esa-sceva/satcom-chunk-collection huggingface dataset:
# Large Constellations of Small Satellites: A Survey of Near Future Challenges and Missions
## 3 Future Challenges
### Constellation Management
The authors in [23] give an example of work in this sense through the application of inventory management approach to the space field. They propose a set of parking orbits and in-plane spares which are refurnished from ground following an optimal policy (i.e., minimize the total Expected Spare Strategy Cost). These spare satellites are then moved into the constellation when needed. Notice that the reliability of the constellation can be regarded also from a building process perspective as stated in [24] (rather than satellite design only) which suggests, for instance, adopting a Failure Mode and Effect Analysis for a robust mass production. The deployment strategy is also fundamental to be included in point 3, given the constellation size trend and the competitiveness of the field. Spacecraft deployment must be accounted for since the beginning because it has a significant impact on the lifecycle cost. In fact, it affects both the number of launches and the complexity of the satellite to be launched. In principle one launch for every orbital plane is needed, also the complexity of the onboard propulsion system (if any) changes based on the post-launch operations to be performed. Few interesting works in this respect are [25; 26; 27; 28]. The approach studied in [25] consists of deploying the spacecraft gradually as they are needed by the market, which is shown to reduce the life cycle cost of a constellation significantly, of about 20% when applied to the Globalstar case study. Similarly, [28] consider a staged deployment which is optimized using genetic algorithms. This approach seems to be particularly appealing as it makes the constellation size adaptable to the market reaction, which is very difficult to predict as Iridium and Globalstar experiences have shown. Other efficient deployment strategies are compared in [26] such as J2 driven deployment and carrier-vehicle deployment. These methods allow one to configure the orbits in space avoiding multiple launches. The use of Earth-Moon Lagrangian point L1 is envisaged in [27], however, this solution seems to be convenient only in combination with carrier vehicles.
## 3 Results
### Algorithm Performance
#### 3.4.2 Minimum PPWL  
This study is conducted to show absorption coefficient results using different PPWL configurations for 1D and 2D schemas. Figure 12 shows the different PPWL values chosen at the 1D FDTD scheme compared to measurements values in Kundt tube. As seen in the figure, no differences can be observed. As a consequence, just for the 1D case, even the minimum value of 6 PPWL yields good results. As NEAM modeling is adaptive to the given input data, and a set of fixed FDTD parameters, 1D FDTD absorption coefficient results shall not vary, thus varying the NEAM coefficients values only. In fact, just mixing combinations between 1D and 2D PPWL will yield different results.  
The four NEAM coefficient results are extracted from the 1D FDTD, which are used in the 2D simulations. Then, just downscaling PPWL interactions between 1D NEAM coefficients to 2D ones were simulated, outlined in Table 4 and represented in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**1D PPWL** & & & **2D PPWL** & & \\ \hline
10 & & 10 1 & & & 6 1 & \\
6 & & & & 6 1 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: PPWL used at 1D-FDTD calculation downscaled to 2D FDTD to compute the absorption coefficient.  
Figure 11: Diffuse absorption coefficient calculated for a 60 mm melamine sheet with Paris’ formulation using the different angle ranges from Table 3. * Range calculated with 10\({}^{\circ}\) step.  
Figure 13 shows that the relationship between PPWL and the result performance holds, regardless of the number of points chosen, as for the 1D case. Knowing that the equivalent coefficients are adaptive and do not seek to be related to physical principles, reducing the number of PPWLs would increase the number of calculations performed, or the volume of the FDTD mesh, thus improving the computational performance.  
Figure 12: Measured normal incidence sound absorption compared to 1D FDTD simulations using different PPWL values for a 60 mm melamine sheet.  
Figure 13: The 60 mm melamine sheet sound absorption calculated with JCAL model and compared to 2D FDTD simulations using downscaled PPWL factors from Table 4. The 1D NEAM coefficient values are calculated at (**a**) 30 PPWL, (**b**) 20 PPWL, (**c**) 10 PPWL, (**d**) 6 PPWL.
## 3 WOA-VMD Denoising Method  
### VMD Parameter Optimization  
In the process of VMD signal decomposition, the mode number \(K\) and penalty parameter \(\alpha\) must be determined. If the value of \(K\) is excessive, the signal is decomposed too much, otherwise, the signal is underdecomposed. If the value of \(\alpha\) is excessive, the signal component will be lost, otherwise, the information will be redundant [17]. In the actual analysis, the optimal parameters should be determined according to the characteristics of each signal. At present, the optimal \(K\) value is usually determined by observing the frequency variation characteristics of the center [13], but this method is subjective and accidental, and the optimal penalty parameter \(\alpha\) cannot be obtained.  
