esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Robot Trajectory Planning and Position/Force Coordination Control in Complex Welding Tasks
## 5 Simulations and Experiments  
### Simulation  
This section mainly verifies the feasibility of position/force coordination for multi-robot based on the symmetrical adaptive variable impedance control. Matlab SimMechanics was used to simulate the multi-robot cooperative welding. In order to verify the effectiveness of the algorithm, the simulation is close to the actual physical experiment. During the experiment, it is assumed that there is a certain expected pressure and external disturbances, the purpose is to test the tracking effect of external and internal forces.  
The object is composed into two rigid pipes, \(R_{a}\) and \(R_{b}\), respectively. The radius \(r_{a}=0.051\) m, \(r_{b}=0.0445\) m, the length \(l_{a}=0.12\) m, \(l_{b}=0.08\) m, respectively. The thickness of the pipe is \(h=0.003\) m, and the density is \(1000\) kg/m\({}^{3}\). The center of the 1 st robot's end-effector and the center of pipe \(R_{a}\) coincide the center of the 2 nd robot's end-effector and the centerline of pipe \(R_{b}\) is coincide.  
The optimal initial welding position [0.5 m \(-0.75\) m 0.404 m] can be obtained by solving Equation (2) through genetic algorithm, the optimization process of genetic algorithm is shown in Figure 8. According to the requirements of the welding task, the variable pose trajectory of the operated object is shown as Equations (5) and (6). Furthermore, the motion of each robot with respect to its respective base coordinate system can be solved according to Equations (9) and (12). Assumingthat the desired internal wrench is [0 N 0 N 15 N 0 N\(\cdot\)m 0 N\(\cdot\)m 0 N\(\cdot\)m 0 N\(\cdot\)m], the desired internal force is among \(z\)-axis. The external disturbance is operated among \(x\) and \(y\) axis are shown in Figure 9.  
The schematic diagram of the system simulation process for multi-robot completing pipe welding is shown in Figure 10.  
The desired motion trajectory of the operated object and the actual motion trajectory after the external disturbance in \(x\)-axis and \(y\)-axis are shown in Figure 11, and the internal force tracking effect of the transfer robots is shown in Figure 12.  
Combining Figures 11 and 12, it shows that after the external disturbance exerting on the \(x\) and \(y\) axis, the closed-chain system is flexible. From Figure 12, it shows that during the coordinated welding process, the tracking of the desired internal force can be achieved, and the internal force is maintained within the allowable tolerance range throughout the entire process.  
Figure 8: The optimization process of genetic algorithm.  
Figure 11: The simulation result of desired trajectory and actual trajectory.  
Figure 9: The external disturbance exerting on the operated object.
## 1 Introduction
The Split Bregman algorithm has been modified to allow for arbitrary coefficient grouping patterns of overlapping or non-overlapping groups with no a priori assumptions on the grouping patterns used. The Split Bregman algorithm has been previously used for TV-based reconstruction of NUS MRSI data [16] and for multi-channel reconstruction of NUS MRI data where the multiple measurement vector (MMV) problem was solved by extending the algorithm to accommodate row-wise grouping of jointly sparse samples [20]. GS reconstruction has previously been applied to the under-sampled \(k_{y}-t\) planes of 2D cine and perfusion cardiac MRI and provided superior results to CS [21,22]. In this article, overviews of the Split Bregman algorithm and GS reconstruction problem are provided in Sections 2.2 and 2.3, respectively. The derivation of the solution to the GS reconstruction problem within the Split Bregman framework is provided in Section 2.4. A brief overview of the CS, TV, and MaxEnt reconstruction problems is provided in Section 2.1, and a description of the non-linear algorithms and parameter settings used to solve them is given in Section 3.2. The experimental setup for the 4D EP-COSI gray matter brain phantom and _in vivo_ brain scans is provided in Section 3.1. Results from retrospectively under-sampled phantom and _in vivo_ datasets then reconstructing the NUS \(k_{y}-t_{1}\) plane with CS, TV, MaxEnt, and GS with overlapping and non-overlapping groups, are shown in Sections 4.1 and 4.2.These experiments show that GS reconstruction with overlapping coefficient groups provides reconstructions with lower metabolite Root Mean Square Errors (RMSE) than the other methods used, as well as qualitatively superior spectra down to 8\(\times\) NUS in both phantom and _in vivo_ datasets. This acceleration translates to a clinically acceptable 5 min 4D EP-COSI brain scan that previously took 40 min to complete.
