esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Management of a Multi-Source Power System
## 2 Literature Review
### Modeling of Power Sources  
There are two crucial factors for batteries which are power dissipation and runtime. [PERSON] and [PERSON] [25] presented an accurate and efficient battery model that could help researchers predict and optimize the battery's runtime as well as the overall system performance. The model accounts for various dynamic characteristics of the battery, including open-circuit voltage, current, temperature, and many more. While testing their model, the researchers observed less than 0.4% runtime error and a mere 30 mV error in the voltage. While designing hybrid systems, consisting of fuel cells along with batteries and super capacitors, the battery is selected based on its energy requirements to reduce its size and weight. This does not take into account the deep discharges of the battery, which have a significant effect on the battery's lifetime. Therefore, [PERSON] et al. [26] and [PERSON] et al. [27] argued that the lifetime of the battery must be considered alongside the energy and power requirements of the battery.  
Another aspect of the system that must be modeled and managed is the fuel cell. This is required to minimize the hydrogen consumption to ensure the appropriate runtime. [PERSON] et al. [28] investigated the effects of the sizing and modeling of the fuel cells in a powertrain powered by fuel cell and energy storage systems. They examined different combinations of fuel cell models alongside energy storage systems to determine which combination results in the least hydrogen consumption to increase the runtime of the powertrain. [PERSON] [29] stated that the efficiency of fuel cells depends on understanding, predicting, and controlling the distinctive performance of fuel cell systems. He provided a number of modeling and control techniques that can be used to ensure quick and stable dynamic system behavior. Also, he discussed various limitations of the controlling techniques and ways to measure the performance of the fuel cell system.  
Lastly, super capacitors are being used more frequently in hybrid vehicles because of their ability to provide a quick burst of current needed during acceleration [8]. [PERSON] and [PERSON] [30] explained how to model a super capacitors. [PERSON] and [PERSON] [31] added that a super capacitor can be used to supply the excess instantaneous power needed by a hybrid system. By doing so, the battery's lifetime can be protected and the dynamic stress on the battery is reduced with the help of the super capacitor.
## 5 Simulations Results and Analysis
### SMC Controller Tuning Results of PACSA  
The GA-based SMC controller is designed to contrast the performance of the proposed SMC controller optimized by PACSA. The relationships between the fitness functions value and the genetic generation of GA and PACSA are shown in Figure 11.  
Figure 10 displays the changes in the objective function of RCSMC and HDSMC during the PACSA tuning optimization. As can be seen from Figure 10, the changes in the objective function of RCSMC tuned by the PACSA are obvious in the first 40 generations and the corresponding convergence speed is fast. However, even until 100 generations, no convergence occurred during the GA tuning process. In addition, the whole tuning process of the GA lasted for 33 h, and the PACSA optimization procedure took only 6 h.  
The optimized parameter results based on the GA and the PACSA are listed in the Table 5.  
According to the optimized parameters in Table 5, the optimized RCSMC and HDSMC are obtained. Then the control of hydro-pneumatic suspension is simulated based on the two optimized controllers respectively, which reflects the optimizing ability for SMC of the PACSA and GA. The control results are obtained as follows.  
Figure 9: Process of the parallel adaptive clonal selection algorithm (PACSA).  
Figure 12a,b shows the dynamic responses of the objection values of the RCSMC and HDSMC. Figure 12c,d describes the dynamic responses of the vehicle vertical acceleration and the roll angle of the vehicle, respectively. Obviously, compared with the optimization results of GA, the values of the objective function, vehicle vertical acceleration, and roll angle optimized by PACSA perform better.  
Figure 11: The relationships between the fitness functions value and the genetic generation of GAand PACSA.  
Figure 10: Vehicle model in MATLAB/Simulink.  
Above all, it can be concluded that the convergence rate, running time, and optimized results of the PACSA approach are better than the GA approach, which indicates that PACSA is more suitable for tuning the parameters of the SMC controller.
