esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The study reported in this paper provides insights on the quantification of power system resilience using historical outage data in TADS. We also discussed multiple challenges to grid resilience under overlapping nearby outages. The assessment of nearby outages in the operation horizon of the BPS goes beyond standard requirements. The comprehensive historical data analysis of cluster outages provides an operating entity with a quantitative method to identify the outages with the highest risks. The knowledge gained from this study shall help companies to understand potential risks and to identify mitigation measures to prevent or minimize the impacts of those outages. The approach presented here can be helpful to the industry in the process of monitoring risks to such time clustered outages.  
In addition to the TADS data analysis in this paper, other NERC required event reports that analyze multiple outages should be cross referenced to TADS reported outages and noted in TADS. Based on these more in-depth after the fact event reports, the associated TADS data should be updated as needed. Similarly, further analysis could include non-automatic outages at lower voltages--including manual switching errors--to quantify their contribution to overall risk.  
Future research to identify better alternative methods, beyond the discussed above Gen DFAX method, is needed to identify electrically close outages. In addition, future research around outage prediction based on machine learning algorithms is needed to proactively cope with overlapping electrically close outages and to improve grid resilience.  
Conceptualization, M.P. and S.E.; methodology, S.E.; software, S.E.; validation, M.P., S.E., and E.C.-S.; formal analysis, S.E.; writing--original draft preparation, M.P., S.E., and E.C.-S.; writing--review and editing, M.P., S.E., and E.C.-S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors are grateful to NERC's TADS Working Group and NERC staff for their support and materials. The definition of clusters in this paper was suggested by [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Simulation Results
### Data Dissemination Results
The scheme has been adapted since in the original expression (4), the retransmission probability depends on a global parameter like the density of vehicles \(\mathbf{\rho}_{s}\). Such global parameter cannot be obtained in a distributed way; therefore, the node's number of neighbors has been used as a local estimator of the density. Regarding the shaping parameter \(\mathbf{v}\), it has been fixed to 20, which has already been demonstrated to provide good results in terms of reachability [30]. Notice that the most optimal configuration of polynomial and irresponsible schemes is out of the scope of the paper. Figure 10 includes the obtained simulation results for irresponsible scheme in terms of \(\mathbf{Re}\), which again validate the obtained dissimilarity metric. Figure 11 contains the obtained results in terms of number of redundant messages. The proposed approach outperforms flooding, but it requires a higher number of messages compared with classical dissimilarity metrics. Figure 11: Comparison of redundancy results for the irresponsible scheme based on the different dissimilarity metric. Figure 10: Comparison of Reachability results for the irresponsible scheme based on the different dissimilarity metric.
# Study on Thermal Radiation Characteristics and the Multi-Point Source Model of Hydrogen Jet Fire
## 3 Theoretical Basis of Numerical Simulation
### Radiation Model  
Considering the combustion characteristics of hydrogen, this study adopted the P1 model to calculate the thermal radiation [31; 32]. The P1 model is a simplified form of the radiative transfer equation (RTE) and is commonly used to describe the transfer of radiation in a medium. This model assumes that the radiative transfer in the atmosphere can be separated, dividing the atmosphere into several layers with relatively independent radiative transfer processes in each layer. The radiative transfer equation is defined as  
\[\frac{\sigma_{\rm s}}{4\pi}\int_{4\pi}I\big{(}\Omega^{\prime}\big{)}\Phi( \Omega^{\prime},\Omega)d\Omega^{\prime} \tag{10}\]where \(\sigma_{\text{s}}\) is the scattering coefficient; \(\Phi\) is the scattering phase function from the \(\Omega^{\prime}\) direction to the \(\Omega\)-direction; and \(I\) is the incident radiation intensity. The P1 model converts the original problem approximately to  
