esa-sceva/satcom-chunk-collection huggingface dataset:
# A Parallel Algorithm for Scheduling a Two-Machine Robotic Cell in Bicycle Frame Welding Process
## 3 Problem Definition  
Cycling Assignment Problem (CAP) considers a robotic cell consisting of \(m\) machines (here, \(m=2\) is usually assumed). The cell is processing \(n\) jobs from a set \(\mathcal{J}=\{1,2,\ldots,n\}\) in a repetitive manner, i.e., each job \(i\in\mathcal{J}\) must be processed precisely every \(T\) time (called cycle time). The processing cycles cannot overlap and the order of jobs is fixed, however each job can be processed on any chosen machine (the same in every cycle). The assignment of jobs to machines is given by \(P\in\mathcal{P}\), where \(P(i),\,i\in\mathcal{J}\) denotes a machine a job \(i\) is assigned to and \(\mathcal{P}\) is a set of all possible assignments. Jobs processing times depend on the assigned machines, for a job \(i\in\mathcal{J}\) the processing time on machine \(a\in\mathcal{M}\) is given by \(p_{i}^{a}\). Between any two jobs \(i,j\in\mathcal{J}\), processed one after another on a machine \(a\in\mathcal{M}\), there is a setup with a duration \(s_{i\to j}^{a}\). Lastly, at most a single job or a single setup can be performed in the cell simultaneously and both job processing and setups are uninterruptible. The problem is to find \(P\in\mathcal{P}\) allowing to minimize cycle time \(T\).  
Let \(S_{i}^{k}\) and \(C_{i}^{k}\) denote start and completion times of job \(i\in\mathcal{J}\) in \(k\)-th production cycle of some schedule. The schedule is feasible iff the aforementioned problem constraints are satisfied, which can be formalized as follows:  
\[\forall i\in\mathcal{J}\setminus\{n\} S_{i+1}^{k}\geq S_{i}^{k}+p_{i}^{P(i)}+setup(P,i+1), \tag{1}\] \[\forall i\in\mathcal{J} C_{i}^{k}=S_{i}^{k}+p_{i}^{P(i)},\] (2) \[\forall i\in\mathcal{J} S_{i}^{k+1}=S_{i}^{k}+T,\] (3) \[S_{1}^{k+1}\geq C_{n}^{k}+setup(P,1), \tag{4}\]  
where \(setup(P,i)\) is a setup time before operation \(i\) according to assignment \(P\),  
\[setup(P,i) =\begin{cases}s_{\max\mathcal{X}(P,i)}^{P(i)}&\text{for: }| \mathcal{X}(P,i)|\geq 1,\\ s_{\max\{j\in\mathcal{J}:\,\,P(j)=P(j)\}}^{P(i)}&\text{otherwise},\end{cases} \tag{5}\] \[\mathcal{X}(P,i) =\Big{\{}j\in\mathcal{J}:\,\,\big{(}j<i\,\,\wedge\,\,P(i)=P(j) \big{)}\Big{\}}. \tag{6}\]  
For a given assignment \(P\in\mathcal{P}\), it was shown in [12], that the minimal cycle time \(T(P)\) for which a feasible schedule exists equals  
\[T(P)=\sum_{i\in\mathcal{J}}\big{(}p_{i}^{P(i)}+setup(P,i)\big{)}. \tag{7}\]  
Therefore, the problem can be formulated as  
\[\min_{P\in\mathcal{P}}T(P)=\min_{P\in\mathcal{P}}\sum_{i\in\mathcal{J}}\big{(} p_{i}^{P(i)}+setup(P,i)\big{)}. \tag{8}\]
## 3 Dynamic Multi-Objective (DYMO) Auction-Based Task Allocation
### Quality Settings  
Assume the task's quality requirements levels are defined by \(Q_{T}=\{a_{1},a_{2},a_{3},\ldots,a_{k}\}\), from the very low quality level \(a_{1}\) to the very high \(a_{k}\), based on the application. To utilize robots and satisfy tasks' quality, each task has to be assigned to a robot where the difference between robot's quality and task's quality is minimized. For example, if we have tasks with quality requirements from 1 to 9, and only three robots with quality 2, 5, and 8, then tasks that require quality 1, 2, or 3 should be assigned to the robot with quality 2, similarly with the other tasks, see Figure 1.  
