esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In the research of heterogeneous wireless sensor networks, clustering is one of the most commonly used energy-saving methods. However, existing clustering methods face challenges when applied to heterogeneous wireless sensor networks, such as energy balance, node heterogeneity, algorithm efficiency, and more. Among these challenges, a well-designed clustering approach can lead to extended node lifetimes. Efficient selection of cluster heads is crucial for achieving optimal clustering. In this paper, we propose an Enhanced Pelican Optimization Algorithm for Cluster Head Selection (EPOA-CHS) to address these issues and enhance cluster head selection for optimal clustering. This method combines the Levy flight process with the traditional POA algorithm, which not only improves the optimization level of the algorithm, but also ensures the selection of the optimal cluster head. The logistic-sine chaotic mapping method is used in the population initialization, and the appropriate cluster head is selected through the new fitness function. Finally, we utilized MATLAB to simulate 100 sensor nodes within a configured area of \(100\times 100\) m\({}^{2}\). These nodes were categorized into four heterogeneous scenarios: \(m=0,\alpha=0,\)\(m=0.1,\alpha=2,\)\(m=0.2,\alpha=3,\) and \(m=0.3,\alpha=1.5\). We conducted verification for four aspects: total residual energy, network survival time, number of surviving nodes, and network throughput, across all protocols. Extensive experimental research ultimately indicates that the EPOA-CHS method outperforms the SEP, DEEC, Z-SEP, and PSO-ECSM protocols in these aspects.  
heterogeneous wireless sensor networks; pelican optimization algorithm; energy efficient; cluster head selection
## 2 Principles and Methods
### Dynamics Derivation of Optomechanical Coupling Active Control System
The first part is the static value \(-\frac{1}{m}\theta_{1}\), which is called the ideal exit angle of \(L_{2}\) when the lens is not misaligned; the second part is the dynamic quantity \(\frac{1}{mf_{1}}[a_{2}(t)-a_{1}(t)]\), which is caused by the lens group of spatial optical filter under different phase microvibration and active control, which is the most important part affecting the beam pointing. Recording the dynamic angular displacement of output light \(\Delta\theta_{2}=\frac{1}{mf_{1}}[a_{2}(t)-a_{1}(t)]\), there are  
\[\Delta\theta_{2}=-[\frac{G(s)}{mf_{1}}\sqrt{A_{1}^{2}+A_{2}^{2}-2A_{1}A_{2} \cos[(\omega_{1}-\omega_{2})t+(\phi_{1}-\phi_{2})]}]\sin(\frac{\omega_{1}+ \omega_{2}}{2}t+\frac{\phi_{1}+\phi_{2}}{2}+\varphi) \tag{15}\]  
Figure  
It can be seen from formula (15) that the dynamic angular displacement of the output light has harmonic property, and the amplitude is related to time, which has certain complexity and variability of working conditions. In the analysis, we focus on the amplitude of the dynamic angular displacement of the output light, and use \(\Theta\) to represent the peak value of the output light deviation from the ideal angle. \[\Theta=\frac{G(s)}{mf_{1}}\sqrt{A_{1}^{2}+A_{2}^{2}-2A_{1}A_{2}\cos[(\omega_{ 1}-\omega_{2})t+(\phi_{1}-\phi_{2})]} \tag{16}\]  
In the following numerical analysis, take \(mf_{1}=f_{2}=16m\)[31]. Formula (16) shows that the peak value of the output light deviation from the ideal angle is related to the amplitude ratio, frequency difference, and phase difference of the excitation. From the derived formula, it can be found that whether the frequency difference is zero or not determines whether the amplitude of the system response is related to time. When the frequency difference is zero, the peak value of the output light angular response is independent of time, and the beam pointing accuracy depends on the amplitude ratio and phase difference of the excitation. When the frequency difference is not zero, the peak value of the angle response of the output light is time-dependent, and the peak value of the angle response is time-varying. The effects of the amplitude ratio, frequency difference, phase difference, and time on the beam pointing should be considered.
