esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Seismic Phase Recognition Method Based on Bi-LSTM Network
## 3 Preparation for the Experiments
### Construction of Automatic Seismic Phase Recognition Dataset
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Information** & **Label** & **Information** \\ \hline back azimuth deg & 59.7 & source distance deg & 0.31 \\ \hline coda end sample & 3534 & source distance km & 34.7 \\ \hline network code & TA & source error sec & 0.6709 \\ \hline p arrival sample & 600.0 & source gap deg & 15.649 \\ \hline p status & manual & source depth uncertainty km & 0.58 \\ \hline p travel sec & 8.819 & source id & 16,012,717 \\ \hline p weight & 0.5 & source latitude & 48.5636 \\ \hline receiver code & A04D & source longitude & \(-123.1136\) \\ \hline receiver elevation m & 13.0 & source magnitude & 3.3 \\ \hline receiver latitude & 48.7201 & source magnitude author & None \\ \hline receiver longitude & \(-122.7063\) & source magnitude type & mb \\ \hline receiver type & BH & snr db & [52.70 54.09] \\ \hline s status & manual & source depth km & 50.84 \\ \hline s weight & 0.5 & trace category & earthquake local \\ \hline source horizontal uncertainty km & 3.55464 & source origin time & 8 February 2011 16:36:36.83 \\ \hline source mechanism strike dip rake & none & trace name & A04D.TA 20110208163638 EV \\ \hline source origin uncertainty sec & 0.94 & trace start time & 8 February 2011 16:36:39.65 \\ \hline s arrival sample & 1305.0 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sample data label information table of STEAD dataset. In order to ensure that the dataset matches the model's input, most of the unused labels in the 35 labels were removed. Only seven pieces of label information, such as trace name, trace start time, p arrival sample, s arrival sample, coda end sample, snr db, and network code, were retained to improve the calculation speed of the model. After preprocessing, we also manually labeled and checked the dataset, removed the blank data, manually modified the obvious errors, and randomly assigned them according to the proportion of 70%, 10% and 20%. A total of 70% of the data was used as the training dataset, 10% as the validation dataset, and 20% as the test dataset.
# Towards the Quantum Internet: Satellite Control Plane Architectures and Protocol Design
## 3 System Model
### Protocol
When the acknowledgment is received, the swapping procedure is started to generate the entanglement at level E2E. A Type 6 message that contains the field is sent to the satellite that has to perform a swapping operation. In this type of message, the Previous and Next fields indicate the satellites that will be interconnected after the swap operation. The internal swapping operations of each domain are performed simultaneously by the controllers on their own domains. When a controller has completed its operations--i.e., the creation of the E2E entanglement between the ground station to which it is connected and the border QSR highlighted with a red circle in Figure 2--it sends a request to the other controller with the purpose of interconnecting the border satellites. This is done with a Type 7 message and, in this case, the Previous and Next fields indicate the satellites between which to perform the teleportation operation. When the teleportation process is completed, a Type 8 message is sent from the border satellite that received the particle to its controller with field C set to 1, indicating that the teleportation between the two border satellites has been completed. At this point, the controller sends a Type 6 message to its border satellite to perform the swapping operation. When this operation is completed, the entanglement E2E is established and the procedure can start again to perform the exchange of successive qubits. When the path is no longer valid, a condition that can be detected by checking the list of paths calculated by the Graph-Based Optimized Path Selection process on the MCS, the satellites close the connections established with the others. Then, the controller processes are deallocated and the controller in the MCS closes the connections with the satellites involved up to that moment. Then, the procedure restarts until the end of the session.
## 3 Uncertainty of Scheduling in the ADN
### Uncertainty of Renewable Energy Generation  
Due to the influence of natural factors, the output of renewable DGs fluctuates greatly. Their output is usually determined by studying the probability model.  
Sunlight intensity can be approximated by obeying the beta distribution:  
\[f(r)=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}\left(\frac{r}{r _{\max}}\right)^{\alpha-1}\left(1-\frac{r}{r_{\max}}\right)^{\beta-1} \tag{2}\]  
The output power of the solar cell panel can be expressed as:  
\[P_{M}=rA\eta \tag{3}\]  
Based on the formula of light intensity and photovoltaic output power, the output probability density function of solar photovoltaic can be obtained by Equation (4):  
\[f(P_{M})=\frac{\Gamma(\alpha+\beta)}{\Gamma(\alpha)\Gamma(\beta)}\left(\frac{ P_{M}}{R_{M}}\right)^{\alpha-1}\left(1-\frac{P_{M}}{R_{M}}\right)^{\beta-1} \tag{4}\]  
Figure 2: User response characteristic curve under certain incentive level.  