\[\
abla\cdot(D_{\text{P1}}\
abla G)-\kappa(G-4\pi I_{\text{b}})=0 \tag{11}\]  
where \(G\) is the incident radiation; \(I_{\text{b}}\) is the blackbody radiation intensity; \(\kappa\) is the absorption coefficient; and \(D_{\text{P1}}\) is the diffusion coefficient of the P1 radiation model. The diffusion coefficient \(D_{\text{P1}}\) is defined as  
\[D_{\text{P1}}=\frac{1}{3\kappa+\sigma_{\text{s}}(3-a_{1})} \tag{12}\]
## 1 Introduction
Previous studies have shown that, during the period from the 1950s to 2000s, \(R_{\rm g}\) changes in China have undergone a shift from darker (decreasing) to brighter (increasing) values [17; 18; 19; 20; 21], whereas other studies have shown that \(R_{\rm g}\) in China has experienced a sustained reduction of about 3.1-4.5 W m\({}^{-2}\) decade\({}^{-1}\)[22; 23; 24]. Studies on the trend of \(R_{\rm g}\) based on an increased number of radiation measurement stations from the 1980s to the 2010s have shown that global radiation shows a distinct increasing trend [25]. Regarding \(R_{\rm d}\), previous studies have reported that, in the context of global dimming, \(R_{\rm d}\) and the diffuse radiation fraction are increasing [19; 22; 25]. For example, [PERSON] et al. [25] observed that national annual mean \(R_{\rm d}\) during the period from 1981 to 2010 increased by 7.03 MJ m\({}^{-2}\) y\({}^{-1}\) by interpolation from 754 meteorological stations and 122 radiation stations across China. [PERSON] et al. [22] reported that the diffuse radiation fraction in China increased by 1.73% per decade from the 1960s to the 1990s. Several studies have simulated the effects of changes in \(R_{\rm d}\) and \(R_{\rm g}\) on GPP at the site scale in China. [PERSON] et al. [26] observed that increments in the diffuse radiation fraction enhanced the LUE of the forest canopy, which compensated for the reduction in GPP caused by the decrease in the PAR fraction in three forest ecosystems in China. [PERSON] et al. [27] reported that the response of canopy GPP to radiation changes was predominantly determined by the \(R_{\rm d}\) in an evergreen coniferous forest in China. However, at a regional scale in China, the impacts of recent changes in \(R_{\rm g}\) and \(R_{\rm d}\) on GPP are unclear, which demands a better understanding and accurate spatiotemporal estimation of such impacts. The aims of the present study were to (i) analyze the trends of \(R_{\rm d}\) and \(R_{\rm g}\) in China from 1981 to 2012, and (ii) assess the effects of variation in the diffuse radiation fraction on terrestrial GPP in China.
## 3 Results  
### Salt Tolerance Index (STI)  
The STI values showed a considerable difference in salt tolerance among the seedlings obtained from magnetoprimed and unprimed seeds germinated in different modulators of NO and ROS. Magnetopriming increased the salt tolerance by 44% in comparison to the UP seeds. The NO donor-SNP also enhanced the STI in the seedlings emerging from UP (43%) and MP (34%) seeds as compared to their respective controls (Figure 1). The NO and ROS inhibitors caused a slight increase (from 2% to 14%) in STI values in seedlings from unprimed seeds. However, these inhibitors significantly reduced the values of magnetopriming-induced salt tolerance index, which was maximally reduced with DPI (45%) followed by ST (39%) and L-NAME (19%) as compared to their magnetoprimed controls (Figure 1).  
\begin{table}
\begin{tabular}{l l l l} \hline
**S. No.** & **Gene Locus** & **Primer Name** & **Primer Sequence (5′ to 3′)** \\ \hline
1. & _Glyma.09G224600 (GmNOS-like 1)_ & GmNOS1-F & AATAAAGAAGAAGAAGA \\  & & GmNOS1-R & TTCGAAGCTGGTGTTCT \\
2. & _Glyma.12G012400 (GmNOS-like 2)_ & GmNOS2-F & TGTGCAAGTTATGACCCAA \\  & & GmNOS2-R & AATCAGCCTTGGGAGCTGAC \\
3. & _Glyma.13G083800 (GmNR1)_ & GmNR1-F & GACCGGTTCAGCAGCTGAC \\  & & GmNR1-R & TTTCCCAAATTAGGCTGAC \\
4. & _Glyma.06G109200 (GmNR2)_ & GmNR2-F & GCTACCCCGCCGCCGCCG \\  & & GmNR2-R & AAACGGACAAGGGAAGTTC \\
5. & _Glyma.19G052400 (GmEF1A)_ & GmEF1A-F & TGAAGCTGATTTTCTAAGG \\  & & GmEF1A-R & GTAACCCAACCTTCTTCAAG \\
6. & _Glyma.20G136000 (GmTULA4)_ & GmTUA4-F & CGTGCAGTGTTGTAGATCTT \\  & & GmTUA4-R & GATCAACAATCTCTTCCCAA \\ \hline \end{tabular}
\end{table}
Table 2: Primers used in the present study.
