esa-sceva/satcom-chunk-collection huggingface dataset:
# Patterns of Arctic Tundra Greenness Based on Spatially Downscaled Solar-Induced Fluorescence
## 5 Conclusions  
In this research, the spatiotemporal patterns of Arctic tundra greenness (2007-2013) were assessed based on SIF data along with two types of NDVI data. In contrast to previously published literature, spatially downscaled SIF was employed first as an indicator of Arctic tundra greenness. It was found that SIF had the potential to exhibit more effectiveness as an indicator of Arctic tundra greenness.  
Specifically, the spatial distribution pattern of Arctic tundra greening and browning was mapped and analyzed. Using the GIMMS NDVI3g and MOD13Q1 indices, the tundra was found to be greening on the North Slope of Alaska and in most parts of western Siberia, and browning in the higher-Arctic Canadian Archipelago, western Alaska (Yukon-Kuskokwim Delta), and eastern Siberia (high latitude area of the Kolyma Lowland and Yana-Indigirka Lowland). However, the Arctic tundra was browning for most parts of the study area below 75\({}^{\circ}\) N based on the SIF data, except for the small regions at the northern edge of the SIF data. Meanwhile, the temporal pattern of tundra in Northern America, Eurasia, and the whole of the Arctic was also interpreted from 2007 to 2013. The greenness value of the Eurasian Arctic tundra was higher than that of Northern America and the entire Arctic tundra for the three greenness indices. From 2010, the Arctic tundra was greening based on MOD13Q1, whereas was browning using the GIMMS NDVI3g. Based on the SIF data, there was a general decreasing trend of Arctic tundra greenness between 2007 and 2013.  
Conceptualization, D.F. and F.S.; methodology, D.F.; software, D.F.; validation, D.F., F.S. and Y.S.; formal analysis, D.F.; investigation, J.W.; resources, D.F.; data curation, D.F.; writing--original draft preparation, D.F. and Y.S.; writing--review and editing, D.F., F.S. and J.W.; visualization, D.F.; supervision, F.S. and J.W.; project administration, D.F.; funding acquisition, D.F.  
This study was supported by National Key R&D Program (No. 2016 YFC1402701). Research grants (41501473) were funded by the National Natural Science Foundation of China, the Youth Science Funds of State Key Laboratory of Resources and Environmental Information System (O8R8A080 YA), and the Chinese Academy of Sciences. Research grants (Y6V60206 YZ) were funded by the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences.  
We would like to thank [PERSON] for providing SIF data (2007-2013).  
The authors declare no conflict of interest.
## 8 Clinical Implications of PD and OSA Co-Management
### Periodontal Therapy's Impact on Systemic Health in OSA Patients  
Emerging research highlights the potential role of periodontal therapy in reducing systemic inflammation in OSA patients, potentially improving cardiovascular and metabolic health--key areas affected by OSA. Managing periodontal inflammation may not only enhance oral health but also alleviate broader systemic consequences associated with chronic OSA-related inflammation. [PERSON] et al. analyzed the risk for OSA among periodontal patients and those who underwent gingivectomy or periodontal flap surgery. The study demonstrated a reduction in the risk for OSA presence following periodontal therapy (OR = 1.36, 95% CI, 1.18-1.56; \(p\) = 0.002) [33]. This improvement may be linked to a reduction in the systemic inflammatory burden associated with chronic periodontitis, suggesting that periodontal treatment could play a role in mitigating OSA symptoms.  
Despite these promising findings, current evidence is inconclusive. The causal relationship between periodontal therapy and respiratory health outcomes, including OSA, remains insufficiently established. Existing studies have yet to confirm whether periodontal treatment can prevent or slow the progression of respiratory diseases in individuals with periodontitis. Reviews by [PERSON] et al. (2001), [PERSON] et al. (2006), and [PERSON] et al. (2012) emphasize significant gaps in the literature regarding the direct impact of periodontal therapy on respiratory health, underscoring the urgent need for further high-quality, longitudinal research [83; 84; 85].