The power generation of the WT is related to the wind speed. The probability density function described by the two-parameter Weibull distribution is generally used to deal with the randomness of wind speed as Equation (5):  
\[f(v)=\frac{k}{c}\Big{(}\frac{v}{c}\Big{)}^{k-1}\exp\left[-\Big{(}\frac{v}{c} \Big{)}^{k}\right] \tag{5}\]  
The power generation of the wind turbine can be expressed as Equation (6):  
\[P_{wt}=\left\{\begin{array}{l}0,0\leq v\leq v_{i}\text{ or }v_{o}\leq v\\ P_{r}\frac{v-v_{i}}{v_{r}-v_{i}},\quad v_{i}\leq v\leq v_{r}\\ P_{r},\quad v_{r}\leq v\leq v_{o}\end{array}\right. \tag{6}\]  
The bus installed on the WT or PV cell can be simplified as a PQ node, assume that the power factor can be kept constant through the automatic switching of capacitors. The reactive power is obtained as:  
\[Q=\frac{P}{\tan\varphi} \tag{7}\]
## 1 Introduction
### Feature Selection  
As mentioned in Section 1.2.2, feature selection is one of the most critical stages of ML modeling since it makes it possible to identify the best relation to the required complexity of the model and its quality at the preprocessing stage. In addition, feature selection makes it possible to find the more representative inputs in the real-world system and to eliminate no representative inputs or those that are redundant. ML models and training algorithms that consider only representative features improve their efficiency and reduce the time required for training [26; 27; 38; 45]. A feature is an observable property in a system. Feature selection aims to select a specific subset of features that maximize the performance of the ML model.  
The feature selection (FS) used here applied one of the most common techniques for removing irrelevant data, reducing dimensionality, increasing predictive accuracy and learning efficiency, and reducing the complexity of the final model [42]. Although there are different approaches for FS, all have theoretical support in their use of different methods, such as filtering, wrapping, and embedding, and involve techniques that use search algorithms, statistical criteria, and information, distance, dependence, and consistency measures [42]. The aim was to use linear and nonlinear methods to implement FS with a dataset acquired from an illumination radiation system.  
This paper focuses on the feature selection stage in order to train a regression model to predict energy consumption in LED lights with specific light recipes in CPPSs. This stage is critical because it identifies the most representative features for training the model, and the other stages depend on it. These tools can enable further in-depth analysis of the energy savings that can be obtained with light recipes and pulsed and continuous light operation modes in artificial LED lighting systems.
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 3 GCS-Routing Design
### Routing Loops in GCS-Routing
#### 3.4.1 Transient Loops in GCS-Routing  
It is essential to guarantee loop-free operation for routing protocols. In GCS-routing, transient loops may be formed during route redistribution when a node receives a new routing table but its child  
Figure 6: Throughput and number of received beacons dependence on distance in line of sight (LoS) and non-LoS (NLoS) environments.  
nodes have not received the updated routing table yet. However, since route messages are distributed in a hop-by-hop manner from the closest drone to the farthest drone from the GCS, transient loops eventually disappear, and the network becomes loop-free. Figure 7 shows an example scenario where a transient loop is formed during route update. The numbers show link costs, and the network consists of three nodes. Figure 7a shows the initial routes to node \(C\) from nodes \(A\) and \(B\). \(NH_{X}(Y)=Z\) means that \(Z\) is the next hop from node \(X\) to node \(Y\). Thus, \(NH_{A}(C)=C\) means the next hop from node \(A\) to node \(C\), \(NH_{B}(C)=A\) means the next hop from node \(B\) to node \(C\), and similarly for \(NH_{C}(A)=A\) and \(NH_{C}(B)=A\).  
In the example scenario, if the link between nodes \(A\) and \(C\) is broken, GCS-routing creates new routing tables for each node. As described in Section 3.2, routing tables are distributed in a hop-by-hop manner. Thus, node \(A\) receives a new routing table from GCS and updates \(NH_{A}(C)\) to \(B\), i.e., \(NH_{A}(C)=B\). However, node \(B\)'s routing table is not yet updated, \(NH_{B}(C)=A\). Figure 7b shows that a transient loop between nodes \(A\) and \(B\) is formed if node \(A\) tries to transmit data to node \(C\), and the presence of the transient loop means the data cannot be transmitted to node \(C\). When node \(B\) receives the new routing table from node \(A\), it updates \(NH_{B}(C)\) to \(C\), and the network becomes loop-free. Then a new routing table is transmitted to node \(C\), both \(NH_{C}(A)\) and \(NH_{C}(B)\) are updated to \(B\), and the network converges, as shown in Figure 7c.
