esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this paper, we come up with three secure chaos-based stream ciphers, implemented on an FPGA board, for data confidentiality and integrity. To do so, first, we performed the statistical security and hardware metrics of certain discrete chaotic map models, such as the Logistic, Skew-Tent, PWLCM, 3D-Chebyshev map, and 32-bit LFSR, which are the main components of the proposed chaotic generators. Based on the performance analysis collected from the discrete chaotic maps, we then designed, implemented, and analyzed the performance of three proposed robust pseudo-random number generators of chaotic sequences (PRNGs-CS) and their corresponding stream ciphers. The proposed PRNGs-CS are based on the predefined coupling matrix M. The latter achieves a weak mixing of the chaotic maps and a chaotic multiplexing technique or XOR operator for the output function. Therefore, the randomness of the sequences generated is expanded as well as their lengths, and divide-and-conquer attacks on chaotic systems are avoided. In addition, the proposed PRNGs-CS contain polynomial mappings of at least degree 2 or 3 to make algebraic attacks very difficult. Various experimental results obtained and analysis of performance in opposition to different kinds of numerical and cryptographic attacks determine the high level of security and good hardware metrics achieved by the proposed chaos system. The proposed system outperformed the state-of-the-art works in terms of high-security level and a high throughput which can be considered an alternative to the standard methods.  
chaos-based stream ciphers; PRNGs-CS; FPGA; security analysis; hardware metrics 2023  
[PERSON], [PERSON], [PERSON], [PERSON], M. Design, Hardware Implementation on FPGA and Performance Analysis of Three Chaos-Based Stream Ciphers. _Fractal Fract._**2023**, 7, 197. [[https://doi.org/10.3390/fractal/2020197](https://doi.org/10.3390/fractal/2020197)]([https://doi.org/10.3390/fractal/2020197](https://doi.org/10.3390/fractal/2020197))
## 5 System Performance Analysis  
In this section, a system-level performance analysis for the integrated passive component block is presented. The purpose of these co-simulations is to provide insights into the system performance of components in an RF chain and to provide accuracy in the link budget and operation range analyses [46]. The integrated passive component block shown in Figure 10 is selected for these simulations as two-port measurements are performed to characterize two critical paths:- Path-1: RF Source -\(>\) PA -\(>\) Diplexer (band n257) -\(>\) Coupler (Thru) -\(>\) Antenna Input; Path-2: RF Source -\(>\) PA -\(>\) Diplexer (band n257) -\(>\) Coupler (Coupled) -\(>\) Power Detector. These paths are highlighted in Figure 16a, and they can be corroborated with the integrated passive component block as shown in Figure 16b. The following system-level simulations are performed to quantify the distortion caused by this block:  
1. Group delay variation at antenna port and power variation at detector port of system as RF source power is varied.
2. EVM of integrated passive component block using RF carrier modulated by \(\pi/4\) DQPSK and 64-QAM.  
To perform these simulations, a suitable PA with a 24 to 34 GHz range of operation and 23 dB gain is selected and modeled in Keysight ADS 2019 [47]. Also, it is assumed that all terminating impedances, such as at the antenna and detector port, are 50 \(\Omega\). The PA is modeled in Keysight ADS along with rest of the components to form a transmitter chain. The component impedances, especially for the PA, are optimized for better operation of the PA rather than 50 \(\Omega\), and thus, the impedances of the components at the PA output can affect its loading. The nature of the output matching network of the PA plays a critical role in design fidelity. This is usually dealt with using system-defined constraints and simulations earlier in the design cycle.Moreover, the non-linearities of local oscillators, frequency upconverters and the PA itself are sources of aberrations in a system and require extensive modeling of each component to set an expectation of system performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Substrate/ Structure** & _f\({}_{\alpha}\)_/**BW (GHz)** & _f\({}_{\alpha}\)_/**BW (GHz)** & **Footprint \& Thickness (mm^{*}\)\(<\)mm)** \\ \hline
[43] & Laminate/Microstrip & 14/- & 28/- & \(\sim\)80 \(\times\) 0.127 \\ \hline
[44] & Laminate/Microstrip & 3.5 & 3.2 & 645.16 \(\times\) 0.508 \\ \hline
[45] & Laminate/SIM & 24.925/ 1.35 & 26.8/1.8 & \(\sim\)2500 \(\times\) 0.5 \\ \hline This Work & Glass/Microstrip & 27/3 & 38.5/3 & 25.86 \(\times\) 0.202 \\ \hline This Work & Glass/Microstrip & 25.875/3.25 & 38.5/ 3 & 20.24 \(\times\) 0.202 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of diplexers using various substrate technologies for mm wave applications.
