esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Repeating Ground Track Orbits  
Repeating ground track orbits are usually used for remote sensing satellites to obtain a good appearance for orbital covering. For satellites on these orbits, the ground tracks can repeat periodically. In this way, the area of Jupiter covered by the satellite can be dynamically monitored to detect the change of the target during this period of time. For  
Figure 8: \(e\sim\omega\) evolution over \(25T\), red points correspond to \(a=1.6832R=120,335.334\) km and \(i=63.497\) deg; green points correspond to \(a=2.1488R=153,622.009\) km and \(i=63.431\) deg.  
example, we can make use of these orbits to detect the change of the Great Red Spot. The interval of adjacent ground track in the equator is  
\[\Delta\lambda=T_{N}\Big{(}n_{I}-\dot{\Omega}\Big{)} \tag{29}\]  
where \(T_{N}=\frac{2\pi}{M+\dot{\omega}}\) is the nodal period of the motion of the spacecraft. Using the mean element theory, we have [36; 37]  
\[\begin{array}{l}\dot{M}=n+\frac{3n_{I}R^{2}}{2p^{2}}\big{(}1-\frac{3}{2}\sin^ {2}i\big{)}\sqrt{1-e^{2}}+\frac{9n_{I}^{2}R^{4}}{4p^{4}}\sqrt{1-e^{2}}\Big{\{} \frac{1}{2}\big{(}1-\frac{3}{2}\sin^{2}i\big{)}^{2}\sqrt{1-e^{2}}\\ +\frac{5}{2}+\frac{10e^{2}}{3}-\sin^{2}i\big{(}\frac{9}{3}+\frac{26e^{2}}{3} \big{)}+\sin^{4}i\big{(}\frac{233}{48}+\frac{100e^{2}}{12}\big{)}\\ +\frac{e^{4}}{1-e^{2}}\Big{(}\frac{35}{12}-\frac{35}{4}\sin^{2}i+\frac{315}{3 2}\sin^{4}i\big{)}-\frac{35I_{1}}{18I_{2}^{2}}e^{2}\Big{(}\frac{9}{14}-\frac {45}{14}\sin^{2}i+\frac{45}{16}\sin^{4}i\Big{)}\bigg{\}},\end{array} \tag{30}\]  
where \(n\) is the mean angular velocity. The conditions for repeating ground track orbits can be formulated as  
\[DT_{N}\Big{(}n_{I}-\dot{\Omega}\Big{)}=D\Delta\lambda=2\pi N \tag{31}\]  
where \(D\) and \(N\) are positive integers. Equation (31) implies satellites will have a repeating ground track after completing \(D\) revolutions in \(N\) Jovian days. The ground track repetition parameter, \(Q=\frac{R}{N}\), which represents the number of orbital resolutions in a Jovian day, is widely used in engineering practice. Using this parameter combined with the definition of \(T_{N}\), Equation (31) is equivalent to  
\[Q=\frac{\dot{M}+\dot{\omega}}{n_{I}-\dot{\Omega}}. \tag{32}\]  
We can see that once \(Q\) is fixed, \(a\), \(e\) and \(i\) will be related by Equation (32). The repeating ground track orbits, which are also sun-synchronous, may play an important role in planetary exploration. For these orbits, Equation (16) was also satisfied. The change of inclinations of sun-synchronous repeating ground track orbits, with respect to the semi-major axis for different values of \(Q\), are presented in Figure 9. One can see that for a given \(Q\), small variations of the semi-major axis produce huge changes in the inclination. Moreover, for orbits with the same semi-major axis, the inclination,\(i\) will be larger if \(Q\) is larger. For the Earth and Mars, the inclination is not so sensitive to the semi-major axis [9].