In this paper, the WOA algorithm is used to optimize the VMD parameters, and envelope entropy is used as the evaluation index of the optimization effect. When the signal contains more noise, the feature information is less, the entropy value is larger, and vice versa. Firstly, the position vector \([K,\alpha]\) of the whale group is initialized to calculate the fitness of each whale. Then, the formula is determined by judging the convergence size, and the optimal solution is obtained by iterating repeatedly until the end. The process is shown in Figure 1.
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 3 Longitudinal Controller Design
### Brake Control Design  
To complete the braking control of the driverless ferry vehicle in the driverless state, the braking torque is calculated using the difference between the desired speed and the current speed. Consequently, the braking oil pressure is an output based on the relationship between the braking torque and the braking oil pressure. The braking control schematic diagram is shown in Figure 11.  
The driverless ferry vehicle's required braking deceleration is computed based on the difference between its desired and actual speeds. The braking deceleration formula is as follows:  
\[a=\frac{v_{ref}-v_{x}}{t} \tag{13}\]  
In Formula (13), the driverless ferry vehicle's braking deceleration is represented by \(a\), its desired speed is represented by \(v_{ref}\), its actual speed is represented by \(v_{x}\), and its braking duration is represented by \(t\).  
According to [PERSON]'s second law, the braking force needed for the driverless ferry is calculated. It contains an equal distribution of the entire braking force across the four wheels. The following is the formula for calculating the braking torque of each wheel:  
\[T=\frac{F}{4}r \tag{14}\]  
In Formula (14), \(T\) is the braking torque required by a single wheel, and \(r\) is the wheel radius.  
The relationship between torque, force, and force area determines how the braking torque is converted to brake oil pressure. The relationship equation is as follows:  
\[F_{b}=\frac{T}{v_{b}} \tag{15}\]  
\[P=\frac{F_{b}}{S} \tag{16}\]  
where \(F_{b}\) represents the braking force exerted by the brake calipers, \(r_{b}\) represents the effective friction radius of the brake disc, \(S\) represents the caliper piston area, and \(P\) represents the brake oil pressure.  
Figure 11: Brake control principle.  
Figure 10: Fuzzy PID acceleration controller.
## 3 Results and Discussion
### The Changes in Pore Volume and Surface Area
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Samples**} & \multicolumn{5}{c}{**Pore Volumes (10\({}^{-2}\) cm\({}^{3}\)/g)**} & \multicolumn{5}{c}{**Pore Surface Area (m\({}^{2}\)/g)**} \\ \cline{3-11}  & & \(V_{\rm Ma}\) & \(V_{\rm Me}\) & \(V_{\rm Tr}\) & \(V_{\rm Mi}\) & \(V_{\rm Cu}\) & \(S_{\rm Ma}\) & \(S_{\rm Me}\) & \(S_{\rm Tr}\) & \(S_{\rm Mi}\) & \(S_{\rm Cu}\) \\ \hline \multirow{2}{*}{Qiyi coal} & Before extraction & 1.62 & 0.80 & 2.28 & 2.45 & 7.15 & 0.016 & 0.091 & 4.182 & 17.442 & 21.731 \\  & After extraction & 1.86 & 0.84 & 2.26 & 2.55 & 7.51 & 0.018 & 0.092 & 4.120 & 18.179 & 22.409 \\ \hline \multirow{2}{*}{Liulin coal} & Before extraction & 1.64 & 0.82 & 1.35 & 1.75 & 5.56 & 0.018 & 0.116 & 3.066 & 14.028 & 17.228 \\  & After extraction & 2.30 & 1.35 & 1.56 & 1.93 & 7.14 & 0.044 & 0.199 & 3.486 & 14.724 & 18.453 \\ \hline \multirow{2}{*}{Dengliazhuang coal} & Before extraction & 1.65 & 0.63 & 1.29 & 1.59 & 5.16 & 0.014 & 0.076 & 2.763 & 14.829 & 17.682 \\  & After extraction & 2.13 & 0.64 & 1.30 & 1.61 & 5.68 & 0.015 & 0.080 & 2.825 & 15.116 & 18.036 \\ \hline \multirow{2}{*}{Shuangliu coal} & Before extraction & 1.21 & 0.47 & 0.83 & 1.33 & 3.84 & 0.011 & 0.047 & 1.765 & 13.086 & 14.909 \\  & After extraction & 1.59 & 0.89 & 0.86 & 1.30 & 4.64 & 0.043 & 0.088 & 1.729 & 12.701 & 14.561 \\ \hline \multirow{2}{*}{Maozequ coal} & Before extraction & 1.07 & 0.38 & 0.97 & 1.51 & 3.93 & 0.007 & 0.075 & 1.495 & 8.837 & 10.414 \\  & After extraction & 1.10 & 0.42 & 1.18 & 1.49 & 4.19 & 0.007 & 0.082 & 1.497 & 8.646 & 10.232 \\ \hline \hline \end{tabular}  
Ma: macropore; Me: mesopore; Tr: transitional pore; Mi: micropore; Cu: cumulative pores.  