## 1 Introduction
Existing studies, although limited in number, highlight the efficacy of wearable sensors in capturing relevant data for HAR in agricultural contexts. However, there is a notable lack of consensus on the most effective anatomical locations for sensor placement, while also the fusion of data from multiple sensors (e.g., accelerometers, gyroscopes, and magnetometers) has not been fully explored. Our research aims to fill these gaps by systematically investigating the optimal anatomical locations for sensor placement, through exploring the effectiveness of three types of sensors (accelerometers, gyroscopes, and magnetometers) and optimizing an ML-based framework for HAR. To acquire the essential sensor data, experimental field tests were carried out involving twenty participants wearing five inertial measurement units (IMUs) positioned on different parts of the body and carrying out six well-defined activities of a collaborative harvesting scenario similar to [9; 12]. An LSTM neural network was used for classifying the activity signatures of the participants, while the dataset is shared publicly in [25]. In conclusion, our approach for a strategic selection and placement of sensors ensures that the most informative signals are captured, which is particularly important in agricultural tasks, where activities can be complex and varied. This approach not only improves accuracy, but also reduces computational costs, minimizes the amount of data needed, and lowers overall system costs. Although the primary objective of our work is to improve HAR in agricultural tasks, with a particular focus on human-robot collaboration, its methodology and key findings could also be applied to other domains. Finally, by providing access to these data, we aim to encourage further research and innovation, accelerating progress and overcoming the barriers of data scarcity that have historically limited the scope of agricultural studies [26; 27]. The remainder of the paper is organized as follows: Section 2 outlines the methodology for data collection, pre-processing, and the developed LSTM-based HAR. Section 3 presents the main results regarding the optimal sensor placement and multimodal sensor fusion, while in Section 4 the results are discussed from a broader perspective along with future research directions. Finally, Section 5 concludes with the key findings.
## 4 Conclusions  
In this study, anion exchange membranes were fabricated with different combinations of GMA, DVB, VBC, and non-woven PET, and functionalized by introducing various amine groups (TMA, TEA, TPA, and DABCO). Water uptake, IEC, ion conductivity, activation energy, thermal stability, and alkaline stability of membranes were investigated for possibility of application in AEMFCs. General relations among water uptake, IEC, and ion conductivity were verified. When VBC contents predominate in the  
Figure 8: Changes in IEC and conductivity of the select membranes with alkaline stability test at 60 \({}^{\circ}\)C for 144 h.  
membrane, TPA shows high IEC and conductivity (1.34 \(\mathrm{meq}\cdot\mathrm{g}^{-1}\) and 0.022 \(\mathrm{S}\cdot\mathrm{cm}^{-1}\)), whereas when GMA content is high, the DABCO shows high IEC and conductivity (1.34 \(\mathrm{meq}\cdot\mathrm{g}^{-1}\) and 0.021 \(\mathrm{S}\cdot\mathrm{cm}^{-1}\)). Membranes quaternized by TMA and TPA with low GMA content are the best candidates considering activation energy (10.40-11.65 \(\mathrm{kJ}\cdot\mathrm{mol}^{-1}\)). Chain structure ammoniums (TMA, TEA, and TPA) thermally endure up to 200 \({}^{\circ}\)C, while heterocyclic structure ammonium (DABCO) thermally endure up to 130 \({}^{\circ}\)C. In particular, TMA with low GMA content shows the highest thermal stability. The TPA reacted with membrane containing 15% GMA by weight and TMA reacted with membrane containing 75% VBC by weight present favorable alkaline stability. Based on characterizations, it is suggested that mono-ammonium groups allow membranes to improve thermal stability. When all the results are comprehensively considered, the GDV15-TPA membrane appears to have the highest applicability for AEMFCs because of having high ion conductivity and thermal resistance in comparison to existing relative researches. Further research will focus on assembling a membrane electrode assembly (MEA) and testing the power density of AEMFCs.
