esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Fatty Acids Profile Analysis  
The fatty acid methyl esters (FAMEs) were synthesised according to the direct method [28]. Samples of 1 g of raw gate were analysed. However, the FAMEs of black cumin oil were prepared according to the AOCS Ce 1b-89 method. A total of 0.09 g of oil was taken for analysis. The fatty acid profile analyses were performed using a gas chromatograph with a flame ionisation detector (Shimadzu GC-2010, Kyoto, Japan) and equipped with an RT(r)-2560 column (100 m \(\times\) 0.25 mm ID and 0.2 \(\upgamma\)m film thickness) (RESTEK, Bellefonte, PA, USA). The oven temperature program was: 4 min of isothermal heating at 140 \({}^{\circ}\)C, then an increase of 4 \({}^{\circ}\)C/min to 240 \({}^{\circ}\)C, which was held for 25 min. The injector temperature was 240 \({}^{\circ}\)C and the detector temperature was 260 \({}^{\circ}\)C. The sample volume injected was 1 \(\upmu\)L with a split ratio of 70:1. The Supelco 37 FAME component standard (Supelco, CRM47885) was used to recognise the peaks. The flow rate of the carrier gas (helium) was 1.0 mL/min. Each sample was analyzed in triplicate. Based on the results of fatty acid profiles, three nutritional indices were calculated: thrombogenicity (TI) [29], atherogenicity (AI) [29], and the ratio of hypo- and hypercholesterol fatty acids (h/H) [30]:  
\[\mathrm{TI}=\frac{\mathrm{C14.0+C16.0+18.0}}{(0.5\times\mathrm{LMUFA})+(0.5 \times\mathrm{E}n-6)+(3\times\mathrm{E}n-3)+(\frac{5n-3}{20-6})}\]  
\[\mathrm{AI}=\frac{\mathrm{C12.0+(4\times\mathrm{C140})+16.0}}{(\mathrm{\Sigma PUFA }-3)+(\mathrm{\Sigma PUFA}-6)+(\mathrm{\Sigma MUFA})}\]  
\[\mathrm{\frac{h}{H}=\frac{\mathrm{C18
# Short Landing for Flying-Wing Unmanned Aircraft with Thrust Vector
## 6 Simulation Verification and Discussion
### Sink-Rate Control under Wind Gust  
To evaluate the performance of the ESO-based sink-rate control proposed in Section 4.4, the implicit NDI controller was chosen for comparison, using the same control bandwidth. Referring to Equation (41), the control law for sink rate control can be expressed as follows:  
\[-a_{zE,d}=k_{i}\int_{0}^{t}\left(\dot{h}_{c}-\dot{h}\right)dt-k_{p}\dot{h}+A_ {1} \tag{59}\]  
where \(k_{p}\) and \(k_{i}\) are the control parameters, which can be determined by referring to Equations (41) and (42). The wind-gust model is described as follows:  
\[\left\{\begin{array}{ll}u_{g}=\frac{u_{H}}{2}\left(1-\cos\left(\frac{\pi x} {d_{x}}\right)\right)&0\leq x\leq d_{x}\\ w_{g}=\frac{w_{m}}{2}\left(1-\cos\left(\frac{\pi x}{d_{z}}\right)\right)&0 \leq x\leq d_{z}\end{array}\right. \tag{60}\]  
where \(u_{g}\) is the gust in the \(u\)-axis; \(w_{g}\) is the gust in the \(w\)-axis; \(x\) is the integration of velocity; \(d_{x}=120\) m and \(d_{z}=80\) m is the gust length; \(u_{m}=3.5\) m/s; and \(w_{m}=3\) m/s is the gust amplitude [40]. As shown in Figure 24, it can be observed that there is no significant difference in the sink-rate response when there is no wind. However, when the wind is introduced at 32 s, the ESO-based sink-rate controller exhibits a better disturbance rejection capability with rapid convergence, while the NDI controller continues to oscillate and shows a trend of divergence. Therefore, it can be concluded that the ESO-based sink-rate controller performs better when faced with external disturbances.