# Design and Implementation of Virtual Security Function Based on Multiple Enclaves
## 6 Result
### Performance of System  
We evaluate the performance of our system from various aspects including system throughput, packet processing time, and CPU occupancy. We consider several cases of our architecture:  
* Baseline represents the unprotected Click-based IDS;
* S-VNFs represents our system with the protection of multiple enclaves.
* S-VNFs (w/opt.) represents an optimized system using DPDK, batch processing, etc.
## 3 Sensitivity Analysis
### Definition of the Sensitivity Analysis
\[S_{i}=\frac{V_{i}}{V} \tag{32}\]  
\[S_{i}^{\mathrm{tot}}=\frac{V_{i}^{\mathrm{tot}}}{V} \tag{33}\]  
As an example, assuming a system with three input parameters, the first-order and the total-effect indices of the first input parameter would be expressed as:  
\[S_{(1)}=S_{1} \tag{34}\]  
\[S_{(1)}^{\mathrm{tot}}=S_{1}+S_{12}+S_{13}+S_{123} \tag{35}\]  
Therefore, some conclusions can be established:  
* If the interactions between the input parameters have no effect at all, \(S_{i}=S_{i}^{\mathrm{tot}}\), and \(\sum S_{i}=1\). * If the interactions between the input parameters affect the output of the system, \(S_{i}<S_{i}^{\mathrm{tot}}\), \(\sum S_{i}<1\), and \(\sum S_{i}^{\mathrm{tot}}>1\). In most cases, the analytical calculation of the previous indices is not possible, so different approximation techniques are usually employed. In this work, the Monte Carlo method is used, which is based on generating a sequence of uniformly and independently distributed points inside the unit hypercube. Being \(d\) the number of input parameters and \(N\) the number of samples employed for each input, a \(N\times 2d\) sample matrix is created. The first \(d\) columns are used as the matrix \(\mathbf{A}\), and the rest of the columns as the matrix \(\mathbf{B}\), obtaining two independent samples of \(N\) points for every input parameter. Based on matrices \(\mathbf{A}\) and \(\mathbf{B}\), \(d\), additional matrices are created. The \(\mathbf{A}_{\mathbf{B}}^{i}\) matrices are equal to matrix \(\mathbf{A}\), except for their \(i\)-th column, which is from matrix \(\mathbf{B}\). The calculations are performed for all the \(\mathbf{A}\), \(\mathbf{B}\) and \(\mathbf{A}_{\mathbf{B}}^{i}\) matrices, and the \(f(\mathbf{A})\), \(f(\mathbf{B})\) and \(f(\mathbf{A}_{\mathbf{B}}^{i})\) values are used to calculate the sensitivity indices. Different estimators are used in the literature, but in this work the improved estimators proposed by [PERSON] et al. [33] were employed, which are represented by Equations (36) and (37). \[S_{i}=\frac{1}{N}\frac{\sum_{j=1}^{N}f(\mathbf{B})_{j}[f(\mathbf{A}_{\mathbf{B }}^{i})_{j}-f(\mathbf{A})_{j}]}{V} \tag{36}\]\[S_{i}^{\rm tot}=\frac{1}{N}\frac{\sum_{j=1}^{N}f(\mathbf{A})_{j}[f(\mathbf{A})_{j}- f(\mathbf{A}_{\mathbf{B}}^{i})_{j}]}{V} \tag{37}\]  
Once the samples for all the input parameters are defined, the output values are calculated for every set of input parameters, and the variances of all the sets are calculated to obtain the influence of each parameter.
# Novel Calibration of MIESM and Reduction of CQI Feedback for Improved Fast Link Adaptation
## 1 Introduction
Firstly, a wide range of additive white Gaussian noise (AWGN) SNR values are considered in the calibration procedure to reflect various channel conditions of users in reality. Secondly, the proposed method sets an effective interval of the effective SNR to screen unnecessary channels which do not help the calibration eventually. To verify the proposed method, we propose an efficient algorithm to check the accuracy of CQIs calculated from the derived calibration factors. As a result, it was confirmed via the system throughput simulation that the calibration factors derived from the proposed method provide more accurate CQI values than the conventional ones. In addition, under the assumption that there is some correlation in time channels, we also propose a CQI feedback bit reduction scheme which is applicable no matter which scheme is used, i.e., wideband CQI or subband CQI. Based on the time correlation between adjacent channels, the CQI difference of two consecutive channels is mostly bounded, which enables reducing the number of bits used for CQI feedback. Although a reduced number of feedback bits are used, it is shown via simulations that the decrease of the system throughput is negligible, which proves that the proposed CQI feedback bit reduction scheme is efficient and also robust in various channel conditions. The remainder of the paper is organized as follows: Section 2 explains MIESM-based FLA and the average calibration method. Section 3 presents the improved calibration method to obtain optimal calibration factors. Section 4 introduces the CQI feedback bit reduction schemes. Results of the simulations and the analysis are shown in Section 5. Finally, this paper is concluded in Section 6.