# Study on Coupled Vertical Vehicle-Bridge Dynamic Performance of Medium and Low-Speed Maglev Train
## 1 Introduction
On the other hand, field tests under different working conditions are carried out to evaluate dynamic performance [18]. Due to the limitation of field tests, the analysis of many influencing factors of coupled vehicle-bridge systems has only been limited to numerical simulation [3,11]. In this paper, a dynamic model of coupled vehicle-bridge systems based on an elastic beam was established, and the dynamic system response with the influence of bridge stiffness and vehicle load was simulated and analyzed. The single-span steel beam (24.94 m) was tested in Shanghai Lingang medium and low-speed maglev base. According to the test data, the time and frequency domain characteristics of vehicles and bridges under different working conditions were analyzed, and the simulation results were verified. Due to the large fluctuation of the suspension gap in the static levitation stage, the phenomenon of vehicle-bridge coupling vibration is more likely to occur [10]. In this paper, the dynamic performance of the train in the static levitation process was analyzed. In addition, the simulation and test results could provide a reference for the design and formulation of maglev traffic regulations. The overall structure of this paper is as follows: in the second section, the coupled vehicle-bridge dynamic model is established based on the flexible beam, and the open-loop instability of the suspension system is explained with the Hurwitz criterion. Moreover, the feedback control algorithm based on voltage control is given for further simulation analysis. In the third section, based on the actual vehicle and track beam parameters, numerical simulation is carried out for the key characteristics of vehicle and track, respectively, and the dynamic response of the system is analyzed. In the fourth section, the relevant scheme designed for the test is given firstly, and then the test analysis is carried out combined with the change of vehicle load and track-beam stiffness. Finally, the relevant conclusions are contained in the fifth section.
# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 5 Conclusions  
The proposed LEACH-MTC algorithm combines the state prediction of moving target and the network monitoring performance parameters, and meets the network monitoring accuracy requirements. This paper constructs a time-varying ellipse monitoring area with the direction changing of the moving target, and it determines the working nodes set. Combined the working node set and the state prediction of moving target, this paper gives a new cluster head selection threshold to construct the cluster head strategy. The LEACH-MTC algorithm increases the number of network nodes to survive, reduces network energy consumption, and prolongs the network life time.  
Conceptualization, C.F.; Investigation, Z.H.; Funding acquisition, Z.H.; Methodology, L.Z.; Software, C.W.; Validation, K.B.; Writing--original draft, L.Z.; Writing--review and editing, C.F. and Y.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Science Foundation Council of China (61771006, 61976080, U1804149, 61701170). Key research projects of university in Henan province of China (21A413002, 19A413006, 20B510001), the Programs for Science and Technology Development of Henan Province (192102210254), the Innovation and Quality Improvement Program Project for Graduate Education of Henan University (SYL20060143), and the Postgraduate Quality Demonstration Courses of Henan University (English Professional Courses) (SYL18030207).  
Not applicable.  
Figure 7: The variation of network residual energy.  
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.
