esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Functional Enrichment Analysis  
Identified DEGs were subjected to functional enrichment analysis using PANTHER (protein analysis through evolutionary relationship) on the basis of biological process, cellular component, molecular function, protein class and pathway enrichment. In comparison-I, the DEGs were enriched in 8 (transcription regulator activity, molecular transducer activity, binding, structural molecule activity, molecular function regulator, catalytic activity, translation regulator activity and transporter activity) different molecular functions, 21 different biological processes, 14 different cellular components, 21 diverse protein classes and 137 various pathways (Tables S1a-S5a). Similarly, in comparison-II the DEGs were enriched in 7 (transcription regulator activity, molecular transducer activity, binding, structural molecule activity, molecular function regulator, catalytic activity and transporter activity) different molecular functions, 20 different biological processes, 14 different cellular components, 19 different protein classes and 85 different pathways (Tables S1b-S5b).
# Making Shopping Easy for People with Visual Impairment Using Mobile Assistive Technologies
## 3 MAT Solutions for PVI Shopping
### Navigation and Product Identification
#### 3.2.3 Hybrid Systems  
In the previous two sections, several solutions to assist PVI in navigating and identifying products have been discussed. These solutions use tags such as RFID and NFC, visual tags such as QR codes and AR Markers, or CV techniques. However, these solutions are not suitable under all situations, because each environment has specific features. For example, CV techniques cannot be used in areas with considerable light, because the quality of the taken image will be poor. In this case, it is better to use a different technology, like RFID or NFC, to improve system accuracy. Another example is when, in a shop, items, which all have needed information, already have barcodes or QR codes. Developers can use these tags for product identification and use CV techniques or non-visual tags (RFID, NFC) for navigation. Two or more such technologies can be combined, which would lead to the development of hybrid systems.  
For example, [PERSON] and co-authors proposed a system that integrates CV techniques with RFID Systems. This system identifies information about relevant objects in the surroundings and sends them to PVI [56]. [PERSON] and co-authors integrate RFID with QR codes, to allow PVI to navigate inside a grocery store [57; 88]. The system used the RFID reader to identify the RFID tags to navigate inside the store. It adopted the smartphone camera to identify QR codes placed on product shelves. Finally, [PERSON] and co-authors developed a solution to help PVI identify objects and navigate in indoor locations using RFID and CV technologies [58]. This system used the RFID reader to receive the current location of PVI and CV techniques to identify objects.
# NAGNE: Node-to-Attribute Generation Network Embedding for Heterogeneous Network
## 5 Experiment
### Experimental Setup
#### 5.1.2 Baselines  
* DeepWalk [1]: A homogeneous network embedding model that integrates random walks and the skip-gram to learn network embeddings.
* Metapath2 Vec [7]: A heterogeneous network embedding method adopting meta-path-based random walks and a heterogeneous skip-gram model.
* HIN2 Vec [32]: A neural network-based approach that learns embeddings through multiple predictive training tasks.
* MAGNN [33]: This model uses node content transformation along with intra- and inter-metapath aggregations to generate node embeddings.
* HAN [8]: This approach utilizes neural networks for heterogeneous networks, generating embeddings by aggregating features from meta-path-based neighbors hierarchically.
* GTN [34]: GTN transforms heterogeneous networks into new graphs using meta-paths and applies convolution to these meta-path-derived graphs to learn node embeddings.
* HGNN-AC [19]: This model transforms a heterogeneous graph into multiple meta-path-defined graphs, learning node representations through convolution and completing attributes for nodes lacking them via weighted aggregation.
* HGSL [20]: HGSL concurrently learns the structure of a heterogeneous graph and the parameters of GNNs for classification tasks.
