esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of End-Effector Compliance on Collisions in Robotic Teleoperation
## 4 Effects of Tool Compliance
### Model of Tool Compliance  
If robot compliance is negligible, the model that describes the collision comprises the rigid robot, the decoupling elements and the tool (see Figure 15).  
The corresponding equations of motion during the collision are as follows:  
\[m_{t}\dot{x}_{t}=c_{t}(\dot{x}_{c}-\dot{x}_{t})+k_{t}(x_{c}-x_{t})-k_{c}\delta^ {m}-\chi\delta^{n}\dot{\delta} \tag{17}\]  
\[m_{p}\ddot{x}_{p}=k_{c}\delta^{m}+\chi\delta^{n}\dot{\delta} \tag{18}\]  
The rigid body moving with imposed motion is the robot flange. The parameters \(k_{t}\) and \(c_{t}\) represent the stiffness and damping characteristics of the system that decouple the tool from the robot. The set of the decoupling system and the tool represents the end-effector. It is worth noting that the mass-spring-damper system that represents the end-effector is excited by the imposed motion \(x_{c}\), which represents the x-coordinate of the robot flange.  
If the two equations are summed, the impact force disappears:  
\[m_{t}\dot{x}_{t}+m_{p}\dot{x}_{p}=c_{t}(\dot{x}_{c}-\dot{x}_{t})+k_{t}(x_{c}-x_ {t}) \tag{19}\]  
If Equation (19) is integrated between the instant (\(t_{1}\)), in which the contact between the tool and the object begins, and the instant (\(t_{2}\)), in which the contact ends, the following result is obtained:  
\[m_{t}V_{t}+m_{p}V_{p}=m_{t}v_{t}+m_{p}v_{p}+c_{t}\int_{t_{1}}^{t_{2}}(\dot{x}_{ c}-\dot{x}_{t})dt+k_{t}\int_{t_{1}}^{t_{2}}(x_{c}-x_{t})dt \tag{20}\]  
Equation (20) shows that the linear momentum of the system is no longer conserved owing to the effect of the impulses of the damping and elastic forces. It is worth noting that the elastic and damping forces increase the final momentum as long as the terms in the integrals are positive. The total impulse generated by the decoupling elements is  
\[I=c_{t}\int_{t_{1}}^{t_{2}}(\dot{x}_{c}-\dot{x}_{t})dt+k_{t}\int_{t_{1}}^{t_{2} }(x_{c}-x_{t})dt \tag{21}\]  
Introducing Equations (1) and (21) into Equation (20) leads to the following equation:  
\[V_{p}=v_{p}-\frac{m_{t}}{m_{t}+m_{p}}(1+e)(v_{p}-v_{t})+\frac{I}{m_{t}+m_{p}} \tag{22}\]  
Equation (22) highlights that the total impulse generated by the decoupling system increases the final velocity of the object.  
Figure 15: Model of the collision between a tool with decoupling elements and an object.
## 3 Multi-Ship Collision Avoidance Decision Making Based on Full Communication
### The CA Logic and Formulation of CA Intention Based on COLREGs  
The CA logic (CAL) of a ship is its macro strategy to pass through a collision-prone situation. As mentioned, the COLREGs are the basic regulations that both ships should comply with in conventional two-ship CA scenarios. There are three types of encounter situations addressed by COLREGs, namely crossing, head-on, and overtaking [19]. The own ship can be regarded as either a give-way ship or a stand-on ship when it is involved in a two-ship encounter scenario, as referred to in [20], which gives a detailed analysis of the stand-on or give-way ship judgment method according to the CPA. According to COLREGs, the give-way ship always has a greater responsibility than the stand-on ship to avoid collisions. The CA measures that the give-way ship can take include a right turn and deceleration, while the stand-on ship can take the opposite measure. The practice of COLREGs in multi-ship encounter situations is discussed in [2], and the main difference between stand-on or give-way ships is whether one ship crosses another before or after it [21]. Both of the involved ships should cooperate to make sure that stand-on ships cross the region before give-way ships.  