## 4 Evaluation Methods  
The evaluation of MT models is extremely important as it measures the degree of reliability of the output from the MT model, and it also informs us when a model requires improvement [27]. Over the years, many evaluation methods have emerged, many of which fall into two categories: human evaluation and automatic evaluation. Automatic evaluation metrics work by comparing the output of an MT system to a set of references generated by humans, also called the gold-standard references [28], and then making use of mathematical and statistical calculations to compute how different the machine-translated output is to the reference translation [28]. The quality of the translation is considered better if the difference between the output and reference is smaller. Automatic metrics use n-grams to calculate the precision scores, where an n-gram is a sequence of n words [28]. This section goes over some commonly used automatic metrics that are used to evaluate MT.
###### Abstract  
To better understand and evaluate the GNSS positioning performance, it is convenient to adopt corresponding measures to reduce the impact of errors on positioning. A GNSS positioning error estimation scheme based on an improved multi-layer perceptron model is proposed. The multi-head attention mechanism and gating operation are integrated into the multi-layer perceptron model to adaptively select and filter features, enhancing the model's ability to understand input features. First, the original positioning error of the satellite is obtained through the Kalman filter positioning method. The data are then preprocessed to extract available features. Finally, the features are input into the constructed model for training and testing to obtain the estimated positioning error value. Two types of comparative experiments were completed. The performance of the presented model is evaluated by the root mean square error. Experimental results show that the proposed method performs well in terms of performance indicators, and has obvious advantages over other state-of-the-art methods. In particular, the root mean square error of the presented method in the first dataset is 0.239 m, which is 39.2% and 17% lower than the current state-of-the-art long short-term memory network and convolutional neural network, respectively. The presented method can provide higher-precision estimated values for studying the GNSS positioning error estimation problem.  
g 2025 17 301
## 3 Results & Discussion
### Analysis of Anode
On the right, a plot of peaks corresponding to binding energies from 500–0 eV. Labels for each relevant peak are included. Figure 6: S/N ratios for each relevant peak in Figure 5. For elements correlated to multiple peaks, the peak with the greatest height is included. For each position, the top bar is composed of elements found in the impregnate (Ca, Al, Ba, and Sc) and the bottom bar comprises elements found in the anode substrate (Mg and W). holes in the (off-white) ceramic spacer, and the cathodes themselves are visible through holes in this housing (green ring in \"zoom-in\", -2 mm in diameter). The copper wiring is _not_ a part of the original cathode assembly, and was added to secure the remaining components after disassembly. Figure 7b-f shows plan-view SEM and EDS results from one of the ring regions of the anode directly opposite the cathodes' emitting surface (Figure 2). Each image is comprised of multiple scans combined to produce a single large area image, allowing for examination of the entire deposition region (red ring in Figure 7b, ~6 mm in diameter). SEM and EDS reveal a radial pattern of deposited material consistent with an emission source located under an anode pinhole (diameter ~0.5 mm), and therefore being consistent with the position of the cathode. EDS shows that the deposited material contains Ba, O, and Ca. Large area scans indicate that, beyond the primary deposition area centered on the pinhole (green ring in panel Figure 7b), the deposited layer is thinner than the 1-2 micron sampling depth for EDS. Little or no deposition is observed beyond ~3 mm from the pinhole. This outer deposition region is consistent with features of the cathode housing, as highlighted by equivalent red ring in Figure 7a,b. The thickness of deposited material increases closer to the primary deposition area (green ring), eventually becoming thick enough to obstruct Mo signal. This area corresponds directly to the size, shape, and position of the cathode itself, as highlighted by equivalent ~2 mm diameter green ring in Figure 7a,b. Portions of the primary deposition area are bare of deposit, as highlighted in Figure 7b-f. Ba, Ca, and Mo scans show that the Mo substrate is exposed in this area, consistent with close examination of SEM images, which show surface scratches on the Mo anode disappearing under the deposited layer, then reappearing. This is hypothesized to be evidence that deposited material flaked off the anode, either during disassembly of the test vehicle or, possibly, during activation or operation.
