esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Sources  
The data used in this research include meteorological data and NDVI datasets (Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Time Scale** & **Spatial Scale** & **Source of Data** \\ \hline Climate dataset & 1982–2020 & – & Meteorological Information Center of the Inner Mongolia Meteorological Bureau \\ NDVI & 1982–2020 & 5 km & [[http://ldr.nascom.nasa.gov](http://ldr.nascom.nasa.gov)]([http://ldr.nascom.nasa.gov](http://ldr.nascom.nasa.gov)) (accessed on 10 January 2022) \\ \hline \end{tabular}
\end{table}
Table 1: Source of dataset.  
Monthly temperature and precipitation data from 115 meteorological stations in Inner Mongolia from 1982 to 2020 were used in this study and obtained from the Meteorological Information Center of the Inner Mongolia Meteorological Bureau. For each station, we calculated three indicators (SPI, standardized temperature index (STI), and SDHI) that characterize drought, high temperature, and compound hot and dry events. This was performed on a station-by-station basis for the average precipitation and temperature during the 12 months of the year.  
The NDVI data were derived from the AVHRR long-term NDVI daily product dataset developed by the National Aeronautics and Space Administration ([[http://ltdr.nascom.nasa.gov](http://ltdr.nascom.nasa.gov)]([http://ltdr.nascom.nasa.gov](http://ltdr.nascom.nasa.gov)) (accessed on 10 January 2022)) with a time range of 1982-2020 and spatial resolution of 5 km. AVHRR aboard the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting environmental satellite for NDVI calculations [38]. Daily NDVI estimates were derived using daily surface reflectance data from the AVHRR instrument on the NOAA platform, which has two spectral channels (infrared and near-infrared) that are used to calculate daily NDVI values. The datasets used in this study were compiled using the NOAA-16, 18, and 19 sensors. LTDR NDVI products have been used to map vegetation areas and assess drought [39]. NDVI ranges from \(-1\) to 1, where positive values represent areas of vegetation cover and negative values represent water, snow, clouds, and non-vegetated surfaces. These data products are processed and distributed by the MEaSUREs Long-Term Data Record project by the Level-1 and Atmosphere Archive & Distribution System (LAADS) Distributed Active Archive Center (DAAC) at the Goddard Space Flight Center.
# Radar Target Localization with Multipath Exploitation in Dense Clutter Environments
## 2 Model  
In this section, we describe the multipath propagation model generated through the ray-tracing technique and the measurement model used by the proposed algorithm in Section 3.
# LightSeg: Local Spatial Perception Convolution for Real-Time Semantic Segmentation
## 4 Experiments
### Block Ablation Studies  
The encoder based on spatially separable convolution was compared with Regseg's encoder. To ensure fairness, RegSeg's decoder was used for both CNNs in the comparison. The FPS and GFLOPs for each test experiment were tested on an RTX 2080 Ti. For 3 \(\times\) 2048 \(\times\) 1024 input images, the spatially separable convolution-based model showed a reduction in GFLOPs of about 14.14% and an increase in FPS of 52.36%. mIoU was reduced by about 2.56% compared to Regseg (see Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **mIoU \(\uparrow\)** & **FPS \(\uparrow\)** & **GFLOPs (Whole Model) \(\downarrow\)** \\ \hline Our & 76.8 & 61.1 & 16.887 G \\ Regseg & 78.1 & 40.1 & 19.670 G \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of frames per second (FPS) and Gigaflops (GFLOPs) with 3 \(\times\) 2048 \(\times\) 1024 Image Resolution.  
Figure 8: We conducted experiments on the positions of different sizes of LSPBlocks in Stage 3 to investigate the preference of different kernel sizes for network depth. In the experiments, \(k\) represents the length of consecutive LSPBlocks, while \(x\) represents the starting block of using LSPBlock.  
Figure 9: We conducted experiments with three different kernel sizes of LSPBlock, namely 5, 7, and 9. The horizontal axis represents the starting position of five consecutive LSPBlocks in Stage 3, while the vertical axis represents the mIoU metric.
