esa-sceva/satcom-chunk-collection huggingface dataset:
# Heat Stress Affects H3K9 me3 Level at Human Alpha Satellite DNA Repeats
## Results
### Dynamics of H3K9 me3 at Alpha Satellite Repeats after HS
For two intronic ARs No 10 and 29 which exhibit an insertion polymorphism (Figure S1) we analyzed H3K9 me3 levels at the corresponding intronic regions in cell lines where these ARs are absent. In HepG2 cells alpha repeat No 10 is heterozygous, and to follow H3K9 me3 levels separately at each allele, we used allele specific primers (see Figure S1C). At the allele with AR No 10, the H3K9 me3 level at the AR and nearby region was increased 3.2\(\times\) and 1.9\(\times\) after HS and at 30 min of recovery, respectively, while at the allele without alpha repeat No 10 we did not detect a change in H3K9 me3 level at the corresponding intronic region of _NR3C1_ gene (Figure 2G). The increase of H3K9 me3 levels at the alpha repeat No 10 was also detected immediately after HS in Cal 27 (2.1\(\times\)) and OV-90 (1.7\(\times\)) cells (Figure 2G). The H3K9 me3 level at the alpha repeat No 29 was increased 2.1\(\times\) immediately after HS in HepG2 cells, while in Cal 27 and OV-90 cells where the _ZNF675_ gene does not contain an alpha repeat (Figure S1B), no significant change in H3K9 me3 level after HS at a corresponding intron region is detected (Figure 2H). Since the _Alu_ repeat in which AR 29 is imbedded remains in Cal 27 and OV-90 cells (Figure S1B), the absence of H3K9 me3 change at the corresponding region, without AR 29 shows that the alpha repeat and not _Alu_ is responsible for H3K9 me3 enrichment upon HS. Finally, we followed
## 4 Simulation Analysis
### Voltage Disturbance Experiment  
SOFC batteries have two states whose parameters are not small or random. Therefore, voltage rise and fall perturbation experiments were conducted for each of the four control algorithms, in which Figure 8a shows the PID control simulation under two different firefly algorithms, and Figure 8b shows the LADRC simulation under two different firefly algorithms where the voltage rises by 6.6% and 7.8% at T = 50 s and T = 100 s and decreases at T = 150 s and T = 200 s by 5.8% and 6.2%.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \(\omega_{0}\) & \(\omega_{c}\) & \(b_{0}\) & **BestCost** \\ \hline IFA-ADRC & 7.500 & 7.500 & 2.5788 & 85.0153 \\ FA-ADRC & 7.4986 & 7.4906 & 2.5221 & 85.4307 \\ \hline \end{tabular}
\end{table}
Table 4: Optimization parameters under LADRC and error integration index.  
Figure 7: Step response curve.  
Figures 9a and 9b, respectively, show the comparison of the boost and stepdown effects of the FA and IFA under the control of the PID controller. Similarly, Figures 9c and 9d, respectively, show the comparison of control effects of the FA and IFA under LADRC control.  
Figure 8: Voltage rise and fall response curve. (**a**) PID controller control. (**b**) LADRC controller control.  
Figure 9: _Cont._  
From the simulation graphs, we can see that the optimized parameters obtained using the IFA can make the response oscillation decrease, and the LADRC controller has a better control effect than the PID controller. From the comparison of graphs (a) and (c), we can see that the time of using the LADRC controller to make the system reach the steady state is 1 s faster than the PID controller, and the same is true for the graphs (b) and (d). It can be seen that the LADRC controller has the characteristics of a fast response.  
Figure 10 compares the control effect of PID control and LADRC under the IFA, and Figure 11a,b shows the enlarged view of the control effect.  
Figure 10: Voltage rise and fall response curves under different controllers.  
Figure 9: Magnification of the stepup and stepdown response curves of two controllers. (**a**) PID controller boost response curves. (**b**) PID controller buck response curves. (**c**) LADRC controller boost response curves. (**d**) LADRC controller buck response curves.  