## 2 Study Area, Data, and Methodology
### Data
#### 2.2.2 Ground-Based Precipitation Data  
Twenty-year (2001 to 2020), quality-controlled, and homogenous gauged precipitation data were collected from the Department of Hydrology and Meteorology (DHM), Government of Nepal. A total of 40 sparsely distributed representative precipitation stations were selected, falling under different climate classes and altitudinal locations, though only 25 stations with less than 10% data gaps were used during the study period (Figure 1).
## 2 Materials and Methods
### Variable Selection and Data Sources
#### 2.2.3 Statistical Methods
They extend traditional econometric models by incorporating spatial factors to capture the mutual influence and dependency between neighboring areas in geographic space. Specifically, spatial econometric models introduce a spatial weight matrix, which measures spatial correlation and spatial adjacency among different areas, reflecting the degree of interconnectivity in geographic space. The matrix includes endogenous interaction (WY) and exogenous interaction (WX). The most commonly used spatial econometric models include the Spatial Lag Models (SLM) and Spatial Error Models (SEM). The Spatial Durbin Model (SDM) simultaneously incorporates \(WY\) and \(WX\) interaction effects and is expressed as follows:  
\[Y=\rho WY+X\beta+WX\theta+\varepsilon,\varepsilon\sim N\Big{(}0,\delta^{2} \Big{)}\]  
where \(Y\) represents the dependent variable, \(W\) is the spatial weight matrix used to capture the spatial correlation between the sample units, \(X\) is a matrix of independent variables, \(\beta\) is a coefficient vector of the independent variables, \(\theta\) is a coefficient for the exogenous interaction effect, \(WX\) is a matrix of spatially lagged independent variables, and \(\varepsilon\) is an error term. Assuming that \(\varepsilon\) follows a multivariate normal distribution, with zero mean and a constant scalar diagonal variance-covariance matrix, \(\delta^{2}\). When \(\theta=0\), it correspondsto the SLM. When \(\theta=-\rho\beta\), it corresponds to the SEM. When the error term of the model exhibits spatial correlation, it is known as the SLM and expressed as follows:  
\[Y=\rho WY+X\beta+\varepsilon,\varepsilon\sim N\Big{(}0,\delta^{2}\Big{)}\]  
When the spatial dependency among the dependent variables leads to spatial correlation in the model, it is referred to as the Spatial Error Model, also known as the SEM, and is expressed as follows:  
\[Y=X\beta+\lambda W_{u}+\varepsilon,\varepsilon\sim N\Big{(}0,\delta^{2}\Big{)}\]  
where \(u\) is the random error vector, and \(\lambda\) is the spatial correlation coefficient among the regression residuals.
# Imitating Human Go Players via Vision Transformer
## 2 Background and Related Work  
### Deep Learning and Go  
Deep learning has been applied across various fields, including time-series data prediction [7], medicine [8, 9], and games [2, 10, 11, 12, 13, 14]. In the domain of board games, winning strategies typically involve two main steps: evaluating the position and identifying the best move. However, designing a hand-crafted evaluation function for complex games such as Go is particularly challenging. To address this, researchers have utilized convolutional neural networks (CNNs) to learn directly from expert gameplay, bypassing the need for manual evaluation function design.  
In 2003, [PERSON] et al. [10] used hand-crafted features and proposed feature extraction methods such as the Eigenspace Separation Transform, combined with dimensional reduction, to train a multi-layer perceptron (MLP). They achieved 25% accuracy on professional games. Subsequent studies saw advancements with the adoption of CNNs. In 2008, [PERSON] and [PERSON] [11] used CNNs to mimic Go experts, employing previous moves and the current position as input. They achieved 34% accuracy for a single CNN model and 39.9% for an ensemble of CNNs. In 2014, [PERSON] and [PERSON] [12] employed a more sophisticated feature set containing edge encoding and Ko constraints to train an eight-layer CNN, achieving 41.1% and 44.4% accuracy on two different Go datasets. In2015, [PERSON] et al. (2015) used a large feature set incorporating 36 feature planes, including the rank of the player and ladder patterns. They also used a deeper 12-layer CNN and achieved 55% accuracy. The work of [PERSON] ([PERSON] et al., 2016) included a supervised learning stage to train the policy network using Go expert data. Their model used a much larger dataset with 48 feature planes and a 12-layer CNN, reaching 55.4% accuracy on the test set. These results highlighted the feasibility of applying CNNs to imitate human expert moves.  