## 3 Distributed Mission Scheduling Model  
### Game Model of DEOS  
To achieve efficient system coordination among multiple satellites, this paper sees the DEOS as a multi-agent system and converts multi-satellite mission planning into a game problem \(G=(N,\{A_{i}\},\{U_{i}\})\), where \(N=S\) is the set of players (agents) in a game. Every satellite \(s_{i}\)\(\in\)\(S\) in this game is seen as a 'rational player' (agent), whose rationality is  
Figure 1: Scheduling solution.  
embodied in that it will continuously adjust its own action strategy \(a_{i}\) in response to the action strategy adopted by opponents, so as to maximize its own payoff \(U_{i}\). Action \(a_{i}\) is the scheduling plan that is only executed by the \(i\)-\(th\) satellite, and it is a subset of the overall scheduling solution defined in Section 2.2.2. As shown in Figure 1, the sub-solution for Sat1 represents the current action adopted by the 1 st satellite in that scenario. In addition, \(A=\prod_{i\in N}A_{i}\) represents the set of joint actions taken by the entire system in one round of the game. For simplicity of expression, let \(a_{-i}=(a_{1},\ldots,a_{i-1},a_{i+1},\ldots,a_{n})\) be the action profile other than \(s_{i}\). To terminate iterations of gaming, we introduce Nash Equilibrium to our model.  
Definition 1: (Nash Equilibrium [39]) For a game problem \(G=(N,\{A_{i}\},\{U_{i}\})\), for any player \(i\)\(\in\)\(N\), we call \(a^{*}\) a Nash equilibrium, when it satisfies:  
\[U_{i}(a^{*}_{i},a^{*}_{-i})=\max_{a_{i}\in A_{i}}U_{i}(a_{i},a^{*}_{-i}) \tag{13}\]  
In iterations of reaching Nash equilibrium of a multi-satellite game, each satellite modifies its own action without directly interfering with other satellites. Due to the optimization objective setting in Section 2.3, each satellite will try to 'capture' as many unscheduled targets as possible to gain a higher individual payoff and meanwhile pay close attention to targets that have been scheduled by others to avoid repeated observation. The gaming process will continue to run until the system reaches a balance when all individuals are unable to obtain higher payoffs by changing their own actions. It is noteworthy that after each round of the game, each satellite must inform other satellites of its action to maintain the consistency of cognition. In Section 2.1, we assume that each satellite with the same payload can interact in real time through inter-satellite links, which provides a basis for the future optimization based on Nash equilibrium.
###### Abstract  
The Global Navigation Satellite System (GNSS) is vulnerable to deliberate spoofing signal attacks. Once the user wrongly locks on the spoofing signal, the wrong position, velocity, and time (PVT) information will be calculated, which will harm the user. GNSS spoofing signals are difficult to carry out spoofing attacks in the direction of arrival (DOA) of the real signal, so the spoofing detection method based on DOA is very effective. On the basis of identifying spoofing signals, accurate DOA information of the signal can be further used to locate the spoofer. At present, the existing DOA monitoring methods for spoofing signals are mainly based on dedicated antenna arrays and receivers, which are costly and difficult to upgrade and are not conducive to large-scale deployment, upgrade, and maintenance. This paper proposes a spoofing detection and direction-finding method based on a low-cost commercial GNSS board component (including an antenna). Based on the traditional principle of using a multi-antenna carrier phase to solve DOA, this paper innovatively solves the following problems: the poor direction-finding accuracy caused by the unstable phase center of low-cost commercial antennas, the low success rate of spoofing detection in a multipath environment, and the inconsistent sampling time among multiple low-cost commercial GNSS boards. Moreover, the corresponding prototype equipment for spoofing detection and direction-finding is developed. The measured results show that it can effectively detect spoofing signals in open environments. Under a certain false alarm rate, the detection success rate can reach 100%, and the typical direction-finding accuracy can reach 5\({}^{\circ}\).  
GNSS; low cost; clock bias; hardware delay; DOA; spoofing detection; spoofing direction finding +
Footnote †: journal: remote sensing  
Article  
A GNSS Spoofing Detection and Direction-Finding Method Based on Low-Cost Commercial Board Components  
[PERSON]  
1 School of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100083, China; [EMAIL_ADDRESS] (P.M.); [EMAIL_ADDRESS] (S.L.)  
