esa-sceva/satcom-chunk-collection huggingface dataset:
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 3 Sensitivity Analysis of Filtering Probability
### Transition Parameter Variation
#### 3.1.3 Algorithm Implementation  
This algorithm summarizes the proposed technique, where \(e\) is the sequence of observations and \(\theta=a_{rs}\), and solves the recursive Equation (7). Once the Transition and Observation matrices are represented in SMF as explained above, the sensitivity coefficients are computed by filling the forward matrices \(F^{k}\) for \(k=1\cdots t\). The details of filling contents of the forward matrices in Algorithm 1 are explained as follows. \(F^{1}\) is initialized by the matrix multiplications of \(O^{1}\), which is a diagonal matrix whose \(v^{th}\) diagonal entry is \(P(y^{1}_{e}|x^{1}_{v})\), and the initial distribution vector \(\Gamma^{\intercal}\),  
\[F^{1}=O^{1}*\Gamma^{\intercal}\]  
The remaining matrices \(F^{k}\) of size \(n\times k,2\leq k\leq t\), are initialized by filling them with zeros. Two temporary matrices \(Ftmp_{1}\) and \(Ftmp_{2}\) are used for computing the coefficients as explained previously. \(Ftmp_{1}\) is computed as the matrix multiplication of \(O^{k}\), \(\bar{A}^{\intercal}\) (which is independent of \(\theta_{a}\)) and \(F^{k-1}\), and a zero vector of size \(n\) ( = the number of states) is appended at its end. \(F^{k-1}\) represents the recursion in the filtering probability. In the same way \(Ftmp_{2}\) is computed as the matrix multiplication of \(O^{k}\), \(\widehat{A}^{\intercal}\) (which is dependent on \(\theta_{a}\)) and \(F^{k-1}\), and a zero vector of size \(n\) is appended to the front. Then \(F^{k}\) is set as the sum of \(Ftmp_{1}\) and \(Ftmp_{2}\):  
\[F^{k}=Ftmp_{1}+Ftmp_{2}\]  
```
0:\(A,O,\Gamma,\mathbf{e}\), and \(\theta_{a}\)
0:\(F^{1},\cdots,F^{t}\)
1:\(\tilde{A}\gets A\)
2:\(\tilde{A}_{r,s}\gets 0\)
3:\(\tilde{A}_{r,s}\leftarrow\tilde{A}_{r,s}\)./\((1-a_{r,s})\)
4:\(\widehat{A}\gets A-\tilde{A}\)
5:\(\widehat{A}_{r,s}\leftarrow\widehat{A}_{r,s}\)./\(a_{r,s}\)
6:\(O^{1}\gets diag(O_{\cdot,\mathbf{e}^{1}})\)
7:\(F^{1}\gets O^{1}*\Gamma^{t}\)
8:for\(k=2\) to \(t\)do
9:\(O^{k}\gets diag(O_{\cdot,\mathbf{e}^{k}})\)
10:\(Ftmp_{1}\leftarrow[O^{k}*\widehat{A}*F^{k-1},zeros(n,1)]\)
11:\(Ftmp_{2}\leftarrow[zeros(n,1),O^{k}*\widehat{A}^{\intercal}*F^{k-1}]\)
12:\(F^{k}\gets Ftmp_{1}+Ftmp_{2}\)
13:Return\(F^{1},\cdots,F^{t}\)
```  
**Algorithm 1** Computes the coefficients of the forward (filtering) probability sensitivity function \(p(\mathbf{x}_{\
u}^{t},\mathbf{y}_{\mathbf{e}}^{1:t})(\theta_{a})\) with \(\theta_{a}=a_{r,s}\) in forward matrices \(F^{k}\) for \(k=1,\cdots,t\) using SMF.