## 3 Modelling Strategies
### Type of Analyses  
Numerical models can be used to perform different types of analyses, which are selected based on the desired outputs. The common analyses used in the seismic assessment of buildings include limit analysis, linear static analysis, nonlinear static analysis (pushover analysis), linear dynamic analysis in the time domain, linear dynamic analysis in the frequency domain, and nonlinear dynamic analysis in the time domain (nonlinear time history analysis) [11; 36].  
Limit analysis implements solutions based on either static or kinematic theorems, requiring minimal input information (geometry and load conditions) and low computational power [11]. Seismic assessment with macroblocks normally employs this analysis type. It provides results on collapse mechanisms and seismic load capacity [36]. All types of linear analyses are inadequate for capturing the dynamic response of URM buildings [11]. Masonry exhibits a high nonlinearity and low tensile strength, making linear analysis unsuitable even for moderate ground motions [36].  
Nonlinear static analysis is a very common and standardised analysis to evaluate the seismic capacity of existing URM buildings [11; 36]. In this case, seismic action is incrementally applied as a lateral force [36]. The analysis accounts for the nonlinear material properties. The nonlinear differential equations are normally linearised and solved by iterative methods, using, for example, the Newton-Raphson method. Implicit solvers may encounter convergence problems when the response is highly nonlinear. However, this approach offers a suitable balance between accuracy and computational demand since it can find the post-peak behaviour [11].  
On the other hand, in nonlinear dynamic analysis, the structure is subjected to time-dependent actions, and the response accounts for the inertia and damping forces [11]. The actions are either recorded earthquakes or artificially generated signals. It is the most advanced and complex type of analysis since it requires time integration methods to solve the differential equations in the time domain [11; 36]. Implicit solvers are commonly used in FEM models, while explicit methods are more frequently applied in DEM models [36]. The computational demand for the implicit solvers is high due to the solution of differential equations, nonlinear behaviour, and convergence issues.
# Proxy-Based Semi-Supervised Cross-Modal Hashing
## 2 Related Works
### Supervised Hashing Methods  
Supervised cross-modal hashing methods utilize labeled data to learn hash codes, aiming to maintain semantic similarity across different modalities based on known label information. Among these methods, DCMH made a groundbreaking contribution by introducing deep learning into cross-modal hashing. It employs an end-to-end approach to learn the feature representations of raw data and optimize hash codes simultaneously [23]. Building on this, the MESDCH method further considers the distinction in similarity magnitudes between data under a multi-label setting, designing a multi-label semantic similarity module to explore inter-modal similarity relationships [24]. Since mapping data into the hash space involves quantization, HHF addresses the incompatibility between metric loss and quantization loss; it alleviates this issue by setting different thresholds based on varying hash code lengths and the number of categories [25]. SCCGDH designs a label network to generate distinct centers for different labels and uses these label centers to guide data learning [26]. DCPH is the first to introduce the concept of category proxies, generating hash proxies through a proxy network to guide the model learning process, enabling the model to fully consider the relationships between data and categories [27]. Considering that the generated category proxies do not account for the data distribution, DAPH adopts a two-step approach: it first integrates data features and labels into a unified framework to learn category proxies, which are then used for guidance [28]. DSPH proposes trainable category proxies and further adds a negative sample constraint to the proxy loss [29]. SCH, addressing the oscillation problem of the loss function, sets upper and lower bounds for similarity measures between data points to mitigate oscillations [30]. Finally, DCGH simultaneously considers relationships between data points as well as between data and categories, ensuring intra-class cohesion and preserving inter-class structural relationships [31].