# Molecularly-Imprinted SERS: A Potential Method for Bioanalysis
## 2 Molecularly Imprinted Polymer (MIP)
### Component of MIP
#### 2.2.2 Templates  
A template is a term given to the compound to be molded in MIP. The template is also usually the compound to be analyzed to obtain high specificity in analytical method. However, in its development, dummy templates or a combination of several templates can also be used. Each of its uses has a different purpose to MIP synthesis. The use of dummy templates is associated with template leakage from MIP particles during the sample preparation procedure [8; 29]. In addition, some disadvantages of MIP are diffusion resistance, hard eluting, low binding rate, and deeply embedded template in the internal also predicted to be overcome by using this dummy template strategy [57]. This limitation can affect the results of the analysis. This solution can result in better analyte recognition capability for MIP. Dummy template might be a material with a similar chemical structure to the analyte to be isolated. For example, the use of 2-chlorophenothiazazine in MIP production for phenothiazazines analysis in meat samples. Because they both have a thiolphenylamine ring in their chemical structure, 2-chlorophenothiazazine was chosen [9]. MIP was also created using dummy templates for the detection hordenine analysis in urine  
Figure 2: Functional Monomer.  
samples [4], fluoroquinolones and sulfonamides in pig and poultry samples [8], benzimidazole analysis [29], caffeine analysis in wastewater samples [50], morphine analysis in urine samples [57], polybrominated diphenyl ethers [58], bisphenol A analysis [59], ratopramine analysis [60], and acrylamide analysis [61].  
Multiple templates can be used to create MIPs. It is designed to analyze specific groups of compounds, and this multi-template selection is more concerned with achieving MIP selectivity for a specific class of compounds. The use of multi-templates in the manufacture of MIP includes ibuprofen, naproxen, and diclofenac for the analysis of acidic active pharmaceutical compounds. The results demonstrated that the multi-template MIP has good molecular recognition properties because it can simultaneously extract all three target compounds. In contrast, the single-template MIP can only extract one analyte [62].  
Multiple templates are used in the preparation of MIP to detect nitrosamines in water and beverage samples. The five templates employed are nitrosamine chemical derivatives observed in water and beverage samples. As indicated by strong adsorption capacity values and good selectivity for the five chemicals, the MIP created could evaluate five nonpolar nitrosamine derivatives [30].
## 3 Results
### Metabolomic Profiling and DEGs in Related Pathways  
To explore the differences in metabolite compounds between the control treatment and salt-stress treatment, metabolomic profiling of tissue-cultured _D. officinal_ plantlets after 14 days of treatment with or without 250 mM NaCl was performed using an untargeted metabolomics method. The results of principal component analysis (PCA) showed that PC1 (75.5%) and PC2 (8.17%) effectively separated the control samples from the saline stress samples (Figure S6A). Compared with the control group, the types of metabolites with increased content under salt stress were more than those of metabolites with decreased content (Figure S6B). A comparison between SCMs of the control and salt-stress treatments showed that the following pathways were enriched: phenylalanine metabolism, biosynthesis of flavone and flavanol, isoflavuoid biosynthesis, biosynthesis of amino acids including leucine and isoleucine, biosynthesis of secondary metabolites, and alpha-linolenic acid metabolism (Figure S6C).  
The contents of many compounds increased under salt stress, especially that of flavonoids (Figure 5A). This change trend is consistent with that of the upregulated expression of genes involved in flavonoid biosynthesis under salt stress (Figure 5B). The increasing content of rutin in leaves is likely attributed to the constant and high expression of these genes under salt stress.  
In addition, salt stress induces the accumulation of other secondary metabolites (Table S5). For example, some primary metabolites such as raffinose, mannose 6-phosphate, and trehalose 6-phosphate (T6P) showed accumulation under the salt treatment compared with under the control treatment (Figure 5A). Moreover, ascorbic acid (AsA) was detected under salt stress (Figure 6A), but its content did not differ significantly compared with that under the control treatment (Table S5). However, the expression levels of the genes encoding GDP-d-mannose pyrophosphate (GMP), GDP-d-mannose-3\({}^{\prime}\),5\({}^{\prime}\)-epimerase (GME), and GDP-l-galactose phosphorylase (GGP) significantly increased under salt stress  
Figure 5: Sugar, amino acid, and flavonoid metabolic pathways and related upregulated genes under salt stress. (**A**) Sugar, amino acids, and flavonoid metabolic pathways. Blue text shows the value of log\({}_{2}\)fold change (FC) in metabolites under salt stress (see Table S5 for details). (**B**) Heat map analysis of upregulated gene expression under salt stress.
