esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Satellite Observation-Relay Transmission-Downloading Coupling Scheduling Method
## 2 Problem Description
### Improvement of Observation Solution Local Search Based on Satellite Cluster
#### 3.3.2 Satellite Cluster Profit-State Evaluation Function
This term implies that when there are satellites with an insufficient satellite-ground data transmission time window in the current solution, neighborhood solutions with more satellite-ground data transmission time will receive a higher evaluation value, guiding the search direction towards solutions with more available satellite-ground data transmission time. \(Flag_{kmpq}\) is calculated as  
\[Flag_{kmpq}=\left\{ \begin{matrix}1&\min(t_{\textit{owska}io})\leq t_{dubsmq},x_{ki}=0,i=1,2,\ldots,n_{t},k=1,2,\ldots,n_{r},o=1,2, \ldots,n_{\textit{owski}},\\ 0&\textit{otherwise}\end{matrix} \right. \tag{19}\]  
where \(\min(t_{\textit{owska}io})\) is the minimum end time of all observation windows of satellite \(k\) for unobserved targets. When there exists an unobserved target and the observation time window end time is earlier than the satellite-ground data transmission time window start time, the satellite-ground data transmission window is considered valid. \(\Delta t_{dRemaink}\) is calculated as  
\[\Delta t_{dRemaink}=t_{duckkmut_{dubsmq}}-t_{dusskmut_{dubsmq}}-\sum\limits_{ i=1}^{n_{t}}\sum\limits_{m=1}^{n_{t}}(t_{dckmi}-t_{dskmi})\quad[t_{\textit{ dskmi}},t_{\textit{dckmi}}]\subseteq[t_{\textit{duskmut_{dubsmq}}},t_{\textit{ duckm}_{dubsmq}}],m=m_{\textit{tskmax}}, \tag{20}\]  
where \(m_{\textit{tskmax}}\) stands for the ground station index corresponding to the maximum end time of satellite \(k\)'s satellite-ground data transmission time window. The equation represents the available satellite-ground data transmission window time of satellite \(k\). The last satellite-ground data transmission time window is representative, so it is used in the equation for calculation.
## Materials and Methods
### Analyses
Nomenclature: \(X\): Biomass concentration (dry cell weight--DCW, g/L); _Gly:_ Glycerol concentration (g/L); \(t\): reaction time (h); \(CA\): Citric acids (citric + iso-citric acid) concentration (g/L); MI: Mannitol concentration (g/L); Ara: Arabitol concentration (g/L); Ery: Erythritol concentration (g/L); _Pol_: Total polyols concentration (g/L); \(q_{Pol}\): specific polyols formation rate, g of polyols per g of biomass per h; \(Y_{PolGly}\): yield coefficient for total polyols production with respect to glycerol consumed, g\({}_{Pol}\)/g\({}_{Gly}\) (g of total polyols produced per g of glycerol consumed); \(Y_{CAGly}\): yield coefficient for citric acid production with respect to glycerol consumed, g\({}_{CA}\)/g\({}_{Gly}\) (g of citric acid produced per g of glycerol consumed); \(Y_{XGly}\): yield coefficient for DCW production with respect to glycerol consumed, g\({}_{X}\)/g\({}_{Gly}\) (g of DCW produced per g of glycerol consumed); r\({}_{CA}\): rate of citric acid production (g\({}_{CA}\) per L per h); r\({}_{Gly}\): rate of glycerol consumption (g\({}_{Gly}\) per L per h); r\({}_{X}\): rate of biomass production (g\({}_{X}\) per L per h); \(\mu\): specific biomass production rate (h\({}^{-1}\)), calculated in batch cultures by the formula \(\mu_{\max}=\frac{\ln\left(\frac{X_{1}}{X_{0}}\right)}{t_{1}-t_{0}}\) where \(X_{1}\) was the first experimental point after inoculation and \(t_{1}\) was the respective time; \(X_{0}\) was the initial DCW quantity and \(t_{0}\) = 0 h; \(P_{CA}\): Citric acids volumetric productivity (g\({}_{CA}\) per L per h); indexes 0 and cons refer to the initial and the consumed quantity of the elements in the experiments carried out; index max refers to the maximum value of a particular parameter in the cultures.