## 2 Related Work
### Multicast Security Support  
The root cause of multicast security problem is derived from open group model [24; 25], in which multicast source can send data to any multicast group, and multicast receiver can join or leave the group freely. This model is beneficial in terms of join/leave operation, group member maintenance and multicast routing. However, the open group membership and open access to send/receive multicast data may result in lots of security vulnerabilities such as DoS, eavesdropping and masquerading [28]. IETF Multicast SECurity (MSEC) work group has published an multicast security architecture [57] which contains the multicast data handling, group key management [58] and multicast security policies. Lots of research efforts are focused on multicast data origin authentication [59], multicast receiver and source access control [60], group key distribution [61], establishment [62] and predistribution [63]. Therefore, to insure multicast security, the multicast system should contain multicast source authentication, multicast receiver authentication and multicast data protection.  
In our scheme, before attaching to the access network of UIN every host has to perform the authentication via AC, which provides a solid foundation for multicast source and receiver authentication. Besides, with the help of AID-RID mapping between access network and core network, the multicast data can be encrypted easily by ASR.
## 2 Working Principle of TEC System  
The proposed thermoelectric cooling system is based upon the Peltier effect as described earlier in the Introduction. A thermoelectric unit is made up of p-type and n-type semiconductors connected by a good conductive material. Semiconductors are electrically connected in series and thermally in parallel, combined with two ceramic plates forming a single-stage cooler's hot and cold side. When it places across the finite temperature difference, a voltage difference is created, known as the Seebeck effect. When an electric current passing through one or more pairs of elements, a finite temperature difference is created between two junctions. Since one junction absorbs heat from the environment; therefore, the temperature at one junction is lower than ambient. Whereas the absorbed heat is carried along with the elements by electron transport as they move from low to high energy state, the temperature at that junction is higher than the ambient temperature. This combined effect is known as the Peltier effect. The mathematical representation of the Peltier effect can be written as Q = \(\pi\)L, whereas \(Q\) is defined as Peltier heat and as Peltier coefficient, and \(I\) as a thermal current. However, the cooling performance of the single module is minimal and cannot be used for practical application. Therefore, an array of such units is connected thermally in parallel and electrically in series to generate a thermoelectric effect [2].
## 5 Cluster-Based Energy Consumption Optimization Problem
### Cluster Formation  
For the cluster formation process, we should balance the load of cluster head energy consumption. The cluster formation depends on the following two parameters.  
(1) The total distance of data transmission  
In order to consume less energy, the total distance of data transmission should be as small as can e acheived. The distance includes two aspects i.e., the inner-cluster distance and sink distance. The inner-cluster distance is the distance between the member node \(S_{i},i=1,2,...,n-k\) and cluster head \(CH_{c},c=1,2,...,k\), and the sink distance is the distance between the cluster head and BS. The total distance is given by the following equation.  
\[total\_dis\left(S_{i}\right)=dis\left(S_{i},CH_{j}\right)+dis\left(CH_{j},BS\right) \tag{16}\]  
where \(dis\left(S_{i},CH_{j}\right)\) is the inner-cluster distance, and \(dis\left(CH_{j},BS\right)\) is the sink distance. \(BS\) denotes the base station. The total distance of transmission is the sum of inner-distance and sink distance.  
(2) The residual energy of cluster head  
In the phase of data transmission, the cluster heads receive data from member nodes and send them to BS after fusion. In general, the energy consumption of cluster head is much higher than member nodes so it is important to guarantee the sufficient residual energy of cluster head. Therefore, a member node should join a cluster head which has a higher residual energy i.e., \(C\_E\left(CH_{j}\right)\).  
To take the above two factors into account at the same time, a weight function of cluster head is given as follows.  
\[C\_W\left(S_{i},CH_{j}\right)=\gamma\times\frac{C\_E\left(CH_{j}\right)}{total \_d\left(S_{i}\right)} \tag{17}\]  
where \(C\_W\left(S_{i},CH_{j}\right)\) is a linear combination of the total distance of data transmission and the residual of energy of cluster head, and \(\gamma\) is a constant defined by user.  
Algorithm 2 shows the process of cluster formation by using cluster weight. The member nodes in the monitoring area select the cluster head of lowest weight to join in.  
