esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data Set**} & \multirow{2}{*}{**Technique**} & \multicolumn{3}{c}{**Training**} & \multicolumn{3}{c}{**Testing**} & \multirow{2}{*}{**Cost**} \\ \cline{3-3} \cline{5-10}  & & **RE** & **RMSE** & **R2** & **RE** & **RMSE** & **R2** & \\ \hline \multirow{4}{*}{**Wind power prediction(winter)**} & PSO & 0.0157 & 11.8303 & 0.9835 & 0.0886 & 55.6747 & 0.9081 & 0.0121 \\ \cline{2-10}  & RSA & 0.0170 & 16.6071 & 0.9657 & 0.1065 & 61.2748 & 0.8890 & 0.0326 \\ \cline{2-10}  & BMO & 0.0959 & 32.5220 & 0.8278 & 0.2318 & 107.9345 & 0.5259 & 0.0444 \\ \cline{2-10}  & AOA & 0.0356 & 26.9534 & 0.8961 & 0.1135 & 59.1465 & 0.8749 & 0.0373 \\ \cline{2-10}  & IRSA & 0.0049 & 8.3396 & **0.9918** & 0.0642 & 32.7880 & **0.9665** & **0.0105** \\ \hline \multirow{4}{*}{**Wind power prediction(summer)**} & PSO & 0.0049 & 2.8918 & 0.9907 & 0.0772 & 9.5937 & 0.9780 & 0.0193 \\ \cline{2-10}  & RSA & 0.0055 & 4.4855 & 0.9778 & 0.6042 & 38.7233 & 0.6423 & 0.0251 \\ \cline{2-10}  & BMO & 0.0808 & 5.8582 & 0.9460 & 0.2983 & 22.3706 & 0.8291 & 0.0428 \\ \cline{2-10}  & AOA & 0.0339 & 4.0871 & 0.9779 & 0.4688 & 30.6282 & 0.7565 & 0.0208 \\ \cline{2-10}  & IRSA & 0.0015 & 2.8416 & **0.9912** & 0.0632 & 9.3401 & **0.9801** & **0.0184** \\ \hline \multirow{4}{*}{**PV power prediction**} & PSO & 0.0235 & 94.9000 & 0.9649 & 0.0762 & 298.7930 & 0.9420 & 0.0535 \\ \cline{2-10}  & RSA & 0.0144 & 119.6847 & 0.9626 & 0.0440 & 223.8701 & 0.9656 & 0.0410 \\ \cline{1-1} \cline{2-10}  & BMO & 0.0359 & 148.5061 & 0.9150 & 0.1148 & 232.2480 & 0.9545 & 0.0827 \\ \cline{1-1} \cline{2-10}  & AOA & 0.0429 & 183.5005 & 0.8987 & 0.1111 & 383.4890 & 0.8913 & 0.0963 \\ \cline{1-1} \cline{2-10}  & IRSA & 0.0146 & 90 & **0.9761** & 0.1285 & 260.8531 & 0.9611 & **0.0234** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Statistical evaluation for wind and solar prediction.
# Performance Evaluation for China's Planned CO\({}_{2}\)-Ipda
## 1 Introduction
Moreover, China's space-based CO\({}_{2}\)-IPDA gives priority to atmospheric CO\({}_{2}\) detection in China and surrounding regions. The notorious air pollution problems in China will pose a unique and serious challenge for the performance of the space-based CO\({}_{2}\)-IPDA [28; 29]. However, previous studies have paid little attention to performance evaluations under high aerosol load conditions [23; 24]. Finally, two important problems should be resolved before production of the final CO\({}_{2}\)-IPDA product for China. One is whether the current parameter configuration can meet the requirements of high-accuracy atmospheric CO\({}_{2}\) detection, and the other is whether there is room to relax the requirements for some parameters without significantly degrading the performance of the CO\({}_{2}\)-IPDA, thereby reducing its power consumption and space requirements. To investigate these problems, we studied the effect of the orbit height and solar radiation on the performance of the satellite-borne CO\({}_{2}\)-IPDA, whose parameters were set according to the current Chinese design. The combined effect of surface reflectance and aerosol optical depth (AOD) on the relative random error was also discussed, and based on this, the existing instrument parameters were optimized using a sensitivity analysis. Finally, real surface reflectance and AOD data for China and surrounding regions were obtained using processed MODIS (Moderate resolution Imaging Spectroradiometer) products to evaluate the performance of the CO\({}_{2}\)-IPDA and discuss the usability of its products. When mapping the spatial distribution of the relative random errors, we focused our performance evaluation on land areas because the reflectance of oceans is fairly homogeneous. This paper is organized as follows: the calculation methods for the errors, data sources, and the parameter configuration are presented in Section 2; results of the sensitivity analysis for the orbit height, solar radiance, surface reflectance, and AOD are shown in Section 3 alongside the parameter optimization for China's CO\({}_{2}\)-IPDA. Section 3 also discusses the performance of China's IPDA based on maps of relative errors generated using real annual reflectance and AOD products. Finally, we summarize the significant results of this study and present our conclusions in Section 4.
