esa-sceva/satcom-chunk-collection huggingface dataset:
# Modified Echo State Network Enabled Dynamic Duty Cycle for Optimal Opportunistic Routing in EH-WSNs
## 1 Introduction
This research article overcomes the limitations of ESN and proposes an improved echo state network which have been named modified echo state network (MESN). Further, in MESN, the interconnection weights are optimally chosen by utilizing a whale optimization algorithm (WOA) [22]. We will describe the MESN model in Section 3. For further improvement, this article proposes the MESN based dynamic DC with optimal opportunistic routing (OOR) for EH-WSNs. This MESN model predicts the expected energy acquisition value for the next time slot. The principal goal of the proposed OOR method is to select the relay set by using multiple energy centric parameters and alter DC by using energy consumption, energy acquisition, and energy threshold. The key contributions of this article are listed as follows:  
* This article proposes the modified echo state network (MESN) which is used as a predictor for finding the expected energy acquisition of the next time slot. The proposed scheme is a novel research in this article and stops the unstable output of ESN due to the random weight selection in the reservoir layer. This model utilizes the whale optimization algorithm (WOA) for optimally selecting the weights of the neurons in the reservoir layer of the echo state network. * In the optimal opportunistic routing (OOR) scheme, the relay set is selected using multiple parameters such as energy consumption, congestion rate and estimated duty-cycled wakeups (EDW). The relay set selection scheme selects appropriate forwarding paths for each forwarder node. * The proposed scheme achieves an optimized style to update duty cycle since it is updated based on energy consumption, energy acquisition of the next slot and energy threshold as the optimal energy is acquired from the prediction of the next slot with the help of the MESN model. * Finally, the comparison of the proposed technique with existing methods such as ESN, AOR, and Max-SNR in terms of performance parameters such as delay, packet delivery ratio and throughput is done. This reveals that the average percentage improvement of the proposed approach outperforms the existing techniques. The organization of the article is as follows: Section 2 provides related work. Section 3 provides complete detail of the proposed MESN technique. Section 4 discusses the working structure of the MESN based OOR scheme. Section 5 describes the results of simulation and performance comparisons. Finally, Section 6 presents the conclusion of the paper.
## 2 Background of the Iris Coding
### Local Binary Pattern  
Local binary pattern (LBP) [23] is an invariant measure for texture with grayscale format. The basic LBP works based on using 3 \(\times\) 3 neighbourhood of each pixel. The mask is used to generate a binary pattern based on comparing each of the surrounding pixels with the central one based on the Equation (6)  
\[f(I(z_{0}),I(z_{i}))=\begin{cases}1,\;if\;I(z_{i})-I(z_{0})>\text{ {threshold}}\\ 0,\;if\;I(z_{i})-I(z_{0})\leq\text{{threshold}}\end{cases} \tag{6}\]  
where  
\(i=1,\ldots 8\) denotes the index of the surrounding pixels  
\(z_{0}\) indicates to the central pixel  
The general representation of LBP with respect to radius \(R\) and neighbour \(P\) is given as in Equation (7)  
\[LBP_{P,R}=\sum_{p=0}^{P-1}s(g_{p}-g_{c})2^{p} \tag{7}\]  
where  
\[s(x)=\begin{cases}1,x\geq 0\\ 0,x<0\end{cases}\]  
\(g_{p}\) denotes the grayscale of the pixel  
\(g_{c}\) denotes the grayscale of the centre
## 1 Introduction
[27] advocated for the modification of path segments not aligning with curvature constraints using B-spline curves. Their aim was to yield paths that harmoniously balance continuity and curvature constraints. A limitation, however, was the absence of a definitive formula for the computation of path curvature. Subsequently, [PERSON] et al. [28] leveraged the innate properties of B-spline curves to diminish the search dimensionality within the bidirectional Rapidly-exploring Random Tree (RRT) algorithm, consequently elevating its search efficiency. Given the emphasis on both safety and comfort during obstacle avoidance for intelligent driving sightseeing vehicles, we propose a dynamic perception-based obstacle avoidance path planning algorithm. Our core contributions span several dimensions: initially, we employ a high-precision GNSS/GPS positioning module to delineate a global path tailored to specific scenarios. Subsequently, real-time dynamic perception of the spatiotemporal environment along this global path is executed, culminating in the identification of obstacle information. Concurrently, we determine control points for the local obstacle avoidance path, guided by both spatiotemporal safety constraints and specific curvature constraints tailored for obstacle avoidance. Through the deployment of the B-spline curve discrete interpolation technique and subsequent optimization of local path segments, we synthesize a robust local obstacle avoidance path. Following this, we architect a path-tracking simulation model within the