```
1:Sensor nodes group: \(s=\left\{s_{1},s_{2},...,s_{n}\right\}\)
2:Cluster head group: \(CH=\left\{CH_{1},CH_{2},CH_{3},...,CH_{k}\right\}\)
3:The residual energy of all sensor nodes: S_E
4:The residual energy of cluster heads: C_E
5:The ids of cluster heads the member nodes join: S_clusterhead
6:for\(i=1\) to \(n\)do
7:if\(s\_E>0\)then
8: Calculate C_W \(\left(S_{i},CH_{1}\right)\)
9:\(temp=C\_W\left(S_{i},CH_{1}\right)\)
10:\(S\_clusterhead\left(i\right)=1\)
11:endif
12:endfor
13:endif
14:endfor
```  
**Algorithm 2**Cluster formation algorithm based on cluster head weight
# On Improving 5G Internet of Radio Light Security Based on LED Fingerprint Identification Method
## 4 Conclusions and Future Work  
In this paper, a novel device identification method was proposed to improve the security of VLC in the 5G network. This method extracts the unique fingerprints of LEDs from their emitted signals. By employing a machine-learning-based classifier to distinguish these LED fingerprints, the devices accessing the 5G network can be reliably identified. The advantage of the proposed method is that it can work in real time and uses OTP (One Time Pad) encryption, which fundamentally avoids the threat of pseudo base-station and KI (Key identifier) disclosure. The feasibility and high accuracy of device identification were verified by using proof-of-concept implementation in a practical indoor VLC-based 5G network. In order to establish the optimal tuning of this approach, the comparison of four types of machine-learning-based classifiers was conducted. The obtained results indicate  
Figure 7: (**a**) The confusion matrix of the SVM classifier; (**b**) The parallel coordinate plots of the SVM classifier.  
that the SVM classifier yields the best performance as it reaches the maximum accuracy of 97.1%. Additionally, the analysis of the confusion matrix and the parallel coordinate plots proved that the identification accuracy relies on the quality of the communication environment.  
Our approach extracted the fingerprints of the transmitter in a VLC system to identify accessing devices. In future research, a bilateral identification mechanism which consists of Tx fingerprints and Rx fingerprints will be investigated to enrich the application scenario and further improve the security of the VLC system in the 5G network.  
Conceptualization, D.S., X.Z. and W.M.; methodology, D.S.; software, D.S.; validation, L.S., Y.Z.; formal analysis, D.S., A.V.; investigation, D.S, K.C.; data curation, B.M., K.A., J.C.; writing--original draft preparation, D.S.; writing--review and editing, W.M., K.C.; supervision, A.V.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the EU Horizon 2020 program towards the Internet of Radio-Light project H2020-ICT 761992.  
Data Availability Statement: Data sharing not applicable.  
The authors gratefully acknowledge the financial support of the EU Horizon 2020 program towards the Internet of Radio-Light project H2020-ICT 761992.  
The authors declare no conflict of interest.
## 1 Introduction  
### Motivation  
Cable-driven parallel manipulators (CDPMs) are very similar to the traditional parallel robot since they also have a static frame, an end-effector, and many elements that connect the static frame and the end-effector. However, CDPM can easily variate its workspace, which is significantly greater than the other, because the flexible cables are used instead of rigid links. Except for those, they also have other great advantages such as economic structure, transportability, and easy assembling or disassembling [1]. With these outstanding advantages, applications of CDPM can be found in many fields: warehousing in logistics [2], contour crafting system in the construction industry [3], camera system in the stadium, etc. Many CDPMs are enhanced its load capacity also the acceleration by increasing the number of cables. Thus, lots of them have redundant actuators and this kind of structure produces an overconstrainedsystem [4; 5; 6]. Furthermore, the cables that are used to link the end-effector and the robot fixed frame have considerable mass and elasticity, which is mentioned. These kinds of CDPM do not have a unique solution for the statistic or dynamic problem, and the kinematic problem cannot be solved alone. The major problem comes from the deformation of the cables: it increases the length of cables. In practice, these elongations cannot be measured and adjusted, resulting in deviations in the position and orientation of the end-effector. To the best of our knowledge in this field, it is merely impossible to directly extract exact solutions in terms of analysis. Therefore, in the kinematic or dynamic problem, with the given pose of the moving platform, the feasible length of cables and the corresponding tensions are obtained via numerical methods. However, by the complexity of the objective functions, these methods are almost impossible to use in real-time. In order to reduce the complexity of the dynamic problem while preserving the usability of the study, quasi-static problem will be considered in this paper. Similar to the dynamic problem, the quasi-static problem is time-dependent. However, the movement of the moving platform is very slow, or the load is very light, therefore the inertia effect is ignorable. That is very useful in many applications such as contour crafting systems and camera systems, since it simplifies the dynamic problem. In this study, we propose a method that produces the approximated model to solve the quasi-static problem. From that, the length of each cable in which the elongations and the adjusting length are also taken account can be determined in an instant.