## 2 Methodology
### Bayesian Optimization-Based Temporal Attention-LSTM Model  
The training of a temporal attention-LSTM model involves a variety of hyperparameters. An appropriate tuning of hyperparameters is crucial for achieving the optimal predictive performance of the model [42]. Current hyperparameter optimization algorithms  
Figure 1: Model structure of (**a**) LSTM, (**b**) GRU.  
Figure 2: Temporal attention-LSTM model.  
include grid search (GS), random search (RS) [43], genetic algorithm (GA) [44], particle swarm optimization (PSO) [45], and Bayesian optimization (BO) [46]. Among them, GA, PSO, and GS are inefficient for the hyperparameter tuning of neural network models due to the vast number of hyperparameters involved [47]. RS randomly selects hyperparameter combinations in the hyperparameter space, but due to its random nature, it may not always find the most optimal hyperparameter settings, potentially missing better solutions. Unlike other methods, Bayesian hyperparameter optimization adjusts and guides the search for hyperparameters based on the results of the previous performance of the loss function. It predicts which hyperparameter combinations might yield better results by analyzing the performance of existing trials, making the search process for the global optimum more efficient [48; 49]. As shown in Figure 3, the Bayesian hyperparameter optimization process for a temporal attention-LSTM model begins with the initialization of the hyperparameter space, where the range of possible values for each hyperparameter is defined. Hyperparameter space includes the number of LSTM units, learning rate, batch size, and attention units. After the temporal attention-LSTM model is trained and evaluated with these hyperparameters, it generates a series of results that reflect the model performance. Then, the tree-structured Parzen estimator (TPE) method is applied to optimize and select a new set of hyperparameters for further training and evaluation. The training results are used to build two probability models that estimate the probability of attaining either better or worse predictive results with a new hyperparameter set. The calculated probability ratio, derived from dividing the probability of achieving better predictive performance by the probability of worse predictive performance, guides the selection of hyperparameters. Hyperparameter combinations with a higher probability ratio are prioritized for the next iterative training and evaluation of the model. This iterative process continues until a preset maximum number of iterations is reached and the optimal hyperparameter combination is obtained.
# Flattening the Curve of Flexible Space Robotics [PERSON]
## 2 Materials and Methods
### Gain Stabilization  
Gain stabilization begins by asserting a specified form of the feedback control. Both classical and modern (state space and optimal) control instantiations ubiquitously impose \(u=-Kx\), where the control is formulated as a negative feedback of state errors multiplied by user-designed gains. Controllers with proportional plus integral plus derivative natures are called PID controllers, and they remain quite popular due to their robustness, simple structure, ease of implementation, and continuing active research [39; 40; 41; 42] in tuning the three gains associated with proportional, integral, and derivative components, respectively [43]. Equation (29) illustrates the equations for a control with proportional plus velocity components whose resulting closed-loop equation is displayed in Equation (30), which may be expressed in terms of active damping, active stiffness, and feedforward in Equation (31). Expression of the closed-loop system in the Laplace domain as a transfer function is represented in Equation (32).  
\[u=K_{p}(\theta_{d}-\theta)+K_{p}\omega\ \to J\dot{\omega}=T_{W}\equiv u(t) \tag{29}\]  
\[I\bar{\theta}+K_{V}\dot{\theta}+K_{p}\theta=K_{P}\theta_{d} \tag{30}\]  
\[I\bar{\theta}+\underbrace{K_{V}\dot{\theta}}_{active}\quad+\underbrace{K_{P} \theta}_{active}\quad=\underbrace{K_{P}\theta_{d}}_{feed}\quad\rightarrow \Bigl{[}Is^{2}+K_{v}s+K_{p}\Bigr{]}\theta(s)=K_{P}\theta_{d}(s) \tag{31}\]  
\[\underline{\theta(s)}=\frac{K_{p}}{Is^{2}+K_{v}s+K_{p}}\to C.E.:\ s^{2}+K_{v}s+K_{p}\Bigr{|}_{I=1}=s^{2}+2\xi \omega_{n}s+{\omega_{n}}^{2} \tag{32}\]  
Gains may be designed foremost for stabilization, and additionally for performance specifications, e.g., rise time, peak overshoot, settling time, etc. While these specifications  
Figure 10: Harmonics used to properly place \(\omega_{0}\). Fourier transform of the periodic signal with fundamental frequency \(\omega_{0}\) with frequency on the abscissa, and the response amplitude on the ordinate. Notice the relatively easier (than using step functions) task of placing \(\omega_{0}\) in (a) at locations other than the locations of the resonant and anti-resonant peaks of Figure 9. The task using harmonic signals is slightly more challenging due to the necessity to properly place \(\omega_{0}\) and all the harmonics depicted in Figure 10.  