## 3 Results and Discussion
### CNT-K Hydrophilicity and Surface Composition  
The hydrophilicity or hydrophobicity of MWCNTs is a hot topic in literature and has been discussed from different perspectives [50; 51; 52; 53; 54; 55]. Commonly, the hydrophilicity of the tubes is attributed to surface defects in the curled layers or to the functionalization of the surface. Both effects lead to the presence of surface charges and improve the interaction with water. If these effects are not present (and when the tubes are ideally shaped), they are described as hydrophobic and uncharged. In our case, the manufacturer refers to the CNT-K as a non-functionalized and rather hydrophobic material, but in the contact angle measurements of the CNT-K carried out by our group, an angle of 80\({}^{\circ}\)[54] or even lower was detected (depending on the density and homogeneity of the sample prepared for the Washburn method), which suggests a slight hydrophilicity. Moreover, the twisted configuration of the tubes leads us to expect defects on the surface, reinforcing the supposition of hydrophilicity (see Figure 1). However, detailed knowledge of the interaction of the MWCNT surface and water is lacking in literature. This knowledge is essential for understanding the impact of the properties of the packed material on the adsorption processes of charged molecules in aqueous media. Therefore, we determined the swelling behavior of the CNT-K in water and analyzed the water binding of dry CNT-K particles. The correlation between dry and wet CNT-K particles is \(CNT_{dry}\left[\mathrm{g}\right]=0.07\text{-}CNT_{wet}\left[\mathrm{g}\right]\), which means that 1 g of wet CNT mud (CNT-K suspended in DI-water, centrifuged at 3200\(\times\) g, 3 min, decantation of CNT-free water) contains only 7% of CNT-K; or on the contrary: 1 g of wet CNT-K mud contains 93% water even after centrifugation. Due to this, the dry CNT-K agglomerates swell to 125% of their original volume, leading to an increase in pore sizes, confirming the existence of macro pores in the system [52].  
The high affinity of the CNT-K to water suggests the existence of surface oxygen, which we confirmed in XPS measurements (see Table 1). The XPS results showed that a small amount of surface oxygen is already present in untreated CNT-K powder. Conditioned CNT-K (acidic treatment to remove the catalyst residue, washed afterwards to neutral conditions and stored in DI-water) showed a similar quantity of oxygen on the surface. [PERSON] et al. [56] and [PERSON] et al. [57] reported comparable values to ours and suggested that these confirm the oxidation of the surface. Samples of CNT-K used as an electrode and treated several times in potential-controlled experiments showed an increase of the surface oxygen content (see Figures S7 and S8), due to redox reactions occurring on the surface, or other electrostatic or covalent molecule-surface interactions appearing during the experiments [58; 59]. These effects will be further discussed in the following sections.
## 3 Numerical Simulation Analysis
### Establishment and Verification of a Finite Element Model for Cylindrical Shells
#### 3.1.2 Validation of Finite Element Calculations  
The comparison between the sound pressure levels inside the shell obtained through finite element simulation and the experimental results from the literature is shown in Figure 7. The experimental and simulated results are highly consistent for frequencies below 1000 Hz. However, when the solving frequency exceeds 1000 Hz, discrepancies between the simulation and experimental data emerge. This is primarily attributed to the higher modal density of the structure in the high-frequency range, leading to reduced accuracy in finite element simulations. As a result, the high-frequency results exhibit significant fluctuations, resulting in mismatches between the simulation and experimental data. Moreover, frequencies beyond this range are outside the scope of the study. However, upon comprehensive analysis, the finite element simulation outcomes for frequencies under 1000 Hz demonstrate a considerable level of consistency with experimental data  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Density (kg/m\({}^{3}\))** & **Elastic Modulus (GPa)** & **Poisson’s Ratio** & **Speed of Sound (m/s)** \\ \hline Steel & 7850 & 210 & 0.30 & \(\backslash\) \\ Water & 1000 & \(\backslash\) & \(\backslash\) & 1500 \\ Air & 1.225 & \(\backslash\) & \(\backslash\) & \(343\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural parameters of annular ribbed double-layer cylindrical shells and fluid material parameters.  
Figure 6: Acoustic finite element model.  
obtained from the literature. The sound pressure level curves almost coincide, indicating the reliability of the approach used in the investigation.