From the above simulation graph, we can see that the system with LADRC has a smaller overshoot, shorter steady-state time, less obvious oscillation, better tracking of set values, a relatively more stable system, and significantly better control than the system with a PID controller.
# A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects
## 4 Experiments
### Experimental Preparation  
#### 4.1.1 Datasets  
Two real-world trajectory datasets are used to evaluate the proposed method. The courier dataset is a self-collected data from a courier of a food delivery platform in Beijing, China. By installing an application that can collect track points on the courier's mobile phone, tracks of the courier on 66 working days from 10 November 2016 to 19 January 2017 are recorded. One of the major concerns of the food delivery system is where the courier will go so as to estimate the travel time to the customer. The courier is in charge of a certain area, so there exists a regular route. As shown in Figure 15a, the tracks in the red rectangle have high density, which means the courier always travel along this routes. Figure 15b shows the trajectories of the courier in the red rectangle in three working days.  
Figure 14: The processes of the model training and location predicting.  
Figure 13: The process of feature engineering.  
The MTA Bus dataset is provided by the Metropolitan Transportation Authority (MTA) and is available online [31]. The dataset contains trajectories of 5725 buses from 315 bus routes across the New York region, from 1 August to 31 October 2014. The 90-days data contains a very large amount of track points in total, and the average number of points each day is nearly 4.2 million. Trajectories of a bus from route BX36 are selected. As shown in Figure 16a, the red regions in the heat map represent the frequent routes of the bus. There are several frequent routes over a long period of time because the bus may change the route due to rescheduling [29]. However, movements of the bus in each route over a short period of time appear regularly. The trajectories in the blue rectangle in the middle of Figure 16a are frequent, as shown in Figure 16b. Trajectories at the top of Figure 16a are frequent, as shown in Figure 16c.  
Figure 16: Visualization of the MTA bus dataset. Trajectories of bus whose ID is 296 are selected. (**a**) The heat map of all the tracks in the MTA bus dataset. (**b**) Trajectories on 2 and 4 August. (**c**) Trajectories on 12 and 15 August.  
Figure 15: Visualization of the trajectories of a courier from the courier dataset. (**a**) The heat map of all the tracks in the courier dataset. (**b**) Trajectories from 10 November to 12 November.  
Table 3 summarizes the characteristics of the two representative datasets. The courier and the MTA bus dataset contain 198 and 149 trajectories respectively, and the time spans are 66 and 90 days. The courier dataset contains 1746 points per day on average and the sampling rate is 0.5 Hz. The sampling rate of the bus dataset is 0.03 Hz, which leads to a smaller number of points per day. The average daily travel distance of the bus is 58 km, far more than the courier. The bus has longer routes than the courier, so the average distance between two points is 23 times more than the courier. Parameters, such as time bin size and distance thresholds, should be set according to the characteristics of the datasets.
# Analytic Time Reentry Cooperative Guidance for Multi-Hypersonic Glide Vehicles
## 5 Simulation and Result Analysis  
In this paper, the idea of bi-level optimization is adopted. First, the outer layer optimizes the designed parameters \(V_{1}\), \(V_{2}\), and \(\sigma_{0}\), and the inner layer optimizes the expected terminal velocity of each guidance cycle that satisfies the range constraint and time constraint, and finally obtains the AOA profile and the bank angle profile that satisfy the cooperative guidance mission. In this paper, five simulation experiments were carried out to verify the effectiveness of the proposed algorithm:  
1. For a single aircraft, we ran simulations consisting of three different flight time constraints against fixed target reentry.  
Figure 1: Analytical time cooperative guidance framework for multi-hypersonic glide vehicles.  
2. Starting from the same reentry point interval, time cooperative simulations of launching multiple aircrafts to hit fixed targets were run in turn.
3. The time-cooperative simulations of multiple aircrafts striking fixed targets at different reentry points at the same time were run.