In the multi-ship encounter scenario, the stand-on/give-way relation between two ships can be applied in a pairwise manner. Thus, a mutual relation matrix is formulated to represent the pairwise relationship between any two involved ships. The CAL of one ship is just the strategy or guidance of CA action. In this paper, CAL is defined as a kind of simple and effective model to analyze the attitude of a ship towards the current encounter situation. Even though there are several feasible trajectories under a particular encounter scenario, the CAL can only be 0 (give-way) or 1 (stand-on). So, it is much easier and more reasonable to infer the target ship's CAL rather than their decisions. After inferring the target ship's CAL, the own ship's CAL can be made accordingly, as follows:  
\[\text{CAL}_{\text{osi}}=\left\{\begin{aligned} 0,&\text{ if OS is give -- way ship to TS}_{\text{i}}\\ 1,&\text{ if OS is stand -- on ship to TS}_{\text{i}}\end{aligned}\right. \tag{1}\]  
For instance, if the target ship's CAL is 1, the own ship's CAL should be 0 to avoid conflict. And the decision of the OS is made on the basis of the OS's CAL. After all, it is the CA decision that determines a ship's status next time. In the method proposed in this paper, CAL is the description of the OS's CA intention at the level of the repulsion field. The repulsion field around TS represents the OS's understanding of the give-way/stand-on relationship with TS and the understanding of the COLREGs.
# An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials
## 2 Preliminary
### Chebyshev Chaotic Mapping
#### 2.1.4 Information Entropy  
Information entropy is usually used to measure the uncertainty of a variable. The greater the uncertainty of variables, the higher the information entropy, which is defined as \(H(x)=\sum_{i=0}^{L-1}\,p(x_{i})log\frac{1}{p(x_{i})}\), where \(X=\{x_{i}|x_{i}\in[0,255]\}\), \(p(x_{i})\) is the probability of \(x_{i}\) and \(L=256\) for grayscale images. The maximum value of information entropy is 8 for grayscale images.  
Figure 5 shows the information entropy diagrams of the Chebyshev chaotic maps. It can be seen that the entropy values of the output sequence generated by the Chebyshev map stabilize within a good range when \(\mu>2\), indicating that the Chebyshev map exhibits a high degree of unpredictability.
## 4 Validation
### Comparison with Numerical Simulation  
To further validate our proposed analytical model, the results of this analytical model are compared with those of a numerical simulation using the FLAC3D5.01 finite differences code. The parameters used for this verification are taken from the literature [10; 36] and are shown in Table 1. A three-dimensional numerical simulation with plane strain condition is carried out. A quarter of the numerical model used for this verification is shown in Figure 5. The left and lower sides of the model are set as axisymmetric boundary conditions, the upper side is set as a free boundary, and other positions are set as displacement-constrained boundary conditions. The thickness, width, and height of the numerical model are set as 1.0 m, 80 m, and 80 m, respectively. The lining is simulated with the solid elements, and its elastic modulus is divided into 20 layers, equally spaced along the radial direction.  
Figure 5: A quarter of the numerical model used for verification.  
Four combinations of two different values for the radially inhomogeneous coefficients (\(\delta\)) and the inner surface pressures of the lining in the radial direction (\(q(\tau)\)) are assumed. For these values, the radial deformation on the rock-lining interface \(u_{\rm i}(\tau)\), and the normalized supporting pressure provided by the lining denoted as \(Q(\tau)/P_{0}\), are obtained by the proposed theoretical model and the three-dimensional simulation. The time-history curves of \(u_{\rm i}(\tau)\) and \(Q(\tau)/P_{0}\) obtained by the two methods are shown in Figure 6, showing good consistency. The maximum difference for all cases is smaller than 10%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **Value** \\ \hline \(P_{0}\) & MPa & 5.89 \\ \(R_{1}\) & m & 3.96 (constant) \\ \(R_{2}\) & m & 4.57 \\ \(d\) & m & 0.61 \\ \(E^{\rm t}{}_{0}\) & GPa & 16.55 \\ \(v_{\rm L}\) & - & 0.2 \\ \(\delta\) & - & \(-0.50\) (0.50) \\ \(\alpha\) & - & 0.68 \\ \(\beta\) & - & 0.60 \\ \(t_{0}\) & a & 0.00 \\ \(\eta_{\rm m}\) & GPa-a & 1590 \\ \(G_{\rm m}\) & MPa & 3447 (constant) \\ \(t_{\rm m}\) & a & 461.27 \\ \(\eta_{\rm k}\) & GPa-a & 7.98 \\ \(G_{\rm k}\) & MPa & 345 (constant) \\ \(t_{\rm k}\) & a & 23.13 \\ \(q(\tau)\) & MPa & 0.00 (0.10) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used for verification.  