2 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; [EMAIL_ADDRESS] (H.Y.); [EMAIL_ADDRESS] (R.L.); [EMAIL_ADDRESS] (R.L.); [EMAIL_ADDRESS] (R.L.); [EMAIL_ADDRESS] (G.Z.); [EMAIL_ADDRESS] (C.F.); [EMAIL_ADDRESS] (J.X)  
Correspondence:[EMAIL_ADDRESS] (X.C.); [EMAIL_ADDRESS] (Y.G.)
# Detecting Deception from Gaze and Speech Using a Multimodal Attention LSTM-Based Framework
## 2 Materials and Methods
### Feature Extraction  
#### 2.2.1 Gaze Features  
From the 26 channels provided by the Gazepoint GP3 Eye Tracker, we selected four of them, Horizontal Fixation Point-Of-Gaze (FPOGX), Vertical Fixation Point-Of-Gaze (FPOGY), Left Pupil Diameter (LPD) and Right Pupil Diameter (RPD), that corresponded to the horizontal and vertical coordinates of the user's fixation point-of-gaze and the diameter of the left and right eye pupil in pixels. The choice of these channels was due to the fact that they contain information about fixations, saccades and pupil size, that are indicators of deception behaviors [5; 6; 8; 9]. Due to the high correlation between the left and right eye diameters, instead of using LPD and RPD channels independently, we computed their average, that is called Mean Pupil Diameter (MPD). Therefore, gaze features were derived from the following three signals: FPOGX, FPOGY and MPD.  
The gaze feature extraction process is depicted in Figure 1a. The first step consisted of dividing the three gaze signals into segments of 3 s length with an overlap of 2.5 s seconds (it meant a shift of 0.5 s between consecutive windows). Incomplete sequences (i.e., segments shorter than 3 s) were padded with masked values in order to not to be used in further computations. This step was required due to the small number of recordings, as this data augmentation process allowed the obtaining of more reliable and representative models. A similar technique has been used for depression detection from speech [37] or Parkinson's Disease detection from drawing movements [38]. After this process, the total number of segments was 5258, distributed in 2736 lies and 2522 truths. Note that from now on, we differentiated two levels: segment level that referred to each of the segments of 3 s length and turn level that corresponded to the full recordings.  
For the LSTM-based systems, the gaze features \(X_{G}\) consisted of a temporal sequence with dimension \(N_{G}\times T_{G}\), where \(N_{G}\) is the number of the eye tracking signals considered and \(T_{G}\) is the number of elements composing each gaze sequence. In this case, \(N_{G}=3\) (FPOGX, FPOGY and MPD) and \(T_{G}=180\) as it was the length corresponding to a segment of 3 s sampled at 60 Hz.  
In the case of the SVM-based systems, as this kind of traditional machine-learning techniques does not allow the adequate modeling of temporal signals, a summarized representation of the frame-level gaze features was required. For doing that, several statistical functionals (in particular, mean, standard deviation, skewness, kurtosis, max and min) were obtained from the raw gaze sequences \(X_{G}\), yielding a compact feature vector \(X_{G}^{C}\) whose dimension is \(N_{G}\times N_{F}\), where \(N_{F}\) is the number of statistics computed (in this case, \(N_{F}=6\)).
# A Comprehensive Survey of Isocontouring Methods: Applications, Limitations and Perspectives
## 7 Discussion
### Tessellation-Based Algorithms  
The tessellation-based algorithms are made up of several different approaches, all of which are based on polygonization or the general use of triangles or tetrahedra. The polygonization algorithms, and the marching tetrahedra algorithm in particular, are based on the fundamental idea of the marching cubes algorithm and therefore originate primarily from the field of medical imaging. However, the methods have also been extended to the generation and triangulation of molecular surfaces, for example. These methods require structured data but often provide topologically more correct surfaces than the marching cubes algorithm. Mesh-based algorithms, on the other hand, originate from a different problem-solving context, where primarily technical problems need to be solved. Structured data are generally not available for these problems, meaning that the algorithms must also provide results with unstructured data. This means that these algorithms could also be used in other areas.  