4. Starting from different reentry points at the same time, time-coordination simulations of multiple aircrafts attacking moving targets were made.
5. Finally, we used a Monte Carlo simulation experiment to test the robustness of our results.  
Here, the CAV-H hypersonic vehicle model is used to carry out the reentry guidance simulations of HGV. We use the aerodynamic model of Reference [27], where the mass is 907.2 kg, and the reference area is 0.484 m\({}^{2}\), the upper limit of heat flux rate in the path constraint is set to 1200 kW/m\({}^{2}\), the upper limit of dynamic pressure is set to 400 kPa, the upper limit of overload is set to 6, and the guidance period is 1 s. The simulation environment CPU is Intel i7 9700 processor, whose main frequency is 3 GHz.
## 1 Introduction
The Sliding Mode Control (SMC) theory has gained popularity in recent years for controlling power converters in different applications due to their advantages: fast dynamic response, robustness, order reduction, implementation simplicity, disturbance rejection, and low sensitivity to parameter variations [23; 24]. Moreover, the SMC theory can also be used to implement observers for power converters to reduce the number of voltage [25] or current [14] sensors. Therefore, this paper proposes an SMC-based control system for a flyback converter connected to a grid-tied PV inverter. The control system is formed by a P&O algorithm, a first-order filter, and an SMC. The P&O algorithm tracks the MPP by modifying the PV module voltage reference, and the first-order filter limits the slew rate of the reference voltage received by the SMC. Conversely, the SMC tracks the voltage reference and rejects the disturbances produced by the grid-connected inverter. The SMC's sliding surface includes two constants, the voltage error, the PV module current, and the magnetizing current, which is estimated online from the MOSFET current, the current at the secondary of the transformer, and the duty cycle. The proposed SMC guarantees the global stability of the system if the voltage reference has a limited slew rate, which is why a first-order filter is implemented between the P&O and SMC. The paper includes a description of the sliding surface definition as well as the detailed procedures to design both the SMC's constants and the first-order filter to guarantee the global stability of the system, which is validated through the analysis of the transversality, reachability, and equivalent control conditions. Moreover, the paper also includes a description of the proposed SMC implementation along with the design of the hysteresis band to ensure a maximum switching frequency. Finally, the proposed control system and design procedures are validated with simulations that demonstrate the system is globally stable and fulfills the desired dynamic performance. The paper has four main contributions: (1) The proposed SMC controller guarantees the system stability for any operating point and the desired dynamic performance for the tracking of the PV module voltage reference. (2) The proposed SMC efficiently rejects 100 Hz or 120 Hz PV module voltage oscillations introduced by the inverter. (3) The proposed design procedures of the SMC and the first-order filter, as well as the implementation details, facilitate the application of the proposed controller for any (i.e., existing or new) flyback converter used in grid-connected PV systems with microinverters or distributed MPPT systems. (4) The paper provides formal stability tests through the analysis of the transversality, reachability, and equivalent control conditions.
# An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine
## 4 Conclusions  
This paper proposes a novel fault diagnosis method for linear analog circuit that combines the circle model-based feature engineering with ELM. In linear analog circuits, we find that the real part \(U_{\sigma}\) and imaginary part \(U_{oj}\) of the output voltage satisfy the circle equation \(\left(U_{\sigma}-a\right)^{2}+\left(U_{\text{oj}}-b\right)^{2}=r^{2}\).The parameters of a, b and r are independent from the parameter value of the component being modeled. It means that no matter how great the parameter shifting is, circle equation is always tenable. Hence, the circle equation characterizes all soft and hard faults. in detail. The circle equation can be confirmed by using three points; hence, only three simulations are needed to determine the three circle parameters a, b and r. Therefore, the time and space complexity are very low. Additionally, only one test node, usually the output node, is needed to model the component. Hence, it is especially applicable to the circuit under test (CUT), which has no inner accessible test nodes. The ELM classification algorithm has low computational complexity and simpler process, so it can make the classification and location of circuit fault faster. If additional test nodes are available, the fault resolution will be further enhanced.  
