esa-sceva/satcom-chunk-collection huggingface dataset:
# Skin Cancer Disease Detection Using Transfer Learning Technique
## 3 Materials and Methods  
Self-learning algorithms are the foundation of artificial intelligence. As new information about the work is received, such algorithms continue to evolve [65]. These techniques are continuously evolving to resolve these issues. Self-learning algorithms can function because these models are based on the human brain [66]. Artificial neural networks (ANNs) are nodes (neurons) connected at various levels, such as human nerve cells. Information is recorded, processed (via positive or negative weighting), and output inside this neuron network. ANNs look especially promising because they have many levels and can recognize more complicated patterns. Deep learning [67; 68] refers to the learning processes that such networks can perform.  
This research introduces a deep transfer learning system to classify melanoma skin cancer. At the first level, pre-processing and various augmentation approaches are used to resolve the issue of class imbalance in the dataset and generate diversity. At the second level,  
\begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Reference** & **Methodology** & **Disease** & **Dataset** & **Accuracy** \\ \hline
[60] & Nasnet Mobile with & Melanoma & HAM10000 skin & 97.90\% \\  & Transfer Learning & & lesion dataset & \\ \hline
[46] & Support Vector Machine & Melanoma, Nevus & DERMIS dataset & 96.0\% \\  & (SVM) & Melanoma, & ISBI 2016 & 86.54\% \\ \hline
[53] & DCNN-FV & Non-Melanoma & challenge & \\ \hline
[54] & Deep Convolutional & Benign, & ISIC Archive & \\  & Neural Network (CNN) & Malignant, Melanoma & Repository & \\ \hline
[47] & SVM & Melanoma, & Ph2 \& ISIC & Ph2 98\%, \\  & & Non-Melanoma & Challenge & ISIC 87.8\% \\ \hline
[7] & Ensemble Model & Malignant, & Xanthous Race (XR), & XR (94.14\%), \\  & Benign & Caucasian Race (CR) & CR (91.11\%) \\ \hline
[48] & FSM \& SVM & Malignant, & Ph2 & 91.90\% \\  & & Benign & & \\ \hline
[49] & DCNN & Melanoma, & Self Contained & \\  & & Nevi & 2800 Images & 96\% \\ \hline
[8] & ANN & Malignant, & Self Contained & \\  & Benign & & 172 Images & 93.6\% \\ \hline
[61] & ResNet-50 & Melanoma, & Self Contained & Sensitivity (82.3\%), \\  & & Nevi & 4204 Images & Specificity (77.9\%) \\ \hline
[55] & Ensemble Model & Melanoma, & ISIC & Avg Precision \\  & & Non-Melanoma & (98.0\%) \\ \hline
[56] & STDP based & Malignant, & \\  & Spiking NN & Benign, Nevi & \\ \hline
[57] & DCNN & Melanocytic, & MED-NODE & MED-NODE \\  & & Non-melanocytic & DermI\& DermQuest & (99.29\%), \\  & & Non-melanocytic & (D\&D), & D\&D(99.15\%), \\  & & ISIC-2017 & ISIC(98.14\%) \\ \hline
[58] & CNN based & Melanoma, & Dermofit, & (90.58\%), \\  & CMD model & Benign & MED-NODE & MED-NODE \\  & & & (90.14\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related work summary.  
auto features are extracted, and then a pre-trained \"MobileNetV2\" model is implemented to classify the malignant melanoma from a benign skin lesion. The flow chart of the proposed technique is presented in Figure 1.
## 3 Experimental Results and Discussion
### Acceleration Responses  
Given the mentioned limitations of sensors, one common alternative to indirectly measure the accelerations is to carry out a second derivative of time-history displacement responses retrieved from the displacement sensors. The acceleration of amplification factor \(\beta\), as an essential index, is used to reflect the structural dynamic amplification effect, because the input peak acceleration is different under each loading earthquake intensity, which can be expressed as follows:  
\[\beta_{i}=\frac{\max(a_{i}(t))}{\max(a_{g}(t))}, \tag{2}\]  
where \(a_{\text{g}}\) and \(a_{\text{i}}\) are the measured maximum acceleration responses at the shaking table and floor \(i\), respectively.  
Figure 18 shows the variation of acceleration amplification factor \(\beta\) with the increasing input PGA from weak to strong. It could be seen that the measured \(\beta\) of the test model under the Taft wave was the largest compared to the other earthquake motions, and all acceleration amplification factors at the second floor were significantly higher than those at the first floor subjected to the three earthquake records. The \(\beta\) value was observed to sharply increase at each acceleration level within a PGA of less than 0.3 g, before frequent earthquake intensity 9, and then reached a maximum, whereas, the value steadily decreased with the increase of PGA from 0.3 to 1.0 g. When the input PGA reached 1.0 g of the rare earthquake intensity 8 for the test model, the acceleration amplification factors at the second floor were 2.3, 2.42, and 2.63, respectively, subjected to the El Centro, Kobe, and Taft waves, while those at the first floor were 1.4, 1.32, and 1.78, respectively. In conclusion, \(\beta\) exhibited a sharp increase before a  
Figure 17: Half-power bandwidth method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cases** & **1a to 5a** & **6a** & **7a** & **8a** & **9a** & **10a** \\ \hline
**Natural Frequencies (Hz)** & 15.68 & 15.68 & 15.57 & 15.35 & 15.01 & 14.82 \\
**Damping Ratios (\%)** & 3.17 & 3.18 & 3.19 & 3.21 & 3.23 & 3.27 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Natural frequencies and damping ratios.  
certain acceleration level and then gradually decreased with the further increasing acceleration owing to the structural stiffness degradation.