Simulink/Carsim framework, employing a pure tracking algorithm as the cornerstone for path-tracking control. To assess the efficacy of our approach, we utilize two distinct vehicle speeds to trace the local obstacle avoidance paths across roads that exhibit varied spatial characteristics. In the concluding stages, the local obstacle avoidance algorithm is transitioned to an authentic vehicle platform, upon which real-world vehicular tests are executed on roads showcasing varied spatial attributes. Both simulation outcomes and tangible vehicular experimental findings affirm that our proposed algorithm adeptly crafts plausible obstacle avoidance paths across roads with distinct spatial nuances. When juxtaposed with conventional Bezier curve obstacle avoidance strategies, the trajectories devised by our algorithm notably emerge as smoother, manifesting diminished front-wheel steering angles and attenuated yaw rate fluctuations. Consequently, the intelligent driving sightseeing vehicle exhibits a more fluid and seamless driving experience during obstacle circumvention.
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 2 Problem Description
### MSOOSP Task Planning Process
In order to reduce the computational burden of the on-board hardware, the pre-processing procedure of the information required for planning, including mission attributes, observation-visible time window (O-VTW, i.e., the period when the satellite is able to observe the ground target), communication-visible time window (C-VTW, i.e., the period when satellites are able to communicate with each other), satellite attitude, etc., is completed on the ground and uploaded to the satellite via the satellite-to-earth link. Phase 2. On-board mission scheduling  
When the satellite receives task information, the task planning algorithm is triggered. Each satellite runs the scheduling algorithm and generates the final scheduled plan through autonomous decision-making and inter-satellite communication. Phase 3. Execute mission planning scheme and download data  
Each satellite performs missions according to the scheduled scheme. When the satellite is visible to the ground station, the observation data is transmitted down to the ground station. Alternatively, it can be transmitted down to the ground station through other satellites using inter-satellite links. This paper focuses on the task planning algorithm for the second part.
## 1 Introduction
This method is easy to implement and highly efficient in computation, making it suitable for real-time detection applications. [PERSON] et al. [39] extended the OBF method to two-dimensional situations using a two-dimensional sliding window. They calculated the square sum of the five independent orthogonal basis components of magnetic dipole sources under strong noise conditions as the energy function to achieve efficient magnetic anomaly detection in two-dimensional situations. This study proposes a new method based on the LM-OBF algorithm to address the inability to estimate magnetic dipole source parameters under low SNR conditions and enhance the robustness of magnetic dipole source parameter estimation. This method transforms the measured magnetic anomaly values into orthogonal space using the Gram-Schmidt orthogonalization process, calculates the coefficient energy square sum as the forward model using orthogonal basis functions and magnetic anomaly values, and then determines the objective function required for parameter optimization within the least squares framework. Following this, the objective function is optimized using the LM algorithm to achieve accurate parameter estimation of the magnetic dipole source. The proposed method is tested on synthetic models and actual measured data, demonstrating that the LM-OBF algorithm can accurately estimate the parameters of magnetic dipole sources under low SNR conditions. This study makes several significant contributions to the field of magnetic dipole source parameter estimation: (1) It introduces a novel LM-OBF algorithm that accurately estimates the parameters of the target source directly under low SNR conditions without any denoising treatment, a rarity in previous studies. (2) It is the first to combine the OBF method with traditional optimization methods, verifying the algorithm's effectiveness in synthetic models and actual measured data and providing new ideas for developing magnetic dipole parameter estimation methods based on other MAD methods in the future.
## 5 Datasets and Experimentation Setup
### Experimentation Setup  
We performed both parameters pruning and quantization on the LeNet5, AlexNet, and VGG-16. For each model, we modified only the last layer with respect to the number of classes in the dataset. For training we used the Stochastic Gradient Descent with a learning rate of \(10^{-2}\). For these three experiments, we used softmax output layer with categorical cross-entropy loss. We trained both simple models and compressed models over 100 epochs using the training-set plant seedlings dataset and 500 epochs for Flavia database because of its small number of images. The input images were all resized to (\(50\times 50\)) without any data augmentation process.