Figure 6: Comparison of results obtained by analytical model and numerical simulation.
# High-Dimensional and Multi-Intensity One-Photon-Interference Quantum Secure Direct Communication
High-dimensional quantum states not only increase the transmission rate but also enhance the probability of detecting eavesdropping [17; 20; 26]. In the meantime, by introducing additional bases into the encoding mode when preparing the quantum states to be transmitted, significant reductions in information leakage can be achieved over long distances [47]. Following these works, a high-dimensional one-photon-interference QSDC (HDOPI-QSDC) protocol is proposed in this paper. The rest of this paper is organized as follows: Section 2 presents a detailed description of the protocol. In Section 3, we analyze the security of the protocol utilizing [PERSON]'s wiretap theory, and discuss its resistance to several common quantum threats. Section 4 is dedicated to a numerical simulation of our scheme to evaluate its performance compared with two other typical QSDC protocols. A short summary and outlook is given at the end, in Section 5.
# Estimating the Observation Area of a Stripmap SAR via an ISAR Image Sequence
## 2 Geometry of Stripmap SAR Observation  
In this section, the characteristics of the spaceborne SAR operating in stripmap mode are introduced, and the observation area is modeled.
## 4 Results
### Model Evaluation Results
#### 4.3.2 Temporal Analysis Results of Large-Scale Landslide Susceptibility in Typical Area  
Figure 11 depicts the results of the one-dimensional linear regression method to fit typical regions linearly. Twelve regions with a high probability of landslide occurrence and significantly increasing trends were chosen: Anhui, Chongqing, Guangdong, Guangxi, Guizhou, Hunan, Jiangxi, Shandong, Sichuan, Hong Kong, Xizang, and Zhejiang (Figure 10b partial red pentagram region). In addition, eight regions with a high probability of landslide occurrence and significantly decreasing trends, Fujian, Gansu, Hainan, Henan, Shaanxi, Shanxi, Taiwan, and Yunnan (Figure 10b, blue pentagram region), were randomly selected, with a total of 20 regional points. The fitting results for Anhui, Chongqing, Guangdong, Guangxi, Guizhou, Hunan, Jiangxi, Shandong, Sichuan, Hong Kong, Xizang, and Zhejiang show a general upward trend at these regional points from 2001 to 2022. There were two clear turning points in 2005 and 2012, when the probability of landslides occurring in these two years was lower than that in the other years. Furthermore, special attention should be paid to Chongqing, Guizhou, and Sichuan, which are three areas prone to landslide disasters, where the probability of landslide occurrence increased by more than 10 percentage points between 2001 to 2022. Conversely, the fitting results for Fujian, Gansu, Hainan, Henan, Shaanxi, Shanxi, Taiwan, and Yunnan demonstrated a general decline in the probability of landslides in these regions between 2001 and 2022. Notably, five regions, Fujian, Gansu, Hainan, Henan, and Taiwan, exhibited minimal declines in landslide susceptibility. In comparison, two regions, Shaanxi and Shanxi, experienced a notable decrease in landslide probability of over 10 percentage points.
# Design and Performance Evaluation of eLoran Monitoring System
## 5 Integrity Monitoring Analysis  
The BPL signal belongs to low-frequency long-wave signals in the same location where the signal trend is relatively stable, and this paper uses the TOA value to analyze the intactness; based on the TOA in the history of the data for prediction, when the gap between the predicted value and the actual value is too large, it is issued as a warning to the BPL time service.
# Quantum 3D FFT in Tomography
## 3 Theory of Tomography  
### Radon Transform  
The Radon transform, which is the basis of analytical reconstruction methods, relates a two-dimensional function to the set of curvilinear integrals of this function. Emission (such as PET and SPECT) or transmission (such as CT) imaging systems take measurements that look similar to fuzzy curvilinear integrals; thus, the above transform model is an idealization of such systems [14]. Applying the Radon transform to an image cube (human body) for a given set of angles can be thought of as computing the projection of the image cube along those angles. The resulting projection, in a 2D representation (Figure 2), is the sum of the pixel luminances in each direction, i.e., a contour integral. The result is a new image \(p(r,\varphi)\). This can be written mathematically by defining  
\[r=x\times cos\varphi+y\times sin\varphi \tag{16}\]  
Thus, we can write the Radon transform as:  
\[p(r,\varphi)=\int_{-\infty}^{+\infty}\int_{-\infty}^{+\infty}f(x,y)\cdot \delta(r-x\cdot\cos\varphi-y\cdot\sin\varphi)\mathrm{d}x\mathrm{d}y \tag{17}\]  
Figure 2: Schematic representation of projection in two dimensions (2D) when performing the Radon transform.  
where \(\delta\) is the known unit impulse.  
