esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The continuous change in formation configurations offers significant potential for formation flight. However, existing methods neglect the potential of autonomous navigation and learning ability based on environmental conditions, which would limit scalability for large formations to some extent. To address this, this paper presents an integrated framework that incorporates an innovative policy for configuration learning into multi-agent flight systems. Firstly, a decoupled trajectory optimization model is proposed, consisting of prediction and learning modules that ensure local adaptivity, obstacle avoidance and dynamic feasibility of formation configuration. Secondly, we design a stage cost function representing obstacle conditions, configuration variability and minimum distance constraints between agents. Then, to enhance adaptability in constrained scenarios, we introduce a reference value generation method that adjusts trajectory learning based on local observations, penalizing errors. Finally, we propose a configuration learning strategy called the local-level action planner and global cost, which coordinates local trajectory optimization with global maintenance. This method integrates distributed model predictive control (DMPC) with modified multi-agent proximal policy optimization (MAPPO), easing the challenge of tracking trajectories from varying rigid geometric shapes. Simulation results demonstrate that the proposed approach improves success rate compared to traditional methods while maintaining formation and avoiding obstacles.  
formation configurations; formation flight; decoupled trajectory optimization; configuration learning strategy Article
## 3 Multi-MAF: A Hybrid Adaptive Algorithm for Routing in WSNs
### Transition Analysis  
Multi-MAF is composed of operating changes between a reactive and a proactive behavior controlled by the sink. In this section, we study what happens during these transitions and their impact on data routing behavior.  
The initial routing state of Multi-MAF is SID and it changes to EF-Tree when a tree-build message is received. Multi-MAF stays in this state while periodical tree-build messages are received. A timeout leads the algorithm back to the SID state. Figure 4 shows the state diagram of Multi-MAF in which the SID state was divided into two operation modes for data forwarding: SID-Ucast and SID-Bcast for unicast and broadcast, respectively. During the time, different nodes in the network can be in different states. This may happen because: (1) The tree is under construction; (2) New nodes were added to the EF-Tree mode network; (3) The tree is not built for the entire network, due to packet losses; (4) The sink sends requisition messages, returning to the SID state, whereas part of the tree is still valid.  
**Proposition 2**: _Considering the proposed forwarding rule, the communication between nodes in different routing states (due to some of the causes described before) is possible._  
Figure 4: State diagram of Multi-MAF.  
**Proof.** We show this characteristic in two parts:  
From SID to EF-Tree state: Data generated or forwarded by a node in the SID-Ucast state can reach a node in the EF-Tree state, due to a tree construction for example (first case). Based on the forwarding rule described before, the packet will follow the tree path. If a data packet is flooded (SID-Bcast state), which occurs when the requisition message path is still not built, this packet can reach the tree in more than one point and it will be forwarded with duplications until it reaches a tree-node which has already received it. The last case is exactly what happens when new nodes are added to the network, because they always start in the SID-Bcast state.  