# Evolutionary Statistical System Based on Novelty Search:
## Appendix A Calibration Experiment  
In this section, we describe a calibration experiment performed for ESS-NS. The motivation of this experiment was to produce information in order to choose sensible parameters for comparison against the other methods in Section 4. Static calibrations are motivated by the fact that metaheuristics usually have a set of parameters that can be very sensitive to the application problem. Therefore, it is often necessary to test a number of combinations of possible values for these parameters in order to find values that are suitable for the kind of problem to be solved. Previous work [22; 46] includes static calibration for several parameters of ESSIM-EA and ESSIM-DE, including number of islands, number of workers per island, and frequency of migration, among others. In the context of our current work, most parameters are fixed in order to perform a fair comparison. For example, the population size and fitness threshold are the same for all methods, and the number of workers is the same for ESS and ESS-NS. This gives all algorithms similar computational resources and restrictions. As a particular case, we have fixed the crossover rate at one since we consider this to be the most direct approach for a novelty-based strategy, given such a value maximizes diversity. These simplifications also help narrow down the space of possible combinations of parameters. Therefore, our calibration was performed by varyingonly two parameters: tournament probability and mutation probability (or mutation rate). The candidate values vary among the following, respectively:  
\[\begin{split}& tour\_prob\in\{0.75,0.8,0.85,0.9\}\\ & mut\_prob\in\{0.1,0.2,0.3,0.4\}\end{split}\]
# L2-CalSat: A Calibration Satellite for Ultra-Sensitive CMB Polarization Space Missions
## 5 L2-CalSat Operation  
As it has been previously explained, the proposed calibration satellite would travel as a piggy-back of the main satellite, to be deployed once in the required Lissajous orbit around the L2 libration point of the Sun-Earth system -see [57] for more details-. One of the main characteristics of this kind of orbit is the small amount of propellant needed to maintain the satellite on it. In fact, the total thrust required for a 3-year calibration mission has been estimated as \(\Delta\
u=17\) m/s, which is around one order of magnitude lower than that required to transfer a micro-satellite to L2.  
After separation, the platform of the main satellite will be the communication link with L2-CalSat. The CMB-Sat will also need to take part in the AOCS of L2-CalSat, providing position and attitude references to calculate relative distances and orientations. The calibration system requirements for pointing at CMB-Sat have been considered, resulting critical the determination of the distance between satellites and the availability of a collision avoidance manoeuvre, although this risk can be estimated as very unlikely. The calibration satellite shall maintain a distance of \(6600\pm 66\) m during operation, in order to avoid the saturation of the CMB mission detectors due to its thermal emission, while being in the far-field of the telescope at the highest frequencies of the experiment (450 GHz, in the particular example described in this work). Additionally, for security reasons it has been established a distance range between both satellites from 4620 to 8580 m (considered calibration distance plus or minus a 30%).  
In-flight calibration must be performed periodically. During this phase L2-CalSat has to be re-positioned and controlled with precision, and a communication link with the main satellite has to be maintained. The CMB-Sat will eventually point to L2-CalSat and will receive the calibration signals. The number, duration and characteristics of these calibration operations have been analysed and the most convenient strategies are proposed in this section.
# Crack Growth and Delamination Analysis in GFR Composite Materials
## 4 Results and Discussion
### Mesh Sensitivity Analysis  
In the framework of continuum mechanics, the stress-strain relations of a material define the constitutive model. In case of strain softening behavior of a material strain, localizations can appear, resulting in strong mesh dependency of the solution. As a result, mesh refinement can decrease the energy dissipation. To alleviate mesh dependency, the ABAQUS solver introduces a characteristic length in the formulations that is related to the size of the element and expresses the strain softening part as a stress displacement relation. Thus, the dissipated energy during damage is given per unit area and not per unit volume and is treated as a material property that is used to calculate the critical displacement for damage initiation. The above method is also consistent with the fracture mechanics concept of the critical energy release rate where it is also treated as a material parameter [34].  
The cross-ply composite model was solved for five different meshes to examine the mesh dependency having 8000, 45,144, 96,000, 208,306, and 352,688 continuum shell elements, respectively, and the results are shown in Figure 8. It can be concluded for all four models that the cracking saturation occurs with the same number of cracks. The only result that significantly differentiates in coarse meshes and seems to stabilize as the mesh becomes finer is the mean delamination length (Figure 9). This result is calculated as an average value from the sum of the delamination length of each crack divided by the number of cracks.  
Figure 8: Final cracking at the model material geometry for five different meshes for quasi-static loading. At the right corner, a magnified local domain encapsulating crack is also given for each of the five different meshes.  
Figure 6: Middle layer transverse cracks at different time steps.  
Figure 7: Crack density as a function of strain for experiment data [33] and simulation model data for quasi-static loading and for total loading time of about 270 s.  
This can be attributed to the small number of elements which translates to a small number of integration points. Inevitably, the results are extrapolated to a larger but unnecessary area. This phenomenon disappears by using an adequate number of elements, which was higher than 45,000 elements for the specific geometry of the presented model. It should be stressed that, for the consistent and successful simulation of the crack profile, more than one row of elements are needed per layer of the composite material.
