esa-sceva/satcom-chunk-collection huggingface dataset:
# Simplified Model Predicts Binder Behavior in Sand Mold Printing
## 2 Materials and Methods
### Materials
#### 2.2.1 Geometry and Mesh  
This study obtained the particle size and porosity of the powders used in the experiment via CT scanning, then set the geometry in the model to 10 mm (w) \(\times\) 10 mm (l) \(\times\) 5 mm (h), as shown in Figure 2a for grid processing. Doing so saves analysis and simulation time and prevents the mesh from breaking or failing to simulate the actual situation smoothly. Since the binder volume was between 0.1-2.5 \(\mu\)L, we set grid smoothing to medium, limited minimum size and maximum size to between 5.0\({}^{-8}\) and 4.096\({}^{-4}\) m, and used the tetrahedron method (most suitable for fluid dynamics simulation) to process the grid, as shown in Figure 2b. The final mesh metrics were below 0.9 (average and stable mesh quality). We then input the parameters needed for the simulation, material properties such as liquid and powder density, viscosity, contact angle, and surface tension, as shown in Tables 1 and 2.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multicolumn{2}{l}{**Sand Particles**} & & & & & & & \\ \hline \multirow{2}{*}{**Item**} & & **Density** & **Particle Size** & **Porosity** & **D\({}_{\mathbf{10}}\)** & **D\({}_{\mathbf{50}}\)** & **D\({}_{\mathbf{90}}\)** & **Contact** \\  & & **(g/cm\({}^{3}\))** & **(\(\mu\)m)** & **(\%)** & **D\({}_{\mathbf{10}}\)** & **D\({}_{\mathbf{50}}\)** & **D\({}_{\mathbf{90}}\)** & **Angle (\({}^{\circ}\))** \\ \hline \multirow{3}{*}{A. SiO\({}_{2}\)} & A-1 & 2.612 & 9–13 & 16.6 & 1.870 & 8.841 & 17.28 & 129.98 \\  & A-2 & 2.594 & 70–200 & 38.2 & 122.8 & 201.9 & 296.0 & 100.71 \\ \cline{1-1}  & A-3 & 2.650 & 198–326 & 40.7 & 98.88 & 185.1 & 266.4 & 79.66 \\ \cline{1-1}  & B. ZrO\({}_{2}\) & 5.890 & 0.28–43.6 & 36.3 & 3.825 & 93.89 & 148.6 & 94.99 \\ \cline{1-1}  & C. Al\({}_{2}\)O\({}_{3}\) & 3.970 & 62.7–149.9 & 7.6 & 0.257 & 7.386 & 38.52 & 108.48 \\ \hline \end{tabular}
\end{table}
Table 2: Particle parameters and effective diameter.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{2}{c}{**Item**} & \multicolumn{3}{c}{**Variables**} \\ \hline \multirow{6}{*}{Geometry} & size & 10 mm\((d)\times 10\) mm\((h)\) & \\  & & & A-1 & 16.6\% \\  & & SiO\({}_{2}\) & A-2 & 38.2\% \\  & porosity & & A-3 & 40.7\% \\  & & & Al\({}_{2}\)O\({}_{3}\) & 7.6\% \\  & & ZrO\({}_{2}\) & & 36.3\% \\  & Smoothing & & medium \\ Mesh & Max size & 4.096\({}^{-4}\) (m) & \\  & Min size & 5.0\({}^{-8}\) (m) & \\ Setup & Method & _VOF_ & \\  & Inlet velocity & 1 (m/s) & \\  & Scheme & PISO \\ Solution Method & Gradient & Green-Gauss node-based \\  & Pressure & PRESTO! \\  & Solver & Second Order Upwind \\ Time step size & Time step size & 1.0\({}^{-6}\) (s) \\  & Number of time steps & 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation Parameters.
## 4 Proposed Methodology of Monitoring
### Stability Analysis of Tower under Smart Active Control  
Table 5 below lists the first five critical load coefficients of various working condition. Figure 10 below compares the critical load coefficients of various working condition.  