From EF-Tree to SID state: This situation happens when the sink node decides to return to the SID mode, sending requisition messages to source nodes that may be sending their data through a still valid tree. As the SID requisitions follow the reverse path of the last received data, they will follow the paths of the tree being used for each source. When the tree becomes invalid in a node which already received a requisition message, its reverse path will be used for data forwarding. Otherwise, the data will be forwarded by flooding, reaching the sink anyway, and the path for the source nodes will be built on the next requisition period. \(\Box\)
## 2 Description of the Proposed Method
### Synthesis of Type-B Universal Filters
The obtained circuit is shown in Figure 6a by moving the injected voltage source equivalent circuit to the merged node of V\({}_{\text{in1}}\), V\({}_{\text{in4}}\) and V\({}_{\text{in5}}\) and grounding nodes V\({}_{\text{in2}}\), V\({}_{\text{in3}}\) and V\({}_{\text{in6}}\). Another allpass function at V\({}_{\text{out1}}\) can be also obtained by injecting the input voltage source to the merged node of V\({}_{\text{in2}}\), V\({}_{\text{in3}}\) and V\({}_{\text{in6}}\) in Figure 6a with nodes V\({}_{\text{in1}}\), V\({}_{\text{in4}}\) and V\({}_{\text{in5}}\) being grounded. In the same way, the above synthesized procedure can be applied to other matrices in Table 2. Four equivalent circuits of synthesized filters are shown in Figure 6. The transfer functions of the synthesized circuit for the case 1 of Table 2 are given by (36) and (37). Figure 7 shows the practical configurations realizing the pathological equivalents in Figure 6. Table 3 shows the comparison of obtained filters using the proposed method and related systematic synthesis approaches [16,17,19,21]. It clarifies the benefits of the proposed method. \[\text{V}_{\text{out1}}= \frac{\text{s}^{2}\text{C}_{1}\text{C}_{2}\text{V}_{\text{in1}}- \text{s}^{2}\text{C}_{1}\text{C}_{2}\text{V}_{\text{in2}}+\text{s}\text{C}_{ 2}\text{G}_{2}\text{V}_{\text{in3}}-\text{s}\text{C}_{2}\text{G}_{2}\text{V} _{\text{in4}}+\text{G}_{2}\text{G}_{3}\text{V
# Offloading of Atomic Tasks in Satellite Networks:
## 4 Method
### Problem Conversion
The paper proposes the idea of solving resource contention based on secondary task offloading. The problem is to improve the overall resource utilization rate and achieve the goal of quickly responding to many atomic tasks and assigning specified computing resources. Figure 2: Mission offloading and re-offloading process of all the satellites. As shown in the Figure 3, the blue satellites in the middle and low orbit satellites on the left represent the initial state of the satellites at a specific time t. Meanwhile, these blue satellites have received many atomic computing task requests. Link resources between low-medium orbit satellites and high-orbit satellites are limited, and it is not advisable to establish links between many low-medium orbit satellites and a specific high-orbit satellite simultaneously. Therefore, allocating the offloading tasks is crucial if multiple satellites need to offload a certain number of atomic tasks to high-orbit satellites at time t. As shown in Figure 3, we consider the satellites as nodes and the links between them as edges to create a small directed network. By calculating the maximum traffic of the network, we can observe the satellite network in red in the virtual box on the left. Meanwhile, we can know the maximum amount of data transmitted on each edge according to the solution result of the maximum flow. According to the edge source node, we can obtain the maximum tasks that the node can unload at the current time t. The calculation tasks that need to be offloaded on other satellites are transmitted to a low-medium orbit satellite that establishes a communication link with the high-orbit satellite. Then the satellite transmits the atomic calculation task request to the high-orbit satellite. As shown in Figure 3, the green node in the middle is the high-orbit satellite. After receiving numerous computing tasks on the high-orbit satellite, if the number of tasks reaches the threshold of tasks acceptable to the high-orbit satellite, then unload the remaining computing tasks that cannot be received by high-orbit satellites to the yellow low-medium orbit satellites in the idle state. Therefore, the above process includes a one-time task cache and two-time task offloads.
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 5 Proposed Continuous Authentication Method
### Feature Extraction  
Intuitively, the entire biometric data processing operations are carried out inside the individual smartphone, which is resource and power limited. Hence critical features with less complex computing requirements are adopted. Initially, the acquired three-axis gyroscope and accelerometer data are denoted as \(A=(a_{x},a_{y},a_{z})\) and \(G=(g_{x},g_{y},g_{z})\) respectively, indicating the measures of angular velocity and acceleration in \(X\)-axis, \(Y\)-axis and \(Z\)-axis. In this case, the magnitude of acceleration and angular velocity signals are respectively calculated as  
\[\begin{cases}\begin{aligned} Mag_{A}=\sqrt{a_{x}^{2}+a_{y}^{2}+a_{ z}^{2}}\\ Mag_{G}=\sqrt{g_{x}^{2}+g_{y}^{2}+g_{z}^{2}}\end{aligned}\end{cases} \tag{20}\]  
Accordingly, a set of time and frequency domain sensor data features are calculated:  
* \(Max(x)\) and \(Min(x)\): The maximum and minimum value of input \(x\).