* Megnn [24]: Megnn merges different bipartite sub-graphs tied to edge types into a novel trainable graph structure using heterogeneous convolution.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 2 Related Work
### VNE Methods  
Since VNE problem has been proven to be NP-hard by being reduced to multiway separator problem [16], its solutions can be grouped into three categories: the exact solutions, meta-heuristic solutions and heuristic solutions. [PERSON] et al. [17] proposed and evaluated an exact algorithm for the VNE problem using integer linear programming (ILP). They used the branch and bound method to embed virtual network requests to multiple physical networks. The results showed that the proposed exact algorithm was effective for solving small-scale problem instances. The VNE problem considered in [18] was formalized as an ILP model. Due to the complexity of ILP, the authors proposed a discrete particle swarm optimization (DPSO) method to solve the VNE problem. Simulation results showed that the DPSO method could get a better convergence performance than existing PSO methods.  
Some heuristic algorithms used network resource attributes or topology attributes to rank nodes in the node mapping stage in VNE. [PERSON] et al. [19] employed the product of the node CPU capacity and its adjacent link bandwidth for node ranking. This method was used in many other research works. However, it only considered the local resource attributes of the node. [PERSON] et al. [20] introduced topology attributes for the first time to embed virtual networks. Referring to the Google PageRank algorithm, they used Markov Random Walk method to rank nodes and performed node mapping in a greedy manner according to the ranking results. [PERSON] et al. [21] introduced the network centrality in complex network theory into the VNE problem and ranked the nodes by calculating their closeness centrality. However, the closeness centrality is only one of the topology properties of the network, which measures the distance between a node and other nodes. A network topology attribute and network resource-considered algorithm was proposed to embed virtual networks in [22]. However, they only defined the local resource attributes. None of these algorithms considered the global network resource attributes as well as reasonably combined the resource attributes and topology attributes to comprehensively evaluate the importance of nodes.
## 3 Control System
### Inverter Modulation  
The modulation method used in this study is the same as that proposed in the original paper of the chosen MLI. It is a low-frequency modulation approach in which all of the switches' switching angles are computed beforehand. The method aims to produce an output that resembles a sinusoid as closely as possible, with each switching angle calculated in relation to a pure sinusoid according to the following simplified equations:  
\[\theta_{\rm j}=\sin^{-1}\biggl{[}\frac{1}{\rm N_{L}-1}(2j-1)\biggr{]}\ \rm for\ j=1,\ 2,\ \ldots\ \frac{\rm N_{L}-1}{2} \tag{17}\]  
\[\theta_{\rm j}=180-\sin^{-1}\biggl{[}\frac{1}{\rm N_{L}-1}(2j-1)\biggr{]}\ \rm for\ j=\frac{\rm N_{L}-1}{2},\ \ldots,\ 2,\ 1 \tag{18}\]  
\[\theta_{\rm N_{L}}=180^{\circ} \tag{19}\]  
\[\theta_{\rm k}=360-\theta_{\rm j}\ \rm for\ j=\rm N_{L}-1,\ \ldots,\ 2,\ 1 \tag{20}\]  
where \(\rm N_{L}\) is the number of MLI output levels, \(\theta_{\rm j}\) is the switching angle position up to \(\rm N_{L}-1\), \(\theta_{\rm k}\) is the switching angle position from \(\rm N_{L}+1\) up to \(\rm 2N_{L}-1\). The depiction of the expected 17 L output is displayed in Figure 6 using (17)-(20).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Regulation** & **Power** & **Number of** & **Comment** \\  & **Range** & **Tracking** & **Controllers** & **Comment** \\ \hline Cascaded DC-DC Converter [25; 28] & High & Yes & 2 & \begin{tabular}{c} Requires 2 DC-DC converters with \\ separate controllers. \\ \end{tabular} \\ \hline Inverter Side Control [29; 30; 31] & Low & No/Yes & 1/2 & \begin{tabular}{c} Requires 2 controllers if power \\ tracking is to be considered. Has \\ lower regulating range at higher \\ inverter output levels. \\ \end{tabular} \\ \hline PI-Based Controller [20; 21; 22] & Low & No & 1 & \
\begin{tabular}{c} Requires different tuning at \\ different operating conditions. \\ \end{tabular} \\ \hline POVC & High & Yes & 1 &
\begin{tabular}{c} Requires only a single controller \\ for both power tracking and \\ voltage regulation. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Voltage control technique comparison.  