# Morphological Analysis of a Collapsing Cavitation Bubble near a Solid Wall with Complex Geometry
## 3 Cavitation Bubble Collapse near a Solid Wall with Complex Geometry
### Evolution of Density Field  
Figure 6 shows the evolution of the density field, where the bubble profiles can be visually distinguished. This figure depicts the dynamic process of cavitation bubble collapse near two different solid walls, i.e., the plane solid wall and solid wall with a complex geometry (\(D=1.03476\)). The offset parameters are set as \(\lambda=1.25\) and \(\lambda=1.5\) in these two cases, respectively. The morphological evolution of the collapsing bubble in the vicinity of the solid wall with a complex geometry is analyzed in detail.  
The initial spherical bubble starts to collapse under the pressure difference \(\Delta p=0.0135994\) mu\(/\) (ts\({}^{2}\)lu). The collapse velocity of the bubble is low in its initial stage, and the bottom bubble wall exhibits a significant deformation at \(t=100\) ts. The bubble is elongated into an ellipsoid before \(t=550\) ts, owing to the retarding effect of the solid wall. With the higher density area diffusing, the upper bubble wall is flattened, then it becomes concave from \(t=550\) ts. There appears a denser density area in the concave region, which accelerates the change of the bubble profiles. Consequently, a micro-jet is formed because of the interaction between the bubble and the solid wall. When the upper and bottom bubble walls collide, the cavitation bubble splits into small bubbles. This phenomenon can be referred to as the first collapse. The bubble deformation is asymmetric, and the deformed bubble tends to collapse into two asymmetric small bubbles owing to the irregular geometry of the solid wall. Subsequently, the small bubbles continue to collapse, and the visible bubble surface completely disappears after the second collapse.  
The processes of bubble collapse near the plane solid wall and solid wall with a complex geometry exhibit a similar dynamic process. However, the solid wall with a complex geometry causes the asymmetric deformation of the bubble. The effect of the solid wall with complex geometry on the collapsing bubble can be reflected in the profile evolution. Meantime, the validity and feasibility of the numerical model to simulate the bubble collapse process are again verified.  
Figure 6: Evolution of density field of the collapsing bubble near a solid wall ((I): plane solid wall and (II): solid wall with complex geometry).
## 5 Experimental Data
### Free Space Detection
As the metal wall becomes farther away from the radar, the position of the main lobe of its echo moves backward and the amplitude becomes weaker. The clutter waves located on either side of it are similarly shifted backward and become weaker. When the distance between antennas and the metal wall is far enough, the clutter is drowned in background noise. Therefore, the range of distances at which the presence of clutter generates false alarms can be measured by varying the distance between antennas and the metal wall. When the radar moves back to a spacing of \(\left(R_{green}\right)_{\text{max}}=3.3\) m from the metal wall, the clutter on both sides of the main lobe of the metal wall echo is so weak that it is submerged in the background noise. At this point, the green axis in Figure 11b moves to the position of \(\left(R_{green}\right)_{\text{max}}=\left(R_{green}\right)_{\text{max}}+ \Delta R\). Therefore, \(\left(R_{green}\right)_{\text{max}}=7.9\) m is the maximum range at which clutter from a strongly reflected target like a metal wall cannot be ignored. At this limit, the position of the green clutter is on the left if \(\left(R_{green}\right)_{\text{max}}-R_{x}<R_{red}\). This means that the farthest position of the left clutter generated by the strongly reflected target still remains to the left of the red axis. Therefore, the left half of the clutter is located to the left of the dividing line and the right half is located to the right of the dividing line. It ensures that the correlation of a pair of symmetric clutter can be calculated. This verifies the correctness of the proposed method to divide the matrix with the boundary of this position.