* \(\mu(x)\): The mean value defined as: \[\mu(x)=\dfrac{1}{n}\sum_{i=1}^{n}x_{i}\] (21)
* \(\sigma(x)\): The overall standard deviation defined as: \[\sigma(x)=\sqrt{\dfrac{1}{n}\sum_{i=1}^{n}(x_{i}-\mu)^{2}}\] (22)* \(\gamma(x)\): The skewness of \(x\) defined as: \[\gamma(x)=\frac{\frac{1}{\overline{n}}\sum\limits_{i=1}^{n}{(x_{i}-\mu)^{3}}}{ \left(\frac{1}{\overline{n}}\sum\limits_{i=1}^{n}{(x_{i}-\mu)^{2}}\right)^{\frac {3}{2}}}\] (23)
* \(\kappa(x)\): The kurtosis of \(x\) defined as: \[\kappa(x)=\frac{\frac{1}{\overline{n}}\sum\limits_{i=1}^{n}{(x_{i}-\mu)^{4}}}{ \left(\frac{1}{\overline{n}}\sum\limits_{i=1}^{n}{(x_{i}-\mu)^{2}}\right)^{2}}\] (24)
* \(\rho_{X,Y}\): The correlation between each pair of axes of the sensor data: \[\begin{cases}\rho_{X,Y}=\frac{\text{cov}(X,Y)}{\sigma_{X}\sigma_{Y}}\\ \text{cov}(X,Y)=\frac{1}{n}\sum\limits_{i=1}^{n}(x_{i}-\mu_{X})(y_{i}-\mu_{Y}) \end{cases}\] (25)
* \(IQR(x)\): Interquartile range of input \(x\).
* \(FFT(x)\): Frequency domain feature of input \(x\).  
Thereafter, standard normalization towards the extracted features is conducted before the training process, which is achieved as  
\[z=\frac{x-\mu}{\sigma}, \tag{26}\]  
where \(z\) denotes the normalized output, \(\mu\) and \(\sigma\) are respectively defined as the mean value and standard deviation of specific features.
## 4 Discussion
### Practice, Policy, and Future Research
#### 4.4.2 Policy  
This review highlights the need for policies to standardise eye-tracking metrics like fixations, saccades, and pupil dynamics, ensuring consistency across research and commercial applications. Ethical data collection practices, including addressing dataset bias and implementing robust security measures, are essential to enhance trust and credibility.  
Policies should also focus on accessibility by supporting the development of low-cost, portable eye-tracking systems for underfunded educational and clinical settings. Encouraging open-source datasets can foster collaboration and innovation, reducing barriers for smaller research groups and organisations.
# A Stochastic Bulk Damage Model Based on Mohr-Coulomb Failure Criterion for Dynamic Rock Fracture
## 2 Formulation
### Coupling of Damage and Elastodynamic Problems  
The equation of motion, corresponding to strong satisfaction of the balance of linear momentum for elastodynamic problem, reads as,  
\[\
abla\cdot\mathbf{\sigma}_{\mathrm{eff}}+\rho\mathbf{b}=\dot{\mathbf{p}}, \tag{7}\]  
where \(\mathbf{\sigma}_{\mathrm{eff}}\), \(\mathbf{b}\), and \(\mathbf{p}\) are the effective stress tensor, body force, and linear momentum density, respectively. The linear momentum density is defined as \(\mathbf{p}=\rho\dot{\mathbf{u}}\), where \(\rho\) is the mass density. This equation is augmented by the compatibility equations between displacement, velocity, and strain, and initial/boundary conditions to form the elastodynamic initial boundary value problem.  