There are other ways of symbolizing this contour integral, each of which may be needed for different purposes. In its most idealized form, the image reconstruction problem is to recover \(f(x,y)\) from the projections \(p(r,\varphi)\), i.e., to apply the inverse Radon transform. To do this, we need to somehow return from the \"projection field\" (i.e., the Radon transform) to the \"object field\" (i.e., the image).
## Materials and Methods
### Finite Element Modeling  
A Python script was used to develop a parametric finite element model through the finite element package ABAQUS 6.11-1 with the detailed information provided as follows. Figure 3a shows the model generated using cylinders with the diameter of \(D\) and alternating strand orientation of 0-90\({}^{\circ}\) to mimic the structure of fabricated scaffolds. The number of strands in each plane is denoted by \(N\) with corresponding subscripts, the amount of penetration within layers by \(\Delta_{0}\), the pore size in the X and Z directions by \(P_{x}\) and \(P_{z}\), respectively, and the length of material exceeding the main borders of the scaffold by \(E_{x}\) and \(E_{z}\) (Figure 3a). It should be mentioned that for applying the compressive load, the upper and lower sides of the modeled scaffolds were trimmed with the value of \(\Delta_{L}\). Using these parameters, the dimensions of the scaffold can be calculated using the following relationships:  
\[L_{x}=2E_{x}+N_{x}D+(N_{x}-1)P_{x} \tag{1}\]  
\[L_{z}=2E_{z}+N_{z}D+(N_{z}-1)P_{z} \tag{2}\]  
\[L_{y}=\begin{cases}2\Big{(}\frac{D}{2}-\Delta L+N_{yz}\left(D-\Delta_{0} \right)\Big{)}&N_{yz}=N_{yx}-1\\ 2\Big{(}\frac{D}{2}-\Delta L+N_{yz}(D-\Delta_{0})\Big{)}-(D-\Delta_{0})&N_{yz}= N_{yx}\end{cases} \tag{3}\]  
where \(L_{x}\), \(L_{y}\), and \(L_{z}\) are the length of the scaffold in each direction.  
As shown in Figure 3b, to simulate the compression test, all the translational degrees of freedom of the bottom side of the scaffold were fixed while the upper face was moved downward with the value  
Figure 3: **(a)** Applied parameters in finite element model including the amount of penetration within layers (\(\Delta_{0}\)), pore size in the X and Z directions (\(P_{x}\) and \(P_{z}\)), \(E_{x}\) and \(E_{z}\) as the extra material exceeding the main borders of the scaffold. \(\Delta_{L}\) is also the amount of trimmed value of the upper and lower sides of the modeled scaffolds for applying the compressive load and \(D\) is the strand diameter and (**b**) applied boundary conditions and meshed part.  
of the desired deformation. Since the model has some symmetric planes, the computational efforts might be reduced by decreasing the size of the model. Hence, the model was considered symmetric in X and Z directions. In addition, appropriate boundary conditions, e.g., fixing the degree of freedom parallel to the plane of symmetry were applied.  
To run the developed model, 20% displacement was applied and the Poisson's ratio was considered as 0.31 as per the previous studies [27,28]. Ten-node modified quadratic tetrahedron elements (four integration points, C3D10) were used to mesh the model (the configuration of the meshed model is available in Figure 3b). In addition, convergence was achieved by using the criteria or conditions that the displacement function within an element is continuous, of rigid-body one, and under the constant strain [29].
## 5 Performance Evaluation
### Real Dataset
#### 5.1.5 Effect of Adaptive Data Collection  
The principal remaining problem is that the underlying physical phenomenon is complex and not constant. The only information available is what can be learned from all reported readings. Then the adaptive data collection will be executed in response to the spatial correlation changes. Once the cluster head detects that a cluster should be relearned, it asks all sensor nodes in the corresponding cluster to collect the reading simultaneously. In the worst case, the sink can re-cluster the whole network when most clusters trend in the network is deteriorating.  