# Co-Design of Mechanical and Vibration Properties of a Star Polygon-Coupled Honeycomb Metamaterial
## 5 Evaluation of Vibration Suppression Performance by Simulation and Experiment
The sampling time is 30 s and the signals are converted and transmitted to the computer through a signal converter. The particle damping is made of tungsten powder with a diameter less than 0.25 mm and has been put into the copper tube, followed by plugging both ends of the tube with rubber stoppers. Different samples are installed in the system for testing. Experimental results of the IM, SPCHM, SPCHM with copper pins, and SPCHM with particle damping are drawn in Figure 13, together with the bandgap regions marked in green. Figure 13a-c point out that the experimental VTLs decrease obviously within the simulated bandgaps, which shows better vibration suppression effect and indicates that the simulation method and results are reliable. Figure 13d displays the comparison of experimental results between the SPCHM with copper pins and the SPCHM with particle damping. The VTL curve of the SPCHM with particle damping locates below that of the SPCHM with copper pins in the whole frequency range, which reveals that adding damping can well enhance the vibration suppression ability of the SPCHM. Noting that the weight of particles is continuously adjustable under the same volume, it has the advantage of replacing any material by designing an equal equivalent density. Moreover, this feature makes it possible to adjust the bandgaps continuously, and the good adaptability of particles provides the potential to be applied in vehicles, transportation devices, aerospace facilities, and other engineering applications with high precision requirements. Figure 13: Experimental VTL results: (**a**) IM; (**b**) SPCHM; (**c**) SPCHM with copper pins; (**d**) SPCHM with copper pins; and SPCHM with particle damping. In general, the metamaterial proposed in this paper has good stiffness and vibration suppression ability, so it has good application potential in the optimization of vehicle suspension system, body, seat, vibration isolation system, ship hull structure, transmission system, propeller, rudder system, and cargo loading device.
###### Abstract  
This paper puts forward a projection-based adaptive command filtered fuzzy nonsingular terminal sliding mode backstepping (PACFTB) control method for the speed control of the linear induction motor (LIM) with unknown end effects. Firstly, the technique of fuzzy logic systems (FLS) is investigated to approximate the nonlinear components of the LIM's mathematical model, which reduces the difficulty and cost of controller design. Then, a constrained command-filtered backstepping controller is designed with a filtering compensator compensating for the inherent error of constrained filter. Moreover, the nonsingular terminal sliding mode control method is combined in the controller design for its advantages of finite-time convergence of the system, and the projection-operator-based adaptive laws are established at the same time. Finally, the stability analysis proves that the boundedness and stability of all signals can be ensured with the proposed PACFTB controller, and the simulation results along with experiment results verify that the proposed control strategy has better control performance than the conventional command filter backstepping and PI controller.  
Keywords:linear induction motor; command filter backstepping; fuzzy logic system; nonsingular terminal sliding mode; projection operator; end effects +
Footnote †: journal: Applied Soft
## 4 Fractional Nonlinear Damage Creep Model
### The Basic Components  
The expressions for both Hooke spring (Figure 12a) and Newton dashpot (Figure 12b) can be written in the form of differential equations:  
\[\begin{cases}\sigma=E\times D^{0}\varepsilon(t)\\ \sigma=\eta_{d}\times D^{1}\varepsilon(t)\end{cases}\longrightarrow\sigma(t) =\eta\times D^{n}\varepsilon(t) \tag{13}\]  
where \(n\in[0,1]\), \(E\) is elastic modulus (GPa), \(\eta_{d}\) is the viscosity coefficient of Newton dashpot (GPa-\(t^{n}\)), \(\eta\) is the viscosity coefficient of materials (GPa-\(t^{n}\)), and \(t\) is creep time.  