## 5 Application
### Local Ice Load Calculation  
Figure 11 compares the mean values of the local ice load peaks on Panel 1 and 2 calculated by each method. In the simplified method, where the geometry of the structure dominates, the same ice loads were calculated in Panels 1 and 2, and the values were larger than the other methods. It can be seen that the direct method gave larger ice loads, but the magnitude and tendency of the results by the direct method and the semi-analytical method were similar to each other.  
Figure 10: Fatigue target point and panels.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Panel 1** & **Panel 2** \\ \hline Influence Factor [MPa/N] & \(8.856\times 10^{-6}\) & \(2.9734\times 10^{-6}\) \\ \hline \end{tabular}
\end{table}
Table 5: Influence factor on each panel.  
Figure 9: Weibull parameters of the ice. (**a**) Scale parameters of each ice length; (**b**) Shape parameters of each ice length.  
Figure 12 shows the cumulative Weibull distribution of ice load amplitudes calculated according to each method. The gentler the slope of the distribution, the greater the randomness included in the calculation process, which means that the variance was large. The direct analysis method based on the finite element model showed the widest load distribution because it can consider enough randomness that can occur in the real environment. The simplified method and the semi-analytical method showed a relatively narrow load distribution.  
Figure 11: Mean of the local ice load peak.  
Figure 12: Weibull cumulative density function of the ice load peak.  
Based on the results of Sections 5.2 and 5.3, the relationship between the probability distribution of the ice load and the corresponding fatigue damage was investigated. Although the simplified method yielded the highest average ice load peak among the three methods, as shown in Figure 11, its fatigue damage was estimated to be similar to that calculated by the direct method. This is because the direct method considers a large amount of randomness, leading to a wider distribution of ice loads. On the other hand, the semi-analytical method showed lower fatigue damage due to most of the ice loads being concentrated near the mean with low levels of randomness. It can be concluded that the ice load and its distribution play a critical role in the evaluation of level ice-induced fatigue damage. Further studies are recommended to investigate the impact of the randomness of ice loads on the simplified and the semi-analytical methods. Based on the results obtained from the direct method and the model test, it is expected that the accuracy of these two methodologies can be improved.
# The Detection of Fake News in Arabic Tweets Using Deep Learning
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Topic**} & \multirow{2}{*}{**Classification**} & \multicolumn{2}{c}{**Feature Type**} & \multirow{2}{*}{**Result**} \\  & & & & & **Approach** & & **News Content** & & **Social Context** \\ \hline
[19] & 2018 & 800 Tweets & General & NB, SVM, DT & ✓ & ✓ & - & Accuracy 0.899 \\ \hline
[20] & 2019 & 177 Tweets & General & EM & ✓ & ✓ & - & F1-score 0.80 \\ \hline
[10] & 2019 & \
\begin{tabular}{c} 268 labeled blog \\ posts, 20,392 \\ unlabeled blog posts \\ \end{tabular} & General & CNN & ✓ & & &
\begin{tabular}{c} Word2 vec(CBOW), \\ char-level \\ embeddings \\ \end{tabular} & F1-score 0.63 \\ \hline
[21] & 2019 & 9000 Tweets & General & RF, SVM, DT, NB & ✓ & ✓ & - & F1-score 0.776 \\ \hline
[22] & 2019 & 1862 Tweets & \begin{tabular}{c} Syrian \\ crisis \\ \end{tabular} & \
\begin{tabular}{c} LR, RF, DT, \\ AdaBoost \\ \end{tabular} & ✓ & ✓ & - &
\begin{tabular}{c} Accuracy \\ 0.76 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of existing detection approaches for fake news in Arabic.