As the results show, the first-order critical Stability coefficient for the tower of Matan Hongshui He Bridge in working condition 1 and 2 is 24.15 and 23.32; the first-order critical Stability coefficient for the tower of Pingnan Third Bridge in working condition 1 and 2 is 25.42 and 27.54. With the application of active load under normal working conditions, the stability coefficient of the structure decreases slightly, and the impact of active control on the first-order critical load coefficient is 3-4%. From the aspect of instability morphology, there are vertical vibration and torsional vibration of the tower under most of the working  
Figure 10: Comparison of critical Stability coefficients under various working conditions. (**a**) Comparison of critical Stability coefficients in Matan Hongshui He Bridge; (**b**) Comparison of critical load coefficient in Pingnan Third Bridge.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Project** & **Instability Order** & \begin{tabular}{c} **Condition 1 Critical** \\ **Load Factor** \\ \end{tabular} & \begin{tabular}{c} **Condition 2 Critical** \\ **Load Factor** \\ \end{tabular} & \begin{tabular}{c} **Regulatory Change** \\ **Rate (\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Matan Hongshui He \\ Bridge \\ \end{tabular} } & 1 & 24.15 & 23.32 & 3.44 \\  & 2 & 30.53 & 29.44 & 3.57 \\ \cline{1-1}  & 3 & 41.76 & 37.81 & 9.46 \\ \cline{1-1}  & 4 & 45.77 & 45.47 & 0.66 \\ \cline{1-1}  & 5 & 45.77 & 40.33 & 11.89 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Pingnan Third Bridge \\ \end{tabular} } & 1 & 25.42 & 27.54 & 8.34 \\  & 2 & 30.40 & 29.11 & 4.24 \\ \cline{1-1}  & 3 & 39.12 & 38.17 & 2.43 \\ \cline{1-1}  & 4 & 45.41 & 46.33 & 2.03 \\ \cline{1-1}  & 5 & 47.54 & 46.71 & 1.75 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The Stability coefficient under various working conditions.  
conditions. Moreover, with the increase of instability order, the stability coefficient increases gradually, that is to say, higher-order instability becomes progressively less possible.
## 4 Finite Element Setup
### Two-Dimensional Modeling Approach  
The 2D FE model captures the cross-sectional behavior of the cables under study. It is particularly suitable for configurations where all conductors are parallel, as the electromagnetic fields remain uniform along the longitudinal axis. Table 2 indicates the total number of elements for each 2D model. The key characteristics of these models are as follows:  
* **Geometry:** The cross-section areas are modeled top-to-bottom, with length parameters specified to six decimal places, ensuring minimal modeling errors;
* **Element Type:** PLANE13 triangular elements with three nodes enabled coupled-field 2D electromagnetic analysis;
* **Material Allocation:** Table 1 properties were assigned to conductor and insulation elements;
* **Degrees of Freedom:** Voltage and magnetic vector potential (AZ) were coupled for electromagnetic continuity, with AZ grounded at the outmost layer to mimic the far-field;
* **Boundary Conditions:** The amplitude of the current excitation is applied to the conductor nodes as force;
* **Mesh Density:** A fine uniform mesh resolved the steady-state current density in conductors, while adaptive meshing (h-method) optimized accuracy and computational efficiency in air regions.
# Functional Safety Networks and Protocols in the Industrial Internet of Things Era
###### Abstract
Notwithstanding, FSNs are required to ensure the transmission of safety-related information among nodes with extremely low error probability and bounded reaction times: as a reference, to achieve a SIL 3 (Safety Integrity Level) as specified by IEC 61508, a FSN has to ensure a residual error rate less than \(10^{-9}\,\mathrm{h}\), which is largely lower than what is typically provided by typical industrial networks. This is needed to cope with the strong requirements imposed by safety applications, such as motion control [10], automotive [11; 12] and nuclear power energy plants [13], to mention some. However, IIoT ecosystems are characterized by ubiquitous connectivity, as well as by high flexibility of the communication infrastructures, that have to be easily reconfigurable to cope with the dynamic changes of the production schedule. These features may be satisfactorily addressed by _wireless_ communications. Thus, it is envisaged that in IIoT ecosystems, industrial wireless networks will be ever more deployed. However, the available functional safety protocols have been designed and tested for wired networks, and hence, their suitability for typical wireless-based IIoT applications, for example, collaborative robotics and transportation via automatic guided vehicles [14], is still to be assessed. Moving from the above considerations, in this paper, we address functional safety networks and protocols in the new IIoT context. Particularly, we focus on the implementation of functional safety protocols over wireless networks. We start introducing, in Section 2, the design approach of FSNs, as specified by the IEC 61784-3 International Standard [15]. Then, in Section 3, we make some general considerations about the challenges concerned with safety protocols over wireless systems. In Section 4, we focus on the behavior of the FSoE protocol on WiFi. Particularly, we provide details about a prototype implementation carried out on commercially available components, along with the outcomes of an extensive experimental session. In Section 5, we discuss the obtained results, make some proposals for performance improving and outline some future developments about the deployment of FSNs in IIoT systems. Finally, Section 6 concludes the paper.
