esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Smart Grid Testbed
#### 4.5.2 Real-World Testbed  
Most of the real-world smart grid testbeds involve several research areas. As a result, for one specific testbed, it is hard to classify which type of testbed it belongs to; however, according to different smart grid configurations, the testbeds can be roughly divided based on their research bias. Nowadays, existing testbeds can be classified into the following categories: wide-area situational awareness; load balancing; decentralised power system structure; power storage; power transportation; cyber-physical threats; network communication; and AMI. In the cyber-physical study range, the related smart grid research areas are 1. large hardware-based testbeds, 2. cyber security analysing testbeds, 3. network communication testbeds, and 4. agent-based control testbeds [95].  
Large hardware-based testbed uses real data acquisition and actuator devices like RTU, PMU, PLC, etc., which are really close to the real-world scenario. It is usually sponsored by nations due to its complexity and high expense. Some famous large hardware-based testbeds are the Idaho National Laboratory [96], the Jeju Island Smart Grid [97], and National Renewable Energy Laboratory [98].  
Cyber security analysing testbeds mainly study potential security vulnerabilities and attack detection methods in all different aspects of the smart grid. For example, different devices like PLC, HMI, IED, etc., and different communication protocols like Modbus, DPN3, C37.118, etc. Some famous cyber security analysing testbeds are University College Dublin the intrusion and defense testbed [99], Queen's University Belfast testbed [100], and SCADASim [101].  
Network communication testbeds study various communication protocols, load balancing, and contingency responses, which focus on the resiliency of the smart grid. The testbeds are usually built on the simulation platforms (RTDS or PowerFactory) using actual devices like PMU and IED. Some famous network communication testbeds are Kansas State University testbed [102] and the University of North Carolina testbed [103].
# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
## 2 Machine Learning Tools and Signal Processing  
As it is highlighted in the previous section, knowledge-based fault classification and location techniques are more appropriate for distribution networks. For a knowledge-based technique to function effectively, the machine should be trained with appropriate information in advance. This training process is called machine learning. The following section briefly describes the types of machine learning tools. It is followed by a detailed discussion on one type of deep learning, called Multilayer feedforward network and extreme learning machine. Signal processing techniques that are applied to extract important features from measured signals are also discussed.
# Time-Dependent Performance of a Multi-Hop Software Defined Network
## 6 Conclusions  
The advent of SDN allows the implementation of smart adaptive routing, which changes network paths so that new connections may be established and inactive may be removed, as well as to deal with changes in traffic loads and incidents that affect network security. This leads to an interesting paradigm shift in network modelling, which has  
Figure 17: Station \(S4\): \(p_{N}(t)\) for different \(C_{A1}^{2}\).  
Figure 18: Mean backlog \(\Psi\) during \(\Delta\) as a function of routing probabilities \(r_{12}\), \(r_{13}=1-r_{12}\), each curve corresponds to a different flow coming from Host 4 to \(S2\).  
traditionally addressed \"long term\" behaviours and computational methods which are appropriate for steady-state analysis. However, when SDN intervenes dynamically to change paths and traffic levels, the network is seldom at a steady state, and optimization must take transients into account.  
Therefore in this paper we have used diffusion approximation modelling for the performance evaluation of a network of SDN switches, that considers both steady-state and transient analysis. We have shown how changes in routing or forwarding decisions by the SDN controller can influence performance parameters such as delay, queue size, and packet loss probability in the transient state. Our results indicate that this method is computationally operational and can provide useful quantitative results for models with realistic parameter values.  
Our analysis captures the interactions among the main parameters of the network, and numerical examples display the dependence of the queue lengths, queueing delays and their dynamics as a function of the changing flow intensity and variance of interarrival times. Our approach also confirms that transient periods play a significant role in the performance of SDN networks, and that they will be useful to analyze much larger networks in future work.  
While the performance evaluations performed in this paper are purely numerical, and based on diffusion approximations models that have been widely validated by simulations [47; 67; 72], in future work we intend to use network emulation tools such as Mininet with real traffic, as well as experiments on a SDN test-bed, to study the influence of time dependent forwarding decisions on the main performance metrics of large SDN networks.  
[PERSON] developed the methodology and [PERSON] wrote the original draft of the article. [PERSON] and [PERSON] developed the software to solve the models, conducted the computations, and obtained the numerical results. [PERSON] then edited the paper. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the SerIoT Research and Innovation Action, funded by the European Commission (EC) under the H2020-IOT-2017 Program, G.A. 780139. The EC's support does not constitute an endorsement of this paper, which reflects the views only of the authors.  