## 3 Results and Discussion
### Volatiles  
#### 3.3.1 Major Volatiles  
The major volatile by-products detected are presented in Table 4. Isoamyl alcohol, 1-propanol, and isobutanol concentrations were significantly (\(p<0.05\)) affected by both the fermentation temperature and the nature of kefir cells (free or immobilized). Ethyl acetate, acetaldehyde, and amyl alcohol concentrations were significantly (\(p<0.05\)) affected only by the fermentation temperature, while 1-hexanol content was significantly (\(p<0.05\)) affected by the nature of the cells, and strong interactions (\(p<0.05\)) were detected between the factors. On the contrary, the methanol content was not affected by any factor.  
Acetaldehyde, the major wine aldehyde, was detected in significantly higher (\(p<0.05\)) levels (up to 97 mg/L) in fermentations performed at 30 \({}^{\circ}\)C with thermally-dried free and immobilized kefir cells on apple pieces and DCM compared to lower temperatures. However, it never raised above the limits considered to be acceptable in wines (100-125 mg/L) [57]. On the contrary, low acetaldehyde concentrations (with no significant changes between different fermentation temperatures) were detected in fermentations performed with thermally-dried immobilized cells on grape skins, thus contributing pleasantly to the wine's aromatic complexity [54].  
Increased ethyl acetate levels were detected in fermentations performed at 20 \({}^{\circ}\)C compared to other temperatures, when thermally-dried immobilized cells were used. However, they never exceeded \(\sim\)50 mg/L, adding pleasant notes [54], considering that concentrations up to 150 mg/L have a positive influence by contributing predominant fruity notes [58].  
Higher alcohols like amyl alcohol, isoamyl alcohol, isobutanol, and 1-propanol are known to contribute to the product's odor complexity by adding fruity characters, when found in low levels [59; 60]. Higher concentrations were detected when thermally-dried immobilized cells were used compared to free cells, although not significantly in all cases. Higher alcohols' formation is known to be greatly affected by high fermentation temperatures [54]. Significantly lower values were detected in fermentations performed at 5 \({}^{\circ}\)C in most cases, except for fermentations with thermally-dried immobilized cells on grape skins, where no concentration fluctuations were detected in general. On the other hand, 1-hexanol and methanol, were detected in very low levels (\(<\)50 mg/L) in all samples.
## 3 Experimental Study in Short Delay Regime
### Locking Condition  
Stable single-mode lasing is a prerequisite to linewidth narrowing, so the locking condition of coupled DFB lasers is studied first. A series of dynamic behaviors emerge from the devices, including mutual injection locking, four wave mixing, periodic oscillation and chaos. The optical spectrum and intensity noise spectrum of these states are plotted in Figure 7a respectively. When the detuning between the sub-DFB lasers are relatively large, four wave mixing occurs. Increasing the coupling strength often results in chaos. Reducing the detuning towards zero will lead to periodic oscillation or mutual injection locking state.  
It was found that the key parameter governing the laser behavior near zero detuning is the coupling phase. The lasing spectrum was recorded under different coupling currents to observe the evolution route. The corresponding map is plotted in the left part of Figure 7b. As the injection current in the coupling region increases from 59 to 82 mA, the laser spectrum evolves from single mode to multiple peaks spaced by relaxation oscillation frequency, corresponding to periodic oscillation. Increasing the coupling current further, the stable state occurs periodically with about a 12 mA interval. This phenomenon is also simulated in the right part of Figure 7b based on Equation (1). In the simulation, the variation of laser frequency is recorded as the coupling phase is swept from 0 to 2\(\pi\). Stable lasing of both sub-DFB lasers with the same frequency occurs for the coupling phase close to integer number of \(\pi\). Since the output power is only augmented by 0.7 dB as the coupling current varies, it is concluded that the coupling current is mainly responsible for phase adjustment and the 12 mA period recorded in our experiment may correspond to one \(\pi\)-shift of the coupling phase. Details for achieving mutual injection locking state is described in Appendix A.