## 2 Dynamic Modeling of Trans-Medium Process
### Dynamic Modeling
#### 2.3.2 Drag Force and Moment  
Because water is much denser than air, the resistance of vehicles when moving in water is also much greater than that in air. Although the drag coefficient is related to the vehicle's characteristic area, angle of attack, velocity, and fluid properties, we consider the drag coefficient a constant in the modeling for simplicity. The drag and drag moment can be considered to be approximately proportional to the square of the vehicle's linear velocity and angular velocity:  
\[F_{rw}=\left[\left(k_{s}k_{xw}+k_{sa}k_{xa}\right)\dot{\left|\dot{x}\right|} \dot{x}\quad\left(k_{s}k_{yw}+k_{sa}k_{ya}\right)\dot{\left|\dot{y}\right|}\dot {y}\quad\left(k_{s}k_{zw}+k_{sa}k_{za}\right)\dot{\left|\dot{z}\right|}^{ \text{T}}\right. \tag{6}\]  
\[M_{rw}=\left[\left(k_{s}k_{\varphi w}+k_{sa}k_{\varphi a}\right)\dot{\left| \dot{\varphi}\right|}\dot{\left|\dot{\varphi}\right|}\dot{\left|\dot{\varphi} \right|}\dot{\left|\dot{\varphi}\right|}\dot{\left|\dot{\varphi}\right|}\right] ^{\text{T}} \tag{7}\]  
where \(K_{dw}=diag\left[k_{ww}\quad k_{yw}\quad k_{zw}\quad k_{\varphi w}\quad k_{ \varphi w}\quad k_{\varphi w}\right]\) represent the drag coefficient matrices in water, respectively, and \(K_{da}=diag\left[k_{xa}\quad k_{ya}\quad k_{za}\quad k_{qa}\quad k_{qa}\quad k_ {qa}\quad k_{qa}\right]\) represent the drag coefficient matrices in air.  
**Remark 2**.: _It is worth noting that the damping coefficient depends on the Reynolds number and the projected area. A detailed discussion on the drag coefficient can be found in reference [27], where only the main factors of drag are considered. Finally, drastic maneuvers are not considered in translational motion, and the Coriolis forces in translational motion are usually not considered._
## 3 Results and Discussion
### Scenario 2 (52)
#### 3.2.3 NH\({}_{4}\)-N  
Scenario 2 was the same as scenario 1. After the implementation of scheme B (HZ1), the EC reduced by 4.45% (Table 13). The implementation of scheme C, which was different from scenario 1, changed the impact on EC due to different working conditions. Scheme C had little impact on the change of NH\({}_{4}\)-N environmental capacity, only 0.62%, which was not very different from the change of COD environmental capacity (0.15%). In schemes D, E and F, the EC of NH\({}_{4}\)-N was reduced by 23.83%, 20.59% and 15.90%, respectively, indicating that the implementation of these schemes had an extensive impact on the surrounding environment.  
The feasibility evaluation results of NH\({}_{4}\)-N environmental capacity indicated that the evaluation index \(R_{2}\) of scheme B was 8, so scheme B was basically feasible; the evaluation index \(R_{2}\) of scheme C was 10, therefore, scheme C was also feasible. The evaluation index \(R_{2}\) of schemes D, E and F was 0, and, therefore, schemes D, E and F were not feasible, that is, working conditions HZ2, HZ4 and HZ5 were prohibited.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reclamation Scheme** & **COD (f/a)** & **EC Change (\%)** & \(R_{1}\) \\ \hline A & 62,444 & - & - \\ B & 61,517 & 1.48 & 10 \\ C & 62,350 & 0.15 & 10 \\ D & 47,803 & 23.44 & 0 \\ E & 48,584 & 22.20 & 0 \\ F & 52,314 & 16.22 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Feasibility evaluation in COD environmental capacity.  