are met, residual oscillatory vibrations remain complicating during on-orbit robotic operations. The transfer function expression particularly reflects the natural frequencies of the system, and these frequencies guide the subsequent steps in the classical approach: structural filtering to tailor the compensation at these frequencies.
## 6 Eukaryotic Aminoacyl-tRNA Protein Transferase and tRNA Substrates  
As mentioned previously, aminoacyl-tRNA protein transferases are also widely found in eukaryotes [89]. The eukaryotic enzyme (Ate1) utilizes tRNA\({}^{\text{Arg}}\) as a substrate and the resultant protein arginylation leads to a number of different outcomes depending on the substrate protein in question. N-terminal arginylation has long been known to result in proteasome dependent protein degradation for many proteins [89], but additional reports have described the targeting of some proteins for autophagy dependent degradation [90] or stabilization [91; 92]. In addition, other examples have been reported that do not alter protein stability but appear to alter the protein function [93; 94; 95]. What may be unique to ATE1 _versus_ the bacterial L/F-transferase is the ability to transfer the amino acid from the tRNA substrate to the side chain of proteins in addition to the N-termini [96; 97]. As with the bacterial L/F-transferase, the novel N-terminal addition of amino acids to peptides from aminoacyl-tRNA substrates have also resulted in the proposed use of these enzymes for biochemical applications [98; 99].  
To date, nothing has been reported regarding tRNA\({}^{\text{Arg}}\) isoacceptor utilization by Ate1, nor is there any insight regarding whether this enzyme must compete for aminoacyl-tRNA substrates with the eukaryotic elongation factors. It is possible that investigations may eventually reveal that there are specialized tRNA\({}^{\text{Arg}}\) isoacceptors in eukaryotes for Ate1. With the high number of genomically encoded tRNA\({}^{\text{Arg}}\) isoacceptors (21 in yeast and 28 in humans) [52], this is an exciting possibility of specialized tRNAs.
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 3 5G Coverage Enhancement Techniques
### Non Orthogonal Multiple Access
#### 3.4.5 NOMA--Future Challenges and Research Issues  
NOMA can be included with the other 5G enabling technologies such as MIMO, CR, HetNets, millimetre wave, and so on. This helps in achieving the 5G key parameters such as improved spectral efficiency, low latency, high data rates, extended coverage capacity, and mass connectivity. However, the inclusion of NOMA with 5G and beyond 5G leads to some issues and challenges such as resource allocation, pairing, decoding, security, and signalling. These issues can be overcome by using some technologies and techniques such as optimization and analytical techniques, game theory, and machine learning algorithms.
# Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding
## 5 Conclusions  
In this work, we present a novel image segmentation algorithm for IR images with stronger boundary adherence capability and reduced trade-off between compactness and segmentation accuracy. Compared with traditional grid seed sampling, our seed sampling strategy based on contour encoding is capable of scattering initial seed points depending on the image content. This seed strategy and contour constraint allow our method to maintain better boundary adherence with fewer superpixels. In smooth regions, a large superpixel is generated by a merging operation, which can avoid over-segmentation; while, in texturally complex regions, adaptively sized superpixels can accurately segment the objects. Experimental results on our self-built IR dataset and the public datasets BSD500 and 3 Dircadb demonstrate that SSCE has a high generalization ability and exhibits better performance than state-of-the-art algorithms, in terms of segmentation accuracy and compactness.  
Since superpixels are generated based on homogeneity within the image region, the associated semantics are reduced. Therefore, future work will focus on increasing the semantics of superpixels. Moreover, the accelerating effect of superpixel segmentation on pattern recognition will potentially be explored as well.  
