X-ray Diffraction (XRD) [31; 44]. For example, [PERSON] et al. [44] represented their XRD pattern as time series and applied a CNN-based model to achieve the rapid prediction and classification of crystallographic dimensionality and space group.  
Although great progress has been achieved through CNN-based networks, there are still some limitations, namely the inability of leveraging the relationship between features in a global view. To utilize both spatial and temporal features from time series, combinations of convolutional operation and recurrent operation [17; 45] or attention mechanism [46; 47] were explored and comprehensively compared.
## 3 Results and Discussion
### Thermal Analysis  
The TG-DSC curves of samples with different w/c ratios are shown in Figure 7a-d. It can be seen from the picture that there are two main endothermic peaks distributed at 80-120 \({}^{\circ}\)C and 700-800 \({}^{\circ}\)C, respectively, in the DSC curve. According to a previous study, the endothermic peak at 80-120 \({}^{\circ}\)C represents the evaporation of ettringite and bound water in the cementitious material, while the endothermic peak at 700-800 \({}^{\circ}\)C represents the decomposition and endothermic of carbonate. Moreover, there is another endothermic peak in the DSC curve at 900-1000 \({}^{\circ}\)C, but no mass loss is observed in the TG curve. It can be known from the literature that the endothermic peak at 900-1000 \({}^{\circ}\)C is caused by the crystal transformation of C-A-S-H gel [19], and because there is no mass loss, it only exists in the DSC curve. We can see all the thermogravimetric analysis areas showing the mixture in Table 4.  
By comparing Figure 7a-d, it can be found that the endothermic enthalpy at 80-120 \({}^{\circ}\)C decreases at first and then increases with an increase in the w/c ratio of the sample, which may be due to the following reasons:  
(1) According to the data of MIP, it can be known that with an increase in the w/c ratio, the total porosity of macropores (\(>\)50 nm) in the sample increased from 4.71% to 5.32%, which resulted in more free water evaporating from the sample during the preparation process. Meanwhile the adsorbed water in the sample decreases, so the endothermic enthalpy decreases.  
(2) The dissolved calcium hydroxide promoted the synergistic effect of EVA rubber powder and dust ash to produce more sodium hydroxide, resulting in more hydrolysis and hydration of GBFS, and more sulfate ions and calcium ions were adsorbed into the gel, resulting in less ettringite formation.  
However, with the continuous increase in the w/c ratio, the endothermic peak of the sample at 80-120 \({}^{\circ}\)C continues to increase until it is almost the same as that of the YN0.37 sample, except that the YN0.37 sample contains more adsorbed water, which confirms that with an increase in the w/c, the pH value of the pore solution decreases, and more ettringite is formed in sample YN0.4. These ettringite filled into the pores to improve the mechanical properties of the sample.  
In addition, through Figure 7a-d, it can be found that the endothermic enthalpy of 900-1000 \({}^{\circ}\)C in different samples increases at first and then decreases with an increase in the w/c ratio, which also verifies that a proper increase in the w/c ratio can promote the hydrolysis of calcium hydroxide in CSM and participate in the synergistic action of EVA rubber powder and sodium desulfurization ash to form more NaOH, thus promoting the hydrolysis and hydration of ore powder and improving the reaction degree. However, with a further increase in the w/c ratio, the pH value of the pore solution decreases, the hydrolysis and hydration of ore powder decrease, and the amount of ettringite formation increases.
## 2 Material and Methods
### Spectrometers  
The visible spectral acquisition system included a spectrometer QE65000 with a range of 200-1135 nm, a light source model LS-1 with a range of 325-2500 nm, the ocean's Y shaped fiber, and computers, which were manufactured by Ocean Optics, Inc. and purchased in Shanghai, China. Leaf visible spectrum acquisition was completed with the assorted control software: Spectrasuite [31]. Parameters: cumulative time of 30 ms, 15 times of scanning, smoothness of 15.  
NIR spectra were collected by Fourier transform spectrometer TENSOR37, which was manufactured by Bruker, Inc. and purchased in Beijing, China. The instrument parameters were as follows: wavelength range was 835-2500 nm and spectral resolution 8 cm\({}^{-1}\). At the same time, it was equipped with an InGaAs detector, gold-plated integral ball and standard background plate.