\end{table} 
Table 2: Pore volumes and surface areas of the samples before and after the Sc-CO\({}_{2}\) extraction.
# Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode
## Abbreviations  
ACC & Area Control Center  
ADSM & Ad Hoc Separation Minima  
AIRAC & Aeronautical Information Regulation and Control  
AM & Avoidance Maneuver  
AMAN & Arrival Manager  
ANSP & Air Navigation Service Provider  
ASMT & Ad Hoc Separation Minima Tool  
ATC & Air Traffic Control  
ATCO & Air Traffic Control Officer  
ATM & Air Traffic Management  
CCO & Continuous Climb Operation  
CD & Conflict Detection  
CDO & Continuous Descent Operation  
CD&R & Conflict Detection and Resolution  
CR & Conflict Resolution  
CRM & Collision Risk Model  
CONOPS & Operational Concept  
DAC & Dynamic Airspace Configuration  
DMAN & Departure Manager  
EASA & European Union Aviation Safety Agency  
ECAC & European Civil Aviation Conference  
FL & Flight Level  
FTS & Fast Time Simulations  
HF & Human Factor  
ICAO & International Civil Aviation Organization  
IFR & Instrumental Flight Rules  
Kg & Kilograms  
KPA & Key Performance Area  
KPI & Key Performance Indicator  
LECM & FIR Madrid  
LEMD & Madrid-Barajas [PERSON] airport  
LPPC & FIR Lisbon  
LPPT & Lisbon [PERSON] airport  
_LOS_ & LOSs of separation  
MONA & Monitoring of Aircraft Trajectories  
MWS & Minimum Wake Vortex Separation  
NEST & Network strategic tool  
NM & Nautical Miles  
PBN & Performance-Based Navigation  
PC & Planner ATCO  
PI & Performance Indicator  
RNP & Required Navigation Performance  
RECAT-EU PWS & European Recategorization Pairwise  
SESAR & Single European Sky ATM Research  
TBS & Time-Based Separation  
TC & Tactical ATCO  
TMA & Terminal Maneuvering Area  
WL & Workload
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 3 Intersection-Based Link-Adaptive Beaconless Forwarding for City Scenarios  
Intersection-based link-adaptive beaconless forwarding for city scenarios (ILBFC) protocol exploits geographic information of road intersections in limiting the duration for which a relay vehicle stays as a default forwarder. Also, an urban road traffic adaptive winner relay management scheme is employed where the relay vehicle when acting as default forwarder decides on every data packet reception whether or not to continue as default forwarder. The details of ILBFC are discussed next.
## 2 DAB Topology and Modeling Analysis
### ZVS Conditions of DAB Converter  
In SiC MOSFET-based DAB converters, ZVS is attainable only when the junction capacitance of the switching device is completely discharged and a reverse current flows through the body diode to initiate conduction. The required polarity of the inductor current for achieving ZVS across different switches is summarized in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Polarity of the Inductor Current** & **Switches** \\ \hline \hline \(i_{\rm L}<0\) & \(S_{1}\), \(S_{4}\), \(S_{6}\), \(S_{7}\) \\ \hline \(i_{\rm L}>0\) & \(S_{2}\), \(S_{3}\), \(S_{5}\), \(S_{8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The required polarity of the inductor current for achieving ZVS in the DAB converter.  
Figure 1: The topology of the DAB converter.  
Figure 2: Triple phase-shift modulation waveforms of DAB.
## 1 Introduction
### The Purpose of Research  
In this paper, we present an example of the use of RTS monitoring followed by physical monitoring using SLDV equipment and a seismograph with an associated geophone. The purpose of this study was to demonstrate the usefulness of the RTS method for determining the dynamic response of a structure. The advantages of this method include its wide usability, the reliability of its results, its ease of use and its economic aspects. The monitoring took place on a dynamically loaded prestressed concrete threshold in a railway line. The purpose of the measurements was to obtain basic information about the dynamic response of a railway sleeper lying on an elastic subgrade in a simple, reliable and safe way. The measurements made can serve as very important information on the influence of the dynamic loading caused by trains running on a railway line on the causes of the damage--mainly cracks--that is nowadays observed in several European Union countries by the maintenance services of the railway infrastructure managers.