However, in Section 1, we pointed out the limitations of CNNs and aimed to demonstrate that Vision Transformers (ViTs) could outperform CNNs, making them a better candidate for imitating Go experts. We compared the performance of ViTs and CNNs on a dataset collected from the KGS Go server ([PERSON] et al., 2015). The models used were based on the same architecture as the policy network from AlphaGo ([PERSON] et al., 2016) and the 12-layer CNN from [PERSON] (2016). The results indicated that ViT could predict more expert moves correctly on the test set.
## 2 Results
### Genes Related to Acteoside Biosynthesis in R. glutinosa  
The biosynthetic pathway of acteoside in _Olea europaea_ has been characterized [22]. In _R. glutinosa_, two biosynthetic pathways are involved in the biosynthesis of the processor of phenylethanolamineglycos: the phenylalanine-derived pathway and the tyrosine-derived pathway [18] (Figure 7A). In the phenylalanine-derived pathway, phenylalanine is metabolized to caffeoyl-CoA through phenylalanine ammonia-lyase (PAL), cinnamate-4-hydroxylase (C4H), coumarate-3-hydroxylase (C3H), and 4-coumarate-CoA ligase (4 CL). In the tyrosine-derived pathway, tyrosine is transformed into hydroxytyrosol glucoside by tyrosine decarboxylase (TyDC)/DOPA decarboxylase (DODC), copper-containing amine oxidase (CuAO), alcohol dehydrogenase (ALDH), polyphenol oxidase (PPO), and UDP-glucose glucosyltransferase (UGT). Caffeoyl-CoA and hydroxytyrosol glucoside were further catalyzed by shikimate \(O\)-hydroxycinnamoyltransferase (HCT) and UGT to form acteoside.  
In total, 223 unigenes encoding nine enzymes involved in acteoside biosynthesis have been identified from the novel _R. glutinosa_ transcriptome. RNA-seq analysis revealed that 21, 24, 11, and 15 were DETs in the four RS vs. nRS comparisons, and 13, 12, 10, and 10 were upregulated genes, respectively (Table S9). A PAL gene, _CL2036.Contig6_, was significantly upregulated in non-radial striations for every cultivar. _CL12387.Contig2_, by contrast, was significantly downregulated in non-radial striations of all cultivars (Table S9, Figure 7B). Four unigenes, including _CL3689.Contig5_ (C4H), _CL8283.Contig2_ (ALDH), _CL4530.Contig2_ (4 CL), and _CL2036.Contig7_ (PAL), were found as DETs in at least three cultivars. It is very interesting that the values of Log\({}_{2}\) (nRS/RS) of _CL7017.Contig3_ (PPO) and _CL4500.Contig3_ (TyDC) in 1706, BJ1, and QH1 were higher than 1, while the Log\({}_{2}\) Ratio (nRS/RS) value in W85-5 was less than 0.5, which is similar to the change in acteoside between radial striation and non-radial striation among the four cultivars (Table S9, Figure 7B). qRT-PCR analysis showed that the three unigenes, including _CL2036.Contig6_ (PAL), _CL4530.Contig2_ (4 CL), and _CL7017.Contig3_ (PPO), were significantly upregulated in non-radial striations of the four cultivars, while _CL4500.Contig3_ (TyDC) was only significant up-regulated in non-radial striation of the three cultivars, including 1706, BJ1, and QH1 (Figure 7C), which coincides well with the transcriptome analysis. The relative expression status of pathway genes between radial striations versus non-radial striations thus provided a realistic association with the biosynthesis of acteoside and phenylethanolamine glycosides.