The coupling between damage and elastodynamic problems is through the effective stress tensor \(\sigma_{\text{eff}}\). In the simplest form, the scalar damage parameter \(D\) linearly degrades the elasticity stiffness tensor, that is \(\sigma_{\text{eff}}=(1-D)\sigma=(1-D)\mathbf{C}\epsilon\)[21]. However, in more advanced damage-elasticity constitutive equations, only certain parts of the elastic stress (or elastic strain) are degraded by \(D\)[48]. By inspecting Figures 1 and 2, it is observed that damage is induced by high tensile and shear stresses and no damage is induced by a hydrostatic compressive stress state (\(\sigma_{1}=\sigma_{2}<0\)). Accordingly, we define a consistent damage-elasticity constitutive equation in which the entire elastic stress, except its hydrostatic compressive part, are degraded by \(D\). That is,  
\[\sigma_{\text{eff}}=(1-D)\sigma_{d}+(1-D)\langle\sigma_{h}\rangle+\langle \sigma_{h}\rangle_{-} \tag{8}\]  
where \(\sigma_{h}\) and \(\sigma_{d}\) are hydrostatic and deviatoric parts of \(\sigma\). The positive and negative (\(\langle\sigma_{h}\rangle_{-}=\sigma_{h}-\langle\sigma_{h}\rangle\)) parts of \(\sigma_{h}\) correspond to the hydrostatic tensile and compressive stresses of \(\sigma\). For example, if \(\sigma_{2}\leq\sigma_{1}\) are the principal values of \(\sigma\), \(\sigma_{d}\), \(\langle\sigma_{h}\rangle\), and \(\langle\sigma_{h}\rangle_{-}\) have the principal values of \([(\sigma_{2}-\sigma_{1})/2,(\sigma_{1}-\sigma_{2})/2]\), \([\langle(\sigma_{2}+\sigma_{1})/2\rangle,\langle(\sigma_{2}+\sigma_{1})/2 \rangle]\), and \([\langle(\sigma_{2}+\sigma_{1})/2\rangle_{-},\langle(\sigma_{2}+\sigma_{1})/2 \rangle_{-}]\), respectively, all with the same principal directions. Clearly, they correspond to the pure shear, tensile, and compressive parts of \(\sigma\).
## 4 Results and discussion  
Integrals appearing in the governing equations of the reduced-order model, including equations (23) and (27), are evaluated using the \(21\times 21\) Gauss quadrature rule. We established convergence of the adopted Gauss quadrature rule by checking that the values of the above integrals do not change by considering more quadrature points for numerical integration. Results presented below are for \(\
u=0.25\).  
For the transverse displacement, we use the following kinematically admissible function in equation (15)  
\[\bar{w}\left(x_{1},x_{2}\right) =A_{0}\left(\frac{\cosh\left(\vartheta_{0}x_{1}\right)-\cos\left( \vartheta_{0}x_{1}\right)}{\cosh\vartheta_{0}-\cos\vartheta_{0}}-\frac{\sinh \left(\vartheta_{0}x_{1}\right)-\sin\left(\vartheta_{0}x_{1}\right)}{\sinh \vartheta_{0}-\sin\vartheta_{0}}\right)\] \[\times\left(\frac{\cosh\left(\frac{\theta_{0}x_{2}}{\varphi} \right)-\cos\left(\frac{\vartheta_{0}x_{2}}{\varphi}\right)}{\cosh\vartheta_ {0}-\cos\vartheta_{0}}-\frac{\sinh\left(\frac{\vartheta_{0}x_{2}}{\varphi} \right)-\sin\left(\frac{\theta_{0}x_{2}}{\varphi}\right)}{\sinh\vartheta_{0} -\sin\vartheta_{0}}\right), \tag{38}\]  
where \(\vartheta_{0}=4.73004\) is the lowest nonzero root of the transcendental equation \(\cosh\vartheta\cos\vartheta=1\), and the constant \(A_{0}\) is chosen by normalizing \(\bar{w}\) with respect to its maximum value. The basis function in equation (38) equals the product of the first transverse modes of vibration of clamped-clamped Euler beams of lengths \(1\) and \(\varphi\), respectively.  