Thus, it can be concluded that the tessellation-based algorithms are primarily used for structured data sets and are only occasionally used for unstructured data sets. The data sets can be two- or three-dimensional. The quality of the various algorithms, if validation is carried out, is predominantly analyzed via a visual inspection. The RooTri() algorithm in [39] is an exception, as it can be used for unstructured data sets and a test routine based on statistically sound methods is performed to evaluate the quality of the algorithm.
## 2 Super-Sized Pore-Throat Network Generation Method
### Fitting of Coordination Number and Assignment of Radii to Pores and Throats  
In a real core, some mineral particles have clear boundaries, but these boundaries do not form a channel through which the fluid can pass, and these mineral particles can be regarded as whole particles. Therefore, it is reasonable to fit the coordination number by removing some pores or throats.  
In this paper, an improved genetic algorithm is used to optimize and match the target coordination number distribution data. As shown in Figure 6, each individual has a single chromosome composed of multiple genes, where each gene is either 0 or 1. In other words, each chromosome represents a binary number, with 0 indicating deletion, 1 indicating retention, and the length of the binary number corresponding to the number of throats in the model  
The flow chart of the genetic algorithm is shown in Figure 7, in which roulette is used for natural selection [34], and a single-point crossover operator is used for gene recombination [35]. The closer the coordination number distribution represented by the chromosome  
Figure 5: Pore range under different coordination numbers. (**a**) Coordination number is 3; (**b**) coordination number is 4.  
Figure 6: An individual’s chromosomes and genes.  
is to the target coordination number distribution data, the higher the individual fitness and the easier the chromosome preservation.  
The fitness function of the genetic algorithm is as follows:  
\[F(c,c_{t})=\sum_{i=1}^{n}\sum_{j=1}^{m}\left|sort\Big{(}c^{ij}\Big{)}-sort\Big{(} c_{t}{}^{ij}\Big{)}\right|+10\times sum\Big{(}c^{ij}<2\Big{)} \tag{4}\]  
where n is the number of partitions in the model, m is the total number of extracted pores in each partition, \(c^{ij}\) is the coordination number of the i-partition and j-th pore from the individual chromosome, \(c_{t}{}^{ij}\) is the distribution of the target coordination number of all pores in each partition, and sort is a function that sorts the one-dimensional values from small to large. The second term on the right side of the equation is the penalty term, preventing the genetic algorithm from dead pores.  
After completing the coordination number optimization and matching, precise control of the pore radius is carried out based on the connected domain analysis algorithm. According to the distribution data of pore radius and throat radius in different partitions, each pore and radius are extracted through traversal, random sampling is carried out within the set interval, and a value is assigned to each pore and radius. The distribution principle is as follows:  
1. For the pores and throats in each region, random sampling is conducted according to the given radius distribution data;
2. The higher the extracted pore area and the more pores combined, the larger pore radius is allocated;
3. The radius of the throat shall not exceed the minimum pore radius in the pores at both ends.
# Calibration and Analysis of Seeding Parameters of Soaked _Cyperus esculentus_ L. Seeds
## 2 Materials and Methods
### Determination of the Contact Parameters of soaked C. esculentus Seeds
#### 2.3.2 Collision Recovery Coefficient  
The collision recovery coefficient is a parameter concerning the amount of recovery after the collision deformation of the material and the contact panel; it is the ratio of the upward separation velocity after the collision to the vertical downward velocity before the collision [22; 23; 24]. The contact material panels were selected as _C. esculentus_ seed plates and steel plate materials, and the falling height of the seeds was selected as a fixed height of 30 cm.  
Randomly selected _C. esculentus_ seeds free-fall from the drop position; after t1 time of collision with the contact plate (recorded time), the free-fall ends, and the fall speed is V. _C. esculentus_ seeds will produce upward torque after the fall; at this time, the high-speed camera (Phantom T1340) will record the maximum height of seed bouncing (h) and the bouncing speed (V1). The test device is shown in Figure 7.  
\[\mathrm{e}=\frac{V_{1}}{V}=\frac{\sqrt{2\mathrm{g}h}}{\sqrt{2\mathrm{g}H}} \tag{6}\]  
where \(\mathrm{e}\) is the collision recovery coefficient; h is the maximum seed bounce height (mm); and H is the seed free-fall height (mm).  