We experimentally test our adaptive data collection scheme to observe the changing fidelity. The settings of parameters are as follows: \(t=0.4\), \(T_{l}=50\), \(T_{d}=4T_{l}=200\). Based on the results in the previous section, the SNR is \(39\%\) when the fidelity threshold is set to \(96\%\). By evaluating the isotonicity between nodes to judge the cluster trend, we get the result shown in Figure 7. The fidelity during the learning phase keeps a constant value of 1. In two periods of data collection, the fidelity curves show a clear decline. However, after each round of data collection, the cluster starts a relearning process when the cluster head detected the deterioration of cluster trend. Consequently, the only suitable candidate is elected as the new cluster head by executing the Algorithm 1. Afterwards, an updated working schedule is created by the cluster head, thus avoiding the continued decline in the fidelity. As we can see in Figure 7, the result is verified that the structure fidelity with the adaptive data collection is acceptable.  
According the Equation (17), \(31.2\%\) energy for the network with 52 sensor nodes is saved. We can set the more strict value of \(t\) or shorten the period of data collection to enhance the fidelity, however, that will not achieve the significant reductions in energy. The tradeoff between the fidelity and the energy saving depend on the tolerance of distortion in the particular sensor applications. Notice that there are several outliers in the second \(T_{d}\), where much lower fidelity scores than they should supply are given. In fact, most of these significant outliers correspond to the sensor readings with large global motions at  
Figure 7: The changing fidelity during the data collection.  
some instants. An outlier detection approach is beyond the scope of this paper, which is primarily aimed at the structural distortion.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 5 Evaluation  
In this section, the proposed parent and PHY selection heuristic is evaluated. First, our experiment setup and methodology are presented. Afterwards, the 6 TiSCH simulator is used to compare the heuristic against the near-optimal, computationally complex GA approach [7]. Finally, the performance of the heuristic running on a real hardware testbed is evaluated using the proposed slot bonding implementation.
## 4 Results and Discussion
### Equivalent Substrates  
To investigate the combined effects of substrate thermal conductivity and thickness on droplet evaporation, this study selected a set of substrates with varying relative thicknesses (\(h_{\text{R}}\)) and adjusted their relative thermal conductivity (\(k_{\text{RS}}\)) to achieve the same dimensionless evaporation rate for water droplets with an initial contact angle of 30\({}^{\circ}\). As shown in Figure 4a, for droplets with the same dimensionless evaporation rate, higher substrate relative thermal conductivity required greater relative thickness. The trends for different dimensionless evaporation rates were consistent. Using a substrate with \(h_{\text{R}0.1}=0.1\) as the reference (denoted as \(k_{\text{RS}0.1}\)), normalization of \(k_{\text{RS}}\) for different evaporation rates was performed (Figure 4b). The overlapping of the normalized curves indicates the existence of a functional relationship between \(k_{\text{RS}}\) and \(h_{\text{R}}\) that maintains a constant evaporation rate. By fitting substrates in Figure 4b, the relationship between the substrate thermal conductivity and substrate thickness required to achieve identical droplet evaporation rates can be approximated by the following equation:  
\[k_{\text{RS}}/k_{\text{RS}0.1}=6.9183\;[1\,-\,\text{exp}\;(-0.14993\;h_{\text{ R}}/h_{\text{R}0.1})] \tag{23}\]  
The equivalent relationship between two distinct substrates that satisfy the equation, with thermal conductivities \(k_{\text{RS}1}\) and \(k_{\text{RS}2}\) with corresponding substrate thicknesses \(l_{\text{R}1}\) and \(h_{\text{R}2}\), can be expressed as follows:  
\[k_{\text{RS}1}/k_{\text{RS}2}=[1\,-\,\text{exp}\;(-1.4993\;h_{\text{R}1})]/[1 \,-\,\text{exp}\;(-1.4993\;h_{\text{R}2})] \tag{24}\]  
Figure 3: The effect of substrate thickness on drop evaporation. (**a**) Temperature distribution during the evaporation of water droplets on glass substrates of different thicknesses; (**b**) evaporate flux along the liquid-vapor interface of the droplet evaporating on glass substrates of different thicknesses; and (**c**) the effect of substrate thickness and the thermal conduction on droplet evaporation.  
This relationship was defined as \"equivalent substrates\". This means that the substrates of different materials will show identical evaporation kinetics if their thickness and thermal conductivity are balanced.