This method has the following main advantages:  
1. Easier to model and lower space complexity. Circle modeling can be realized by simulating three sampling points at one time;
2. Higher completeness. All fault parameters (soft fault and hard fault) are characterized in a unified way.
3. Higher fault accuracy.
4. Less time cost  
[PERSON] and [PERSON] proposed the methodology and paper organization; [PERSON] and [PERSON] carried out the simulations and analyzed the experimental data; [PERSON] prepared the original draft and supervised the project; [PERSON] validated the obtained results; [PERSON] revised and edited the original manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of Shandong Province (China) under grant number ZR2018 MEE022 and Key Research and Development Program of Shandong Province (China) under grant number 2019 GHY112072 and 2019 GHY112051.  
The authors would like to thank editors and anonymous reviewers who made constructive comments. This work was supported in part by the Natural Science Foundation of Shandong Province, China (Grant No. ZR2018 MEE022) and Key Research and Development Program of Shandong Province, China (Grant No. 2019 GHY112072 & 2019 GHY112051).  
The authors declare no conflict of interest.
## 2 State of the Art
### Nonlinear Features  
Several techniques have been proposed to study the nonlinear characteristics involved in the FHR signal in relation to fetal health status. In this context, mutual information (MI) has been employed to design efficient features for FHR signal analysis [33] and study the UC and FHR coupling [34]. Multivariate analysis based on linear and nonlinear features has been proposed to discriminate between normal and intrauterine growth-restricted fetuses [35]. Multiscale entropy (MSE) has been used to estimate the signal complexity [36; 37] and regularity [38] of FHR recordings. Besides, as presented in Section 3.2.3, empirical mode decomposition (EMD) has been used in different approaches proposed for CTG signal analysis.
## 1 Introduction  
The appearance of the commercial lithium-ion battery (LIB) in 1991 [1] has opened a new era for the boom in electronic and mobile devices, leading to the participation in social networks as well as the electrification of automobiles. The LIB has become an essential part of smartphones as well as electric vehicles (EVs), helping to increase human connection, expand the capabilities of electronic devices, and, eventually, enable the reduction of the use of fossil fuels. With the dire concerns about climate changes due to greenhouse gas emissions from the combustion engine vehicles [2], EV has become a promising solution to replace the conventional vehicles with the support of the LIB [3]. The continuous increase in the role of the LIB has led to further improvements and enhancements of the battery performance. Especially for the successful deployment of EV, it is critical to minimize the charging time of the LIB so that users feel comfortable with the refueling of vehicles at charging stations [4]. To improve user convenience through the fast charging of EV, two different aspects have to be improved together. Firstly, the infrastructures of EV service equipment, namely the chargers and related electric supply facilities, have to be developed to be able to supply high enough current rate to the EV battery pack. This is known to be relatively simple compared to the improvement of battery functionality itself. Secondly, the chemistry of the LIB, namely the battery functionality itself, has to be further refined to be able to accommodate the higher current charging while keeping the battery capacity without significant degradation over the acceptable lifecycle of the EV applications. Unlike the case of EV service equipment development, this battery chemistry refinement for the rapid charging speed is known to be extremely challenging [5]. To fully understand the charging process of the LIB and possibly improve the performance in that regard, three major processes are to be investigated carefully: (i) rapid lithium de-intercalation from the positive electrode; (ii) rapid diffusion of lithium through the electrolyte; and (iii) rapid lithium insertion into the negative electrode. In fact, the complexity of designing and manufacturing the LIB makes it very difficult to satisfy all of these factors simultaneously [6].  