## 3 Blockchain-Enhanced Security and Operational Efficiency in VANETs
### Reputation Models and Calculation
#### 3.2.1 Reputation Evaluation through a Bayesian Approach  
In our solution, the reputation evaluation process is underpinned by a multifactorial Bayesian inference approach, which integrates various factors to determine the trustworthiness of each node:  
1. Historical Data Analysis: The history of a node's actions and interactions within the VANET plays a pivotal role. This includes data on previous communications, transactions, and behavioral patterns.
2. Node Interaction Frequency and Nature: The frequency and nature of a node's interactions with other nodes are scrutinized. Regular, positive interactions contribute to a higher reputation score.  
Responses from Other Nodes: The feedback or responses that a node receives from others in the network are crucial. Positive endorsements from other reputable nodes can enhance a node's reputation.
4. Recent Behavior Analysis: More recent actions of a node are given greater weight, as they more accurately reflect the node's current status and intentions.  
Bayesian inference is a statistical method that updates the probability for a hypothesis as more evidence or information becomes available. In the context of VANETs, it allows for the dynamic updating of reputation scores based on new data. The process is as follows:  
1. Initial Probability Estimation: Each node starts with an initial reputation score based on a predefined trust level.
2. Evidence Accumulation: As nodes interact within the VANET, evidence regarding their behavior accumulates. This includes data from the factors mentioned above.
3. Probabilistic Updating: The reputation score of a node is updated probabilistically, thereby considering the new evidence. Bayesian inference calculates the posterior probability of a node being trustworthy given the accumulated evidence.
4. Dynamic Adaptation: The system continuously adapts the reputation scores based on the latest interactions and feedback, thereby ensuring that the scores are reflective of the current behavior and reliability of the nodes (1). \(P(\text{Trustworthy}|\text{Evidence})\) represents the posterior probability of a node being trustworthy given the new evidence. This approach allows for a nuanced and evidence-based reputation management system in VANETs, thus enhancing the overall security and reliability of the network.  
\[P(\text{Trustworthy}|\text{Evidence})=\frac{P(\text{Evidence}|\text{Trustworthy })\times P(\text{Trustworthy})}{P(\text{Evidence})} \tag{1}\]
## 2 Related Works
### Clustering-Based Techniques
[20] proposed a fault-tolerant dynamic static clustering (FT-DSC) to improve the performance of the existing dynamic static clustering (DSC) [21] protocol in terms of fault-tolerance and energy efficiency. In the proposed FT-DSC protocol, each non-CH node A will send either the sensed data (i.e., subscribed events) or a special packet to the CH in its allocation time slot. Special packets inform the CH that A is still alive in the case of no subscribed event occurring in A to notify the CH in that time slot. If the CH does not receive any data or a special packet from A, the CH will assume that A has failed. Then, the CH will exclude the failure node from the allocated time slot (fault detection). The size of the special packet is much smaller than that of the sensed event or data. Hence, sending a special packet consumes less energy compared to that of a data packet. At the end of a round, if the base station (BS) does not receive any data from a CH, the BS will set a timer and send a \"Hello\" message to that CH. If the BS does not hear any acknowledgment (ACK) message from the CH before the timer expires, the BS will assume that the CH has failed; then the BS will assign the highest residual energy node of the cluster as a new CH. Usually, at the end of each round, the CH assigns the highest remaining energy node as a new CH. If the BS is not aware of the failure of a CH, the new CH assignment at the end of a round will not be performed. FT-DSC can detect the failure of cluster heads (CH) and non-CH nodes and hence provides more reliability than that of the DSC protocol.