## 3 Performance Analysis of iPyroxy Block
### UV Absorption and Band Gap  
The band gap and absorption properties of iPyroxy blocks may be efficiently examined by UV spectroscopy--an essential method for assessing the optical properties of materials. This technique entails subjecting iPyroxy materials to ultraviolet light and analyzing their absorption spectra. The initiation of UV absorption in the spectrum facilitates the band gap calculation, defined as the energy disparity between the conduction and valence bands. The observed band gap of iPyroxy is 1.3 eV, categorizing it as a semiconductor (Figure 3). The band gap denotes the energy necessary for electron transitions and indicates moderate electrical conductivity. The absorption peaks identified in UV spectroscopy elucidate the electronic alterations within the material, while distinct absorption values at different wavelengths reveal the interaction of UV light with the iPyroxy structure, enhancing comprehension of its electronic characteristics. Through UV spectroscopy, researchers can enhance the efficacy of iPyroxy for essential applications, including the creation of electronic devices and optoelectronic materials. The 1.3 eV band gap indicates advantageous optical properties within a defined frequency spectrum, rendering iPyroxy blocks appropriate for antenna substrates to improve data transmission. Moreover, their semiconductor characteristics make them suitable for optoelectronic devices, including LEDs and solar cells. The distinctive absorption properties of iPyroxy render it suitable for sensors, electrical components, and photonic applications, allowing effective light manipulation and detection. Comprehensive these features is essential for developing technologies dependent on efficient light interaction.  
Figure 3: iPyroxy band gap.
# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## Materials and Methods
### Data
#### 2.2.4 Fire Emissions Based on MODIS  
Satellite-based thermal information is suitable to detect fire events. We estimated CO\({}_{2}\) emissions from anthropogenic-induced and wildfires as another flux in the calculation of CO\({}_{2}\) balance. During a fire event, various kinds of trace gases are released [41]. In this study, we focused on CO\({}_{2}\) emissions. CO\({}_{2}\) emissions from fire events were estimated by using biomass, burned areas, fuel types, combustion severities, and emission factors. For biomass, we used the vegetation integrative simulator for trace gases (VISIT), a process-based model, with 0.5\({}^{\circ}\) spatial and monthly temporal resolutions [28; 42; 43]. In the VISIT, aboveground biomass (AGB) and SOM are calculated through process-based algorithms of the terrestrial carbon cycle. The MODIS normalized difference vegetation index (NDVI) (a 1 km spatial resolution) was used to downscale the VISIT-based biomass estimation into a 1 km resolution by using logistic regression functions for each IGBP biome type in MCD12Q1 (Table A1) [44]. Burned areas and their dates were obtained from a MODIS fire product, MCD45A1 [45]. Fuel types were obtained from the MCD12Q1 500 m land cover map. Differences in the NDVI before and after fire events were directly reflected the loss of AGB. Combustion severities were represented by fire radiative power (FRP) in MODIS fire products. [PERSON] et al. [46] derived a combustion completeness ratio by using FRP. The merits of using FRP include its ability to describe daily temporal dynamics of fire severity. In addition, its spatial resolution is high (1 km). Emission factors were obtained from the global fire emissions database (GFED) 4.1s [47], meaning the proportion of CO\({}_{2}\) emissions within biome-specific carbon gases by biomass burning. For the CO\({}_{2}\) emission ratios of AGB and SOM, 0.49 and 0.57 were applied, respectively [33].
## 3 Results  
### Model Validation and the Emergence of Castration-Resistance  
A subset of the experimental data used for model calibration was reserved for model validation. As can be seen in Figure 3 (red circles), our model is able to recapitulate immune infiltrate data that was not used in the fitting process. These data represent immune cell numbers in the tumor once castration resistance has emerged.  