## 4 Simulation Verification
### Point Stabilization  
In this test, the goal and initial states are predefined. Here, the goal and initial position are set to (\(3\) m, \(2\) m, \(\pi/3\) rad) and (\(0\) m, \(0\) m, \(0\) rad), respectively. In order to simulate the real movement of the OMR, the noise is added to the output state.  
In each prediction period, a control matrix \(\bar{u}\) is obtained through solving the QP problem (26). The \(\bar{u}\) is the velocity input sequence to control the movement of the OMR. Actually, only the first control vector in \(u\) (\(j\)) is used as the effective velocity input. The velocity input throughout the whole motion process is shown in Figure 7. In this figure, there are two red dotted horizontal lines, which present the velocity constraints. It can be seen from this figure, with the application of MPC algorithm, that the control velocities of three wheels converge to zero gradually and smoothly. Meanwhile, velocity \(u_{3}\) is affected by the maximum control input constraint at the beginning.  
The state trajectories of the OMR in Cartesian coordinates are shown in Figure 8. Two position parameters, \(x_{c}\) and \(y_{c}\), are given in the upper subfigure of Figure 8. The initial position of \((x_{c},y_{c})\) is set to (0 m, 0 m) and the target position is set to (3 m, 2 m). The orientation angle \(\theta_{c}\) of the OMR in the moving process is presented in the lower subfigure of Figure 8. The initial orientation angle is 0 rad and the target orientation angle is set to \(\pi/3\) rad. As shown in Figure 8, the MPC controller can steer the OMR to the target quickly and steadily.  
The coordinates, \((x_{c},y_{c})\), of the OMR in moving process are shown in Figure 9. It can be seen from this figure that the movement trajectory is very close to the target trajectory. The tracking error between the movement trajectory and the target trajectory is shown in Figure 10. The maximal tracking error is about 4 cm. From Figures 8 and 10, it can be seen that the OMR moves along the predefined target trajectory to the target point, which demonstrates the effectiveness of the developed MPC algorithm.  
Figure 8: The state trajectories of the OMR in Cartesian coordinates.  
Figure 7: The velocity inputs of three mecanum wheels.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 2 Review of Early Fault Diagnosis Approaches
### Variational Mode Decomposition
#### 2.1.3 Sparse Decomposition  
As a new branch of the signal processing methods, sparse representation has received considerable attention, and has been widely used in the field of EFD of rotating machinery [46; 47; 48; 49; 50; 51; 52].  
The main goal of sparse representation is to seek a signal representation that is as concise as possible. In the mathematical sense, a sparse signal representation is desired, i.e. to represent a signal accurately via a few atoms, so the number of nonzero representation coefficients is as small as possible. It is essentially an optimization problem, in which an objective function is denoted by the norm of vectors. Here, the definition of the sparsest or nearly sparsest representation over a dictionary is called sparse representation. Its mathematical model can be formulated as:  
\[\min_{B,\alpha_{i}}\sum_{i=1}^{m}|x_{i}-B\alpha_{i}|^{2}+\lambda\sum_{i=1}^{m} |\alpha_{i}| \tag{10}\]  
where \(x_{i}\) denotes the \(i\)-th sample, \(B\) denotes the dictionary matrix, and \(\alpha_{i}\) represents the sparse representation of \(x_{i}\) and \(\lambda\) represents a positive parameter.