All of the authors contributed to this study. Conceptualization, W.L. and F.L.; Methodology, W.L. and F.L.; Software, W.L.; Data curation, Z.M. and J.M.; Funding acquisition, F.L.; Writing--Original draft preparation, W.L.; Writing--review and editing, W.L., Z.M. and J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shanghai Key Laboratory of Criminal Scene Evidence funded Foundation (Grant No. 2017 xcwzk08) and the Innovation Fund of Shanghai Institute of Technical Physics (Grant No. CX-267).  
Not applicable.  
The authors declare no conflict of interest.
# Rotation Accuracy Analysis of Aerostatic Spindle Considering Shaft's Roundness and Cylindricity
## 2 Mathematical Model
### Numerical Analysis  
With reference to paper [20], some items in Equation (7) are rewritten as follows by using the finite difference method:  
\[\frac{\partial}{\partial X}\left(H^{3}\frac{\partial P^{2}}{\partial X}\right) =\frac{H_{i+0.5,j}^{3}}{\Delta X^{2}}\left(P_{i+1,j}^{2}-P_{i,j}^{2} \right)-\frac{H_{i-0.5,j}^{3}}{\Delta X^{2}}\left(P_{i,j}^{2}-P_{i-1,j}^{2}\right) \tag{8}\]  
\[\frac{\partial}{\partial Z}\left(H^{3}\frac{\partial P^{2}}{\partial Z}\right) =\frac{H_{i,j+0.5}^{3}}{\Delta Z^{2}}\left(P_{i,j+1}^{2}-P_{i,j}^{2} \right)-\frac{H_{i,j-0.5}^{3}}{\Delta Z^{2}}\left(P_{i,j}^{2}-P_{i,j-1}^{2}\right) \tag{9}\]  
\[\frac{\partial PH}{\partial X}=\frac{H_{i+0.5,j}-H_{i-0.5,j}}{2\Delta X}P_{i,j} +\frac{H_{i+0.5,j}}{2\Delta X}P_{i+1,j}-\frac{H_{i-0.5,j}}{2\Delta X}P_{i-1,j} \tag{10}\]  
\[\frac{\partial PH}{\partial\tau}=\frac{H_{i,j}}{\Delta\tau}P_{i,j}-\frac{H_{( \tau-\Delta\tau)(i,j)}}{\Delta\tau}P_{(\tau-\Delta\tau)(i,j)} \tag{11}\]  
where \(H_{(\tau-\Delta\tau)(i,j)}\) and \(P_{(\tau-\Delta\tau)(i,j)}\) in Equation (11) are \(H_{i,j}\) and \(P_{i,j}\) in \((\tau-\Delta\tau)\) time, respectively.  
Figure 2: Circumference cylindrical coordinates.  
By substituting Equations (8)-(11) into Equation (7), the equation is simplified as  
\[E_{i,j}P_{i,j}^{2}+(F_{i,j}+I_{i,j})P_{i,j}-K_{i,j}=0 \tag{12}\]  
with  
\[A_{i,j}=\frac{H_{i+0.5,j}^{3}}{\Delta X^{2}} \tag{13}\]  
\[B_{i,j}=\frac{H_{i-0.5,j}^{3}}{\Delta X^{2}} \tag{14}\]  
\[C_{i,j}=\frac{x_{0}^{2}}{z_{0}^{2}}\frac{H_{i,j+0.5}^{3}}{\Delta Z^{2}} \tag{15}\]  
\[D_{i,j}=\frac{x_{0}^{2}}{z_{0}^{2}}\frac{H_{i,j-0.5}^{3}}{\Delta Z^{2}} \tag{16}\]  
\[E_{i,j}=A_{i,j}+B_{i,j}+C_{i,j}+D_{i,j} \tag{17}\]  
\[F_{i,j}=\Lambda\frac{H_{i+0.5,j}-H_{i-0.5,j}}{2\Delta X} \tag{18}\]  
\[G_{i,j}=\Lambda\frac{H_{i+0.5,j}}{2\Delta X} \tag{19}\]  
\[H_{i,j}=-\Lambda\frac{H_{i-0.5,j}}{2\Delta X} \tag{20}\]  
\[I_{i,j}=2\Lambda\frac{H_{i,j}}{\Delta\tau}+Q \tag{21}\]  
\[J_{i,j}=-2\Lambda\frac{H_{(t-\Delta\tau)(i,j)}}{\Delta\tau} \tag{22}\]  
\[K_{i,j}=A_{i,j}P_{i+1,j}^{2}+B_{i,j}P_{i-1,j}^{2}+C_{i,j}P_{i,j+1}^{2}+D_{i,j} P_{i,j-1}^{2}-G_{i,j}P_{i+1,j}-H_{i,j}P_{i-1,j}-J_{i,j}P_{(t-\Delta\tau)(i,j)} \tag{23}\]  
The dimensionless gas film pressure in \(\tau\) time is given by  
\[P_{i,j}=\frac{\sqrt{\left(F_{i,j}+I_{i,j}\right)^{2}+4E_{i,j}K_{i,j}-F_{i,j}-I _{i,j}}}{2E_{i,j}} \tag{24}\]