## 2 Hybrid Edge-Cloud Approach
### Motivation for the Edge-Cloud Approach
#### 2.1.1 IEC 61850-90-5 Evaluation--Tested Setup  
The testbed used for the evaluation is presented in Figure 1. The PMU emulator was an experimental PMU device with the disabled PMU functionality and an installed DEKRA Performance Test Tool to emulate the network traffic of a PMU. The emulator was connected to the UXM device via a radio frequency (RF) coaxial cable, whereas a secondary data connection to the testbed PC, required for the experiment's control, was made via the emulator's Ethernet port and testbed network. The UXM device from Keysight is an LTE base-station emulator device that emulates custom LTE networks with configurable network conditions. For experimental control and sequencing, the Test Automation Platform from Keysight was used and dedicated scripts were written for controlling the emulated PMU and UMX as well as for data recording and the evaluation of experiment. The DEKRA Performance Tool was used as a traffic generator on the emulated PMU and as a calculator of key performance indicators, namely throughput, latency (one-way delay) and packet loss. The performance tool consists of a server and a client and can be configured to emulate various network traffic types or patterns. The Dekra server was run on a testbed PC, whereas the client was run on the simulated PMU.
# A Notched Long-Period Fiber Grating Magnetic Field Sensor Based on Nanoparticle Magnetic Fluid
## 4 Conclusions  
In conclusion, this paper demonstrated the manufacturing process of an NLPFG magnetic field sensor utilizing MF as the sensing material. The results depict the feasibility of our proposed NLPFG magnetic field sensor based on ferromagnetic fluid containing nanoparticles. In addition to being used as a magnetic sensor, the proposed NLPFG sensing structure can be used as a current sensor as well. When the applied external magnetic flux density increases to 27.74 mT, the transmission loss variation of the dip resonant wavelength is 3.48 dB. The sensitivity of the sensor is nearly 0.125 dB/mT. The sensor also has the advantages of a low-cost fabrication procedure and suitability for mass production.  
This work was supported by the Ministry of Science and Technology, Taiwan (grant number MOST 103-2221-E-151-009-MY3).  
[PERSON] designed methods of experiment and analyzed the data. [PERSON] performed experimental works, analyzed the experimental data and wrote the paper.  
The authors declare no conflict of interest.  
Figure 5: The spectra of the NLPFG magnetic field sensor under different magnetic flux densities for magnetic field sensing.  
Figure 6: The relationship of the transmission loss for the NLPFG magnetic field sensor under distinct magnetic flux densities.
## 3 Materials and Methods
### Complexity Analysis  
In this section, the computational complexity of our proposed components is analyzed. The proposed framework is divided into two parts: the preprocessing procedure and the simulation. We assume that the dataset is sufficiently large; therefore, \(|V|\) and \(|E|\) are much larger than the other variables. In Sections 3.3.1 and 3.3.2, preprocessing involves identifying seed bots and accounts that may follow, retweet, or mention, which takes \(O(|V|)\) time complexity. The corresponding simulation in every step is \(O(1)\). The user clustering presented in Section 3.4 incurs a time complexity of \(O(p^{2}|V|+p^{3}+k|V|T)=O(|V|)\), where \(p\) is the number of features, \(k\) is the number of clusters, and \(T\) is the number of iteration. Training the profile editor consisting of two MLPs has a time complexity of \(O(n^{2}E|V|)=O(|V|)\), where \(n\) is the number of neurons and \(E\) is the number of epochs. Using the profile editor to generate a profile takes time consumption of \(O(n^{2})\). Therefore, the preprocessing, which includes building the virtual environment and action space, has a time complexity of \(O(|V|+|E|)\). During the simulation, at each step, an action is generated (\(O(n^{2})\)), followed by the potential influence/user engagement simulation and profile editing mentioned previously (\(O(1+n^{2})=O(n^{2})\)) and concluding with the detector predicting the bot score. Hence, the time complexity of a simulation is \(O(t_{m}(n^{2}+f))\), where \(f\) is the time complexity of the detector. Finally, the space complexity of the proposed framework is \(O(|V|+|E|)\), as constructing the virtual environment dominates memory consumption compared to other operations.