Figure 6: Switching angle positions.
## 4 Experimental Setups and Data Description
### Crossvalidation for Datasets  
Datasets dividing into the training dataset, validation dataset, and testing dataset with 6:2:2 ratio and the validation are utilized to evaluate the performance of the proposed deep learning algorithm. However, the single split cannot ensure achieving an optimal result of identifying structural damage conditions among subdataset. Therefore, the \(K\)-fold crossvalidation method is adopted to reduce the bias during the testing process. More specifically, the datasets are divided into the training and testing datasets with a ratio of 8:2. Then, the training dataset is split into K equal portions where one portion is validation, and the remaining datasets are training. In this study, the \(K\) = 4 is selected, and the crossvalidation of datasets is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Structural Conditions** & **Label** & **Description** \\ \hline
1 & State\#08 & Gap = 0.13 mm \\
2 & State\#09 & Gap = 0.10 mm \\
3 & State\#10 & Gap = 0.05 mm \\
4 & State\#11 & Gap = 0.15 mm \\
5 & State\#12 & Gap = 0.20 mm \\
6 & State\#14 & Gap = 0.20 mm + mass on the 1 st floor \\
7 & State\#15 & Gap = 0.10 mm + mass on the 1 st floor \\
8 & State\#17 & Column: 1 BD \(-50\%\) stiffness reduction \\
9 & State\#21 & Column: 3 BD \(-50\%\) stiffness reduction \\
10 & State\#24 & Column: 2 BD \(-50\%\) stiffness reduction \\
11 & State\#18 & Column: 1 AD + 1 BD \(-50\%\) stiffness reduction \\
12 & State\#22 & Column: 3 AD + 3 BD \(-50\%\) stiffness reduction \\
13 & State\#23 & Column: 2 AD + 2 BD \(-50\%\) stiffness reduction \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different scenarios of the structure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Condition Type** & **State Selection** &
\begin{tabular}{c} **Dataset Division** \\ **(Training/Validation/Testing)** \\ \hline
10\% damaged condition & State 08, State 09, State 10, \\  & State 11, State 12 \\
20\% damaged condition & State 14, State 15 \\
30\% damaged condition & State 17, State 21, State 24 \\
40\% damaged condition & State 18, State 22, State 23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of used datasets.
# Non-Destructive pXRF on Prehistoric Obsidian Artifacts from the Central Mediterranean
## 3 Analytical Methods
The portability, easy operation, and low maintenance for hand-held XRFs also enabled many archaeologists to conduct analyses themselves, without depending on a geoscientist or laboratory staff to prepare and run their samples. The homogeneity of obsidian, the relatively flat areas on stone tools that may be tested, and little if any surface weathering make it a perfect material for non-destructive X-ray fluorescence analysis. Figure 4: K-line energy peaks for two obsidian samples. In blue is from Monte Arci (Sardinia) SC; note the high strontium, zirconium, and barium peaks when compared to SA (in red). The blue lines represent the multiple peaks for barium. For a number of years, however, there were concerns raised about the integrity of analyses being conducted and, in particular, the production of data with actual concentration values [23; 30]. How do we deal without calibrated quantitative results for major elements silicon (typically 65-75% for SiO\({}_{2}\) in obsidian), aluminum, sodium, potassium, and magnesium? How do we deal with matrix effects on secondary X-rays? What standard reference materials may be used for inter-laboratory comparisons? These issues were mostly for archaeology users who needed to compare their analytical data for artifacts with those of other scholars' analyses of geological source samples in their geographic region. Obsidian calibration software was developed and shared, however, by 2008 for the Bruker pXRF instruments, based on 40 geological obsidian samples analyzed by INAA, LA-ICP-MS, and XRF [22; 31]. Separately, pXRF users who analyzed sufficient geological samples of known origin could also make a direct comparison of the uncalibrated raw data with that from the archaeological samples that they analyzed with the same instrument. Nevertheless, the use of calibrated data is expected for publications in many journals. For archaeological obsidian artifact studies, nearly all research is conducted by scholars with geological samples from their region of interest.