The spontaneous emergence of castration resistance in the Myc-Cap xenograft model (Figure 2a) occurs due to an amplification of the androgen receptor gene [34]. Following [10], we approximate this process by assuming that an initial pool of castration-resistant cells is already present at the time of ADT initiation. Prior to treatment, the xenograft grows exponentially. ADT administration induces apoptosis, especially in the androgen-sensitive cell population (Figure 2b), resulting in a transient decrease in tumor volume. This cell death elicits a strong immune response from the host. Dendritic cells are recruited to the tumor site (Figure 3a), which transform into APCs and migrate to the lymphoid compartment  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Units** \\ \hline \(\alpha_{N}\) & 0.3196 & per day \\ \(\varepsilon_{N}\) & [0.2788,1] & dimensionless \\ \(\delta_{NA}\) & 0.0922 & per day \\ \(\omega_{N}\) & [1,3.5] & dimensionless \\ \(\alpha_{M}\) & 0.3516 & per day \\ \(\varepsilon_{M}\) & 1 & dimensionless \\ \(\delta_{MA}\) & 0.0922 & per day \\ \(\omega_{M}\) & 1 & dimensionless \\ \(\delta_{K}\) & 1 & per day \\ \(k_{T}\) & 0.01 & tens of millions of cells \\ \(\delta_{D}\) & 3.2486 & per day \\ \(\rho\) & 0.0046 & tens of millions of cells per day \\ \(k_{D}\) & 20.0926 & tens of millions of cells \\ \(\lambda_{A}\) & 1.2539 & per cell per day \\ \(\mu_{A}\) & 9.1990 & per day \\ \(\mu_{T4}\) & 0.0258 & per day \\ \(\lambda_{T8}\) & 16.9130 & per cell per day \\ \(\kappa_{T8}\) & 0.3000 & per day \\ \(k_{I}\) & 0.3 & tens of millions of cells \\ \(\lambda_{T4}\) & 28.7035 & per cell per day \\ \(\mu_{T8}\) & 0.0218 & per day \\ \(k_{G}\) & \(1.5\times 10^{-12}\) & ng/mL \\ \(\delta_{R}\) & 1.2907 & per day \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter values estimated by data fitting.  
where they activate resident T cell populations. Activated T cells subsequently localize to the tumor site, reaching peaks around 10 days post-ADT initiation (Figure 3b-d).  
As dead tumor cells are cleared, there is a transient decrease in immune cell numbers at the tumor site, starting around day 30. Now, castration-resistant cells emerge as the dominant cell type and the tumor begins to regrow (Figure 2a). This leads to a concomitant increase in total immune cell numbers starting around day 45 (Figure 3). However, the _density_ of immune cells within the tumor continues to decrease (data not shown) with a decline in the number of dead tumor cells, reflecting progression to a state of depressed immune presence.
## 2 Basic Theory  
The characterization of data and their edge distributions are represented by \(X\) and \(P(X)\). Thus, \(D=\left\{X,P(X)\right\}\) represents a domain. Two domains that are not the same indicate differences in the characterization or edge distributions. \(D^{s}=\left\{X^{s},P(X^{s})\right\}\) denotes the source domain, while \(D^{t}=\left\{X^{t},P(X^{t})\right\}\) denotes the target domain. These two domains comprise bearing vibration data under various loads with the same labels but distinct edge distributions, \(P(X^{s})\
eq~{}P(X^{t})\). The purpose of transfer learning is to aid in accomplishing tasks in the target domain by leveraging messages from the source domain. The domain-adversarial neural network (DANN) [26] is one of the classical methods of transfer learning.  
Domain-adversarial neural networks (DANNs) mainly include a feature extractor \(G_{F}\), a domain discriminant module \(G_{D}\), and a fault recognition module \(G_{C}\). The feature extractor primarily performs deep feature extraction on the input data, while the fault identification module is responsible for identifying faults in the target domain test data samples after the feature extraction. The domain discriminant module serves as a binary classifier with an added gradient inversion layer.  