###### Abstract  
Global navigation satellite system (GNSS)-reflectometry is a type of remote sensing technology and can be applied to soil moisture retrieval. Until now, various GNSS-R soil moisture retrieval methods have been reported. However, there still exist some problems due to the complexity of modeling and retrieval process, as well as the extreme uncertainty of the experimental environment and equipment. To investigate the behavior of bistatic GNSS-R soil moisture retrieval process, two ground-truth measurements with different soil conditions were carried out and the performance of the input variables was analyzed from the mathematical statistical aspect. Moreover, the feature of XGBoost method was utilized as well. As a recently developed ensemble machine learning method, the XGBoost method just emerged for the classification of remote sensing and geographic data, to investigate the characterization of the input variables in the GNSS-R soil moisture retrieval. It showed a good correlation with the statistical analysis of ground-truth measurements. The variable contributions for the input data can also be seen and evaluated. The study of the paper provides some experimental insights into the behavior of the GNSS-R soil moisture retrieval. It is worthwhile before establishing models and can also help with understanding the underlying GNSS-R phenomena and interpreting data.  
Keywords:global navigation satellite system (GNSS)-reflectometry; soil moisture retrieval; signal-to-noise ratio (SNR); XGBoost +
Footnote †: journal:
# Structure-Guided Image Inpainting Based on Multi-Scale Attention Pyramid Network
## 4 Experimental Results and Datasets
### Quantitative Comparison  
To thoroughly assess the effectiveness of the proposed image inpainting framework, this study utilizes two commonly employed metrics in the field of image inpainting: pixel similarity and structural similarity index measure (SSIM). These metrics are pivotal for evaluating the accuracy with which the inpainted images replicate the pixel-level details and maintain structural integrity compared to the original images. The mean absolute error (MAE) is a widely utilized metric in the field of image restoration and repair, offering a straightforward way to evaluate the performance of models that aim to reconstruct or inpaint damaged, corrupted, or missing parts of an image. MAE calculates the average of the absolute differences between the predicted pixel values of the restored image and the corresponding pixel values of the ground truth (original, undamaged) image. In addition, the Frechet inception distance (FID) is incorporated as an essential supplementary metric to gauge the visual perceptual quality of the restored images. FID is particularly useful in capturing the holistic image quality and the fidelity of textures and colors as perceived by the human visual system. The experimental setup includes rigorous comparative tests against several cutting-edge methods in the field, specifically FFTI [44], SWT [45], FMGDN [46], and MFMAM [3]. These methods represent a diverse set of approaches, ranging from texture-focused techniques to those prioritizing structural integrity in image inpainting. The results of these comparisons are meticulously documented in Tables 1 and 2, where the superior performance of the proposed method is clearly delineated across all the metrics used. The analysis includes tests on two well-known datasets: Paris Street View and CelebA. The Paris Street View dataset, known for its urban landscapes and architectural elements, tests the method's ability to handle images with straight lines and geometric structures. The CelebA dataset, on the other hand, evaluates how well the method restores human facial features, which require precise treatment of organic shapes and skin textures. The study's findings underscore that the proposed method not only achieves higher pixel and structural similarity scores but also significantly improves the visual perceptual quality of the restored images, as evidenced by its lower FID scores compared to the benchmarks. This enhancement in performance can be attributed to the innovative integration of multi-scale attention mechanisms and the use of structure-guided strategies, which together facilitate a more nuanced and context-aware restoration process.