# Low-Latency Short-Packet Transmission over a Large Spatial Scale
###### Abstract  
Short-packet transmission has attracted considerable attention due to its potential to achieve ultralow latency in automated driving, telesurgery, the Industrial Internet of Things (IIoT), and other applications emerging in the coming era of the Six-Generation (6G) wireless networks. In 6G systems, a paradigm-shifting infrastructure is anticipated to provide seamless coverage by integrating low-Earth orbit (LEO) satellite networks, which enable long-distance wireless relaying. However, how to efficiently transmit short packets over a sizeable spatial scale remains open. In this paper, we are interested in low-latency short-packet transmissions between two distant nodes, in which neither propagation delay, nor propagation loss can be ignored. Decode-and-forward (DF) relays can be deployed to regenerate packets reliably during their delivery over a long distance, thereby reducing the signal-to-noise ratio (SNR) loss. However, they also cause decoding delay in each hop, the sum of which may become large and cannot be ignored given the stringent latency constraints. This paper presents an optimal relay deployment to minimize the error probability while meeting both the latency and transmission power constraints. Based on an asymptotic analysis, a theoretical performance bound for distant short-packet transmission is also characterized by the optimal distance-latency-reliability tradeoff, which is expected to provide insights into designing integrated LEO satellite communications in 6G.  
6G; short-packet transmission; URLLC; finite-blocklength coding; large spatial scale; relaying; propagation delay; end-to-end delay; decoding delay; asymptotic analysis +
Footnote †: journal: _Entropy_  
0000-0001-4000-0001-4000][PERSON]  
0000-0001-4000-0001-4000][PERSON]  
0000-0001-4000-0001-4000][PERSON]
## 2 Materials and Methods
### Methods
#### 2.3.4 Spatial Interpolation Method  
Spatial interpolation is very important for obtaining the spatial distribution characteristics of climate variables when meteorological stations are few and widely separated. In this paper, the gradient plus inverse distance squared (GIDS) method is used for interpolating climate variables and \(ET_{0}\)[36]. It is an inverse distance weighting method that takes into account the changes of meteorological factors with latitude, longitude and altitude. Previous studies have shown that this method has good applicability in areas with large terrain undulations and uneven site distribution [37]. The interpolating climate variable is calculated as follows [36; 37]:  
\[Z=[\sum_{i=1}^{N}\frac{Z_{i}+(X-X_{i})\times C_{x}+(Y-Y_{i})\times C_{y}+(E-E_ {i})\times C_{e}}{d_{i}^{2}}]/(\sum_{i=1}^{N}\frac{1}{d_{i}^{2}}] \tag{7}\]  
where \(Z\) is the estimated climatic variable, \(Z_{i}\) is the value at climate station \(i\), \(d_{i}\) is the distance from the site to climate station \(i\) and \(N\) is the number of climate stations used for the interpolation. \(X\), \(Y\) and \(E\) are the longitude, latitude, and altitude of the point to be estimated, respectively, \(X_{i}\), \(Y_{i}\) and \(E_{i}\) are the longitude, latitude, and altitude of climate station \(i\), respectively, and \(C_{x}\), \(C_{y}\) and \(C_{e}\) are regression coefficients for \(X\), \(Y\) and \(E\), respectively.  