To generalize, assume the robots' quality levels are given by \(Q_{R}=q_{1},q_{2},q_{3},\ldots,q_{z}\); then we propose the following equation to interrelate the robots' quality levels to the tasks' quality levels:  
\[q_{i}=(i-1)*\beta+\alpha, \tag{1}\]  
where \(\beta\) is the range of task qualities that can be fulfilled by each robot's quality level, \(\alpha\) is used to compute \(q_{i}\), and they are computed as follows:  
\[\beta=\frac{k}{z} \tag{2}\]  
\[\alpha=\frac{\beta+a_{1}}{2}, \tag{3}\]  
where \(k\) and \(z\) are the number of task quality requirement levels and the number of robot quality levels, respectively. As a proof of concept, we assume nine levels of quality requirements for the tasks (\(k=9\)), represented by numbers from \(a_{1}\) (very low quality) to \(a_{9}\) (very high quality). Using Equation (1), the robot qualities are as follows: \(Q_{R}=2,5,9\), whereas \(\beta=9/3=3\) and \(\alpha=(3+1)/2=2\).
# Design and Implementation of Virtual Security Function Based on Multiple Enclaves
## 6 Result
### Performance of System
#### 6.2.4 Result Analysis  
In conclusion, the performance of our system is affected by several factors, and the additional overhead is caused by SGX protection. Firstly, most of the overhead is caused by ecall and ocall instruction, which is the most influential factor in the performance of SGX, according to our test. When the number of enclaves is too large, the performance of the whole system is seriously affected because of entering and exiting enclaves. Secondly, the security check will also affect the performance of the system. It is necessary to use remote attestation and local attestation to ensure that the data will not be tampered with or stolen. As a result, a series of encryption and decryption operations are used and bring some extra overhead. Besides, the parameter transmission process also affects system performance. In our multi-enclave-based design, the result of packet classification needs to be passed to other enclaves. If the entire packet is transferred between multiple enclaves, the overhead is too large. Therefore, we have used shared memory to optimize packet transmission, and a packet pointer is designed to transfer the packet. Besides, there are three other performance optimizations for improving the performance of the entire system. By using the high-performance I/O library DPDK, the performance of I/O operations is improved. Moreover, by reducing the number of system calls, the use of ecall and ocall instruction can be reduced. Through batch processing, multiple small data packets can be processed uniformly, which increases the length of data packets processed at one time and improves efficiency.
## 2 PQD Signal Visualization Processing Based on Lissajous Trajectory  
### Model Construction of PQDs  
The generation of PQDs often accompanies abnormal faults in system equipment and interference from external factors. If they cannot be detected and identified in a timely and accurate manner, it will be impossible to complete the repair of abnormal states the first time, which poses serious safety risks and economic losses to the power grid. The modeling method for PQDs can be obtained according to IEEE Std 1159-2019 [14]. Its representative equations are shown in Table 1. Among them, the nine types of PQDs are all single disturbances, represented by C1-C9, and the normal signal is represented by C0. The waveform diagrams of C0-C9 are shown in Figure 1.  
Figure 1: Signal waveforms of PQDs.  
Complex PQDs are usually composed of multiple single PQDs, which contain various categories and different start and end times. The resulting composite waveforms are more complex and variable, with stronger randomness. This presents challenges for the generalization of traditional identification algorithms. The complex PQDs can be expressed as follows:  
\[f(t)= \lambda(t)\sin(\omega t)+\varphi(t) \tag{1}\]  
where \(\omega\) is the nominal system frequency, and \(\lambda(t)\) represents the multiplication coefficient module of the PQD signal, including sag, swell, interruption, and flicker. \(\varphi(t)\) represents the superposition module of PQD signal, including harmonic, oscillatory transient, pulse, notch, and spike.
# Parametric Optimization Study of Novel Winglets for Transonic Aircraft Wings
## 5 Optimization Studies
### Taguchi Method  
The achieved signal-to-noise (S/N) ratio, which employs Taguchi analysis to detect the loss in quality of the variables in diverse issues, indicates the relative significance of the parameters and their ideal combination. The nature of variability and the mean of the quality characteristics are considered simultaneously by both quality loss and variables of the S/N ratio [41]. Moreover, this approach helps researchers focus their attention on quality losses or the SN ratio when solving multi-objective optimisation problems, which has led to an increase in the interest in reducing complexity.  