## 2 Network Model and Operation
### Time Schedule
#### 2.2.1 Definitions  
A schedule can be represented by a matrix \(Q\)[19], where element \(q_{t,j}\) denotes the state of node \(j\) during time slot \(t\): \(q_{t,j}=\ell\) when node \(j\) will be planned to transmit to node \(\ell\), and \(q_{t,j}=-\ell\) when node \(j\) will be planned to receive from node \(\ell\). Finally, \(q_{t,j}=0\) means node \(j\) is in an idle state.  
A schedule has a period \(T\) if it repeats every \(T\) time slots: \(q_{t,j}=q_{t+T,j}\;\;\forall t,j\). In essence, TDMA has a periodic operation, so our interest will be focused on periodic schedules. Period \(T\) is the frame length. We denote by \(Q^{(T)}\) the matrix that represents the schedule in a period.  
A perfect schedule is a matrix \(Q^{(T)}\) with no zero entries (no node has an idle state in the frame). A schedule is called optimal in case of providing maximum throughput. In [19], it is shown that every network has an optimal schedule that is periodic, so the periodicity is an important feature to search for the optimal schedule. Since it is impossible to find a perfect schedule for a linear array of \(N>2\) nodes [19], our goal is to find an optimal periodic schedule containing the largest number of transmissions or, which is the same, the smallest number of idle states.  
The average throughput \(S\) of a periodic schedule \(Q^{(T)}\) can be calculated as the number of either transmissions or receptions in one frame divided by the frame length [19; 25]:  
\[S\;=\;\frac{1}{T}\;\sum_{t,j}\;\;1_{(q_{t,j}<0)}\quad, \tag{1}\]  
where \(1_{(A)}\) is the indicator function with value 1 if the logical expression \(A\) is true and 0 otherwise.  
The constraints shown in Figure 3 can be mathematically expressed for any discrete time \(t\) as follows:  
\[q_{t,j}=\;-\ell\;\;\;\Rightarrow\;\;\ell\;=\;j+1\qquad\text{with}\;\;j\in\{2, \,3,\;\ldots,\;N\text{-}1\}\;, \tag{2}\]  
\[q_{t,j}\;=\;j-1\;\;\Leftrightarrow\;\;q_{t+1,j-1}=\;-j\qquad\text{with}\;\;j \in\{2,\,3,\;\ldots,\;N\text{-}1\}\;, \tag{3}\]  
\[q_{t,j}\;=\;j-1\;\;\Rightarrow\;\;q_{t,j-2},\;q_{t,j+2}\leq 0\qquad\text{with}\;\;j \in\{3,\;\ldots,\;N\text{-}2\}\;, \tag{4}\]  
\[q_{t,1}\;\in\;\{0,\;-2\}, \tag{5}\]  
\[q_{t,N}\;\in\;\{0,\;N\text{-}1\}. \tag{6}\]  
Equations (2) and (3) mean that a node can only transmit to its down-stream neighbor, Equation (4) states that nodes \(j\) and \(j+2\) cannot transmit simultaneously, and Equations (5) and (6) set the boundary conditions for a finite network: Equation (5) means that the Gateway (node 1) either collects packets from node 2 or remains idle, and Equation (6) means that the upper-stream node \(N\) never receives packets from other nodes. It is important to note that positive values represent nodes in a transmission state, negative values represent nodes in a reception state, and zero means idle. Since a transmission is always associated with a reception, there will be as many positive valued elements as negative ones.
## 3 Beam Steering/Beam Forming Using Reconfigurable Components
### Varactor Diodes
A detailed case study has been presented in [97] of the practical constraints of two-dimensional beam steering using a varactor diode-based meta-material at micro and mm-wave frequencies. The practical impacts of the varactor diode utilized with a meta-material-based RTA are deeply observed, as shown in Figure 8. Various results of the internal resistance, capacitance, and inductance have been compared in the reported paper. Four prototypes are developed on different frequencies, including 5, 14, 28, and 60 GHz, to understand the effect of internal resistance Rs and internal inductance Ls of varactor diodes. It is concluded from the above study that internal resistance Rs have various impacts on RTAs. On the other hand, internal inductance Ls affects the proposed TA's bandwidth and operational frequency band response. As internal inductance Ls increases, higher frequencies shift towards lower frequencies, increasing the bandwidth of the reported elements. These result in a reduction in the overall phase shift accomplished  
Figure 7: **(a)** Layer stack view of proposed unit cell, **(b)** layout view, **(c)** 3D view, **(d)** top layer, **(e)** bottom layer (reprinted with permission of [94]. Copyright 2019 [PERSON], [PERSON], et al.). by multilayers of unit cells that may have the impact of reducing the scanning range of TAs to perform beam steering with a transmitarray.