\(X\) represents the input data, and the characterization extractor \(G_{F}\) learns the features of the data samples by updating the parameter \(\theta_{F
# Minimizing the Number of Distrustful Nodes on the Path of IP Packet Transmission
## 2 Materials and Methods
### Statement of Research
To find a path that does not include distrustful nodes, we can simply not consider them as neighbors of the current node, using the classic Dijkstra algorithm. Note that the constructed tree of paths will not contain distrustful nodes, so the paths to them will be unknown. But the shortest path tree must include paths to them as well. With the proposed approach, the following options are possible:  
* There are workarounds to all distrust nodes and we obtain the shortest path tree using the usual Dijkstra algorithm. After that, the shortest paths to distrustful nodes must be added to the tree of shortest paths. If there is one such node, then it can be found using the classical algorithm, but if there are more nodes, it requires more detailed analysis and decision-making. * There is no workaround for at least one distrust node and we will obtain confirmation of this. In this case, it is necessary to proceed to ensure the second requirement described above. It is necessary to supplement the tree with paths containing distrustful nodes as transit, but their number should be kept to a minimum. There are several approaches to minimizing the number of distrust nodes on the way to the end node. One of these approaches suggests the following additional procedure in the classical algorithm when passing the current node and counting the weight of the channel from it to all its neighbors. If you encounter a distrust node on the way, it is necessary to assign some sufficiently large positive constant to the weight of the channels associated with it, as is the same for all distrustful nodes. Thus, the inclusion of each distrustful node in the path strongly increases the running weight of the path. Note that it is the running weight that increases when passing [PERSON]'s algorithm, but not the actual weight of the path, which is calculated by the sum of all weights on the edges between all vertices of the path. The larger the amount of such distrustful nodes in the path, the larger the running path weight for [PERSON]'s algorithm. As a result, a path with a minimum number of distrust nodes will be found. Another approach is to immediately look for paths to trustful and distrustful nodes, as well as either a detour or a path through the smallest number of distrustful nodes.
## 1 Introduction  
Oil shale, an organic-rich sedimentary rock, holds significant potential as an unconventional energy source due to its abundant kerogen content, which can be converted into shale oil and gas through various extraction processes such as pyrolysis, retorting, and in situ conversion. With the world's increasing demand for energy [1], the exploration and exploitation of oil shale resources have gained considerable attention in recent years. However, the development of oil shale reserves presents several challenges, including complex geological structures, variable quality, and environmental concerns. Therefore, a comprehensive understanding of oil shale characteristics, formation processes, and extraction technologies is crucial for the sustainable and efficient utilization of this resource.  
Previous studies have focused on the geological and geochemical aspects of oil shale, examining its occurrence, depositional environment, organic matter content, and hydrocarbon potential [2; 3; 4]. In addition, several reviews have addressed the challenges and advancements in oil shale extraction techniques, including retorting, pyrolysis, and in situ conversion, along with their environmental implications.  
Despite the wealth of existing knowledge, gaps remain in our understanding of oil shale's multifaceted nature, particularly concerning the interplay between its geological,geochemical, and technological aspects. Furthermore, there is a need to integrate the latest findings and advancements in oil shale research to provide a comprehensive and up-to-date overview of the field.  
This review aims to bridge these gaps by thoroughly analyzing oil shale's geology, geochemistry, and extraction technologies, drawing from recent studies, and incorporating novel insights. The review will also discuss the environmental impacts of oil shale exploitation, including effects on geological environments, water resources, and air quality, and will explore strategies and technologies implemented to mitigate these adverse consequences. Lastly, the review will highlight the areas where previous reviews have fallen short and the motivation behind the present study (Figure 1).  
By providing a comprehensive and current synthesis of oil shale research, this review seeks to facilitate the sustainable development of oil shale resources, foster innovation in extraction technologies, and contribute to addressing the global energy challenge. The findings and insights presented herein will be valuable for researchers, policymakers, and industry stakeholders engaged in the exploration, exploitation, and management of oil shale resources.
# A Node Density Control Learning Method for the Internet of Things
###### Abstract  
When examining density control learning methods for wireless sensor nodes, control time is often long and power consumption is usually very high. This paper proposes a node density control learning method for wireless sensor nodes and applies it to an environment based on Internet of Things architectures. Firstly, the characteristics of wireless sensors networks and the structure of mobile nodes are analyzed. Combined with the flexibility of wireless sensor networks and the degree of freedom of real-time processing and configuration of field programmable gate array (FPGA) data, a one-step transition probability matrix is introduced. In addition, the probability of arrival of signals between any pair of mobile nodes is also studied and calculated. Finally, the probability of signal connection between mobile nodes is close to 1, approximating the minimum node density at T. We simulate using a fully connected network identifying a worst-case test environment. Detailed experimental results show that our novel proposed method has shorter completion time and lower power consumption than previous attempts. We achieve high node density control as well at close to 90%.  
Internet of Things; wireless sensors; mobile nodes; density control; probability Article  
A Node Density Control Learning Method for the Internet of Things  
[PERSON], [PERSON], [PERSON], [PERSON].  
and network power consumption of WSNs are directly affected by the density of nodes [3; 4]. Relevant scholars have proposed some better methods.