In this study, four winglet design parameters of a transonic wing, namely, cant angle of the first tip, taper ratio, sweep angle, and tip twist, are selected with four levels. The range of the selected design parameters is decided by taking reference from the research paper [24] and is listed in Table 3. CFD analysis is performed, employing the Taguchi L16 orthogonal array for the 16 conditions listed in Table 4 to determine C\({}_{\text{L}}\), C\({}_{\text{D}}\), and C\({}_{\text{M}}\) (bending moment of wing root chord at quarter chord location).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Design Parameters** & **Symbol** & **1 st Level** & **2 nd Level** & **3 rd Level** & **4 th Level** \\ \hline Cant angle of the first tip & \(\Phi\) & \(0^{\circ}\) & \(25^{\circ}\) & \(50^{\circ}\) & \(75^{\circ}\) \\ Taper ratio & \(\lambda\) & 0.2 & 0.4 & 0.6 & 0.8 \\ Sweep angle & \(\Lambda\) & \(10^{\circ}\) & \(25^{\circ}\) & \(45^{\circ}\) & \(60^{\circ}\) \\ Tip Twist & \(\mathbb{C}\) & \(-8^{\circ}\) & \(-6^{\circ}\) & \(-4^{\circ}\) & \(-2^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Selected design parameters and their levels.  
Figure 11: Variations in S/N ratios for different parameters at the \(\rm C_{L}\)/\(\rm C_{D}\) condition.  
Figure 12: Variations in S/N ratios for different parameters at the \(\rm C_{M}\) condition.
## 4 Simulation Result Comparisons of the N-TWSRIM  
### Non-Copper Plating Model  
To verify composite multilayer theory, when the thickness of the copper layer is 0, a 2D non-copper plating model of N-TWSRIM is established. The flux distribution is shown in Figure 8.  
Figure 7: Complete N-TWSRIM computational block diagram.  
The electromagnetic properties of the non-copper plating model, including output torque, stator current, and power factor, are calculated by ECM and FEM. The results are shown in Figure 9 and Table 4.  
Comparison of the analytical and finite element results shows that the torque gradually increases with the increase in slip, which is similar to the traditional solid rotor torque curve and has monotone increasing characteristics. However, the stator current and power factor only increase slightly as the slip increases, and almost no change is observed. This feature makes N-TWSRIM capable of running at overload or block conditions for a long time, and the proposed motor can be used for specific  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Slip**} & \multicolumn{2}{c}{**Output Torque**} & \multicolumn{2}{c}{**Power Factor**} & \multicolumn{2}{c}{**Stator Current**} \\ \cline{2-7}  & **FEM** & **ECM** & **FEM** & **ECM** & **FEM** & **ECM** \\ \hline
0.3 & 0.028 & 0.033 & 0.4424 & 0.4716 & 1.920 & 1.949 \\
0.4 & 0.082 & 0.116 & 0.4458 & 0.4735 & 1.922 & 1.951 \\
0.5 & 0.203 &  
applications. However, according to the torque and power factor curves, the torque and power factor of the motor are low. This disadvantage can be improved by copper plating on the rotor surface.