## Conflicts of Interest: The authors declare no conflict of interest.  
### Nomenclature  
COPCoefficient of performance  
c\({}_{\rm p}\)Specific heat at constant pressure, (kJ\(\cdot\)(kg\({}^{-1}\)\({}^{,}\)C\({}^{-1}\)))  
EEVElectronic expansion valve  
\(\dot{E}_{D}\)Energy destruction rate, (kW)  
\(\dot{E}_{loss}\)Energy loss rate, (kW)  
\(\dot{E}_{p}\)Product exergy rate, (kW)  
\(\mathbf{e}\)Specific exergy, (kJ\(\cdot\)kg\({}^{-1}\))  
GWPGlobal warming potential  
HFCHydrofluorocarbons  
HXHeat exchanger  
hSpecific enthalpy, (kJ\(\cdot\)kg\({}^{-1}\))  
\(\dot{m}\)Mass flow rate, (kg\(\cdot\)s\({}^{-1}\))  
\(M_{actual}\)Actual refrigerant charge, (kg)  
\(M_{super}\)Mass at saturated vapor, (kg)  
\(M_{liquid}\)Mass at saturated liquid, (kg)  
MACMobile air conditioning  
NRCNormalized refrigerant charge  
ODEEV opening degree, (%)  
ODPOzone depletion potential  
PPressure, (MPa)  
\(\dot{Q}\)Heat transfer rate, (kW)  
RGas constant, (kJ\(\cdot\)kg\({}^{-1}\)\(\cdot\)K\({}^{-1}\))  
TTemperature, (\({}^{\circ}\)C)  
RPM Round per minute, (r\(\cdot\)min\({}^{-1}\))  
sSpecific entropy, (kJ\(\cdot\)(kg\({}^{-1}\)\(\cdot\)K\({}^{-1}\)))  
\(\dot{V}\)Air volume flow rate, (m\({}^{3}\)\(\cdot\)h\({}^{-1}\))  
\(\dot{W}\)Power input, (kW)  
\(\eta\)Energy efficiency, (%)  
\(\omega\)Absolute humidity of air, (kgwater/kgdry air)  
\(\rho_{air}\)Air density, (kg\(\cdot\)m\({}^{-3}\))  
**Subscripts**  
0 Dead state  
aAir  
com Comporsor  
dis Discharge of compressor  
e Evaporater  
in Inlet  
opt Optimal  
out Outlet  
r Refrigerant  
succ  
Suction of compressor  
tot Total  
v Vapor
# Area-Time Efficient Hardware Architecture for CRYSTALS-Kyber
###### Abstract  
This paper presents a novel area-time efficient hardware architecture of the lattice-based CRYSTALS-Kyber, which has entered the third round of the post-quantum cryptography standardization competition hosted by the National Institute of Standards and Technology. By developing a dual-path delay feedback number theoretic transform multiplier dedicating for Kyber parameter set and deploying this multiplier in the Kyber architecture, the key generation, encryption, and decryption operations are accelerated substantially. Furthermore, the proposed architecture offers the best value of area-time product in comparison with existing approaches. The implementation results on Xilinx Vivado targeted for Virtex-7 FPGA board demonstrate that the proposed Kyber cryptoprocessor completes encryption and decryption operations in approximately 57.5 us at the highest frequency of 226 MHz. Furthermore, the area-time product value when using the proposed Kyber architecture is improved by at least twofold compared with existing architectures.  
CRYSTALS-Kyber; decryption; encryption; number theoretic transform (NTT); polynomial multiplier; post-quantum cryptography +
Footnote †: journal: Applied  
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The remaining parts of INNT architecture are similar to NTT architecture, with \(\omega_{n}\) being replaced by \(\omega_{n}^{-1}\), as shown in Figure 1b. Montgomery reduction and Barret reduction are also used in INTT architecture.
# Safety Risk Assessment in Urban Public Space Using Structural Equation Modelling
## 4 Result  
### Reliability and the Validity Test  
The data from 219 questionnaires involving 21 factors were preliminarily analyzed, and the KMO value was 0.903 \(>\) 0.8, which indicated that the data reliability of the questionnaire was reliable. However, since the CITC values of Q2 and Q14 are both less than 0.2, the relationship between these two items and other items is very weak. After deleting Q2 and Q14, the new reliability coefficient is 0.925, which is a further improvement from the previous 0.903, as shown in Table 5.  