###### Abstract
The tool path adaptation for robotic deburring implemented usually needs to supplement extra equipment or processes, e.g., using a vision system described in [27; 36] or direct teaching [29]. Other studies on the process parameter control for robotic deburring have certain constraints, e.g., the deburring tool is an abrasive diamond disc in the control strategy for the process parameter control, and other deburring tools are not considered [30]; the designed control action and implementation are more intricate, such as [25; 31; 32; 34; 36]; the procedure of the proposed approach is more complicated, such as [33]; or detailed deburring process parameters such as robotic feed and spindle speed for the deburring industrial robot are not considered [35]. The deburring orientation adjustment of current industrial robots is usually not dexterous, especially at the far deburring position while simultaneously considering obstacle avoidance. There is an urgent need to develop a robot manipulator for robotic deburring of complex shaped parts with highly dexterous manipulation and very efficient five-face deburring in one setup. Moreover, there are very few related references for the tool path strategies which are implemented as proprietary software packages of robot manufacturers. It is particularly necessary to conduct the research on the tool path planning which is highly suitable for the deburring characteristics of the self-developed robot manipulator. Also, in order to perform an adaptive deburring process and finer deburring quality, it is very important to investigate an easy-to-implement and efficient process parameter control method with automatic-online errors correction. The rest of this article is organized as follows. The structure of the robot manipulator and related research are described in Section 2. A robotic deburring tool path planning method and a robotic deburring process parameter control method are proposed for robotic deburring in Sections 3 and 4, respectively. A dexterous manipulation verification experiment is presented in Section 5. Two robotic deburring experiments, disc deburring experiment of an automobile hub and multifaceted edges deburring experiment of an automobile steering booster housing, are conducted in Section 6. Finally, conclusions are drawn in Section 7.
# 5G Beyond 3 GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
## 2 5G Technologies beyond Release 15
### Broadcast and Multicast  
Under some conditions and road events, traffic may require urgent alerting in multiple destinations within a local area and for a specific time. To facilitate this delivery model and serve local messages in an efficient manner, broadcast and multicast communication services can make the difference. These communication models might contribute to public and vehicular safety, thereby enhancing operational scalability [23]. Rel-14 included only broadcast without feedback, while Rel-16 will include feedback for increased reliability [24].
## 2 Types of Surfactant-Modified Polymer Nanocomposites
### Surfactant-Modified Organic Nanofiller/Polymer Nanocomposites  
Table 4 indicates the examples of organic nanofillers, surfactants, polymer matrices, and preparation processes of polymer nanocomposites. Organic fillers, such as CNCs, could be modified by an anionic surfactant, like Beycostat A B09, to produce surfactant-modified CNCs (s-CNCs) [15]. The s-CNCs were mixed with PLA via solution blending and solvent casting processes for the preparation of s-CNC/PLA nanocomposites [15]. CNPs have also been used for the preparation of polymer nanocomposites. CNPs could be modified by a non-ionic surfactant, such as Span 60, to improve the dispersion of hydrophilic CNPs in a hydrophobic PS matrix [30]. Span 60-modified CNPs were mixed with a PS solution via the solution blending process, followed by a solvent casting process to prepare CNP/PS nanocomposites [30]. On the other hand, NCFs could be suspended in a silicon-based surfactant, such as Niax L-595, with dispersant oils to facilitate the dispersion of NCFs in a PEVA matrix [8]. The NCF suspension was added to the PEVA through the melt blending process, followed by a compression molding process to obtain NCF/PEVA nanocomposites [8].  
In addition, Pluronic could treat CNWs to improve the interactions between the CNWs and the DGEBA epoxy [7]. CNW/DGEBA nanocomposites could be prepared by means of solvent-free blending and pour casting processes [7]. NCFs could also be modified by a non-ionic surfactant, like Ultraric PE 105, for promoting the interfacial interaction between the NCF and PBAT phases [19]. NCFs were mixed with the surfactant and PBAT via solution blending and solvent casting processes for the preparation of NCF/PBAT nanocomposites [19]. CNWs could also be modified by a non-ionic surfactant, such as PEGMONO, to improve CNW dispersion in a non-polar PLA matrix [16]. PEGMONO-modified CNWs were mixed with PLA solution via the solution blending process, followed by a solvent casting process to prepare CNP/PLA nanocomposites [16]. Table 4 demonstrates that the surfactant-modified organic nanofiller/polymer nanocomposites could mostly be prepared through a solvent casting process by using organic solvents.