## 4 Experiments and Results
### Inter-Class Separability and Intra-Class Consistency Validation Experiments
#### 4.3.1 K-Means Clustering and ISODATA Clustering Test for Inter-Class Separability
If Kappa is positive, a greater kappa indicates a higher classification accuracy; if Kappa is negative, it indicates a particularly poor classification accuracy. The classification evaluation results of the original images, the TCT images and the pseudo-TCT images of each combination of region 2, region 3 and region 4 are shown in Table 14. To show the differences between the images with the best evaluation result and the rest of the images, we selected the second-best, medium and worst pseudo-TCT combination images for the evaluation result, as shown in Figure 5. There were 24 combination images, so only region 2 was selected to be shown in Figure 14 to further demonstrate the differences in the different combination images. \[\text{Precision}= \frac{\text{TP}}{\text{TP}+\text{FP}} \tag{12}\]0 _Cont._  
Although these images do not have the best evaluation result, they all have improved visualization compared to the original images. Vegetation, buildings and roads are more prominent in these images. Because the 0123-combination image had the best clustering effect in K-Menas, so regardless of other combinations, only the 0123-combination image of region 4 and image B were selected for ISODATA clustering. The ground objects in remote sensing images can be roughly divided into seven categories, which are bare soil, buildings, mountains, roads, shadows, vegetation and water bodies, so the classification number of ISODATA clustering is set from 4 to 10. The remaining parameters only need to ensure that the 0123 combination of region 4 is the same as that of image B, the number of iterations is set to five, the change threshold is set to 5%, the minimum number of pixels in each category is set to one and the maximum standard deviation of each category is set to one, and the maximum value of merged classes is set to two. If the main part of a category is buildings and roads, it is selected as the result of clustering. The clustering results are shown in \[\text{Precision}= \frac{\text{TP}}{\text{TP}+\text{FP}} \tag{12}\]1  
The accuracy of ISODATA clustering results was evaluated using the same metrics as K-Means clustering, and the results were shown in \[\text{Precision}= \frac{\text{TP}}{\text{TP}+\text{FP}} \tag{12}\]2
## 4 Renewable Carbon Sources for the Biosynthesis of 3-HP
### Microbial Production of 3-HP from Glycerol  
Glycerol is the main by-product of the production of biodiesel, as the production of 1 ton of biodiesel is accompanied by the generation of 100 kg of glycerol [54]. As the global demand for biodiesel is continuously increasing, it is urgent to find ways to deal with the crude glycerol derived from biodiesel, ideally through its conversion into high-value-added products. Currently, the research on the production of 3-HP from glycerol is mainly focused on the coenzyme A-dependent pathway and coenzyme A-independent pathway. Diverse host strains, including _K. pneumoniae_[27], _E. coli_[31], _L. reuteri_[39], etc., have been developed as microbial cell factories for 3-HP production via the glycerol pathway and achieved high titers. Recently, a strategy was proposed to develop a _Pichia pastoris_ cell factory for 3-HP production from glycerol via the malonyl-CoA pathway.
# Potential Metal Contamination in Foods of Animal Origin--Food Safety Aspects
## 4 Exposure Calculation for Risk Management
### Estimated Daily Intake  
The tolerance of contaminants depends on different factors, such as the concentration of the metal in food consumed, the daily consumption of food, and the body weight of the consumer. EDI was introduced and recommended to assess daily exposure [33; 63; 111; 113].  
To estimate the potential exposure level of contaminants, the following formula is used for the calculation of EDI [153]:  
\[\text{EDI = (Conc \times Cons)/BW} \tag{2}\]  
where Conc is the concentration of the metals (mg/kg) in the examined food; Cons means the daily average consumption of the investigated food (g/day/person); and BW is an average human body weight (kg).  
The estimated EDI value must be compared to the dietary reference doses (RfD) of chemicals. RfD means the estimated daily exposure of a chemical to the human population that is unlikely to induce observable significant risk of adverse, undesirable effects during a lifetime. In the cases of major toxic metals discussed in this review, the dietary RfD is 0.3 \(\upmu\)g/kg/day for As [33; 154], 1 \(\upmu\)g/kg/day for Cd [63; 154], 0.3 \(\upmu\)g/kg/day for Hg [155; 131], and 0.16 (adult) and 0.26 (children) \(\upmu\)g/kg/day for Pb [111; 154].