# An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment
## 3 Algorithm Implementation
### Quantization Strategy  
The model size of SegNet is 112.3 MB, which should be compressed by pruning, quantization, coding and other means [20] before running on the embedded platform to reduce the computational cost and storage consumption. The method used in this paper is linear quantization, and the overall process is shown in Figure 3.  
First, convolution and Batch Normalization (BN) are combined into one operation, because the model parameters are fixed during inference. For SegNet, each convolution layer is followed by a BN layer. Merging these two layers can reduce the use of computing resources and precision loss and improve the inference speed. Next, the data distribution of filters in a convolution layer is calculated. Filters with similar data distribution are merged into one group, corresponding to the same scale factor \(f\). The \(f\) determines the decimal point position of the quantized dynamic fixed-point number. Equations (1) and (2) take the weight as an example to introduce the calculation of the \(f\). The \(w\) and \(w^{\prime}\) represent the weight before and after quantization, and \(N\) represents the bit width of the fixed-point number. \(f\) is designed as the integer power of 2, so that the shift can be used instead of the multiplication, which is well implemented on FPGA. Load the preprocessed input image for convolution, and the generated output activations are arranged in the channel dimension. Use the same method described above to calculate the decimal position of the output activation \(f_{o}\), and the \(f_{o}\) of the current layer is the \(f_{i}\) (decimal position of the input data) of the next layer. To ensure the following correct calculation, a set of \(1\times 1\) filter is used to complete the reordering of feature maps. Subsequent pooling and unpooling operations are unchanged. After the final convolution layer, the quantized model and the corresponding scale factor are exported. The whole quantization operation is processed  
Figure 2: The DS-SLAM framework on Heterogeneous Extensible Robot Open (HERO).  
in the host CPU in advance, and will not increase the inference latency on FPGA. The 8-bit quantized SegNet algorithm is evaluated on the Pascal VOC dataset [21]. The global accuracy is 87.3%, the class accuracy is 69.4%, and the mean IoU is 51.5%, which is 0.8%, 1.1%, and 1.6% lower than the original model. The quantized model dramatically reduces the storage requirements and computational complexity without significant precision loss.  
\[f_{w}=ceil(log_{2}\frac{2^{N}-1}{max(|w|)}) \tag{1}\]  
\[w^{\prime}=round(\frac{w}{2^{-f_{w}}}). \tag{2}\]  
Figure 3: The process of quantization.
## 3 Results
### Cost Analysis of AM vs. CM for GIS Aluminum Components
#### 3.2.2 Cost Analysis of Spare Parts and Prototyping  
Spare parts management comes with a long list of challenges; high storage and logistics costs, overproduction caused by minimum purchase volumes, and long waiting times are standard and prevalent and, sometimes, the supplier no longer offers the part by the time it is needed. AM offers a solution here. Spare parts made from metals can be produced on demand by a 3D printer without tools and in the exact quantity required, without a minimum purchase volume. This avoids unnecessary overproduction and guarantees maximum customer satisfaction. The underlying idea is instead of resource-intensive warehouse logistics, the CAD data of the spare parts are saved and only sent when necessary. Moreover, as the pioneering technology leader, Hitachi Energy collaborates with customers and partners to enable a sustainable energy future--for today's generations and those to come [15]. Therefore, besides the advantages of spare parts-on-demand, AM's aim at clean and sustainable technologies is especially considerable.  
Prototyping for technology and R&D centers also has the same situation as spare parts manufacturing in terms of lead time. The design and manufacturing of special tools and a long lead time of up to 6 months for each part in the development phase are significant challenges for designers. AM is a suitable solution that can be considered for prototyping with short order to delivery time and without any need for additional tools.  