The authors declare no conflict of interest.
## 3 Application of the COR Values--A Case Study
### Rockfall Simulation Results
#### 3.3.3 Empirical Formula Validation of Numerical Model  
Due to the lack of historical data and field tests in application cases, an empirical distance formula (Equation (8)) with a similar reach angle and volume of rockfalls was selected for verification [7] to investigate the rationality of this numerical model.  
\[L=1.1906H-7.4717 \tag{8}\]  
where \(L\) is the block mass, \(H\) is the angular acceleration vector (Figure 17).  
This empirical formula based on the two-dimensional slope model describes the relationship between rockfall elevation \(H\) and movement distance \(L\). In order to apply this formula to the numerical model, three-dimensional expansion was required. Figure 18a shows the contour lines of the simulation area. In order to facilitate calculation, the figure's lower left corner was set as the origin, and the distance was marked in the X and Y directions. The distribution points of rockfall show that the rockfall movement direction presented an included angle of 25 \({}^{\circ}\) with the x axis. From this, we could roughly determine the rockfall movement distance:  
\[S=L/\cos 25^{\circ} \tag{9}\]  
In Figure 18a, 10 contour lines with an elevation of 1107-1332 and an interval of 25 m are marked with 1-10, respectively. On the basis of elevation \(H_{0}=1545\) m of the starting point of rockfalls, \(\Delta H\) could be obtained by subtracting the elevation of the 10 contour lines above. Then, \(\Delta L\) and \(\Delta S\) were calculated with Equations (8) and (9), and are summarized in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**Elevation**} & \multicolumn{2}{c}{**Distance**} & **Displacement** \\ \cline{2-5}  & **\(H\) (m)** & \(\Delta H\) (m)** & \(\Delta L\) (m) & \(\Delta S\) (m) \\ \hline
1 & 1107 & 438 & 514.01 & 567.16 \\
2 & 1132 & 413 & 484.25 & 534.32 \\
3 & 1157 & 388 & 454.48 & 501.47 \\
4 & 1182 & 363 & 424.72 & 468.63 \\
5 & 1207 &In Figure 18b, 10 contour lines are colored according to the legend of displacement magnitude after the empirical formula calculates the corresponding distance S. The results show that most of the rockfall distance in the numerical simulation was consistent with the estimated distance of different contours. The larger mass and less damping may have caused the mismatch between the farthest rockfall distances and the formula results.
# Stimuli-Responsive Polymeric Nanosystems for Controlled Drug Delivery
## 3 Stimuli-Responsive Polymeric Nanosystems
### Endogenous Stimuli-Responsive Polymeric Nanosystems
#### 3.1.2 pH-Responsive Polymeric Nanosystems  
The high rate of glycolysis in tumor cells and the overproduction of lactic acid lead to the mild acidity (pH 6.2-6.8) in tumor microenvironment compared to the physiological condition in normal tissues (pH 7.2-7.4) [127; 128]. The pH difference between the tumor microenvironment and normal tissue has led to the development of pH-activatable polymeric nanoplatforms for controlled drug release at tumor sites. Additionally, the high acidity of endosomes and lysosomes (pH 4.0\(-\)6.0) has been utilized as endogenous stimulus to trigger the intracellular cargo release [21; 129]. The amine groups-rich polymers that can switch between hydrophobic and hydrophilic states in response to pH changes have been extensively explored for the establishment of pH-responsive nanoplatforms. This kind of polymeric nanomicelle would be disassembled in response to pH changes, leading to the cargo release at a specific microenvironment. Recently, [PERSON] et al. reported a dual pH-responsive nanomicelle drug delivery nanoplatform self-assembled from poly(2-(dimethylamino) ethyl methacrylate)-block-poly(2-(diisopropylamino)ethyl methacrylate) (PDMA-b-PDPA) diblock copolymers for both amphoteric B and siRNA delivery (Figure 4) [130]. In addition, the polymers beard with acid-responsive functionalities such as boronic acid esters, oxime, and hydrazone bonds are widely explored to construct pH-sensitive drug nanocarriers.  