# Multi-UAV Cooperative Anti-Submarine Search Based on a Rule-Driven MAC Scheme
## 4 Simulation Experiment and Analysis  
In order to verify the effectiveness of the proposed algorithm, a simulation environment for multi-UAV collaborative search is established in the MATLAB environment. The 20 km \(\times\) 20 km search mission area is divided into 40 \(\times\) 40 grids, and the initial coordinates, speeds, and headings of the four UAVs are shown in Table 2, assuming that the detection range of the UAVs' magnetic detectors is a \(1\times 1\) unit grid; the detection probability and the false alarm probability are \(p_{d}=0.89\) and \(p_{f}=0.05\), respectively; and the following three scenarios are considered:  
* Scenario 1: multi-UAV search without association rules;
* Scenario 2: multi-UAV search using association rules;
* Scenario 3: comparison of the number of targets searched with or without association rules.
## Construction and Commissioning of PAL-XFEL Facility
###### Abstract  
The construction of Pohang Accelerator Laboratory X-ray Free-Electron Laser (PAL-XFEL), a 0.1-nm hard X-ray free-electron laser (FEL) facility based on a 10-GeV S-band linear accelerator (LINAC), is achieved in Pohang, Korea by the end of 2016. The construction of the 1.11 km-long building was completed by the end of 2014, and the installation of the 10-GeV LINAC and undulators started in January 2015. The installation of the 10-GeV LINAC, together with the undulators and beamlines, was completed by the end of 2015. The commissioning began in April 2016, and thefirst lasing of the hard X-ray FEL line was achieved on 14 June 2016. The progress of the PAL-XFEL construction and its commission are reported here.  
Keywords:FEL; free electron laser; PAL; PAL-XFEL; construction; commissioning; LINAC; beamline +
Footnote †: journal: Journal of Physics B
# An Innovative and Efficient Method for Reverse Design of Wheel-Rail Profiles
## 5 Application Example
### Dynamic Performance Analysis
When the optimized wheels match the CHN60 rail, the maximum lateral displacement of the front guide wheels of MU CRH2 is around 8.5 mm. The rolling angle of the first wheelset with optimized wheels is close to that of the LMa profile, the maximum yaw angle difference of the first wheelset is 0.08 rad. The outer wheel friction force, derailment coefficient and lateral wheel-rail force of the first wheelset with optimized wheels are better than those of the LMa profile. The results show that indexes regarding the curve negotiation capability of optimized wheels are better than those of LMa profiles when matching CHN60, except the lateral displacement of the first wheelset is slightly higher than that of LMa profiles. Figure 23: Lateral stability index.
## 2 Decomposition of the Standard JRC Profile
### Standard JRC Profile Decomposition Method  
#### 2.1.1 Empirical Mode Decomposition Method  
Empirical Mode Decomposition (EMD) is a signal decomposition method based on the characteristic timescale of the signal itself. It does not need a fixed a priori basis function, and it is self-adaptive. It decomposes the fluctuation or trend at different scales in the signal, step by step, and generates a series of data sequences with different characteristic scales, namely, the intrinsic mode function (IMF).  
IMF is a function satisfying the following two conditions [69]:  
1. In the entire data set, the number of crossing zero points and extreme value points is the same or the difference between them is 1;
2. At any point on the signal, the mean value of the upper envelope defined by the local maximum and the lower envelope defined by the local minimum are 0, which means that the signal is locally symmetric about the time axis.  
The following three assumptions need to be satisfied when the EMD method is used to signal decomposition:  
1. The signal has at least two extreme points: a maximum point and a minimum point;
2. The characteristic timescale is defined as the time interval between adjacent extreme points;
3. If the signal does not have an extreme point and has only an inflection point, it should be differentiated one or more times to obtain the extremum point before decomposition, and then the corresponding component can be obtained by integrating the obtained result.  