Since \(\bar{w}\) in equation (38) is symmetric about the axes \(x_{1}=1/2\) and \(x_{2}=\varphi/2\), the basis functions \(u_{(p)i}\) in equation (21) with \(\mathrm{mod}\left(\mathrm{mod}(m,2)+\mathrm{mod}(n,2),2\right)=0\) imply \(\Xi_{(p)}=0\) in equation (23). Here, \(\mathrm{mod}(m,n)\) gives the remainder in the division of \(m\) by \(n\). Therefore, the number of degrees of freedom in equation (14b) is given by  
\[P=\frac{1}{2}\left(\bar{p}\bar{q}-\mathrm{mod}\left(\bar{p}\bar{q},2\right) \right), \tag{39}\]  
where only basis functions \(u_{(p)i}\) such that \(\mathrm{mod}\left(\mathrm{mod}(m,2)+\mathrm{mod}(n,2),2\right)=1\) are considered. Results below are computed by selecting \(\bar{p}=\bar{q}\).  
In [20], it is shown that convergent solutions for pull-in parameters of rectangular von Karman microplates are obtained for \(\bar{p}=\bar{q}=4\), corresponding to \(P=8\). Thus, results presented herein are computed with \(9\) basis functions in the reduced-order model.
# A Deep Reinforcement Learning-Based Decision-Making Approach for Routing Problems
## 4 Experiments
### Comparison Analysis
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**TSP20**} & \multicolumn{3}{c}{**TSP50**} & \multicolumn{3}{c}{**TSP100**} \\ \cline{2-10}  & **Length** & **Gap** & **Time** & **Length** & **Gap** & **Time** & **Length** & **Gap** & **Time** \\ \hline Concorde [14] & 3.84 & 0.00\% & 6 min & 5.70 & 0.00\% & 15 min & 7.76 & 0.00\% & 1 h \\ Gurobi [28] & 3.84 & 0.00\% & 8 s & 5.70 & 0.00\% & 3 min & 7.76 & 0.00\% & 19 min \\ LKH-3 [29] & 3.84 & 0.00\% & 45 s & 5.70 & 0.00\% & 8 min & 7.76 & 0.00\% & 28 min \\ \hline Nearest Search & 4.33 & 12.76\% & 1 s & 6.78 & 18.95\% & 3 s & 9.47 & 22.04\% & 8 s \\ Random Search & 4.01 & 4.43\% & 1 s & 6.15 & 7.89\% & 2 s & 8.55 & 10.18\% & 3 s \\ Farthest Search & 3.95 & 2.86\% & 1 s & 6.03 & 5.79\% & 4 s & 8.37 & 7.86\% & 9 s \\ \hline OR Tools [30] & 3.87 & 0.78\% & 1 min & 5.86 & 2.81\% & 6 min & 8.09 & 4.25\% & 25 min \\ GA [31] & 3.86 & 0.52\% & 1 min & 5.84 & 2.46\% & 5 min & 8.05 & 3.74\% & 24 min \\ SA [32] & 3.86 & 0.52\% & 1 min & 5.83 & 2.28\% & 5 min & 8.03 & 3.48\% & 23 min \\ ACO [33] & 3.86 & 0.52\% & 1 min & 5.82 & 2.11\% & 4 min & 8.02 & 3.35\% & 22 min \\ \hline AM [22] & 3.86 & 0.52\% & 2 s & 5.80 & 1.75\% & 5 s & 7.96 & 2.58\% & 14 s \\ [PERSON] et al. [24] & 3.85 & 0.26\% & 13 min & 5.78 & 1.40\% & 18 min & 7.94 & 2.32\% & 28 min \\ DACT [25] & 3.84 & 0.00\% & 9 s & 5.74 & 0.70\% & 23 s & 7.92 & 2.06\% & 1 min \\ Neural-2-Opt [34] & 3.84 & 0.00\% & 16 min & 5.73 & 0.53\% & 32 min & 7.89 & 1.68\% & 45 min \\ POMO [23] & 3.84 & 0.00\% & 5 s & 5.72 & 0.35\% & 22 s & 7.83 & 0.90\% & 1 min \\ Ours & 3.84 & 0.00\% & 5 s & 5.71 & 0.18\% & 19 s & 7.80 & 0.52\% & 49 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison results among baseline methods on TSP20-100. Pertaining to CVRPs, the optimal solutions are achieved by the powerful approach LKH-3 [29], as shown in Table 2. In comparison with the heuristic optimizer, the mean route length of our approach is 0.34, 1.85, and 1.37 shorter than that of OR Tools [30], which shows that DRL-DM engages a powerful decision-making capability to tackle complex routing problems. Moreover, the proposed approach also exhibits superior routing performance when compared with DRL-based methods. Specifically, DRL-DM achieves remarkable optimality gap reductions of 6.65%, 3.45%, 2.62%, 2.17%, and 0.96% than AM [22], [PERSON] et al. [24], DACT [25], NeuRewritter [35], and POMO [23], respectively. It is worth noting that our proposed approach outperforms the second-best model, POMO [23], by a gap reduction of 0.49%, 0.77%, and 0.96% on CVRPs with 20, 50, and 100 nodes, respectively. These exciting results demonstrate the excellent ability of our DRL-DM to tackle different types of routing problems.