Figure 3: Chemical structures of two different pro-antitumor ether lipids (proAELs) that have been synthesized and investigated with respect to their physical properties and ability to constitute a novel liposome-based drug-delivery system. Secretory phospholipase A2 (sPLA2) hydrolyzes the liposome membrane, thereby releasing both activated antitumor ether lipids (AELs) and the encapsulated drug. The AELs are cytotoxic to cancer cells. Furthermore, synergistically with the generated fatty acids, they function as permeability enhancers that promote drug uptake by the cancer cells. Reproduced from ref. [126] with permission from the American Chemical Society.
## 3 Results and Discussion
### Vortex-Induced Vibration of the Risers
As can be seen in Figures 4-7, a flow velocity of 0.2 m/s would not lead to distinct vibration for all the risers, and the frequencies of VIV for the UPVC riser with a 5 N top tension force obtained using power spectral density (PSD) at the flow velocities of 0.6 m/s and 1.0 m/s are shown in Figures 8 and 9. Figure 7: Response microstrain curves of the three risers at z = 0.5 m with a 65 N top tension force and the S-F end condition. (**a**) 0.2 m/s, Al, case 10; (**b**) 0.6m/s, Al, case 11; (**c**) 1.0 m/s, Al, case 12; (**d**) 0.2 m/s, PMMA, case 22; (**e**) 0.6 m/s, PMMA, case 23; (**f**) 1.0 m/s, PMMA, case 24; (**g**) 0.2 m/s, UPVC, case 34; (**h**) 0.6 m/s, UPVC, case 35. From Figures 8 and 9, the frequencies of VIV for the UPVC riser with 5 N top tension force and S-S end condition are 5.18 Hz and 5.405 Hz for velocities of 0.6 m/s and 1.0 m/s, respectively, while the frequencies of the UPVC riser with 5 N top tension force and S-F end condition are 8.408 Hz and 8.559 Hz for velocities of 0.6 m/s and 1.0 m/s, respectively. This shows that the S-F end condition leads to greater frequencies of VIV for the risers. Strouhal number (S\({}_{\rm t}\)) is a dimensionless number that describes oscillating flow mechanisms and can be calculated using S\({}_{\rm t}=\frac{\rm f_{s}D}{\rm U}\), where f\({}_{\rm s}\) is vortex shedding frequency, D is the outside diameter of the riser and U is the flow velocity. Therefore, for cases 26, 27, 29 and 30, the S\({}_{\rm t}\) (s) are 0.17, 0.11, 0.28 and 0.17, respectively. As calculated in Section 2.3, the Re (s) are 11,881, 19,802, 11,881 and 19,802, respectively, for these 4 cases. Based on the relationship between Re and S\({}_{\rm t}\), St should be about 0.2. It is found that the value of 0.2 for S\({}_{\rm t}\) is a little different from those calculated using vortex shedding frequency. The reasons for this might be: (1) the effect of different parameters, such as tension force, material property and end condition, is not involved in the Re calculation in Section 2.3; or (2) the experimental precision of the test leading to inaccurate results of vortex shedding frequency. Figure 10 illustrates the effects of top tension force on the amplitude of the VIV for UPVC riser. Figure 8: PSD of the UPVC risers with two different end conditions (U = 0.6 m/s). Figure 9: PSD of the UPVC risers with two different end conditions (U = 1.0 m/s). From Figure 10, when the flow velocity is 0.2 m/s, the change of top tension force would not lead to the change of amplitude of VIV for the riser. When the velocity rises to 0.6 m/s, the amplitude of VIV for the riser with 65 N top tension force decreases to 18.2% of that with 5 N top tension force and S-S end condition and 57% of that with 5 N top tension force and S-F end condition. Similarly, for the case of a velocity of 1.0 m/s, the effect of larger top tension force is also significant (9.5% with S-S end condition and 75% with S-F end condition, for those with 5 N).
# A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
## 2 Related Works
### Retinex Model  
Retinex theory [5] was first proposed in 1970s and it is now widely used for enhancement of the low-light images. Figure 1 shows the process of an image capturing by a Charge Coupled Device (CCD) sensor, the object is illuminated by sunlight, the reflected rays from the object surface is captured by CCD camera and a human vision system see that object.  