An empirical method of correcting measurements of pressure in a capillary rheometer for viscous heating effects was developed by [PERSON] and [PERSON] for the case of adiabatic walls [13]. Their treatment may be considered as a generalized extension of the classical Rabinowitsch-Mooney method for estimating temperature-corrected viscosity in capillary flow. [PERSON] and coworkers have recently carried out rheological measurements up to shear rates of \(10^{6}\) s\({}^{-1}\) for a polypropylene using a special rheological mold very similar to a standard injection mold [33]. By defining three thermal flow regimes according to the Cameron number (Ca), they also successfully calculated temperature corrected viscosity by taking into account a rise in average fluid temperature over the whole slit volume due to shear heating and compression heating [7; 30; 33]. In another work, they used a thermocouple to measure the temperature rise of a rubber compound issued from a conical die, and the measured temperatures were in good agreement with the prediction that takes into account both shear and elongational heating [17]. It should be apparent that most of the methods discussed above have contained simplifying assumptions. Furthermore, these methods have not been extensively tested against experimental capillary flow data, from which the uncorrected viscosity is determined. As a result, these methods have not met with complete success. In this article we begin by demonstrating the necessity of performing the viscous heating correction in capillary rheometry for a series of thermoplastic melts that exhibit a distinct sensitivity of viscosity to temperature. On the basis of a newly derived expression for the average viscosity in nonisothermal flows, we elucidate the details of the correction of shear viscosity for fluid temperature rises. It appears to us, however, that no earlier studies have made a direct assessment of the generality of the viscous heating correction method proposed. For its simplicity and reliability, capillary rheometry provides a unique opportunity for extracting the viscous heating contribution to pressure drop by means of the 3-D nonisothermal flow simulation. It is shown that, in the shear-dominated capillary flow, the prediction of the viscous Cross model based on the temperature-corrected viscosity agrees favorably with experimental results. Furthermore, a near material-independent scaling relation is found and can be expressed in terms of the correlation between the dimensionless fluid temperature rise (\(\Theta\)) and the Nahme number (Na), from which the temperature rise due to viscous heating as well as the resultant viscosity change can be predicted.
## 4 Results and Discussion
### Geophysical Survey
#### 4.1.2 Temporal Variation of Conductivity in the Area
Geophysical data available for this period does not allow for any further discussion. For 2001-2006, geophysical data provided a better scenario as measurements covered the area comprehensively. For this period, contamination spread was shown to recede for directions N30\({}^{\circ}\) W, N45\({}^{\circ}\) W, and N60\({}^{\circ}\) W, that is, the northern part of the area. This is particularly evident for the higher conductivity curves. Thus, if high conductivity values are attributed to contamination, it is likely that contamination receded during this period in this region. On the other hand, directions N75\({}^{\circ}\) W, N105\({}^{\circ}\) W, and N120\({}^{\circ}\) W showed a  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**VF—Vertical Fields**} \\ \hline \multicolumn{1}{c}{} & \multicolumn{1}{c}{**1994–2001**} & \multicolumn{1}{c}{**2001–2006**} & \multicolumn{1}{c}{**2006–2016**} & \multicolumn{1}{c}{} \\ \hline \(\sigma\) & N60\({}^{\circ}\) & N75\({}^{\circ}\) & N120\({}^{\circ}\) & N30\({}^{\circ}\) & N45\({}^{\circ}\) & N60\({}^{\circ}\) & N75\({}^{\circ}\) & N105\({}^{\circ}\) & N120\({}^{\circ}\) & N30\({}^{\circ}\) & N45\({}^{\circ}\) & N60\({}^{\circ}\) & N75\({}^{\circ}\) & N105\({}^{\circ}\) & N120\({}^{\circ}\) \\