## 3 Proposed Methodology for Out-of-Stock Prediction Model Using BCHO-TCN LightGBM
### Working of TCN LightGBM in Out-of-Stock Prediction
#### 3.5.1 LightGBM  
LightGBM is a distributed gradient-boosting framework used for machine learning that stands out for its implementation of decision trees in tasks such as classification, ranking, and regression. The purpose of LightGBM is to provide a fast and scalable algorithm capable of handling large datasets. It is recognized for its fast training speed and effective memory utilization. Compared to XGBoost, LightGBM has been found to be faster and more accurate in various benchmarks and experiments. LightGBM's unique approach to leaf-wise tree growth sets it apart from other tree-based learning algorithms, like XGBoost, which typically use level-wise tree growth. While level-based tree growth advances the structure of a tree step by step, leaf-based tree growth prioritizes expanding the tree according to the nodes, resulting in the largest reduction in loss.  
The leaf-wise tree method has smaller tree nodes compared to the level-wise tree method of the same depth, resulting in faster training on large datasets. LightGBM utilizes two unique techniques: Gradient One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB). GOSS removes data instances with minimal gradients and keeps the rest of the data to calculate information gain, as instances with higher gradients have a bigger influence on information gain calculations. By using a smaller dataset, GOSS can accurately estimate information gain. EFB, the second technique, reduces the number of features by combining mutually exclusive features. The decision to use LightGBM is based on its considerable success, which is attributed to its advantages over other algorithms. LightGBM is well known for its rapid training speed, accurate results, and efficient use of memory. Additionally, it is praised for its ability to support parallel, distributed, and GPU learning, as well as its capacity to effectively handle extensive datasets. These advantages have made LightGBM a popular choice in many winning solutions in machine learning competitions.
## 5 Cutting Performance Evaluation  
### Penetration Index  
The penetration index was also widely used to assess the difficulty of rock breaking with cutting tools. [PERSON] et al. [52] established a drillability index \(p^{\prime}/F\), by measuring the crater depth (\(p^{\prime}\)) produced at the threshold force (\(F\)) at which chips form. i.e., the peak force. The method is not suitable for situations where rock fragments are not formed. The different drillability index in this study can be applied to any condition, whether chip formation  
Figure 14: Contact force chain formed between rock particles at the penetration depth of 0.5 mm.  
occurs or not. The indentation force and the penetration depth were automatically recorded during the indentation process, and the penetration index (\(PI\)) is the ratio of the maximum force at each penetration depth (\(F_{m}\)) to the maximum penetration depth (\(p\)), indicating how difficult it is for an indenter to penetrate the rock, as calculated by Equation (7).  
\[PI=\frac{F_{m}}{p} \tag{7}\]  
where \(PI\) is the penetration index, \(F_{m}\) is the maximum force at each penetration depth (kN), and \(p\) is the maximum penetration depth (mm).  
The \(PI\) values for saddle, spherical, wedge, parabolic, and conical buttons were 45, 39, 38, 17, and 16 kN/mm, respectively. This indicated that the conical button had the highest penetration ability, while the saddle button was the worst. The conical button was easier to penetrate into the rock. However, the force-penetration curve only reflects the performance of the cutter in penetrating into rocks, without considering the volume of rock fragmentation. Therefore, the crack propagation pattern and energy consumption should be carefully studied.
# Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm
## 3 Proposed Models  
As stated above, for the purpose of the research that was presented in this paper we conducted two types of cloud computing resource scheduling simulations: one with a real data set and one with an artificial data set. In each simulation, we used slightly different resource scheduling models.  
When performing resource scheduling simulations with a real data set, we used the single-objective model, where we took the makespan (MS) as the objective, as in [75]. In simulations with an artificial data set we employed multi-objective resource scheduling with MS and budget cost objectives, as in [26; 64].  
After we show a basic background information, both models will be presented.  
The cloud hardware infrastructure is organized in the cloud data centers. The cloud data centers have a limited number of physical servers, which are usually refereed as hosts. Each host has many attributes such as a host unique identifier (hostID), number of processing elements (PE), performance of each PE defined in MIPS (million instructions per second), etc. Each physical server can host multiple VMs that utilize a time-shared or space-shared VM scheduling policy.  