In Figure 1, \(L(x,y)\) refers to the solar illumination component, \(R(x,y)\) is the reflected light rays from the object surface, and \(J(x,y)\) indicates the observed image which is captured by a CCD camera. The physical model can be described by the following mathematical functions:  
\[J(x,y)=L(x,y)\ R(x,y) \tag{1}\]  
The physical meaning of the above formula can be simply expressed as that the observed image (i.e., low-light image) is equal to the product of the reflectance of image \(R(x,y)\) (i.e., desired illuminated image) and the illumination component \(L(x,y)\) (i.e., degree of darkness which degrades input image).
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 3 Wireless Standards at 60 GHz mmWave Band
### IEEE 802.11 ad  
IEEE 802.11 ad is a standalone unlicensed protocol that provides data rates up to 8 Gbps. It spans from 57 to 71 GHz with six sub-divided channels, each with a bandwidth of 2.16 GHz. It supports only single-channel transmission of 2.16 GHz bandwidth and does not support multi-user MIMO (MU-MIMO). It uses carrier sense multiple access with collision avoidance (CSMA-CA) [77]. There are different modulation and coding schemes that can be grouped together for high SNR, power efficiency, low complexity, robustness, and high achievable data rates. The idea of antenna sectors is introduced in 802.11 ad, which corresponds to a discretization of the antenna space, reducing the number of possible beam directions to test. This is analogous to beamforming. The sector-level sweep (SLS) phase and the beam refinement protocol (BRP) phase are the two steps of beamforming training. The SLS is required to establish a link between the two stations, with one station attempting different antenna sector configurations consecutively while the other has its antennas set up in a quasi-omnidirectional fashion. In the case of phased antenna arrays, the BRP is then  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**IEEE Standard** & **Forum Type** & \
\begin{tabular}{c} **Peak Data Rate** \\ **(Gbps)** \\ \end{tabular} &
\begin{tabular}{c} **Bandwidth** \\ **(GHz)** \\ \end{tabular} \\ \hline IEEE 802.11 ay & International standard & 100 & 8.64 \\ IEEE 802.11 ad & Industry consortium & 8 & 2.16 \\ IEEE 802.15.3c & International standard & 5.7 & \textless{}3 \\ WirelessHD & Industry consortium & 4 & 2 \\ ECMA387 & International standard & 4.032 & 2.16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The 60 GHz mmWave ISM band standards suitable for indoor industrial communication.  
Figure 5: Worldwide allocation of 60 GHz mmWave spectrum.  
used to fine-tune this arrangement by employing narrower beams and possibly optimizing the antenna weight vectors.  
For a pair of transmitter-receiver stations, beamforming training identifies the optimum receive and transmit antenna sectors. A bidirectional training frame sequence is transmitted to accomplish this. IEEE 802.11 ad may achieve a maximum throughput of up to 8 Gbps with a fully trained transmit and receive antenna beamforming configuration. Furthermore, the beamforming protocol provides a low antenna gain device training procedure and can send training parameters to a central network coordinator for channel access scheduling. A detailed analysis of this protocol has been presented in [52].
# High-Precision Roller Supported by Active Magnetic Bearings
## 2 Automatic Alignment System Using Magnetic Bearings
### Magnetic Bearing System Configuration  
Magnetic bearings can measure and control the position of the rotor using electromagnets, sensors, and control systems. Figure 1 shows the schematic diagram of the magnetic bearing control system. The position of the shaft within the air gap is measured using the gap sensors placed in the x and y directions. The magnetic bearing controller adjusts the position of the shaft core by controlling the input current of the magnetic bearing coil supplied through the current driver. The magnetic bearing controller consists of a proportional-integral-derivative (PID) controller with a sampling frequency of 10 kHz, a lead compensator, and a low-pass filter. The PID gain was optimized to stably levitate the rotor and secure a phase margin of more than 20\({}^{\circ}\).
## 2 Materials and Methods
### Workflow of Power-Efficient Cluster-Based Routing Algorithm  
Figure 2 below illustrates the flow of the PECR algorithm.  
The main focus of this study is to find the factors behind energy exhaustion and the efficient use of energy assets. As discussed, in previous algorithms, there are a lot of loopholes that drain sensor nodes' energy more quickly. In this study, the proposed methodology includes intermediate levels during the transmission phase. The first level is from the node to the CH, where transmission and reception energy consumption is estimated by considering the length of the data packet, the free space energy, and the distance of the respective node as given in Equations (4) and (5). At the second level (CH to MCH) along with the previous parameters, aggregation energy is also considered, as given in Equation (7). The third level (MCH to BS) is important in the proposed study. At this level, energy consumption is estimated using all previous parameters as given in Equations (8)-(10). In the proposed energy consumption model of the PECR algorithm, energy has a direct relation with distance (d). Therefore, a multi-hop scheme is designed to conserve the energy of each node. Additionally, there is no direct data transmission between the sensor nodes to the BS. In short, the PECR algorithm is focusing on solving the limitations of existing algorithms using a clustering scheme, CH and MCH functionalities, optimum path selection in a forward direction, the rotation process, and energy consumption estimation at each level of data transmission.