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 2 Data
### Validation Data
Generally, CEMS-derived products do not include flooded vegetation if they are produced using SAR data. To verify the AUTOWADE capability to detect flooded vegetation adjacent to open water, two events recently that occurred in Italy have been considered as test cases (Table 4). For the first event that occurred in October 2020 in the Vercelli district, an S2 image acquired on the same day as the S1 flood image has been used as reference data. For the flood that occurred in January 2021 (also mapped by CEMS: EMSR496 in Table 2), aerial photos were available as reference. From Table 4, it can be noted that pre-flood S2 data have been utilized to estimate \(NDVI\) under non-flooded conditions (Section 2.2).
# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 3 Methods
### Algorithm
#### 3.2.1 Structure of HGA  
The HGA begins by generating an initial solution and subsequently creates multiple populations, as populations \(M\), derived from this initial solution. It then iteratively applies device-based and sequence-based optimizations to refine and update the population, aiming to identify the optimal solution. The algorithm framework is illustrated in Figure 5.
## 3 Results and Discussions
### Yeast Films Characterization
#### 3.3.2 Imaging of Yeast Cells in Films  
Top-view SEM images of YFs indicated a very smooth, uniform film surface, without air bubbles, any pores, or cracks (YF1, YF2, and YF3 shown in Figure 3a); the cross-sections of YFs magnified 1000\(\times\) (YF1, YF2, and YF3 shown in Figure 3b) indicated the absence of pores, punctures, or agglomerates, with recognizable yeast cell wall structures; the 5000\(\times\) SEM microstructures revealed yeast cells with a modified morphology due to intense cellular sonication, leading to yeast cell wall disruption, liberating cytoplasm and exhibiting the internal yeast organelles, together with the change in cell volume (Figure 3a-c). Part of yeast cells became more turgid, presenting shriveled walls. Some were swelled just before cell wall disruption, and others were elongated with a wilted appearance (YF1, YF2, and YF3 shown in Figure 3c). The shriveled walls may have allowed the CLEO spheres/droplets to be accommodated inside. Similar observations were presented by other researchers for yeast cells disrupted by sonication [31; 38].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Yeast Film Samples** & **Thickness, mm** & **Dry Matter, \%** & **a\({}_{\mathbf{w}}\)** \\ \hline YF1 & 0.21 \(\pm\) 0.02 \({}^{\mathrm{a}}\) & 72.74 \(\pm\) 0.07 \({}^{\mathrm{a}}\) & 0.561 \(\pm\) 0.090 \({}^{\mathrm{c}}\) \\ YF2 & 0.24 \(\pm\) 0.02 \({}^{\mathrm{a}}\) & 72.10 \(\pm\) 0.14 \({}^{\mathrm{a,b}}\) & 0.562 \(\pm\) 0.080 \({}^{\mathrm{c}}\) \\ YF3 & 0.22 \(\pm\) 0.02 \({}^{\mathrm{a}}\) & 72.37 \(\pm\) 0.36 \({}^{\mathrm{a}}\) & 0.589 \(\pm\) 0.070 \({}^{\mathrm{b,c}}\) \\ YF1c & 0.22 \(\pm\) 0.02 \({}^{\mathrm{a}}\) & 71.35 \(\pm\) 0.25 \({}^{\mathrm{c}}\) & 0.751 \(\pm\) 0.000 \({}^{\mathrm{a,b}}\) \\ YF2c & 0.21 \(\pm\) 0.01 \({}^{\mathrm{a}}\) & 71.24 \(\pm\) 0.20 \({}^{\mathrm{c}}\) & 0.746 \(\pm\) 0.040 \({}^{\mathrm{a,b}}\) \\ YF3c & 0.23 \(\pm\) 0.03 \({}^{\mathrm{a}}\) & 71.45 \(\pm\) 0.41 \({}^{\mathrm{b,c}}\) & 0.808 \(\pm\) 0.000 \({}^{\mathrm{a}}\) \\ \(p\)-value & 0.486 & 0.000 & 0.000 \\ \hline \hline \end{tabular} The values are the mean of duplicate analyses. On the columns, different letters indicate significant differences (\(p<0.05\)) between films determined by ANOVA and [PERSON]’s post hoc test.  
\end{table}
Table 2: Thickness, dry matter, and water activity of YFs.  
Figure 2: Viscosity of the film-forming emulsions based on yeast and CLEO during the peak hold test at a shear rate of 100 s\({}^{-1}\). Lowercase letters, having the same letter, indicate non-significant differences among samples by established by ANOVA (\(p\)-value = 1.00) and [PERSON]’s post hoc test.