# Off-Resonance Control and All-Optical Switching: Expanded Dimensions in Nonlinear Optics
## 4 Laser-Modified Fluorescence
Under such conditions, it has been shown that the following expression for the anisotropy arises [56]  
\[r=\frac{3\cos^{2}\!\phi-1+(Kl^{\prime}/c\epsilon_{0})\!\left(\cos^{2}\!\phi-1 \right)}{5+(Kl^{\prime}/\gamma c\epsilon_{0})\!\left(20-11\!\cos^{2}\!\phi \right)} \tag{11}\]  
where \(\phi\) is the angle between the absorption and emission transition moments, the former relating to the resonant laser beam that induces excitation prior to the fluorescence, and the hyperpolarizabilityhas been simplified to \(K\big{|}\mu^{0\alpha}\big{|}^{3}\) for calculational convenience (the dipole moments cancel out in \(r\)). For the limiting case when \(I=0\), i.e., the off-resonant beam is absent, the well-known expression [57]\(r=(1/5)\big{(}3\mathrm{cos}^{2}\phi-1\big{)}\) is recovered. The modification of individual molecular emission rates is measurable in more than one other respect. An aspect that is principally of spectroscopic interest is the associated change in the decay lifetime, signifying the kinetics of ensemble photophysics. Once again, the capacity to controllably modify this parameter, itself the direct focus in fluorescence lifetime imaging (FLIM) studies for example [58], affords another dimension to resolve differences between chemically distinct fluorophores in close proximity, especially in cases where the corresponding extent of emission spectrum overlap fails to permit speciation by wavelength alone. It is significant that the input of the off-resonant beam can also alter the near-field spatial distribution of the fluorescent emission [59; 60]. Via the nonlinear couplings, the emitted electromagnetic field has an increasingly 'directed' character, i.e., a propagation behavior typically observed in the far-field, as the intensity of the off-resonant beam increases. The phenomenon, therefore, offers the ability to optically control the near-field distribution of fluorescence. Contour maps illustrating the effects of the off-resonant beam on the emission field are provided in Figure 4. It is interesting to note the possibility of using the actively controllable effect we have described either instead of, or in conjunction with, other passive schemes for achieving highly directed output. The virtues of plasmonic enhancement and phased-array nanoantennas for directed emission have recently received prominent interest (see, for example, [61; 62; 63; 64]).
## 3 Results
### Polar Distribution of NITS Temporal Intensity
Next, we focus our research on the properties of the main radiation peak. The full-width of half-maximum (FWHM) of the main peak and pulse interval between peaks when \(a_{0}\) from 0.1 to 10 and \(\gamma_{0}\) from 5 to 100 are calculated and shown in Figure 8, respectively. The pulse width becomes shorter with \(a_{0}\) and \(\gamma_{0}\) increasing as shown in Figure 8a, which is caused by more severe nonlinear effects. The more intense the laser field is, the greater the ponderomotive force that the electron is subjected to in the laser field. A larger ponderomotive force and a higher electron initial energy will lead to more severe nonlinear effects, causing the electrons to radiate higher order harmonics [24,25]; thus, resulting in a shorter radiation pulse width. In extreme cases when \(a_{0}=10\) and \(\gamma_{0}=100\), the FWAM reaches the yoctosecond level [Figure 6c], being \(10^{6}\) times shorter than when \(a_{0}=0.1\) and \(\gamma_{0}=5\). However, the changes pulse interval of radiated pulses is positively correlated with \(a_{0}\) and negatively correlated with \(\gamma_{0}\) according to Figure 8b. It is of great significance for the selection of parameters experimentally obtaining single or continuous ultra-short light pulses. Figure 8: With different normalized laser intensity \(a_{0}\) and different electrons’ normalized initial energy \(\gamma_{0}\), the main pulse FWHM of NITS radiation peak in (**a**), and the pulse interval between main peak and secondary peak in (**b**). Figure 7: The joint distribution of NITS radiation temporal power and polar angle \(\theta\) when \(a_{0}=5\) and \(\gamma_{0}=50\) in (**a**). This distribution changes periodically as the laser’s initial phase \(\psi\) changes while the azimuth angle \(\phi\) is fixed (see Video S3). (**b**) is an overhead view after taking the logarithm of (**a**), in which the dash-lines connect the radiation peaks.
## 4 Communication Architecture  
### Modeling of Intra-Level Communication Architecture  
The functioning of our architecture can be modeled through a set of State-Transition diagrams. We define the behavior diagram of all the sensors used in the architecture. The behavior of the different sensors depends largely on their nature, their expected functionalities and their positions. It should be remembered that depending on the architecture, the sensors deployed at the first level have no predecessor. Similarly, the well has no successor. We model in the following the functional states as well as the possible transitions for the different behaviors of each sensor that can induce a change in the global behavior of the network.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 5 Different VANET Technologies Based on Transmission Range
### Long-Range Communications Technology  
#### 5.3.1 C-V2X Technology  
The vehicles communicate with their surroundings through the cellular network [165]. The C-V2X technology was introduced by 3 GPP release 14, and it was further developed by 3 GPP release 15 in order to fulfil the criteria of 5G communications [30]. C-V2X technology offers multiple benefits listed below [24].  
* C-V2X is highly reliable and can perform communication between the vehicles at high speed.
* The C-V2X technology can perfectly operate in dense traffic conditions, therefore it overcomes the congestion issues faced by DSRC.