## 4 Laboratory Tests
### Phase 3: Mechanical Behaviour of Soil Mixtures
#### 4.2.3 Design Parameters: California Bearing Ratio (CBR) and Compressive Strength  
The structural behaviour of the road layers is determined by the load bearing capacity. According to the type of road layer executed, this bearing capacity is measured according to the CBR (California Bearing ratio) or unconfined compressive strength.  
The CBR index measures the bearing capacity of soils and compacted aggregates used in the construction of road bases or subbases. The CBR index depends on the density and moisture conditions of the samples. In this study, the samples were compacted according to the optimum moisture obtained in the Modified Proctor Test to reach the maximum dry density and the highest possible bearing capacity. The CBR value is carried out in accordance with UNE 103-502.  
The unconfined compressive strength (UCS) was performed according to the NLT-305/90 standard in specimens 177.8 mm high and 152.4 mm in diameter. The UCS measured the resistance in cohesive soil or cement treatment soil or granular materials. Like the CBR samples, these samples were manufactured under the optimal compaction conditions obtained in the Modified Proctor Test.  
Spanish specifications [34] establish that lime-stabilised soil must comply with the minimum CBR index; however, cement-treated soil must exceed a minimum value of  
Figure 7: Moisture–dry density curves.  
unconfined compressive strength. The results obtained in the laboratory test are shown in Figure 8.  
Analysing the results shown in Figure 8, the use of different nanosilica improve the bearing capacity of soils.  
The addition of 0.056 w% of N1 and 0.112 w% of N2&N3 increase the CBR index by 31.5% and 33.2%, respectively.  
Similar increases in the CBR index have been observed in previous studies [38] with similar dosages, probably due to the reactions of these nanomaterials with cement and the soil minerals themselves, leading to pozzolanic cementitious reactions. This behaviour has been observed in previous studies with higher increases in the bearing capacity or simple compressive strength, due to the higher addition percentages of the nanomaterials, between 1 and 7% [37; 39].  
The AM-1, AM-2 and AM-3 mixtures showed similar CBR index values, in the order of 100% of the CBR index with slightly higher values in the mixtures with nanomaterials, which shows an excellent bearing capacity. At the same time, unconfined compressive strength values of between 1.08 and 1.23 MPa were obtained, relatively high values for mixes with a total of 1 w% cement, normal values for stabilisation being a minimum of 3%, according to Spanish specifications [34].
## 3 Network Optimization of the Coal Mine Safety Risk Assessment
### Model Evaluation Indicators  
For a comprehensive assessment of safety risk in underground mining, three statistical metrics (coefficient of determination \(R^{2}\), mean square error \(MSE\), and mean absolute percentage error \(MAPE\)) were used to determine the average performance of the optimal model and the accuracy of the prediction. The coefficient of determination (\(R^{2}\)) is the percentage of variance of the dependent variable that can be predicted using the independent variables [78]. The coefficient of determination (\(R^{2}\)) was used to measure the effectiveness of the neural network model, and therefore the optimal model was determined based on this principle. The coefficient of determination (\(R^{2}\)) can have values between 0 and 1. An \(R^{2}\) value closer to 1 indicates higher correlation and better agreement between the predicted results and the target values [79]. \(MSE\) is the mean or average square of the difference between actual and estimated values. This means that the \(MSE\) is obtained by dividing the square of the difference between the predicted and actual target variables by the number of data points. Positive values are always preferable, while values close to zero are optimal. The smaller the values of \(MAPE\) and \(MSE\), the better the accuracy of the prediction model [80]. The equations for the three statistical metrics are given below.  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(X_{i}-Y_{i})^{2}}{\sum_{i=1}^{n}(\bar{Y}-Y_{i})^{ 2}} \tag{21}\]  
\[MAPE=\frac{\sum_{i=1}^{n}\lvert X_{i}-Y_{i}\rvert}{n}*100 \tag{22}\]  
\[MSE=\frac{1}{n}{\sum_{i=1}^{n}(X_{i}-Y_{i})^{2}} \tag{23}\]  
where \(n\) is the total number of the data points, \(X_{i}\) is the actual value of the ith sample, \(Y_{i}\) is the predicted value of the ith sample, and \(\bar{Y}\) is the mean value of the actual values.
# Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?
## 5 Conclusions  
In this study, four data assimilation scenarios were adapted to evaluate whether the assimilation of SM, LAI, and SIF can jointly increase the prediction performance of MLDAS. Data assimilation experiments were conducted during the growing season over 3 years at an AmeriFlux site. With more accurate initial conditions provided by data assimilation, predictions from MLDAS outperform those from OL. On average, MLDAS produces 28.65%, 27.79%, and 19.15% lower root square deviations (RMSD) for daily H, LE, and SM prediction compared to OL. Moreover, the results also indicate that the impact of assimilation on SM prediction can last longer (around 100 days) than that of LE (around 60 days). Our work proves that data assimilation can potentially increase the short-term prediction skill of LSMs. Considering that our assimilation-prediction test was conducted at only one site, more sites with different climates and land cover conditions need to be included in our assimilation-prediction experiments to draw some more general conclusions in the future. Furthermore, Noah-MP should be further coupled with atmospheric models (e.g., WRF) to test whether data assimilation influences meteorological forcing, which can give feedback on real-time land surface variable forecasts.  
Conceptualization, T.X.; methodology, X.H.; software, Y.T. and X.H.; validation, T.X., F.C., S.L. and X.H.; formal analysis, Y.T.; writing--original draft preparation, Y.T.; writing--review and editing, all; visualization, Y.T.; supervision, T.X.; project administration, T.X.; funding acquisition, T.X. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China (42171315) and supported by the State Key Laboratory of Earth Surface Processes and Resource Ecology (2021-ZD-04).  
Meteorological forcing data of the Mead site including air temperature, pressure, humidity, precipitation, wind speed, incoming shortwave and longwave radiation were downloaded from AmeriFlux ([[http://ameriflux.lbl.gov/](http://ameriflux.lbl.gov/)]([http://ameriflux.lbl.gov/](http://ameriflux.lbl.gov/)) accessed on 7 October 2021). LAI and SM are also acquired from the AmeriFlux database. The remotely sensed solar-induced chlorophyll fluorescence (SIF) products were downloaded from: [[https://osf.io/8](https://osf.io/8) xqy6/?view_only=4d14a64b7]([https://osf.io/8](https://osf.io/8) xqy6/?view_only=4d14a64b7) be644d-39564c82a371e1d20 (accessed on 1 November 2021).  
We would like to thank the academic editor and three anonymous reviewers for their kind help and valuable comments that greatly improved this manuscript. We also would like to thank the high-performance computing support from the Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University.  
The authors declare no conflict of interest.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 2 The Real-Time Metaverse  
The real-time metaverse is a cutting-edge component of the ongoing digital revolution [32], serving as an advanced expansion of the broader metaverse concept. The real-time metaverse represents the next evolution in virtual worlds, transforming static digital environments into dynamic, interactive spaces continuously updated based on real-world data. The real-time metaverse goes beyond the traditional metaverse's pre-built, static worlds [33]--where users can socialize, work, play, and explore--by incorporating actual changes in the real world as they happen. This shift unlocks a new realm of possibilities, blending physical and digital realities to create more immersive and interactive experiences.  
The core of the real-time metaverse instantaneously syncs real-world activities and environments with their digital counterparts. This is achieved through advanced sensor technologies and other inputs that continuously capture and send data from the physical world. These sensors gather critical information about an environment's geometry, movement, and visual details, feeding it into powerful data processing systems [34]. Data fusion techniques combine these diverse data streams into a single coherent model [35], which is then rendered in real-time within the virtual space. The result is a virtual environment that evolves in response to real-world changes, whether it is the movement of objects, changes in lighting, or adding new elements to a scene.