# Image Dehazing and Enhancement Using Principal Component Analysis and Modified Haze Features
## 3 Haze Removal Using PCA and Haze-Relevant Features
### Training the Random Forest  
The proposed method used a set of high-resolution haze-free images collected from the internet to generate the training dataset for the learning of random forest [19]. In the proposed method, the random forest is trained using the haze-relevant features of the local maximum contrast and dark channel. However, since the local maximum contrast and dark channel features are more salient in the non-sky region, we extracted the haze-free patches from non-sky regions. In addition, since it is hard to obtain the transmission of observed haze image directly, we synthesized haze patches using transmission values from 0.1 to 0.9 based on the patch-based haze model in (9). We generated 10,000 pairs of haze and haze-free patches for each \(t\) and extracted the haze-relevant features to train the random forest. Eighty percent of the training dataset was used for training and the rest was used for testing for each \(t\) in (9).  
In addition, we extracted the haze-free patches from a set of image pyramid to generate a pair of haze and haze-free patches, as shown in Figure 6a. Since transmission is constant at regions of the same depth, the transmissions of patches \(p_{1}\), \(p_{2}\), and \(p_{3}\) extracted at in- and across-scales should also be constant. For that reason, multiscaled patches of the same size can be regarded as a set of different scenes at the same depth. Figure 6c,d respectively show the comparison of estimated transmissions using random forest.  
Figure 6c is the transmission estimated using the random forest trained by the haze-relevant features estimated from the haze patches, which were synthesized using haze-free patches extracted from a single in-scale space. On the other hand, the random forest used in Figure 6d was trained using the haze-relevant features estimated from the haze patches, which were synthesized using haze-free patches extracted from multiscaled spaces. As shown in Figure 6d, the haze-free patches extracted from multiscale spaces are more related to the relationship between the haze-relevant features and transmission compared with Figure 6c. For that reason, the proposed method can estimate a more accurate transmission at the same scene depth from an image pyramid and random forest.
## 2 Materials and Methods
### Fundamentals of Brittle Failure Numerical Modelling
As shown in Figure 4, showing the constitutive behaviour of a cohesive crack element, if the opening is smaller than a peak value \(\mathrm{o_{p}}\), the behaviour is elastic (Figure 4a); otherwise, tensile strength \(\mathrm{f_{f}}\) is reached and yielding starts with a strain-softening post-peak behaviour with a residual opening \(\mathrm{o_{r}}\) governed by Mode I fracture energy \(\mathrm{Gf_{I}}\). The same goes for Mode II (Figure 4b). Usually, the dominant fracturing mode is the mixed one (Figure 4c). Figure 4: Built-in FDEM fracture model in (**a**) Mode I, (**b**) Mode II, and (**c**) Mixed mode (modified from [28]; \(\mathrm{f_{s}}\) = shear strength, \(\mathrm{f_{t}}\) = tensile strength, \(\mathrm{G_{lc}}\) = Mode I fracture energy, \(\mathrm{G_{Ilc}}\) = Mode II fracture energy, \(\mathrm{o_{p}}\) = peak opening, \(\mathrm{o_{r}}\) = residual opening, \(\mathrm{p_{f}}\) = fracture penalty, \(\mathrm{p_{t}}\) = tangential penalty, \(\mathrm{s_{p}}\) = peak slip, \(\mathrm{s_{r}}\) = residual slip, Mode I = opening mode, and Mode II = sliding mode). Figure 3: (**a**) Schematic of Fracture Process Zone (FPZ) in brittle geomaterials (after [PERSON] et al. (1985); (**b**) theoretical FPZ model (modified by [28]). Examples of the use of FDEM for modelling brittle failure can be found, to name some, in [29] with a research on the influence of loading rate and sample orientation in a Brazilian test simulation on a layered rock sample, in [30] that simulated biaxial loading on a homogeneous rock sample capturing failure localisation, fracture initiation and growth, increase of strength with confinement, brittle-ductile transition, in [17] that used an FDEM model with a micro-Discrete Fracture Network (\(\upmu\)DFN) to account for micro-heterogeneities and reproduce spalling around the Mine-By tunnel, in [31] with reference to the study of failure mechanisms of anisotropic rock masses around unsupported deep tunnels and relative influencing factors. For a detailed review of discrete modelling techniques for rock mass fracturing processes, the interested reader can refer to [32].