In this study, since the exact effect of the delivery time of spare parts to the customer is uncertain, we tried to make this comparison based on only the cost of the required tools and consumables. As a result, if only the cost of production is considered, AM and 3D printing are efficient for parts currently produced by the casting method and require the design and manufacturing of special steel molds. The reason is that in the production of spare parts and prototyping, the tool's price is considered for one part, and this tool may not be used anymore.  
According to the evaluations carried out in the case study (GIS-ELK-04/145 kV), between USD 8000 and 14000 should be paid for the design and production of the casting mold. If we assume the cost of the mold is USD 10,000 per unit on average and consider that the cost of AM and 3D printing is USD 0.8 per gram for aluminum alloys, then AM for-spare parts or prototypes weighing up to 12-13 kg will save money. This savings in cost is in addition to the fact that the waiting time is reduced from 3-6 months to two weeks.
## 5 Case Study  
### Test Setting  
The using state estimation and residual life prediction of electrical connectors studied in this paper are based on the intermittent failure re-appearance test. The random vibration stress is selected for intermittent failure re-presentation [20]. For improving efficiency, the accelerated vibration stress is applied, which helps to predict the residual life of connectors in a short time. The random vibration stress is generated by the DC1000 electrical vibration table made by the SuShi company (Suzhou, China). The connector is installed on the vibration table. While the table drives the connector vibration, the value of contact resistance is measured using the four-wire method. The linear DC power supply of Motech LPS-305 (MOTECH, Taiwan) is used for inputting constant current to exciting circuit. The output voltage is collected by the NI PXI4472 data acquisition card (NI, Austin, TX, USA). The contact resistance is the ratio of output voltage to constant current. The test window is set as 0.5 ms and the step length of the test window is set as 0.4 ms and the contact resistance curve is obtained in the intermittent failure re-appearance test. Combined with the fault threshold (\(R_{\text{th}}=50\)\(\Omega\)), the intermittent failure curve can be plotted, then the generalized severity of intermittent failure can be extracted using Equation (19).
## 5 Algorithm
### Parameters  
The parameters required by VLE are: \(\alpha\), \(\beta\), \(\delta\), \(char\), and \(tsys\).  
The parameter \(\alpha\) sets the number of solutions that will be evaluated in the search area, either in a big area (a _wide area neighbourhood_) or a little area (a _local area neighbourhood_). This parameter can be any odd number greater or equal to 3. By increasing \(\alpha\), the number of evaluations increases between one effective movement and another, therefore increasing the possibility of finding a local optimum.  
Figure 11: Movement for \(x_{2}\) in the exploitation stage: search of \(x_{2,t+1}\) starting from \(x_{2,t}\).  
Figure 12: Final value of \(x_{1}\) in the exploration stage: search of \(x_{1,t+1}\) starting from \(x_{1,t}\).  
The parameter \(\beta\) determines the minimum acceptance probability of bad solutions during the search process. The \(\beta\) value can be fixed or variable. If it is considered variable, its value will gradually decrease as a function of \(m\), the identification number of the current movement, from 1 to some value close to 0. The implicit assumption in making \(\beta\) variable is that, as \(m\) increases, the probability of accepting poor quality solutions decreases. In other words, when beta is nearby 1, there are not so much opportunities as for intensifying the search in a _local area neighbourhood_ starting from a bad solution, but when it is nearby 0, the opportunities are high.  
The parameter \(\delta\) puts a limit in the descending movements, allowing the algorithm to leave the local area when not any appreciable change is obtained in the fitness during the descend.  
The parameter _char_ allows characterizes again the chemical species after each restart. This parameter can be equal 1 or 0. The chemical species are characterised again when _char_ is equal to 1. This parameter introduces high randomness in the search.  
Last, the _tsys_ parameter establish the type of chemical system to be simulated. The value is 1 for system with chemical compounds very similar between them, 2 for compounds not so similar or not so different between them, or 3 for very different chemical compounds. If compounds are very similar or very different, the values of the decision values change slightly or significantly, respectively. In other words, the choice of the system, allows to direct the search either towards exploitation (_tsys_ = 1), towards a balance between exploitation and exploration (_tsys_ = 2), or towards exploration (_tsys_ = 3).  