###### Abstract  
Based on the observations of Ionospheric Bubble Index (IBI) data from the Swarm mission, the characteristics of plasma bubbles are investigated during different types of geomagnetic storms recorded from 2014 to 2020. The geometrical constellation of the Swarm mission enabled us to investigate the altitudinal profile of the IBIs during different activity levels in a statistical mean. Results show that the majority of IBIs associated with moderate storms are observed at low altitudes and the probability of observing IBIs at high altitudes (Swarm-B) increases with the increase in storm level. This is confirmed by observing the F2 layer peak height (hmF2) during super storm events at larger altitudes using COSMIC data. The maximum number of IBIs is recorded within the South Atlantic Anomaly (SAA) region with a long duration time and tends to increase only during dusk time. Both the large duration time and number of IBIs over the South Atlantic Anomaly (SAA) suggest that the gradient in the electron density and the depression in the magnetic field are the main factors controlling IBI events. Also, the IBIs at high altitudes are larger at sunset and at low altitudes pre-midnight. In addition, the occurrence of IBIs is always larger in the northern hemisphere than in the southern hemisphere irrespective of the type of storm, as well as during the summer months. Moreover, there is no correlation between the duration time of IBIs and both the altitudinal observation of the IBIs and the storm type. Seasonal occurrence of IBIs is larger during equinoxes and vice versa during solstices irrespective of both the type of storm and the altitude of the satellite. The large number of IBIs during equinoxes agrees with the previous studies, which expect that the large electron density is a developer of steeper \(\
abla_{n}\). Large occurrences of super storm IBIs observed within the pre-midnight during summer and at sunset during equinoxes are a novel observation that needs further investigation. Also, the majority of IBIs are observed a few hours after geomagnetic substorms, which reflects the role of the Disturbance Dynamo Electric Field (DDEF) as a main driver of IBIs.  
plasma bubble; ionospheric irregularity; geomagnetic storm; Swarm mission +
Footnote †: journal: _Journal of Physics A: Mathematical and Theoretical Physics_  
1
Footnote 1: institutetext: Physics Department, Faculty of Science, Fayoum University, Fayoum 63514, Egypt; [EMAIL_ADDRESS] (F.H.); [EMAIL_ADDRESS] (A.F)  
2
Footnote 2: institutetext: Physics Department, Faculty of Science, Fayoum University, Fayoum 63514, Egypt; [EMAIL_ADDRESS] (F.H.); [EMAIL_ADDRESS] (A.F)  
3
Footnote 3: institutetext: Academy of Scientific Research and Technology, Cairo 11516, Egypt  
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Footnote 4: institutetext: Correspondence: [EMAIL_ADDRESS]
# An Efficient Finite-Difference Stencil with High-Order Temporal Accuracy for Scalar Wave Modeling
## 2 Method
### A New Efficient FD Stencil with High-Order Spatial and Temporal Accuracies  
To further improve the computational efficiency, we simplify the cross-rhombus stencil to a new FD stencil with a radial shape. As shown in Figure 1, the new stencil involves grid nodes only with \((\pi n/4)\) degrees, where \(n\) is an arbitrary integer. Compared with the standard cross-rhombus stencil, the new stencil has less grid nodes, especially in the high-order cases. We can prove that the stencil still has a higher-order temporal approximation, and the details of the proof are shown in Appendix A.  
According to the definition of the new stencil (Figure 1), we obtained the new FD scheme:  
\[\begin{split}& a_{0}p_{0,0}^{0}+\underbrace{\sum_{m=1}^{M}a_{m} \Big{(}p_{-m,0}^{0}+p_{m,0}^{0}+p_{0,-m}^{0}+p_{0,m}^{0}\Big{)}}_{cross\ stencil}\\ &+\underbrace{\sum_{n=1}^{N-1}b_{n}\Big{(}p_{-n,-n}^{0}+p_{n,n}^ {0}+p_{n,-n}^{0}+p_{n,-n}^{0}\Big{)}}_{rotated\ cross\ stencil\ with\ a\ (\pi/4)-degree}\\ &\approx r^{-2}\Big{(}p^{1}-2p^{0}+p^{-1}\Big{)}.\end{split} \tag{4}\]  
Figure 1: Schematic diagram of the new stencil, where the new radiation stencil is composed of a standard cross-stencil and a rotated cross-stencil of \((\pi/4)\) degrees.  
The new radiation stencil is a combination of a standard cross-stencil and a rotated cross-stencil with \((\pi/4)\) degrees (Figure 1). Let \(a_{m}\) represent the FD coefficients of the standard cross-stencil and \(b_{n}\) represent the FD coefficients of the rotated cross-stencil. In the following parts, we present two methods to obtain the FD coefficients of the proposed stencil and analyze the approximate accuracy theoretically.