The daily \(ET_{0}\) of 73 meteorological stations on the TP in 1970-2018 is calculated and transformed into \(ET_{0}\) at annual scale. The annual average \(ET_{0}\) on the TP is obtained by interpolating the annual \(ET_{0}\) of 73 stations to the centroid of the area using the GIDS method.
# Optimization of BP Neural Network Model for Rockburst Prediction under Multiple Influence Factors
## 3 Index Selection and Data Analysis  
The mechanism of rockburst is complex, and its causes are numerous. Considering the internal cause (physical and mechanical properties of the rock itself) and the external cause (e.g., stress concentration caused by excavation), this paper selected the ratio of maximum tangential stress of surrounding rock to rock uniaxial compressive strength (\(\sigma_{\rm{0}}/\sigma_{\rm{c}}\)), the ratio of rock uniaxial compressive strength to rock uniaxial tensile strength (\(\sigma_{\rm{c}}/\sigma_{\rm{t}}\)) and the elastic energy index (\(W_{\rm{et}}\)) [32] as prediction indexes. The reasons for selecting these indexes are as follows: \(\sigma_{\rm{0}}/\sigma_{\rm{c}}\)-this index considers the adjustment and change in stress after excavation of underground space and the stress concentration phenomenon of surrounding rock, which reflects the potential influence of the stress state of surrounding rock on the surrounding rock, and reflects the stress condition background of rockburst. The \(\sigma_{\rm{c}}/\sigma_{\rm{t}}\) index is the strength brittleness coefficient, which refers to the dynamic process of rockburst. The rock mass undergoes brittle fracture, and the surrounding rock also produces unstable failure, so the degree of brittle failure is closely related to rock eruption. \(W_{\rm{et}}\) is the elastic energy index that describes the conditions of rock eruption from the perspective of energy; its value reflects the energy storage characteristics of the rock mass, and the higher the rockburst, the more energy is released when a rockburst occurs. The intensity of the rockburst is graded into four levels: level I (none rockburst), level II(weak rockburst), level III (moderate rockburst) and level IV (strong rockburst).  
In this paper, 100 groups of rockburst samples were extensively collected from around the world [33; 34; 35; 36], including 19 level I samples, 27 level II samples, 36 level III samples and 18 level IV samples. The full sample data can be found in Appendix A \"Sample data\". Figure 2 shows the boxplot of the rockburst prediction indexes, which shows the mean value; the upper and lower bounds of the 95% confidence interval for the mean value; the median; the variance; the standard deviation; and the maximum value for the rockburst prediction index, minimum value, range, interquartile range, skewness and kurtosis, and some characteristic values are shown in Table 1.  
Figure 1: Structure of the BP neural network.
## 3 Results and Analysis
### Performance Evaluation of Modified Nano-SiO\({}_{2}\)  
#### 3.2.1 Contact Angle  
Wettability is very important as it directly affects the effect of pressure reduction and injection. Core wettability was evaluated by measuring the contact angle. Figure 5 shows the contact angles of core slices immersed in modified nano-SiO\({}_{2}\) dispersion and saline. In the control experiment of core sections soaked in salt water, the core contact angle was 37.34\({}^{\circ}\), indicating that the initial surface wettability of the core was hydrophilic. However, the contact angle of the core slices immersed in the modified nano-SiO\({}_{2}\) dispersion solution was 104.26\({}^{\circ}\), indicating that the modified nano-SiO\({}_{2}\) can change the core from hydrophilic wetting to hydrophobic wetting, and the change in wettability can reduce the adhesion force of the rock surface to water to promote water flow. Consequently, the effect of lowering the pressure and increasing injection was achieved. With an increased concentration of modified nano-SiO\({}_{2}\), the contact angle increased from 104.26\({}^{\circ}\) to 136.36\({}^{\circ}\) and gradually became stable. This finding indicated that with increased concentration, the adsorption amount on the core surface increased, and the hydrophobic degree on the core surface also increased. However, with a further increased concentration of modified nano-SiO\({}_{2}\), the adsorption gradually became saturated. The adsorption capacity was basically unchanged and the contact angle tended to be stable.