Figure 8: Computed region of marine EC (scenario 2). Computed region of plan A; computed region of plan B; computed region of plan C; computed region of plan D; computed region of plan F.
# Topology Optimisation under Uncertainties with Neural Networks
## 2 Topology Optimisation under Uncertainties
### Deterministic Model Formulation
#### 2.1.3 Adjoint Equation  
To define the optimisation problem, we introduce the _Ginsburg-Landau functional_\(E^{\varepsilon}:\mathbb{R}\to\mathbb{R}\), which serves as a penalty term for undesired variations and is defined by  
\[E^{\varepsilon}(\varphi)=\int_{D}\frac{\varepsilon}{2}|\
abla\varphi|^{2}+ \frac{1}{2\varepsilon}\psi_{0}(\varphi)\,\mathrm{d}\mu,\]  
where \(|\cdot|\) is the Euclidean norm. This ensures that the solution to the optimisation problem can be interpreted as an actual smooth shape. The _double well functional_\(\psi_{0}:\mathbb{R}^{d}\to\mathbb{R}\) with \(\psi_{0}(x)=(\varphi(x)-\varphi(x)^{2})^{2}\) penalises values of \(\varphi\) that differ from \(0\) or \(1\) and the leading term limits the changes of \(\varphi\). This results in the cost functional \(J^{\varepsilon}:\mathbb{R}^{d}\to\mathbb{R}\) to be minimised,  
\[J^{\varepsilon}(\varphi,u)=\int_{\Gamma_{g}}g\cdot u\,\mathrm{d}\mu+\gamma E^ {\varepsilon}(\varphi),\quad\gamma>0. \tag{3}\]  
The adaptivity parameter \(\gamma\) controls the weight of the interface penalty and hence has a direct influence on the minimum respective to the characteristics of the resulting shape of \(\varphi\). In fact, \(\gamma\) is chosen adaptively to avoid non-physical or highly porous topologies, see [2]. Additionally, we require the volume constraint \(\int_{D}\varphi\,\mathrm{d}\mu=m|D|\) with \(m\in[0,1]\) to limit the amount of overall material.  
The (displacement) state \(u\) from Equation (3) is obtained by solving the state Equation (2), which is used in the optimisation problem  
\[\text{minimize }J^{\varepsilon}(\varphi,u)\text{ over }\varphi\in H^{1}(D) \tag{4}\]  
\[\text{s.t. Equation (\ref{  
is shown in [2] that for \(I^{\varepsilon}\) the corresponding adjoint problem can be formulated as: find \(p\in H^{1}(D)\) such that  
\[\int_{D}\sigma(\varphi,p))\mathcal{E}(v_{p})\,\mathrm{d}\mu=\int_{\Gamma_{g}}g \cdot v_{p}\,\mathrm{d}\mu\qquad\forall\,v_{p}\in H^{1}_{0}(D), \tag{5}\]  
which is identical to the state equation. Hence, the respective adjoint solution \(p\) is equal to the solution \(u\) of Equation (2) and no additional system has to be solved.
## 5 Use Case Description with the OGC SensorThings Standard  
In this section, the typical behaviour of a user/farmer is analysed in the proposed system. Figure 5 shows a diagram that represents the various users, how they use the system, and their permissions. The two most common ways for users/farmers to interface with the system are as follows: requesting and viewing data, and sending data to the database. In the first case, the farmer logs in, formulates a query that is translated by the client into the OGC standard, and waits for the response from the server. In the case of a positive outcome, the data will be displayed in the client. In the second case, the farmer logs in with privileges for data submission and sends the data to the server, which will check that the data are in the correct format and consistent with the database before saving them to the latter.  
**Farmer**: The farmer receives and views data through the client. The client communicates with the server via the HTTP protocol. Requests to the server are made by the client through the service interface of the SensorThings standard, in a way that is transparent to the farmer.  