## 5 Experimental Analysis of the Interaction of Anchor Bolts and Concrete Foundation
### Measurement under Static Loading  
This testing methodology consists of gradually increasing the pressure in the torque wrench from 0 to 60 bar, then 110 bar, then 160 bar, and then finally 200 bar. At each force corresponding to the pressure in the tightening device, a time delay of 5 min was allowed, during which the reading on the strain gauge apparatus was not allowed to increase, confirming the perfect interaction between the anchor bolt and the concrete foundation. The axial forces corresponding to the respective pressures are given in Table 1. At a maximum pressure of 200 bar, with cleaned and lubricated threads, an axial force of 350 kN was achieved according to the calibration diagram for the given state of the threads and their lubrication, and the friction of the nut against the washer. In the testing of the interaction of the anchor bolts with the concrete foundation, newly designed nuts were used by the authors so as to prevent failure of the anchor bolts or plastic deformations at the location of the first load-bearing thread.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Tightening Device Pressure [bar]** & **Axial Force in Bolt [kN]** \\ \hline
60 & 93 \\
110 & 178 \\
160 & 266 \\
200 & 345 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Axial forces at defined pressures.  
Figure 7: Fields of equivalent stress around the root of the first thread on the anchor bolt when using: (a) normalised nut; (b) designed nut.  
The testing of the interactions of the anchor bolts and foundations was carried out by means of dynamometers and newly designed nuts by adjusting the pressure parameter on the hydraulic tightening device, which corresponds to the respective preload. It should be noted that, although there are some variations in the set preloads determined from the calibration diagrams, these differences did not reach 5%.
# A Polarization-Based Method for Maritime Image Dehazing
## 5 Conclusions  
This research study presents an improved polarimetric method that addresses the constraints of traditional polarimetric dehazing techniques in marine settings. By employing a quadrant technique alongside polarization difference images, the automated and efficient extraction of \(A_{\infty}\) is achieved. Additionally, segmenting the sea area based on the intensity of the polarization image aids in subsequent dehazing procedures, effectively overcoming the defect encountered by conventional polarimetric dehazing methods in marine environments. Comparative analysis with the DCP method reveals an increase in image entropy ranging from 6% to 12%, a rise in standard deviation by 26% to 49%, and an average contrast enhancement of 29%. In contrast to the BCCR method, the proposed approach demonstrates an increase in image entropy between 7% and 13%, an increase in standard deviation from 9% to 64%, and a contrast elevation of 51% to 199%. Notably, it displays significant performance improvements compared to the Retinex method. The local contrast of the images is improved by over 30% compared to that of the original foggy images. Experimental findings demonstrate the suitability of the proposed method for marine scenes, effectively leveraging polarization data for dehazing and successfully restoring image detail information. However, as described in Section 3.2, we used multiple experiments to establish the predetermined threshold area, and in the future, we plan to employ more systematic solutions, such as the Nyquist-Shannon Criterion. In addition, as discussed in Section 4.3, our method may encounter mis-segmentation in certain specific environments. We will seek to further improve this in future work.  
Conceptualization, Z.Z. and R.M.; methodology, R.M.; software, R.M.; validation, Z.Z. and S.L.; formal analysis, R.M.; investigation, S.Z.; resources, S.Z.; data curation, R.M. and Z.W.; writing--original draft preparation, R.M.; writing--review and editing, Z.Z., S.L. and R.M.; visualization, Z.W. and S.Z.; supervision, Z.Z.; project administration, Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
We appreciate the assistance provided by [PERSON] during the early stages of the experiment.  
The authors declare no conflicts of interest.  
Figure 12: Failed segmentation case. (**a**) the \(S_{0}\) image; (**b**) the degree of polarization image; (**c**) the segmentation binary image.