The average values of the collision recovery coefficients between the _C. esculentus_ seeds and the seed and steel plates were measured as 0.46 and 0.55, respectively, based on Equation (6).
# Energy-Saving Routing Protocols for Smart Cities
## 2 Materials and Methods
### Egli's Propagation Loss Model  
Network simulation tools use computational models for their operations, from component and device models to environmental condition and mobility behaviour models, as well as for signal propagation. Propagation loss models are mathematical models used to estimate the attenuation between RF transmitters and RF receivers in order to obtain the received signal power according to specific conditions (frequency, antenna gain, etc.) [31].  
The Egli model is a widely used propagation loss model derived from experimental results using actual measurements of television broadcast systems [32]; it is suitable for cellular communications where there are a number of both fixed and mobile devices. Furthermore, it is applicable in scenarios where transmission occurs across uneven terrain without the presence of vegetation in the communication link [33]. The Egli model can be adopted for frequency ranges between 40 MHz and 1 GHz and for distances up to 60 km [34], and takes into consideration the line of sight between the devices, The Path Loss is denoted as \(P_{L}\), and is provided by the formula  
\[P_{L}=G_{t}G_{r}\left(\frac{h_{t}h_{r}}{d^{2}}\right)^{2}\left(\frac{40}{f_{c }}\right)^{2} \tag{1}\]  
where \(G_{t}\) and \(G_{r}\) are the gains of the transmitter and receiver antennas, respectively, \(h_{t}\) and \(h_{r}\) are the respective heights of the transmitter and receiver antennas, \(d\) is the distance between them, and \(f_{c}\) is the carrier frequency in MHz.
## 2 Tri-Stable Stochastic Resonance Theory
### Principle of Stochastic Resonance
#### 2.2.1 Langevin Equation  
In order to describe the motion law of Brownian particles in liquid, the dynamic equation of Brownian particles is given, and on this basis, the Langevin Equation is established. The macroscopic equation for the motion of a Brownian particle of mass in a liquid is  
\[m\dot{v}=-\alpha v, \tag{8}\]  
Among them, \(v\) is the velocity of movement of the Brownian particles in the liquid, and \(-\alpha v\) is the viscous force. When the mass of the Brownian particles is large, in addition to the macroscopic viscous force, the Brownian particles are also subjected to random motion in all directions by the force of the liquid molecules. The smaller the mass of the Brownian particles, the greater the magnitude of the motion, so we add the force \(F(t)\) of the molecular clutter to Equation (8).  
\[mv=-\alpha v+F(t), \tag{9}\]  
where \(F(t)\) is the total force generated by the collision of liquid molecules with Brownian particles. By dividing both sides of Equation (9) by \(m\), can get  
\[\dot{v}+\gamma v=\Gamma(t), \tag{10}\]  
And, \(r=\frac{\alpha}{m}\), \(\Gamma(t)=\frac{F(t)}{m}\) is the damping coefficient per unit mass and the molecular collisional fluctuation force, \(\Gamma(t)\) is called Langevin force, and Formula (10). It is called the Langevin equation. Suppose that the particle \(t=0\) has the velocity \(v_{0}\) and the position is at \(x_{0}\), then \(v=\dot{x}\) is taken into Equation (10).  
\[\ddot{x}+\gamma\dot{x}=\Gamma(t), \tag{11}\]Since the Brownian particles are still subjected to external forces, the Langevin equation under the action of the external field becomes  
\[\dot{x}+\gamma\dot{x}=f(x)+\Gamma(t), \tag{12}\]  
where \(f(x)\) is the external force to which the average unit mass of Brownian particles is subjected. In the case of over-damping, the left side of Equation (12) is the damping term, which plays a major role, so the inertia term \(\ddot{x}\) can be ignored, and \(\gamma=1\), (12) can be reduced to  
\[\dot{x}=f(x)+\Gamma(t), \tag{13}\]  
When the external force \(f(x)\) of the average unit mass Brownian particle is a nonlinear multipotential function, the equation shown by Equation (13) is called the nonlinear Langevin equation [18; 19; 20].