For example, when the farmer wants to view the first 10 results of the \"Observations\" entity sorted by ID, the client will send a GET request to the server at the following address: [[http://example.org/v1.0/Observations?protuctT1](http://example.org/v1.0/Observations?protuctT1)]([http://example.org/v1.0/Observations?protuctT1](http://example.org/v1.0/Observations?protuctT1))\text{text{text  
org/v1.0/Things(1) (accessed on 1 January 2025). The \"description\" property of the instance will be overwritten and replaced with the one sent by the administrator.
## 5 Discussion
### Phase D\({}_{2}\) Deformation: Timing and Dynamical Context  
At both the outcrop and microstructural scales, D\({}_{2}\)-phase folding overprints D\({}_{1}\)-phase shear deformation, expressed as the transposition of S\({}_{1}\) fabrics by S\({}_{2}\) foliation. Lithological competence contrasts result in diverse fold geometries. D\({}_{2}\)-phase deformation is characterized by longitudinal flexural folding, during which the folding of the S\({}_{1}\) shear foliation produces axial planar cleavage (S\({}_{2}\)). Subordinate folds with S-shaped, M-shaped, and Z-shaped geometries are ubiquitously developed. This folding event is observed in Jurassic and Cretaceous strata [30] but is absent in strata younger than the Paleocene. Previous studies indicate that the Yeba Formation in the Jiama-Qulong district underwent phase D\({}_{2}\) brittle-ductile deformation following phase D\({}_{1}\) ductile deformation. Structural analysis indicates that this deformation phase is characterized by top-to-the-south (hanging wall moving southward) shear kinematics, forming a series of north-vergent thrust-related folds [13]. In addition, the folding observed in the Early Cretaceous Takena Formation and the Late Cretaceous Sheaving Formation, caused by nearly N-S-directed compression [30], is highly consistent with the D\({}_{2}\)-phase structural deformation characteristics identified in this study.  
During the Early Cenozoic (\(\sim\)65-45 Ma), the collision between the Indian and Eurasian plates triggered widespread magmatism in the Gangdese belt, including the emplacement of extensive Linzzingo volcanic rocks and voluminous granitoid intrusions (Figure 7b) [8; 31; 32; 33]. The Linzzingo volcanic rocks exposed in the Linzhou Basin, located north of the study area, are predominantly terrestrial volcanic rocks of the Dianzhong Formation (65-58 Ma). These volcanic rocks are essentially undeformed and unconformably overlie the Late Cretaceous Sheaving Formation at a high angle (Figure 7b) [22]. Therefore, the corresponding phase of structural deformation must have occurred prior to the deposition of the Paleocene Dianzhong Formation.  
It is also noteworthy that the Takena Formation underwent a folding and erosion event during 80-70 Ma [30]. Considering the relative sequence of D\({}_{1}\) and D\({}_{2}\) deformation phases established in this study, we infer that the D\({}_{2}\)-phase deformation occurred during \(\sim\)79-70 Ma. This timing still falls within the tectonic framework of the northward subduction of the Neo-Tethys Ocean beneath the Lhasa terrane [34; 35].
## 3 Results
(**A,C**) Ca\({}^{2+}\)-responses of A-172 cells to the application of thapsigargin (TG, 10 \(\mu\)M) in a calcium-free medium contained 0.5 mM EGTA after 24-h (**A**) and 48-h (**C**) incubation with different concentrations (1, 5, 10 \(\mu\)g/mL) of selenium nanorods (SeNrs) or selenium nanoparticles (SeNPs). (**B,D**) Ca\({}^{2+}\)-responses of L-929 cells to the application of thapsigargin (TG, 10 \(\mu\)M) in a calcium-free medium supplemented with 0.5 mM EGTA after 24-h (**B**) and 48-h (**D**) pre-incubation with different concentrations (1, 5, 10 \(\mu\)g/mL) of selenium nanorods (SeNrs) or selenium nanoparticles (SeNPs). Ca\({}^{2+}\) responses for panels (**A–D**) are averaged over several dozens of cells. Pre-incubation of L-929 mouse fibroblasts with SeNPs or SeNrs did not lead to the depletion of Ca\({}^{2+}\) stores of the ER (Figure 9B,D) comparable to the effects of these nanocomposites on A-172 cells (Figure 9C). In the case of SeNPs exposure to L-929 cells within 24 h, only a certain trend was observed to suppress the amplitude of cellular Ca\({}^{2+}\) signals upon the application of TG after exposure to SeNPs, but not to SeNrs. The incubation time of L-929 cells with the studied nanocomposites was increased up to 48 h (Figure 9D) and also did not result in a noticeable suppression of Ca\({}^{2+}\) signals to the application of TG. This shows the lack of a pronounced impact of these nanoparticles on the sex of healthy ER Ca\({}^{2+}\) cells. Thus, the exhaustion of the ER Ca\({}^{2+}\) pool caused by the effect of SeNrs or SeNPs occurred only in human glioblastoma A-172 cells, but was absent in healthy fibroblasts. At the same time, SeNrs are likely to be more effective at an incubation interval of 24 h. Together with the data from the PCR analysis, the results of Ca\({}^{2+}\) imaging indicate ER stress under the action of the studied nanocomposites in cancer cells.