EMD is a method of decomposing signals through the sifting process. The sifting process can be divided into three steps:  
Step 1: First, determine all extreme points for a target signal \(X(t)\). The upper envelope is obtained by connecting all the maximum points with a cubic spline function, and the lower envelope is obtained by connecting all the minimum points with a cubic spline function. The difference between the signal \(X(t)\) and the mean of the upper and lower envelopes is denoted as \(h_{1}\), as follows:  
\[h_{1}=X-\left(E^{+}(t)+E^{-}(t)\right)/2 \tag{1}\]  
Then, \(h_{1}\) is regarded as the new \(X(t)\), and Step 1 is repeated until \(h_{\rm i}\) meets the two limiting conditions of the _IMF_; the first-order IMF, called \(IMF_{1}\), is then sieved from the signal.  
Step 2: Separate \(IMF1\) from \(X(t)\) to obtain a different signal called \(RES_{1}\) with the \(IMF_{1}\) component removed.  
\[RES_{1}=X(t)-IMF_{1} \tag{2}\]  
\(RES_{1}\) is taken as a new signal, and Step 1 is repeated until the nth order residual sequence (\(RES_{n}\)) becomes a monotone function, and the \(IMF\) component cannot be further sieved.  
\[RES_{n}=RES_{n-1}-IMF_{n} \tag{3}\]  
Step 3: From a mathematical perspective, regard the initial signal \(X(t)\) as the sum of the \(IMF\) component and residual component \(RES\)[42]:  
\[X(t)=\sum_{i=1}^{n}IMF_{i}+RES_{i} \tag{4}\]  
where \(IMF_{i}\) represents the detail component, and \(RES_{i}\) represents the approximate component.  
Its flowchart is shown in Figure 1[70].
## 4 PM\({}_{2.5}\) Estimation
### Model Validation  
The total number of the matching dataset, covering all parameters at the 65 stations in Malaysia from 2018 to 2019, is 13,376. The matching data were randomly partitioned at a fraction of 70% for model calibration (model development) and 30% for model validation. In the model development, a sample based 10-fold cross validation technique was used, where the calibration data were randomly divided into 10 subsets; at any single moment,one subset was used for validation and the remaining subsets were used for calibrating the model. The average value of the results of the 10 subsets was adopted as the model accuracy. The sample based 10 cross validation (CV) performed validation with matchup sample from both spatial and temporal dimension. This is a commonly used CV-based technique to reveal the overall predictive ability of PM\({}_{2.5}\) estimation models [130]. Then, the final models were validated using the 30% of the remaining data. Statistical indicators such as the coefficient of determination (R\({}^{2}\)), Root Mean Square Error (\(RMSE\)), mean bias error (\(MBE\)) and Nash-Sutcliffe Efficiency (\(NSE\)) were used to evaluate the accuracy of the models. The \(NSE\) is a normalized statistic, which can determine the magnitude of the residual variance to the measured data variance and indicates how well the measured PM\({}_{2.5}\) versus estimated PM\({}_{2.5}\) data fits the 1:1 line (best fit line).  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{N}{{{{(I_{m}-l_{c})}^{2}}}}}{{{N_{t}}}}} \tag{1}\]  
\[MBE=\frac{\sum\limits_{i=1}^{N}{{{{(I_{m}-l_{c})}^{2}}}}}{{{N_{t}}}} \tag{2}\]  
\[NSE=1-\frac{\sum\limits_{i=1}^{N}{{{{(I_{m}-{l_{c})}^{2}}}}}}{\sum\limits_{i= 1}^{N}{{{{(I_{m}-{T_{m})}^{2}}}}}} \tag{3}\]  
where \(I_{m}\) and \(l_{c}\) are the measured and estimated PM\({}_{2.5}\) concentrations, respectively, \(\overline{I_{m}}\) is the average of the measured PM\({}_{2.5}\) and \(N\) the total number of measurements. \(NSE=1\) indicates an ideal model performance, while \(NSE=0\) shows model predictions as accurate as the mean of the observed data. Lower \(RMSE\) and \(MBE\) values correspond to better performance and to lower biases from the ML models.