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
## 5 Parametric Study
### Influence of Perfobond Size  
Figure 20 illustrates the significant impact of the perfobond connector's thickness on the pull-out performance of notched T-perfobond connectors. The pull-out resistance increased dramatically as the thickness of the notched perfobond rib was incremented from 2 mm to 4 mm, 6 mm, 8 mm, and 10 mm, with respective increases of 93.22%, 146.44%, 156.71%, and 147.87%. The enhancement in pull-out resistance is primarily attributed to the robust anchoring effect of the bottom flange of the perfobond rib. For thicknesses between 2 mm and 6 mm, the perfobond rib weaker than the other structural elements; thus, the pull-out resistance increases linearly with the thickness of the perfobond rib. Once the thickness exceeds 6 mm, the perfobond rib's strength surpasses the constraints imposed by the concrete and rebars in the holes. Consequently, further increases in the perfobond rib's thickness lead to a continued rise in pull-out resistance. However, beyond a certain point, additional thickness does not yield a significant change in pull-out resistance.  
Figure 18: Influence of hole height. (**a**) Load-separation curves; (**b**) influence analysis; (**c**) failure mode.  
Figure 19: Influence of hole length. (**a**) Load–separation curves; (**b**) influence analysis; (**c**) failure mode.  
Figure 20: Influence of perfobond thickness. (**a**) Load–separation curves; (**b**) influence analysis; (**c**) failure mode.  
Figure 21 demonstrates the significant impact of the notched perfobond rib's length on the pull-out performance of notched T-perfobond connectors. As the perfobond rib's length increased from 60 mm to 70 mm, 80 mm, 90 mm, and 100 mm, the pull-out resistance of the notched T-perfobond connectors correspondingly rose by 45.2%, 68.8%, 85.83%, and 83.11%, respectively. The enhancement in pull-out resistance is primarily due to the substantial anchoring effect of the bottom flange of the perfobond rib. Between lengths of 60 mm and 90 mm, the perfobond rib is weaker than the other structural constraints, and the ultimate resistance of the connectors increases linearly with the length of the perfobond rib. When the length exceeds 90 mm, the strength of the perfobond rib surpasses the constraints imposed by the concrete and rebars in the holes, suggesting that further increases in rib length may not significantly enhance pull-out resistance.
## 3 Results and Discussion
### Anticancer Activity  
GA, saponins, and PC display anticancer activities [25; 63]. _G. sylvestre_ extracts were active against human lung adenocarcinoma and breast carcinoma cell lines [64]. Presently, the cytotoxic activity of CuO NP treated and non-treated CSC extracts against MCF-7 and HT-29 cancer cells were investigated. The cells were exposed to various levels of CuO NP treated and non-treated CSC extracts. The percentage inhibition of the cells increased with increasing concentrations of CSC extracts (Figure 7). Greater inhibition was noted at 200 \(\upmu\)g/mL of the CSC extracts (Figure 7a,b), at which the CuO NP treated CSC extracts exhibited high cancer inhibition, whereas the non-treated CSC extracts displayed less inhibition. Similarly, _G. sylvestre_ and _Eclipta prostrata_ extracts obtained from AgNP treated plants also displayed higher cytotoxicity against HeLa cells [63]. The present findings agree with the earlier observation studies that that the CuO NP-elicited hairy roots displayed more potent cytotoxic activities than non-elicited hairy roots [29]. This avid cytotoxic activity in these extracts may be due to the high amounts of GA II and PC.  
Figure 7: Effect of CuO NPs on cell viability of MCF-7 cells (**a**) and HT-29 cells (**b**) in CSC of _G. sylvestre_. Different letters indicate a significant difference at \(p\leq 0.05\).
## 2 Structure of Hybrid AC/DC Microgrid  
A typical hybrid microgrid structure consists of an AC network, DC network, utility grid, and interface stage. Hybrid AC/DC microgrid incorporates both individual AC and DC microgrids through interfacing stages. Based on the connection of distributed generators and energy storage systems to the main bus and interconnection of the main bus with the utility grid, the hybrid microgrid can be divided into three topologies such as AC coupled, DC coupled, and AC-DC coupled. The major difference between them is the nature of the main grid present [7]. In [8], the authors compare different batteries for optimal operation of renewable energy resources in a hybrid microgrid with the help of iHOGA software. It shows that the existing techniques for life span prediction of batteries are not accurate, and comparatively, the lithium batteries exhibit less cost of energy. A more detailed discussion of power management schemes of hybrid AC/DC microgrid encompassing the DC and AC sources/loads is presented in [9].