## 4 The Algorithms
### Complex Experiments--Experiments Focused on the Interactions between Dynamic Removal and Addition  
The multi-environment exploration space \(S_{real}\) contained two subspaces \(S_{real}=\{s_{\{1,i\}},s_{\{2,j\}}\}\) in this experiment. The subspaces \(s_{\{1,i\}}\) and \(s_{\{2,j\}}\) have entrances \(E_{1}=\{e_{1_{1}}\}\), \(e_{1_{1}}\subseteq s_{\{1,i\}}\), and \(E_{2}=\{e_{2_{1}}\}\), \(e_{2_{1}}\subseteq s_{\{2,j\}}\), respectively. The subspaces are of equal size of \(10\times 8\times 5\) cells. The entrance \(e_{1_{1}}\) is also the entrance to the space \(S_{real}\), \(e_{S_{real}}=e_{1_{1}}\subseteq s_{\{1,i\}}\). The subspace \(s_{\{2,j\}}\) is enveloped by \(s_{\{1,i\}}\). Agents are defined as general entities able to operate in a certain environment and transport other agents from one environment to another. The set of agents \(A_{i}=\{a_{\{i,\{k,1\}}},a_{\{i,k,2\}},...,a_{\{i,k,25\}}\}\) can operate in the subspace \(s_{\{1,i\}}\) and the set \(A_{j}=\{a_{\{j,k,1\}},a_{\{j,k,2\}},...,a_{\{j,k,25\}}\}\) in the subspace \(s_{\{2,j\}}\). Each agent from the set \(A_{i}\) can transport one agent from the set \(A_{j}\) at a time from the \(e_{S_{real}}\) to the \(e_{2_{1}}\) of the subspace \(s_{\{2,j\}}\) (Definition 9 and Section 4.3). The result of this experiment is shown in Figure 15. The subspaces were explored by groups of four agents.  
Another experiment was focused on the investigation of the agents' removal process following the artificial addition and removal of agents in heterogeneous space. The results are illustrated in Figure 16. The experiment was performed in a space that consisted of four subspaces \(S_{real}=\{s_{\{1j\}},s_{\{2,j\}},s_{\{3,j\}},s_{\{4,j\}}\}\) (the space illustrated in Figure 1). The subspaces had entrances \(E_{1}=\{e_{1_{1}}\}\), \(e_{1_{1}}\subseteq s_{\{1,i\}}\), \(E_{2}=\{e_{2_{1}}\}\), \(e_{2_{1}}\subseteq s_{\{2,j\}}\), \(E_{3}=\{e_{3_{1}}\}\), \(e_{3_{1}}\subseteq s_{\{3,j\}}\), and \(E_{4}=\{e_{4_{1}}\}\), \(e_{4_{1}}\subseteq s_{\{4,j\}}\). The sizes of the individual subspaces varied. The subspaces \(s_{\{1,i\}}\), \(s_{\{2,j\}}\), and \(s_{\{3,j\}}\), and \(s_{\{
# MGKGR: Multimodal Semantic Fusion for Geographic Knowledge Graph Representation
## 5 Discussion
### The Impact of Diversity in Relation Semantics  
This section discusses the impact of rich-semantic adjacency relations and sparse semantic adjacency relations on the experimental results. Rich-semantic adjacency is represented by concatenating the categories of the head and tail entities with the adjacency relation, forming the pattern \"_EntityCategory-adj-EntityCategory_\". Sparse-semantic adjacency is simply represented by \"_adj_\". In the experiments above, rich-semantic adjacency relations are used for training, which provide rich information and enable the model to capture the latent relationships between entity categories and spatial topology. However,  
Figure 3: Model performance on attribute relations, adjacency relations, and mixed relations.  
while rich-semantic adjacency relations enriched the information available to the model, it also increased the number of relations, thereby raising the complexity of model training. On the other hand, sparse-semantic adjacency triples contain only adjacency information, providing solely spatial topological relations between geographic entities. We selected several models for this comparative experiment, including the translation-based TransE, the bilinear model DistMult, the neural network-based RGCN, and the multimodal models MKGFormer and MGKGR. The experimental results are shown in the Table 4.  
It can be observed that for the DistMult model, sparse-semantic adjacency leads to better prediction results. This is because DistMult excels at learning symmetric relationships and transforming rich-semantic adjacency, converting a large number of asymmetric relationships into symmetric ones, resulting in improved performance. For other models, the experimental results on rich-semantic adjacency relationships which contain more detailed information are superior. This indicates that combining rich semantic information with adjacency relationships can effectively enhance the model's performance in representing and understanding knowledge graphs.