## 4 Quantify the Spire Retrieval Accuracy and Uncertainty
### Initial Comparison of UCAR Spire Products with STAR Spire Retrievals  
RO retrievals may vary when implementing different processing procedures [20; 21; 22; 23; 24; 25]. To quantify the UCAR retrieval uncertainty owing to the RO processing, we also compare the UCAR Spire data with the STAR Spire retrievals (see Appendix A). STAR has become an RO processing and science center [31; 33; 34; 49]. We developed RO inversion packages for COSMIC-1, KOMPSAT-5, COSMIC-2, and GeoOptics data [32; 33]. In addition, we also developed a processing package for the CWDP Spire mission (see Appendix A). Like the approaches used to derive the COSMIC-2 data products, we use a similar inversion package to derive the STAR Spire bending angle and refractivity profiles. The STAR Spire BA is close to the ERA5 data interpolated at the Spire location and time.  
Figure 14 shows that the relative bending angle difference between the STAR and UCAR Spire retrievals from an 8 km to 30 km altitude is close to zero. Table 4 lists the means and standards of the STAR-UCAR Spire fractional refractivity for GPS, GLONASS, and GALILEO from the surface to a 40 km altitude. The mean fractional refractivity difference is less than 0.06% from the surface to 40 km altitudes for the GPS, GLONASS, and GALILEO signals. GLONASS's standard deviation is larger than GPS and GALILEO's.
# Cross-Domain Fusion Constellation Design of Communication, Navigation and Remote Sensing
## 4 Experimental Analysis
### System Cost  
In this letter, the cost of the three basic constellations and the cost of the CDFC are estimated, and the cost model uses formula (11), as shown in Figure 7. Although the cost of the integrated constellation system is higher than three independent constellations, it has significant advantages over the total cost of the three constellations, with a total cost reduction of 18.31%, indicating that the CDFC can effectively reduce costs while maintaining or even exceeding the service quality of the three functional domains.  
Figure 5: Constellation coverage map.  
Figure 6: GDOP of CDFC.
# Strengthening of RC Beams with CFC Panels for Improving Impact Resistance
## 5 Analytical Program Verification
### Impact Response  
Figure 20 presents the analytical results against the experimental results, in terms of the displacement and impact force time histories. The results demonstrate that the proposed numerical method can adequately reproduce the impact response of CFC-strengthened RC beams.  
The repeated impacts of RC_FB_CFC and RC_F3S_CFC are shown in Figure 21. As the figure demonstrates, except for RC_F3S_CFC, the displacement responses of all the specimens under repeated impact loading were accurately reproduced. This confirms that the proposed analysis method can predict the impact response and failure process of RC beams strengthened with bottom panels, such as the RC_FB_CFC specimen; however, the proposed method could not adequately simulate the displacement response of the RC_F3S_CFC during the third impact. This may be attributed to the simplified implementation of an elastic-plastic relationship to model the flexural-tensile behavior of the CFC panels. Therefore, to accurately reproduce the impact response, it is necessary to use a more advanced mechanical model, in which the inside of the panel reaches the elastic limit before the side of the CFC panel peels off.  
Figure 22 shows the relationship between the cumulative residual displacements and number of repeated impacts for each of the four types of beams. In this study, a residual displacement of 10 mm (1/100 of the span length) was set as the design limit. As shown in Figure 22, the proposed analysis method can accurately predict the number of repeated impacts that each beam type can resist before reaching the design limit, although there is a slight difference between the residual displacement at the time of the second and third impacts estimated by the numerical analysis of the RC_F3S_CFC types. The simulation confirmed that the number of repeated impacts until each type of residual displacement reached the design limit could be roughly predicted.  
Figure 20: Analytical versus experimental impact response of different specimens during the 1 st impact: (**a**) displacement at midspan; (**b**) impact force at midspan.  
Figure 21: Analytical versus experimental impact response of specimens during repeated impacts: (a) displacement of RC_FB_CFC at midspan; (b) impact force of RC_FB_CFC at midspan; (c) displacement of RC_F3S_CFC at midspan; (d) impact force of RC_F3S_CFC at midspan.  
Figure 22: (a) Analytical versus experimental: relationship between cumulative residual displacement and number of impacts; (b) analytical results of cumulative displacement distribution of RC_F3S_CFC along the beam axis.