It can be seen from Table 6 that the common factor variance of all indexes is higher than 0.4, indicating that the indexes can be effectively extracted. In addition, the \(p\) value (Sig.) is 0, and the KMO value is 0.910, which is greater than 0.6, indicating that there is a high correlation between the indexes established in the questionnaire. Therefore, the validity is guaranteed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Observed Variable**} & \multicolumn{5}{c}{**Latent Variable**} & \multicolumn{2}{c}{**Common Factor**} \\ \cline{2-7}  & **Factor 1** & **Factor 2** & **Factor 3** & **Factor 4** & **Factor 5** & **Variance** \\ \hline Q1 & 0.114 & 0.815 & \(-\)0.013 & 0.115 & \(-\)0.152 & 0.713 \\ Q3 & 0.078 & 0.025 & 0.148 & 0.886 & \(-\)0.001 & 0.813 \\ Q4 & 0.081 & 0.083 & 0.033 & 0.914 & 0.016 & 0.851 \\ Q5 & 0.557 & 0.246 & 0.422 & 0.121 & \(-\)0.211 & 0.608 \\ Q6 & 0.226 & 0.141 & 0.797 & 0.229 & 0.066 & 0.764 \\ Q7 & 0.243 & 0.097 & 0.365 & \(-\)0.096 & 0.618 & 0.591 \\ Q8 & 0.801 & 0.191 & 0.218 & 0.120 & \(-\)0.112 & 0.752 \\ Q9 & 0.840 & 0.066 & 0.306 & \(-\)0.072 & 0.003 & 0.801 \\ Q10 & 0.894 & 0.028 & 0.208 & \(-\)0.030 & 0.038 & 0.846 \\ Q11 & 0.822 & 0.256 & \(-\)0.025 & 0.208 & 0.067 & 0.789 \\ Q12 & 0.777 & 0.346 & \(-\)0.033 & 0.150 & 0.099 & 0.757 \\ Q13 & 0.836 & 0.220 & 0.135 & 0.080 & 0.172 & 0.801 \\ Q15 & \(-\)0.045 & \(-\)0.002 & \(-\)0.093 & 0.078 & 0.909 & 0.846 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Validity analysis of 19 indexes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Latent Variable**} & **Index** & **CITC** & **Cronbach’s \(\alpha\) Coefficient** \\ \hline \multirow{4}{*}{Conscious behavior} & Q1 & 0.379 & 0.926 \\  & Q3 & 0.205 & 0.928 \\  & Q4 & 0.200 & 0.928 \\ \hline \multirow{4}{*}{Social governance} & Q5 & 0.614 & 0.921 \\  & Q6 & 0.512 & 0.923 \\  & Q7 & 0.428 & 0.929 \\  & Q8 & 0.742 & 0.918 \\ \hline \multirow{4}{*}{Construction equipment} & Q9 & 0.743 & 0.918 \\  & Q10 & 0.760 & 0.918 \\  & Q11 & 0.754 & 0.918 \\  & Q12 & 0.751 & 0.918 \\  & Q13 & 0.820 & 0.917 \\ \hline \multirow{4}{*}{Urban environment} & Q15 & 0.709 & 0.937 \\  & Q16 & 0.729 & 0.918 \\  & Q17 & 0.682 & 0.920 \\ \hline \multirow{4}{*}{Road traffic} & Q18 & 0.832 & 0.916 \\  & Q19 & 0.763 & 0.918 \\ \cline{1-1}  & Q20 & 0.819 & 0.916 \\ \cline{1-1}  & Q21 & 0.818 & 0.917 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Reliability analysis of 19 indexes.
## 4 Materials and Methods
### Quantitative Real-Time PCR Assay  
A quantitative real-time PCR assay (qRT-PCR) was used to validate the expression profiles of the candidate genes. Total RNA samples were the same samples used for the cDNA library preparation. RNA was extracted as described for cDNA library construction, and cDNA was reversely transcribed using a PrimeScript RT reagent kit containing gDNA Eraser (Takara, Dalian, China). Primers were designed with Primer Premier 5.0 software (Primier Biosof international., Quebec, QC, Canada). The primers designed were synthesized in Sangon Biotech (Shanghai) Co., Ltd. qRT-PCR was constructed using Maxima(r) SYBR Green/ROX qPCR Master Mix (2X) (Thermo Fisher Scientific, Waltham, MA, USA) on CFX96 Real-Time PCR Manager 3.1 (BioRad, Hercules, CA, USA). A 10 mL reaction containing 1 mL synthesized cDNA, 5 mL 2-TB Green Premix Ex Taq mix, 0.5 mL 10 mM forward primer, 0.3 mL 10 mM reverse primer, and 3 mL sterile distilled water was amplified. PCR amplification was performed as follows: preheating at 94 \({}^{\circ}\)C for 10 min, followed by 40 cycles at 94 \({}^{\circ}\)C for 15 s, 60 \({}^{\circ}\)C for 30 s, 72 \({}^{\circ}\)C for 30 s, and the melting curves were generated from 65 \({}^{\circ}\)C to 95 \({}^{\circ}\)C with increments of 0.5 \({}^{\circ}\)C every 5 s. The reference gene SKI-interacting protein was applied as the internal control [38], and the primers used in this study are listed in Table S1. Each sample was performed with three biological replicates and three technical replicates. The relative expression levels of genes were computed by the 2\({}^{-\Delta\Delta\text{CT}}\) method [85].