# Recognizing Urban Functional Zones by GF-7 Satellite Stereo Imagery and POI Data
## 4 Results and Discussion
### Results
#### 4.1.2 UFZs Identification Results  
The identification results of UFZs are shown in Figure 9, and the quantitative evaluation results are summarized in Table 4. Qualitative results show no significant difference in the performance of the different classifiers. Quantitative indicators OA and kappa coefficient are not much different. Among them, SVM outperforms the others with the highest OA and kappa coefficient, has obvious advantages in identifying public services and shantytowns. KNN follows with the second-best performance, particularly excelling in identifying green land and education. RF obtains the lowest OA and kappa coefficient, but it has obvious advantages in identifying commercial zones and development. For different categories of UFZs, the accuracy of identifying public services and education is relatively low, and the identification accuracy is mostly less than 70%. The accuracy of identifying residential zones, green land, and development surpasses 90%.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **SVM (\%)** & **KNN (\%)** & **RF (\%)** \\ \hline Residential zone & 94.00 & **94.72** & 91.61 \\ Commercial zone & 83.68 & 85.36 & **87.45** \\ Shantytown & **92.16** & 87.25 & 90.20 \\ Public service & **55.41** & 47.30 & 47.30 \\ Development & 96.43 & 94.64 & **98.21** \\ Education & 64.71 & **70.59** & 47.06 \\ Green land & 94.06 & **96.04** & 95.05 \\ OA & **88.17** & 87.97 & 87.18 \\ Kappa coefficient & **83.91** & 83.60 & 82.65 \\ \hline \end{tabular}
\end{table}
Table 4: Overall classification results and per category results. The bold values represent the best performance of each indicator.
## 3 Research Methodology
### Air-Fuel Ratio (AFR) Control  
An Internal Combustion (IC) engine is a heat engine that uses air to burn fuel in a combustion chamber. In industrial operations, IC engines are commonly utilized as prime movers. Chemical energy is turned into mechanical rotation, which is then used to power the engines' compressors and alternators. The two types of IC engines are Spark Ignition (SI) and Compression-Ignition (CI). The compression causes combustion in CI engines, whereas spark plugs cause combustion in SI engines. During the combustion phase, the AFR is a measurement of how often air and fuel are mixed in a specified ratio. It is required for better engine performance, reduced fuel consumption, and less pollution. The general architecture of the AFR of an SI IC engine is shown in Figure 1.  
The mathematical equation of AFR is:  
\[\text{AFR}=\frac{m_{air}}{m_{fuel}} \tag{1}\]  
Figure 1: The architecture of the air–fuel system of an SI IC Engine [4].  
The chemical equation is given as:  
\[25\text{O}_{2}+2\text{C}_{8}\text{H}_{18}\to 16\text{CO}_{2}+18\text{H}_{2} \text{O}+\text{Energy} \tag{2}\]  
The AFR is referred to as the stoichiometric ratio in this equation, and its value for gasoline is 14.6:1. During the combustion of fuel, the AFR ranges from 6:1 to 20:1. A rich mixture has a lower value than the stoichiometric ratio, whereas a lean mixture has a higher value than this ratio. The 16.5:1 AFR, for example, is lean, while the 13.7:1 AFR is rich in gasoline. Rich and lean mixtures are also deemed dangerous to the engine because they weaken the catalyst while also lowering engine performance and fuel economy. The value of AFR varies depending on the type of petrol. The value for ethanol amounts to 9:1, methanol to 6.47:1, and hydrogen to 34.3:1. Four sensors are important in preserving AFR control of the SI IC engines:  
_Manifold Absolute Pressure (MAP) Sensor:_ It gives the controller a precise suction air pressure rating.  
_Throttle Sensor:_ It provides the controller with an air throttle position.  
_Exhaust Gas Oxygen (EGO) Sensor:_ It is used to assess the oxygen substance in the drained gas and manages the fuel supply to ensure efficient ignition.  
_Speed Sensor:_ Measures the rotational speed of the crankshaft of an engine; to provide this speed to the controller, a speed sensor is used.  
Because when these sensors fail, the engine shuts down, hence fault tolerance is required. It is desirable to generate virtual redundant sensors in the FDI unit that have a nonlinear response similar to actual sensors. That is why we used the estimation techniques (GA, PSO, and NLR) in AFTCS architecture to approximate the system.