* The C-V2X technology supports both short-range and long-range communication between the vehicles and with the RSU.
* The C-V2X technology is able to provide a 360-degree view non-line of sight (NLoS) sensing.
* C-V2X can support much higher data rates compared to the short- and medium-range technologies discussed and, thus, it is able to support applications which require higher data rates.  
The C-V2X technology has two modes for vehicular communication. Mode-3 allows the vehicle to communicate via the base-station while Mode-4 allows the vehicles to communicate with each other directly [166]. Mode-4 uses the PC5 interface at 5.9 GHz while Mode-3 uses the traditional LTE interface [13; 167]. In addition, Mode-3 depends on centralised scheduling while Mode-4 is completely distributed. The purpose of direct communication between the vehicles (through Mode-4) reduces the latency requirements, necessary for most of time-critical applications in ITS. The vehicles may switch between the desired modes dynamically [167]. LTE and LTE-A have severe security vulnerabilities; the details are provided in [168] (and references therein). Security threats faced by C-V2X is provided in [169]. Furthermore, countermeasures for C-V2X are discussed in detail in [169; 170] (and references therein).
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 6 Privacy-Preserving Architectures for Scalable IoT-Based Healthcare Systems
### Decentralized Data Aggregation and Processing  
Aggregating and processing data in a decentralized manner is adopted by privacy-preserving architectures to support the protection of patients' confidentiality during data analysis. The distribution of data processing to edge devices reduces the need for centralized data while minimizing large data breaches. Furthermore, it supports the resolution of latency issues during data transmission to a central server for processing [13]. Such an approach requires a robust solution to preserve privacy at the edge. Blockchain technology plays a crucial role, as it provides a tamper-resistant and transparent platform for storing health records within decentralized networks by creating a decentralized ledger of transactions. Blockchain technology provides patients with considerable control over their medical data, prevents unauthorized data modifications, and ensures data integrity.  
The decentralized nature of blockchains aligns seamlessly with the goals of decentralized data aggregation and processing, offering an additional layer of security and trust in healthcare data management. However, challenges include scalability, energy consumption, and ensuring data privacy within a public blockchain.
## 5 Conclusions and Future Work  
The analysis performed has shown that companies are facing the steadily increasing regulating pressure in the sphere of information security. It can lead to a situation where the same requirements coming from different standards in the same organization might be covered by different technical solutions, thus increasing the company's expenses. The problem can be solved by the consequent mapping of compulsory standards and further determination of MSB. Unfortunately, currently available methods for MSB identification are mainly expert-based, which are not affordable for SMEs and can be subjective.  
A method for solving two different problems--MSB identification and its verification against controls already implemented by the organization--was proposed. The method is based on graph theory and graph optimization algorithms: minimum vertex cover and subgraph isomorphism, respectively. The method was formally described and later experimentally verified.  
For proof of method feasibility, three regulating documents (ISO27002, PCI DSS, and GDPR) were presented as graphs and later mapped, forming an initial graph with 1276 vertexes. The initial graph was amended according to the method restrictions, and a new polynomial time, vertex cover algorithm was applied. The resulting MSB graph of 322 vertexes was compared against the graph, representing controls deployed by a fictional ACME Corporation, and coinciding controls were identified. The experimental test has shown the following:  
* Application of graph theory and graph optimization algorithms, such as minimum vertex cover algorithms, to the standards mapping graph can be effectively used for removing duplicating requirements and ensuring spending minimization on information security;
* The method is capable of processing original graphs with relatively high numbers of vertexes, and the optimization rate of removed duplicated vertexes has reached 74.5% in the case of our experiment and can be even higher if a more significant number of regulating documents have to be applied;  
Figure 13: Identified isomorphic parts of MSB and DCG.  
* Application of isomorphism features provides a user-friendly way of evaluating the current state of controls deployed by the organization against the desired MSB state.  
Further research on the topic should be concentrated on minimum vertex cover algorithm and subgraph isomorphism algorithm selection and optimization for better performance, automation of actions that currently are made manually, and integration of the proposed method with our previously proposed approaches based on security ontology fostering symbiosis of these two approaches.  
All authors contributed to designing and performing measurements, data analysis, scientific discussions, and writing the article.  
This research received no external funding.  
The authors declare no conflict of interest.