# Vision, Enabling Technologies, and Scenarios for a 6G-Enabled Internet of Verticals (6G-IoV)
## 3 Future Scenarios for the Internet of Verticals
### The Internet of Robotics
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **Purpose** & **Expected Communication Rate** \\ \hline Robot-environment & Control of and adaption to & \\ Communications & environment /exchange of & Mbps/Gbps \\ Robot-Things & sensory data with & \\ Communications & environment & \\ \hline \multirow{2}{*}{Robot–Human Communications} & Control, & \\  & cooperation/coordination, & \\  & information and context & \\  & sharing, problem solving & \\ \hline Robot–Robot & & \\ Communications & Control, & \\ (voice, video, data, VR/AR, & cooperation/coordination, & \\  & information and context, & \\  & sharing, problem solving & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Future scenarios and communication requirements of robotic networks.
## 2 Modeling the Orbital Dynamics
### Perturbation Solution of Atmospheric Drag  
The secular terms of atmospheric drag are  
\[\left\{\begin{aligned}  a_{\text{sec,drag}}&=-B_{1}a^{2}n \bigg{[}\big{(}I_{0}+2 eI_{1}\big{)}+C\cos 2\omega I_{2}+\
u z_{0}^{2}\bigg{(} \frac{3}{4}I_{0}-I_{1}+\frac{1}{4}I_{2}\bigg{)}\bigg{]},\\ \epsilon_{\text{sec,drag}}&=-B_{1}an\bigg{[}\bigg{(} \frac{e}{2}I_{0}+I_{1}+\frac{e}{2}I_{2}\bigg{)}+\frac{C}{2}\cos 2\omega(I_{1}+I_{3}) \\ &\quad+\
u z_{0}^{2}\bigg{(}-\frac{1}{2}I_{0}+\frac{7}{8}I_{1}- \frac{1}{2}I_{2}+\frac{1}{8}I_{3}\bigg{)}\bigg{]},\\ i_{\text{sec,drag}}&=-\frac{1}{4}B_{2}a\sin i\,(I_ {0}+\cos 2\omega I_{2}),\\ \Omega_{\text{sec,drag}}&=-\frac{1}{4}B_{2}a\sin 2 \omega I_{2},\\ \omega_{\text{sec,drag}}&=-\cos i\Omega_{\text{sec, drag}}-B_{1}an\frac{1}{e}C\sin 2\omega\bigg{[}\frac{e}{4}I_{0}-\frac{1}{2}I_{1}-eI_{2}+ \frac{1}{2}I_{3}+\frac{3}{4}eI_{4}\bigg{]},\\ M_{\text{sec,drag}}&=-(\omega_{\text{sec, drag}}+\cos i\Omega_{\text{sec,drag}})-\frac{3n}{4a}a_{\text{sec,drag}}(t-t_{0}), \end{aligned}\right. \tag{8}\]  
where \(\
u\) is the atmospheric density scale height rate, \(I_{m}(z)\) is the Bessel function of the first type of virtual variables, and variable \(z\) abides by  
\[z=\frac{ae}{H_{p_{0}}}. \tag{9}\]  
Other intermediate quantities are given as follows:  
\[I_{m}(z)=\sum_{k\geqslant 0}\frac{1}{k!(m+k)!}\bigg{(}\frac{z}{2}\bigg{)}^{m+2 k}, \tag{10}\]  
\[B_{1}=\frac{C_{D}S}{m}\rho_{p_{0}}F^{2}\exp\bigg{(}-\frac{1}{H_{p_{0}}}(a-a_{0 }+a_{0}e_{0})-C\cos 2\omega_{0}\bigg{)}, \tag{11}\]  
\[B_{2}=\frac{C_{D}S}{m}\rho_{p_{0}}Fn_{e}\exp\bigg{(}-\frac{1}{H_{p_{0}}}(a-a_{ 0}+a_{0}e_{0})-C\cos 2\omega_{0}\bigg{)}, \tag{12}\]  
\[F=1-\frac{r_{p_{0}}n_{e}}{v_{p_{0}}}\cos i_{0},\;C=\frac{1}{2}\bigg{(}\frac{ \epsilon_{e}}{H_{p_{0}}}r_{p_{0}}\bigg{)}\sin^{2}i_{0}, \tag{13}\]  
where \(S\) is the cross-sectional reference area, \(m\) is the satellite mass, \(C_{D}\) is the drag coefficient, \(n_{e}\) is the Earth spin rate, the elements \((a_{0},e_{0},i_{0},\omega_{0})\) are the initial Keplerian elements, \(\rho_{p_{0}}\) and \(H_{p_{0}}\) are atmospheric density and the density scale height of the initial perigee of the satellite orbit, and \(r_{p_{0}}\) and \(v_{p_{0}}\) are the position and velocity at the same point.