# Flowering Stage and Daytime Affect Scent Emission of _Malus ioeensis_ \"Prairie Rose\"
###### Abstract  
Flowering crabapple is an important ornamental flower. It is vital to understand the floral scent properties and the associated release dynamics for carrying out fragrant flower breeding or floral regulation of crabapple. Static headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry was used to detect the volatile compounds in _Malus ioeensis_ \"Prairie Rose\" flowers at different flowering stages and at different day-night time. The results showed that methylheptenone, phenylethanol, geranylacetone, 2-(4-methoxyphenyl)lethal, \(\alpha\)-cedrene were the major compounds in _M. ioensis_ \"Prairie Rose\", but the compounds released during different stages and different day-night time were significantly different (\(P<0.0001\)). A total of 25 volatile compounds were identified from the four flowering stages. The floral scents in the initial and flowering stages were the most similar (dissimilarity 0.21). The main compounds in these two stages were geranylacetone and methylheptenone, and the contents of geranylacetone and phenylethanol were positively correlated with the flowering stages. From the bud stage to the end of flowering, the total amount of volatile compounds released showed an initial increase followed by a decrease and the amounts of compounds released during the initial flowering stage were the highest. The aliphatic and benzenoids content was significant higher in the daytime than at night. A total of 15 compounds were detected in the five time periods. Methylheptenone and phenylethanol were particularly released in the 10:00-12:00 and 15:00-17:00 time periods. There were only three common compounds among the five time periods and the types of flower volatiles released during the daytime were obviously higher than those released at night. From the nocturnal to diurnal, the amount of flower volatiles released first increased, then decreased, and the release reached a peak between 10 am and 12 noon, which was consistent with the pollination biological characteristics of _Malus_ flowers. Our findings are important for understanding the mechanism of insect visits to crabapple and the regulation of crabapple flower scent.  
crabapple; flower fragrance; emission rule; SPME-GC/MS +
Footnote †: journal: molecules
## 3 Data and Methodology
### Preparation of Training and Validation Datasets  
Landslide spatial prediction can be treated as a binary classification problem, where landslides and non-landslides represent positive cases and negative cases, respectively [53]. It is usually recommended that an equal number of negative and positive cases should be prepared [54]. To select representative negative cases, K-means clustering was used to determine non-landslides from extensive landslide-free areas. Accordingly, the information of all non-landslide grid units was imported to SPSS 22 software, and those grid units were classified into 388 categories by using the K-means clustering function. The nearest unit of each category to the clustering center was taken as a negative case because the distance of clustering represents the extent of approximation to a category [55]. The calculated 388 non-landslide locations are shown in Figure 4a. Then, landslides and non-landslides were randomly divided into two parts with a same configuration (70% for training and 30% for validation) (Figure 4b).  
Figure 4: (**a**) Locations of the selected non-landslide; (**b**) Distributions of the training dataset and validation dataset. DEM: digital elevation model.  
Figure 3: Landslide conditioning factors (**a**) Elevation, (**b**) Slope angle, (**c**) Slope aspect, (**d**) Plan curvature, (**e**) Profile curvature, (**f**) ERG, (**g**) Land use, (**h**) Distance to roads, (**i**) Distance to rivers, (**j**) Distance to faults, (**k**) Annual rainfall, (**l**) Normalized difference vegetation index (NDVI).
# Mining Pool Selection under Block WithHolding Attack +
## 3 Evolutionary Game for Mining Pool Selection
### Expected Payoff  
We first derive the expected payoff of a miner in pool \(i\), which is the revenue of the miner minus the cost for mining. The revenue can be given by the product of the miner's hash power and the revenue density of pool \(i\) (i.e., the revenue obtained per unit hash power). The cost comes from the power consumption (e.g., electricity fee) for mining. We use \(p\) to denote the power charge required for unit hash power and use \(r_{i}\) to denote the revenue density of pool \(i\), which is a function of \(\mathbf{x}\), \(\mathbf{\omega}\), \(s_{i}\) and \(\mathbf{a}\) as will be shown in Section 3.2.1. The expected payoff of miners in pool \(i\) can be expressed as  
\[y_{i}(\mathbf{x},\mathbf{\omega},s_{i},\mathbf{a})=(r_{i}(\mathbf{x},\mathbf{\omega},s_{i},\mathbf{a}) -p)\omega_{i}. \tag{1}\]