In the current research, our team paid close attention to the third aspect, the rapid lithium insertion into the negative electrode because the solid electrolyte interface (SEI) definitely experiences build up during the repeated charging processes and is well known to be a major mechanism of battery capacity degradation. More importantly, the frequency of charging pulse was carefully considered for its possible contribution to maintaining the battery health through minimizing the growth of the SEI. To understand and explain the fast-charging acceptance of a mass-produced LIB by applying the pulse current charging, the change in battery performance was investigated by observing its lifecycle, as well as using electrochemical impedance spectroscopy (EIS) [7].
## 4 CSAMT Experiment in the Jinding Lead-Zinc Deposit
### CSAMT Data Quality Control
#### 4.2.1 CSAMT Signal-to-Noise Ratio Experiment  
To calculate the signal-to-noise ratio (SNR) of CSAMT data, both the noise signal and the data signal need to be obtained. Firstly, the open-pit mine at the center of the work area was selected as the experimental site. Data acquisition was conducted when the artificial field source was not transmitting, and the measured signals were considered noise signals. Secondly, under the same experimental setup conditions, when the artificial field source was transmitting, the collected signals were considered data signals. Finally, the signal-to-noise ratio (SNR) formula was used to calculate the SNR. By comparing the SNR values, the quality of the collected data was evaluated, which further indicated the strength of the received signal. The experimental results of the signal-to-noise ratio are shown in Figure 8.  
In Figure 8, there are four sub-figures: the electric field noise signal amplitude diagram, the magnetic field noise signal amplitude diagram, the electric field data signal amplitude diagram, and the magnetic field data signal amplitude diagram. From the experimental results, it can be observed that the noise signals have a large range of amplitude value fluctuations and significant error bars. Conversely, the data signals exhibit smooth and continuous amplitude curves with small error bars, and their values are more than 10 timesthose of the noise signals. Therefore, the signal-to-noise ratio experiment we designed is reasonable and feasible and can be used as a criterion for evaluating the strength of the received signal.  
The calculation formula for the signal-to-noise ratio is as follows:  
\[SNR=20*Log\frac{signal}{noise} \tag{20}\]  
The calculation results of the signal-to-noise ratio for the electric field and the magnetic field at the test point are shown in Figure 9.  
In Figure 9, within the frequency band of 5000-10 Hz, the signal-to-noise ratio of both the electric field and the magnetic field is greater than 20 dB, and the noise percentage is less than 10%. This indicates that the signal strength is sufficiently large, and the quality of the measured CSAMT field data is high. In the high-frequency and low-frequency bands, the SNR is less than 10 dB, and the noise percentage is greater than 30%, indicating the low quality of the collected data. This is due to two main reasons: the V8 instrument and magnetic probe exhibit response distortion in both high- and low-frequency bands, resulting in low measurement accuracy; and when transmitting high frequencies, the transmission current is small, making the signal more susceptible to interference; in the  
Figure 8: Comparison of the amplitude between the noise and total signals at the test point. (**a**) Amplitude of the noise signal in E; (**b**) amplitude of the noise signal in H; (**c**) amplitude of the total signal in E; (**d**) amplitude of the total signal in H.  
case of low frequencies, factors such as shorter data collection time and stronger interfering signals contribute to a relatively lower signal-to-noise ratio in the data.
# ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion
## 1 Introduction  
With the advancement of space remote sensing technology, the observation capability of optical remote sensing satellites has significantly improved, leading to an increase in the spatial resolution of remote sensing images. Currently, the spatial resolution can reach 0.3 m per pixel [1], providing high-quality data for various scientific research applications. Computer vision technology plays a crucial role in extracting information from these images for tasks such as geological mapping, terrain measurement, and land cover change detection [2]. Object detection and recognition in remote sensing images are particularly important, especially in identifying ships, which hold strategic value in both economic and military domains. The accurate detection and recognition of ships in port areas and key waterways are vital for civilian and military purposes. However, the presence of complex backgrounds and variations in object scales in remote sensing images [3] pose significant challenges to achieving satisfactory ship detection accuracy.