As shown in Equation (13), the stress of the Hooke spring and the Newton dashpot is proportional to the zero-order and one-order derivative of the strain, respectively. Considering that creep starts at time \(t=0\) under a constant stress, let \(a=0\) and \(f(t)=\sigma(t)=\sigma\) in Equation (9). \(\varepsilon(t)\) can then be expressed as:  
\[\varepsilon(t)=\frac{I^{n}[\sigma(t)]}{\eta}=-\frac{\sigma}{\eta}\frac{1}{ \Gamma(n)}\int_{0}^{t}(t-x)^{n-1}d(t-x)=\frac{\sigma}{\eta}\frac{t^{n}}{n \Gamma(n)} \tag{14}\]  
From the properties of the Gamma function, \(n\cdot\Gamma(n)=\Gamma(n+1)\). Therefore, we obtain the final expression of Abel dashpot (Figure 12c):  
\[\varepsilon(t)=\frac{\sigma}{\eta}\frac{t^{n}}{\Gamma(1+n)} \tag{15}\]  
Let \(\sigma/\eta^{n}=1\). The strain-time curve of the Abel dashpot is illustrated in Figure 13. The results indicate that different fractional orders in Formula (16) allow the Abel dashpot to exhibit responses over multiple time scales.  
Figure 12: The basic mechanical components: (**a**) Hooke spring; (**b**) Newton dashpot; (**c**) fractional Abel dashpot; (**d**) nonlinear viscous dashpot.  
A nonlinear dashpot with strain triggering is introduced to describe the deformation during the accelerating creep stage (see Figure 12d). The term'strain triggering' means that when the total strain is less than \(\varepsilon_{a}\) (the strain value at the moment when the rock starts to enter the tertiary creep stage), the dashpot acts as a rigid body and has no effect. When the total strain exceeds \(\varepsilon_{a}\), the nonlinear dashpot starts to activate. Let \(\eta_{n1}\) be the viscosity coefficient of the nonlinear dashpot and define the constitutive relationship [19] as follows:  
\[\begin{cases}\sigma=\eta_{n1}\tilde{\varepsilon}_{n1}&(\varepsilon\geq \varepsilon_{a})\\ \varepsilon_{n1}=0&(\varepsilon<\varepsilon_{a})\end{cases} \tag{16}\]
\begin{table}
\begin{tabular}{c|c|c|c|c|c} \hline \hline \multirow{2}{*}{**Object**} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Parameters**} \\ \cline{3-6}  & & **Initial** & **Release** & **Diameter** & **Ref** \\  & & **Pressure** & **Flow Rate** & **[mm]** & \\ \hline Hydrogen fuel & \multirow{2}{*}{Simulation/FLUENT} & \multirow{2}{*}{10 MPa} & \multirow{2}{*}{0.467} & \multirow{2}{*}{10} & \multirow{2}{*}{[12]} \\ cell ship & & & & & \\ \hline Hydrogen & \multirow{2}{*}{Simulation/FLUENT} & \multirow{2}{*}{42 MPa} & \multirow{2}{*}{-} & \multirow{2}{*}{0.5, 1, 2, 5} & \multirow{2}{*}{[13]} \\ storage tank & & & & & \\ \hline Hydrogen fuel & \multirow{2}{*}{Simulation/FDS} & \multirow{2}{*}{-} & \(6.7\times 10^{-4}\) & \multirow{2}{*}{-} & \multirow{2}{*}{[14]} \\ cell vehicle & & & & & \\ \hline Semi-closed & \multirow{2}{*}{Simulation/STAR-} & \multirow{2}{*}{2, 5, 20 bar} & \multirow{2}{*}{-} & \multirow{2}{*}{0.4572, 0.8} & \multirow{2}{*}{[15]} \\ space & & & & & \\ \hline Simple geometric & \multirow{2}{*}{Experiment} & \multirow{2}{*}{-} & \multirow{2}{*}{0.2, 1 kg/s} & \multirow{2}{*}{5, 10, 20} & \multirow{2}{*}{[16]} \\ spaces & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recent studies related to hydrogen release and diffusion. need for selecting an appropriate location for installing a detector by considering kinetic characteristics such as motion types, directions, and periods. The rest of this manuscript is organized as follows: Section 2 presents the methodology for analyzing hydrogen diffusion behavior while considering the ship's motion. Section 3 provides definitions of the ship's motion caused by hydrogen diffusion based on the ship motion and hydrogen diffusion characteristics for scenarios including ship motion type, period, and direction. Finally, Section 4 summarizes the main results of our study and reviews its limitations.