**S/m** & W & W & W & W & W & W & W & W & W & W & W & W & W & W & W \\
**10** & \(-11\) & 86 & & 10 & 5 & 4 & 12 & & & & & & & & & & \\
**20** & \(-10\) & 18 & 1 & 24 & 1 & 1 & 38 & 127 & 33 & & 91 & 31 & 82 & 19 \\
**30** & \(-10\) & 35 & 14 & 14 & 2 & 1 & 5 & 136 & 25 & & 77 & 35 & 8 & 31 \\
**40** & \(-12\) & 36 & & 5 & 1 & 1 & 12 & 121 & 70 & & 68 & 28 & \(-22\) & 19 & 22 \\
**50** & \(-15\) & 27 & & \(-12\) & \(-3\) & \(-1\) & 42 & 110 & 129 & 21 & 63 & 30 & \(-22\) & 22 & 10 \\
**60** & \(-20\) & 66 & & \(-29\) & \(-12\) & \(-2\) & 35 & 75 & & 29 & 62 & 31 & \(-18\) & 20 & \\
**70** & & & & \(-44\) & 1 & & & 59 & & 35 & 51 & & & 4 & \\
**80** & & & & \(-55\) & & & & & & 39 & & & & & & \\
**90** & & & & \(-62\) & & & & & & & & & & & & & \\ \hline \multicolumn{10}{c}{**Hf—Horizontal Fields**} \\ \hline \multicolumn{10}{c}{} \\ \hline \multicolumn{10}{c}{} \\ \hline \(\sigma\) & N60\({}^{\circ}\) & N75\({}^{\circ}\) & N  
general increase in contamination spreading, thus the highest velocities were estimated and, furthermore, spreading affected all the conductivity curves in this region. In particular, values for horizontal coils, or data containing information from the deeper aquifer, depicted the highest calculated dislocations of conductivity curves. From 2006 onwards, there was an increase in directions N30\({}^{\circ}\) W, N45\({}^{\circ}\) W, and N60\({}^{\circ}\) W, possibly in association with the Vala da Breja ditch, which is in the northern part of the region. For all the other directions, for example to the west and southwest, spreading either recededed or the calculated velocity decreased significantly, possibly because of the remediation works carried out in the 2003-2005 period.
## 4 Numerical Simulation of Soliton Molecules
### Out-of-Phase Two-Soliton Bound States  
The initial condition is set as two Gaussian pulses with 1/e pulse width of 200 fs, pulse energy of 1 pJ, pulse separation of 0.1 ps and phase difference of \(\pi/2\). As we can see from Figure 16a, the initial noise gets amplified and shaped after numbers of round trips in the cavity and finally converges to a bound state consisting of two identical pulses with fixed separation. Figure 16b depicts the optical spectrum of such soliton molecules in both linear (blue) and logarithmic (orange) scales, giving modulation depth of 100% and modulation period of 11.6 nm. It corresponds to temporal separation of 0.69 ps between the bound solitons, which is also confirmed by the pulse trace in time domain in Figure 16c. In detail, the two pulses are of mirror symmetric profile with respect to each other and they are partly overlapped on their edges. Both optical spectrum and temporal phase information indicate a phase difference of exactly \(\pi\). In addition, the pulse duration is 0.16 ps, satisfying transform-limited relationship of sech-shaped pulse when relating to the spectral width (time-bandwidth product of 0.315). In this case, the pulse separation is 4.4 times the pulse duration, indicating a strong direct interaction via pulse trail.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Fibre Segment**} & **Second-Order** & **Third-Order** & **Nonlinear Kerr** & \\  & **Dispersion \(\beta_{2}\)** & **Dispersion \(\beta_{3}\)** & **Coefficient f** & **Length \(L\) (m)** \\  & **(ps\({}^{2}\)/km)** & **(ps\({}^{3}\)/km)** & **(W\({}^{-1}\)-km\({}^{-1}\))** & \\ \hline SMF 1 & \(-7.5\) & \(0\) & \(2.3\) & \(0.2\) \\ SMF 2 & \(-22\) & \(0\) & \(1.2\) & \(0.2\) \\ EDF & \(-18\) & \(0\) & \(3.2\) & \(0.4\) \\ \hline \hline \end{tabular}  
* SMF: single-mode fibre; EDF: erbium-doped fibre.  
\end{table}
Table 2: Parameters of optical fibres in a fibre laser cavity for numerical simulation.  
Figure 15: The fibre laser cavity used in simulation. SMF: single-mode fibre. Other abbreviations are the same as those in the main text.  
When we set another initial condition, namely, change the phase difference of the two noise pulses to \(\pi\), the pulse evolution can proceed differently, see Figure 17. The stable state is another soliton molecule state with pulse separation of 3.51 ps and pulse width of 0.15 ps. Therefore, the RSW value is 23.4, making the binding situation looser. It indicates that the initial parameters, i.e., the historical operation status, play an important role in the ultimate steady state of the system. In a dissipative system which contains different attractors, initial situations act in a way somewhat similar to a precursor in chemistry, namely, to guide the system to reach one of the attractors.