# A Single-Launch Deployment Strategy for Lunar Constellations
## 2 Low-Energy Captures in the Sun-Earth-Moon System  
### The Circular Restricted 3-Body Problem  
The CR3 BP represents the most common dynamic framework to study the motion of a spacecraft in the Earth-Moon system. This model is based on the hypothesis that the mass \(m\) of the spacecraft is negligible compared to that of the primaries, the Earth (\(m_{1}\)) and the Moon (\(m_{2}\)), and that in the inertial space, the relative trajectory of \(m_{2}\) about \(m_{1}\) is circular [36].  
The dynamic equations of motion for the CR3 BP are expressed in a reference frame \(\mathcal{F}_{r}=[\hat{x},\hat{y},\hat{z}]\), centered at the center of mass of the system \(O\), with \(\hat{x}\) pointing from \(m_{1}\) to \(m_{2}\), \(\hat{z}\) orthogonal the orbital plane of the primaries and \(\hat{y}\) completing the rectangular reference frame [37]  
\[\left\{\begin{aligned} \dot{x}&=v_{x}\\ \dot{y}&=v_{y}\\ \dot{z}&=v_{z}\\ \dot{v}_{x}&=2v_{y}+\frac{\partial U}{\partial x}\\ \dot{v}_{y}&=-2v_{x}+\frac{\partial U}{\partial y}\\ \dot{v}_{z}&=\frac{\partial U}{\partial z}\\ \end{aligned}\right. \tag{1}\]  
with  
\[U=\frac{1}{2}\left(x^{2}+y^{2}\right)+\frac{1-\mu}{r_{1}}+\frac{\mu}{r_{2}} \tag{2}\]  
\[\mu=\frac{m_{2}}{m_{1}+m_{2}} \tag{3}\]  
where \(r_{i}\) indicates the distance between the spacecraft and the i-th primary. It is worth recalling that Equation (1) are expressed in terms of units of distance \(DU=a_{M}\) and time \(TU=1/\omega_{M}\), where \(a_{M}\) and \(\omega_{M}\) are, respectively, the mean distance and the orbital angular speed of the Moon with respect to the Earth.  
The CR3 BP admits five equilibrium points \(L_{i}\), also named the Lagrange or libration points [38], and one constant integral of motion, named the Jacobi constant  
\[C=-\left(\dot{x}^{2}+\dot{y}^{2}+\dot{z}^{2}\right)+2U \tag{4}\]  
In fact, the Jacobi constant is proportional to the opposite of the total energy \(\mathcal{E}\) of the spacecraft; therefore, \(C\) decreases as \(\mathcal{E}\) increases.  
Trajectories that can evolve in the neighborhood of both the primaries are characterized by \(C<C_{1}\), where \(C_{1}\) is the Jacobi constant calculated in \(L_{1}=[x_{L_{1}},0,0,0,0,0]^{T}\), the libration point lying on \(\hat{x}\) between \(m_{1}\) and \(m_{2}\)[13; 39]. The deployment strategy proposed in this work is based on the use of those low-energy trajectories, which verify the condition \(C=C_{1}-2h\), where \(h>0\) is an arbitrarily small constant named the energy level. The characterization of these solutions is discussed in the following section.