## 3 Results
### Multiple Regression Models  
#### 3.3.1 Multivariate Poisson Regression Model of Limitations in Basic Activities of Daily Living  
A Poisson regression model was constructed to identify factors associated with the number of limitations in BADLs. The main risk factors for a higher number of BADL limitations were having +3 mobility and upper limb/fine motor difficulties, physical inactivity, and the presence of a long-term illness, along with other variables such as pain, male sex, depression, and low weight. In contrast, a higher HGS ratio was significantly associated with fewer BADL limitations and emerged as the primary protective factor. Specifically, having +3 mobility and upper limb/fine motor limitations was associated with an 8.17 increase in the mean number of BADL limitations. Physical inactivity and long-term illness were also linked to increased risk, 2.21 and 2.03, respectively. Conversely, individuals with the highest HGS ratios experienced a 90% reduction in BADL limitations compared to those with lower ratios (Figure 3 and Table S2).  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & **Mdn** & **(IQR)** & **Mdn** & **(SD)** & **H** & **df** & \(p\)**-Value** & **Post Hoc U** & \(p\)**-Value** \\ \hline \multicolumn{8}{l}{**Basic Activities of Daily Living Limitations**} \\ \hline No Pain (1) & 0 & (0.0) & 0.1 & (0.6) & & & & 1 vs. 2 & \(<\)0.001 *** \\ Localized Pain (2) & 0 & (0.0) & 0.4 & (1.1) & 4569.4 & 2 & \(<\)0.001 & 1 vs. 3 & \(<\)0.001 *** \\ Widespread Pain (3) & 0 & (2.0) & 1.2 & (1.9) & & & & & \\ \hline \multicolumn{8}{l}{**Instrumental Activities of Daily Living Limitations**} \\ \hline No Pain (1) & 0 & (0.0) & 0.3 & (1.3) & & & & 1 vs. 2 & \(<\)0.001 *** \\ Localized Pain (2) & 0 & (1.0) & 0.8 & (1.8) & 3731.4 & 2 & \(<\)0.001 & 1 vs. 3 & \(<\)0.001 *** \\ Widespread Pain (3) & 1 & (4.0) & 2.2 & (3.0) & & & 2 vs. 3 & \(<\)0.  
.3.2 Multivariate Poisson Regression Model of Limitations in Instrumental Activities of Daily Living  
Regarding limitations in IADL, the main factors associated with a higher number of limitations were +3 mobility and upper limb/fine motor limitations, arm and fine motor difficulties (multiplying the number of limitations by 5.33), physical inactivity (multiplying it by 2.13), and having a long-term illness (multiplying it by 1.89). Once again, a higher HGS ratio was identified with fewer IADL limitations. People with the highest HGS ratio had an 84% reduction in the number of IADL limitations compared to people with a lower HGS ratio (Figure 4 and Table S3).  
Figure 3: Multivariate Poisson regression including the number of limitations in the Basic Activities of Daily Living as the dependent variable. CI: confidence interval; * _p_-value \(<\) 0.05; ** _p_-value \(<\) 0.01; and *** _p_-value \(<\) 0.001.