Summarizing, _alfa_, _beta_ and _tsys_ influence a lot in the search of the best solution.  
The experiments considered _alfa_ from 3 to 35, _beta_ variable, and _tsys_ equal to 1, assuming that all the reference functions were extremely complex.
# Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review
## 4 Applications of Microseismic Monitoring in Coal Mine
### Water Intush
[109] used theoretical analysis, finite element modeling, and microseismic monitoring to reveal the generation mechanism of the water-conducting channel in the floor of the deep mining face of the Xingdong coal mine and assess the performance of countermeasures for disaster control employing multi-level cooperative grouting reinforcement. [PERSON] et al. [110] used the variation in microseismic event counts and spatial location to study the fracture and development of crack structures in the floor of a longwall panel of the Xingdong coal mine; furthermore, they established a warning system for floor water inrush based on monitoring technologies of crack structures.
# Advancing Multi-UAV Inspection Dispatch Based on Bilevel Optimization and GA-NSGA-II
## 3 Solution Algorithm  
In response to the multi-UAV power grid inspection scheduling problem, this paper innovatively proposes a bilevel optimization-based GA-NSGA-II algorithm framework, as shown in Figure 3. Through the segmentation of power grid lines, the large-scale inspection task is decomposed into smaller and easy to manage unit tasks, and each unit task is regarded as the minimum dispatching unit. The algorithm employs a bilevel optimization strategy: the inner-layer optimization uses an adaptive genetic algorithm to optimize the task execution sequence within a single UAV's domain, while the outer-layer optimization utilizes the NSGA-II algorithm for task assignment and optimization.
## 2 Methodology
### Evolutionary Spectral Clustering
#### 2.4.1 Preserving Cluster Quality (PCQ)  
In the context of spectral clustering, we consider two partitions, \(Z_{t}\) and \(Z_{t}^{\prime}\), which are the results of clustering the transport network at a time period \(t\). We assume that the two partitions give similar performance at time period \(t\), but in the case of clustering historic data, the clustering performance using partition \(Z_{t}\) is preferred because it is more consistent with historic data. In this case, the evolutionary normalized cut's total cost can be expressed as follows [18]:  
\[TotalCost=\alpha\;NC_{t}|_{Z_{t}}+(1-\alpha)\;NC_{(t-1)}|_{Z_{t}}, \tag{10}\]  
where \(|_{Z_{t}}\) means evaluated by the partition \(Z_{t}\). For a given partition, the normalized cut can be written following Equation 7. After some manipulation, we obtain the following total cost:  
\[TotalCost=K-Tr\Big{[}X_{t}^{T}\Big{(}\alpha\,D_{t}^{-1/2}W_{t}D_{t}^{-1/2}+(1- \alpha)\,D_{t-1}^{-1/2}W_{t-1}D_{t-1}^{-1/2}\Big{)}X_{t}\Big{]}. \tag{11}\]  
The PCQ framework consists of finding the optimal solution that minimizes the total cost. This problem can be converted to a trace maximization problem, where a solution is the matrix \(X_{t}\) whose columns are the K-eigenvectors associated with the top K-eigenvaluesof the evolutionary Laplacian matrix defined by the combination of both normalized similarity matrices at time period \(t\) and \((t-1)\)[18]:  
\[L_{pcq}=\alpha\left(D_{t}^{-1/2}W_{t}D_{t}^{-1/2}\right)+(1-\alpha)\left(D_{t-1 }^{-1/2}W_{t-1}D_{t-1}^{-1/2}\right). \tag{12}\]  
This equation is used in two consecutive times \(t\) and \(t-1\). After obtaining \(X_{t}\) at time period \(t\), we can project data points into \(span(X_{t})\) and then apply the K-means clustering algorithm to obtain the final clusters.