## 2 Materials and Methods  
### SWCNT Film Fabrication  
The first fundamental step in the realization of a thin film based on CNTs [7] involves their dispersion in a liquid medium. In our procedure, we used sodium-dodecyl-sulphate (SDS) produced by Sigma-Aldrich, St. Louis, NO, USA (assay > 98.5%) as the anionic surfactant [25], preparing a solution of distilled water with SDS, within which highly pure CoMoCAT single walled carbon nanotubes (Sigma-Aldrich, assay > 90%, with a diameter of 0.7-0.9 nm and composition 10% metallic and 90% semiconductor) powder was inserted with a concentration of 26 ug/mL. The dispersion was tip ultrasonicated (Branson, St. Louis, MO, USA, S250A, 200 W, 20% power, 20 kHz) in an ice-bath for one hour. During this process, the SDS molecules were arranged around the nanotubes, separating them from one another and keeping them dispersed in water. The result was a well-dispersed suspension which is stable for several months without the formation and the precipitation of CNT aggregations. The next step of the SWCNT film realization is a low vacuum filtration process [8]. Different volume aliquots of the dispersion were introduced into a filtration system, composed in the lower part of a porous septum upon which a mixed cellulose ester filter was placed (Pall GN6, 1 inch in diameter and with 0.45 um pore diameter). Distilled water and a conceived solution of ethanol, methanol, and water were used to rinse the cellulose filter after the deposition of SWCNTs to remove SDS molecules. At the end of the process, homogeneous and stable filters were obtained, and the CNT film was deposited onto a solid substrate.
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 3 Development Status and Key Technologies of ISTNs
### Key Technologies of ISTNs
#### 3.2.2 THz of ISTNs  
The short wavelength of THz is negatively affected by the reflectance, transmittance, and absorption of materials, especially in the atmosphere, resulting in high propagation losses. However, the concentration of water vapor molecules near the earth's surface decreases with the increase in altitude, making the THz frequency range highly promising for communication in an environment approximating vacuum, such as space. It is estimated that satellite-to-aircraft communication links have sufficient budget for THz communication [78]. Terahertz/radio frequency hybrid communication is of great significance for space-air-ground integrated networks to improve transmission efficiency and achieve 6G smart connectivity. Therefore, satellite THz communication has attracted widespread attention. In [79], the authors proposed an analytical framework to study the coverage probability and average achievable rate of a downlink THz LEO satellite-aircraft network with directional antennas. In [80], a comprehensive analysis and optimization of integrated space-air-ground communication with multi-band terahertz/radio frequency communication was presented. Based on this model, expressions for SINR and rate coverage probability were derived, and the system coverage and transmission rate performance were evaluated. A simulated annealing-based algorithm was proposed to maximize node performance. In [81], the authors developed a complex mathematical framework to simulate directional interference between crosslinks in millimeter-wave/THz satellite communication networks. It focused on crosslink interference, analyzed sources of interference, and proposed interference mitigation solutions. In practical applications, the high power of the THz signal generator introduces higher noise figures. In the future, research on satellite THz technology will focus on the aspects of antenna layout, beam design, transmitter design, and so on.
## 3 Materials and Methods
### User Satisfaction Modeling  
We previously discussed measuring the effect of user positive or negative sentiments toward each UXD on user satisfaction using a bidirectional LSTM model. The bidirectional LISTM model overcame the problems of the existing model used for user satisfaction models such as Gaussian distribution [13] and regression analysis.  
Most of the existing models for user satisfaction assume that the online rating given by a user is a linear amalgamation of the sentiment regarding all the dimensions discussed  
Figure 8: Work flow of integrating must-link into the Gibbs sampler.  
Figure 9: Workflow of topic labeling into UX dimensions.  
in the online reviews. However, this assumption is not valid; there are many issues, such as a complex combination of sentiments towards most of the dimensions in user online reviews. In order to resolve this issue, bidirectional LSTM outperforms when compared to other models for modeling user satisfaction. Based on this reason, we employed this model for measuring the sentiment effects towards each UXD. We used the user rating as label attributes for each review, along with generated data as discussed the in subsequent section for model training.