# Application of Reinforcement Learning in Decision Systems: Lift Control Case Study
## 4 The Proposed Solution
### State Representation  
The state of the environment observed by the agent at time-step \(t\) is represented by the environmental state vector \(s_{t}\), determined based on the current state of the simulator. The specific components of this vector construction for the system adopted in this work are detailed below:  
\[s_{t}=[p_{t},\ Z_{t}^{1},Z_{t}^{2},\ \ldots,\ Z_{t}^{N},\ D_{t}^{2},D_{t}^{3}, \ \ldots,\ D_{t}^{N},\ U_{t}^{1},U_{t}^{2},\ \ldots,\ U_{t}^{(N-1)}]^{T}, \tag{4}\]  
where:  
* \(p_{t}\in\{1,2,\ \ldots,N\}\) represents the current position of the lift cabin at time-step \(t\).
* \(Z_{t}^{i}\in\{0,1\},i=1,2,\ \ldots,N\) indicates the state of the lift cabin button for floor \(i\) at time-step \(t\). It is equal to 1 if there is at least one passenger inside the cabin with floor \(i\) as their destination and 0 otherwise.
* \(U_{t}^{i}\in\{0,1\},i=1,2,\ \ldots,(N-1)\) denotes the state of a call button on floor \(i\) associated with the intention to go up. It is set to 1 if at least one passenger is on floor \(i\) with a destination floor \(j>i\), and 0 otherwise.
* \(D_{t}^{i}\in\{0,1\},i=2,3,\ \ldots,N\) represents the state of a call button on floor \(i\) associated with the intention to go down. It is valued at 1 if at least one passenger is on floor \(i\) with a destination floor \(j<i\), and 0 otherwise.
## 2 Materials and Methods
### Preparation of Bacterial Suspensions  
Bacterial cultures, inoculum sizes, growth conditions, and tests specifications were adjusted in accordance with the standards recommended by the Clinical and Laboratory Standards Institute (CLSI) [35; 36; 37]. The C-CaOH\({}_{2}\) NPs were tested against the following seven bacterial strains: three Gram-positive aerobes (_Staphylococcus aureus_ ATCC\({}^{\copyright}\)29213, _Staphylococcus Epidermidis_ ATCC\({}^{\copyright}\)12228, and _Enterococcus Faecalis_ ATCC\({}^{\copyright}\)29212); three Gram-negative aerobes (_Escherichia coli_ ATCC\({}^{\copyright}\)25922, _Klebsiella Pneumonia_ ATCC\({}^{\copyright}\)700603, and _Pseudomonas aeruginosa_ [clinical isolate]); and one Gram-positive anaerobic strain (_Parcimonas micra_ [clinical isolate]). The clinical bacteria isolates were obtained from the Microbiology Laboratory, College of Dentistry Research Center, King Saud University, Riyad, Saudi Arabia. All procedures and culture handling were performed aseptically in a class II biological safety cabinet (SteriIGARD(r) III Advance). All bacteria were initially cultured on Brain Heart Infusion agar (BHIA) and incubated in their suitable conditions (aerobic for 24 h; anaerobic for 48 h) at 37 \({}^{\circ}\)C. After incubation, two distinct bacterial colonies were inoculated into 3 mL of sterile Mueller-Hinton broth (MHB) and incubated at 37 \({}^{\circ}\)C. The final inoculum sizes were adjusted to 1 \(\times\) 10\({}^{8}\) CFU/mL (0.5 McFarland). The antibacterial activity of C-CaOH\({}_{2}\) NPs was explored utilizing three essential in vitro assays: (1) agar well diffusion to assess inhibition zones (ZOI); (2) resazurin-based microdilution assay to determine the minimal inhibitory concentration (MIC); and (3) minimum bactericidal concentration (MBC). All tests were performed in triplicate for each bacterial strain.