When clients (end users) send tasks to the cloud system environment, the tasks first arrive at the task manager component, which organizes the tasks and provides the status of each requested task to the end user. It then forwards the requested tasks to the next component, the task scheduler. The task scheduler assigns all the tasks to the available and suitable VMs by employing the scheduling algorithm. Each VM is considered available if it has finished the processing of previous tasks, or if it does not have a task up ahead. The goal is to effectively use available VMs for tasks processing, but not to overload the cloud system. The result of overloading the cloud system would be malfunctions, end-user dissatisfaction, etc.  
The basic goal of tackling the resource scheduling problem in cloud infrastructure is allocation of available cloud system resources to submitted tasks, by achieving one or more objectives, like minimization of the makespan (MS) or time completion of the tasks from the queue, minimization of the total cost and energy consumption by cloud resources, etc. [26; 75].  
In modern cloud computing literature, the most commonly used resources are VMs. For that reason, throughout the rest of this paper, the terms VMs and resources will be used interchangeably.
## 1 Introduction  
Surface traffic cannot meet the development needs of a large city, therefore underground tunnel construction is gradually becoming the primary option for urban transportation projects [1]. Earth pressure balance (EPB) is a common method for underground construction in cities with dense surface buildings. Viewed from the construction perspective, the main construction area of an urban tunnel is concentrated in the soft layer under the dense building group in cities [2]. This will inevitably cause settlement due to the effects of two major factors: shallow depth and soft ground [3; 4; 5]. Once ground settlement occurs, building safety and other public utilities above the construction area are seriously threatened. Therefore, predicting ground settlement caused by tunneling is an essential part of the risk analysis of urban tunnel projects [6; 7; 8]. To provide an important foundation for assessing potential risks and adjusting construction plans [9], four methods are commonly used to assess ground settlement caused by tunnel construction, i.e., empirical [10; 11], analytical [12], numerical [13; 14], and artificial intelligence methods [15; 16; 17; 18]. Although the empirical method has a longer history of application, which not only involves incomplete parameters, it also does not have the best prediction accuracy [19]. Some widely used empirical equations are listed in Table 1. Subsequently, analytical, and numerical simulation methods were used to calculate ground settlement. In most real-world situations, the analytical method struggles to recreate the excavation process during the calculation; as a result, numerous simplifications were applied in the calculation process, leading to biased results [20]. [PERSON] and [PERSON] [21] pointed out the shortcomings of numerical simulation methods in terms of timeliness and the problem of considering boundary difficulties. Therefore, avoiding the complex process of reverting tunnel construction in the calculation process and the method of exploring the relationship between numerous weakly correlated parameters becomes a challenge in ground settlement prediction. Table 1 displays a selection of studies pertaining to empirical, analytical, and numerical methods for calculating ground settlement. Artificial intelligence methods have received focused attention due to the fact that they do not require prior experience as support, as well as having the ability to work with multiple influential variables. [PERSON] [44] introduced a support vector machine (SVM) approach for predicting ground settlement in shallow soils and analyzed the importance of parameters affecting settlement prediction. [PERSON] et al. [45] found that artificial neural network (ANN) has the better performance for the pile foundation settlement prediction than other conventional artificial intelligence methods. Moreover, artificial intelligence
# LEO-Based Satellite Constellation for Moving Target Detection
###### Abstract  
With the rapid development of cooperative detection technology, target fusion detection with regard of LEO satellites can be realized by means of their diverse observation configurations. However, the existing constant false alarm ratio (CFAR) detection research rarely involves the space-based target fusion detection theory. In this paper, a novel multi-source fusion detection method based on LEO satellites is presented. Firstly, the pre-compensation function is constructed by employing the range and Doppler history of the cell where the antenna beam center is pointed. As a result, not only is the Doppler band broadening problem caused by the high-speed movement of the satellite platform, but the Doppler frequency rate (DFR) offset issue resulted from different observation configurations are alleviated synchronously. Then, the theoretical upper and lower limits of DFR are designed to achieve the effective clutter suppression and the accurate target echo fusion. Finally, the CFAR detection threshold based on the exponential weighted likelihood ratio is derived, which effectively increases the contrast ratio between the target cell and other background cells, and thus to provide an effective multi-source fusion detection method for LEO-based satellite constellation. Simulation results verify the effectiveness of the proposed algorithm.  
LEO; fusion detection; clutter suppression; doppler ambiguity; CFAR Article  
LEO-Based Satellite Constellation for Moving Target Detection