## 4 The Proposed Key Distribution Framework
### A Multipath-Multi-Key Distribution Algorithm
#### 4.2.2 Routing Procedure  
The search for optimal routing combination paths (RCS) is a critical step in the proposed QKD network. In traditional networks, routing algorithms such as Dijkstra prioritize the shortest path based on additive metrics such as distance or time. However, QKD networks require a focus on security due to the quantum nature of the key distribution [19]. The proposed multiplicative security probability metric is defined as follows:  
\[\epsilon=\prod_{j=1}^{m_{j}}p_{j} \tag{3}\]where \(p_{j}\) represents the security probability of node j and \(m_{i}\) is the total number of nodes on the path \(R_{i}\), effectively capturing the compounded security risks associated with each relay. This approach ensures that paths with higher overall security are preferred, aligning with the need for secure key distribution in QKD networks [39]. This multiplicative metric contrasts with the additive metrics used in classical algorithms such as Dijkstra, necessitating a modified approach for path-finding. Therefore, the proposed model adapts the classical Dijkstra algorithm into what might be termed 'ExDijkstra', which optimizes paths based on a non-additive, monotonically decreasing function of path security, reflecting a more holistic approach to secure communications in quantum networks. This modification allows the algorithm to prioritize paths that offer a balance between length and security, which is crucial for the efficient operation of QKD networks [41].  
Addressing time constraints through proactive key generation at the source node is a practical solution. By generating QKD key pairs in advance, the network can select faster routes without altering the physical path, thereby enhancing both efficiency and security [42]. This strategy aligns with the need for timely key distribution in QKD systems.  
Finally, in the proposed model, the implementation of a test mode to detect and analyze the node's suspicious behavior adds an essential layer of security. This feature enables the network to identify and respond to potential threats promptly while maintaining the integrity of the key distribution process. Although node-disjoint paths prevent untrusted nodes from accessing multiple keys, they can lead to longer paths and increased resource consumption [19; 28]. The reliance of the proposed model on behavioral evaluations over time rather than predefined trust values offers a more adaptable solution suitable for zero-trust environments, as illustrated in Figure 5.
# FeRHA: Fuzzy-Extractor-Based RF and Hardware Fingerprinting Two-Factor Authentication
## 5 Combined PUF- and RFF-Based Authentication
### Replay Attack Mitigation  
Replay attacks pose a significant threat to communication protocols, particularly in situations where message integrity and authenticity are paramount. Such an attack occurs when an adversary captures a legitimate packet and re-transmits it, potentially resulting in misleading a receiver into accepting a previously valid but now outdated or redundant message. To effectively mitigate replay attacks, FeRHA employs a novel counter-based mechanism that relies upon the unique collaboration between the prover and the verifier. To elaborate, we initialize a counter value \(count\) to be intimately linked to the verifier's state. The mechanism is represented in the following steps:  
* Initializing the counter: the initialization value of \(count\) is established through the application of \(PUF_{x}\) to the helper string \(\mathcal{H}_{RFF_{y}}\). The mathematical representation of this initialization process is \(count=PUF_{x}(\mathcal{H}_{RFF_{y}})\). This method assures that the initial state of the counter is unpredictable and associated with the hardware characteristics of the involved devices, providing a robust foundation for the counter's integrity.
* Counter obfuscation: to mitigate the risk of interception or exploitation of \(count\), we factor in the token that is shared between the prover and verifier. To obscure the counter value, an \(XOR\) operation is employed to mask \(count\) with the \(Key\), resulting in \((counter\oplus Key)\), as shown in Figure 5. The \(Key\) value is chosen from the token list \(\gamma_{x\to y}\) through a pre-agreement between the prover, \(\delta_{x}\), and the verifier, \(\delta_{y}\); such a pre-agreement could be simply determined by the server by setting a default entry in the pairwise challenge-token lists for the entire IoT network, as noted in Section 5.1. It should be noted that \(Key\) will not be used as an authentication token, but rather assigned and selected to be the \(Key\). This mechanism is dependent on the verifier side, thereby rendering an adversary who attempts to eavesdrop over multiple links to target \(\delta_{x}\) unsuccessful, since \(\gamma_{x\to y}\
eq\gamma_{x\to z}\), as illustrated in Figure 4. This operation is instrumental in ensuring that the actual counter value transmitted over the network remains indiscernible and cannot be directly leveraged by an adversary.
* Verification operation: upon receiving a packet, the \(Key\) is used to perform an \(XOR\) operation, enabling the recovery of the original counter value, \(count\). Thereafter, the verifier performs a counter value check, ensuring that it is new and has not been previously utilized during the session.