## 3 System Architecture
### Electric Storage System Model  
These days, the electrical energy storage devices are playing a significant role in maintaining the system reliability, enhance the power quality (PQ), and achieve green energy goals. Therefore, an ESS was installed for storing electrical energy in the proposed model to improve the performance of the HEMS. When the electricity prices were low, the ESS stores form the utility or excess PV energy.  
In this section, the battery ESS was modelled and integrated with the proposed system. The stored energy of battery \(E_{bat}(h)\) in any time interval is presented in Equation (2). A positive value of \(E_{bat}(h)\) represents the charging state of the battery and its negative value denotes the discharging state. The battery charging and discharging efficiencies are represented by \(\eta_{ch}\) and \(\eta_{dch}\), respectively. To avoid the rapid charging and discharging of the battery, the constraints given in Equations (3) and (4) should be meet. Equation (3) represents the maximum charging limit and Equation (4) represents the minimum discharging limit of a battery in any scheduling time interval. \(S_{bat}\) is a binary variable to represent charging state (\(S_{bat}=1\)) and discharging state (\(S_{bat}=0\)) of the battery during any hour \(h\) of a day.  
\[E_{bat}(h)=\frac{E_{bat}^{ch}(h)}{\eta_{ch}}\cdot S_{bat}(h)-E_{bat}^{chch}(h) \cdot\eta_{dch}\cdot(1-S_{bat}(h)) \tag{2}\]  
\[0\leq\frac{E_{bat}^{ch}(h)}{\eta_{ch}}\leq E_{ch}^{max}\cdot S_{bat}(h) \tag{3}\]  
\[0\leq E_{bat}^{chch}(h)\cdot\eta_{dch}\leq E_{dch}^{max}\cdot(1-S_{bat}(h)) \tag{4}\]  
\[SOC(h)=SOC(h-1)+\ \frac{\left(E_{bat}^{ch}(h)-|E_{bat}^{chch}(h)|\right) \cdot h}{C^{battery}} \tag{5}\]  
\[SOC_{min}\ <SOC(h)<\ \ SOC_{max} \tag{6}\]  
Equation (5) indicates the battery state of charge \(SOC\) and Equation (6) represents the maximum and minimum limits of battery SOC in each time slot. \(E_{bat}^{ch}(h)\) and \(E_{bat}^{chch}(h)\) signify the charging and discharging state, respectively, of a battery in time slot \(h\).
## 5 Tubing Fatigue Precision Fracture Separation Finite Element Simulation  
### Tubing Fatigue Fracture Precision Separation Finite Element Model  
Extended finite element method is a new numerical analysis method to study crack propagation. With the advantages of conventional finite element method (CFEM), it can deal with various discontinuities, such as crack growth and complex fluids, and it is widely used in the field of material failure. Through ABAQUS finite element simulation software, the tubing fatigue fracture finite element model is established. In addition, XFEM is used to analyze the fatigue crack propagation process.  
The established tubing and prefabricated crack are assembled, as shown in Figure 12. In order to make the calculation results in crack propagation easier to converge, the loading process of the tubing is simplified as the cyclic reciprocating motion of the loaded end in the same direction. In the loop module, the tubing loaded section is coupled to its center RP-1. Then, it is constrained in boundary conditions. When the boundary condition constraints are imposed on the tubing, the six freedom degrees of 20 mm fixed end at the left end are all constrained. Point RP-1 is subjected to cyclic displacement. The cycle time displacement curve is shown in Figure 13, where \(T\) is the time used for one cycle of cyclic loading, with the amplitude of the displacement value of \(\Delta\text{X}=6\) mm.  
Figure 11: Variation of axial stress at four positions of V-groove section under eccentric wheel loading: (**a**) Variation of axial stress at point a and point c; (**b**) Variation of axial stress at point b and point d.  
Figure 12: Application of boundary condition.  
The 304 stainless steel tubing is selected as the research object. For detailed data, the elastic modulus is 2.1 \(\times\) 10\({}^{5}\) MPa, the Poisson's ratio is 0.3, and the density is 7850 kg/m\({}^{3}\). In addition to material properties, the analysis of crack propagation requires additional parameters to be added to the keyword [19; 20]. The maximum principal stress criterion is chosen as the failure criterion, and the energy-based mixed-mode exponential evolution law is chosen for the damage evolution. When dividing the tubing mesh, the area near the V-groove is densified, with a cell size of 0.5 mm \(\times\) 0.5 mm \(\times\) 0.25 mm. The size of the rest of the grid cells is 1.0 mm \(\times\) 0.5 mm \(\times\) 0.5 mm. Prefabricated crack does not require meshing.
## 2 Materials and Methods
### Recording and Data Acquisition  
Electromyographic (EMG) activity was recorded from TA and soleus (SOL) of the right leg; AD and posterior deltoid (PD) of the right arm; and the right SCM and OO. EMG activity was recorded using pairs of Ag/AgCl electrodes (NeuroPlus A10040, Vermed, Bellows Falls, VT, USA) placed on the skin over the bellies of the intended muscles, with an inter-electrode distance of about 2 cm. Ground electrodes were placed over the right clavicle and the anterior tibia of the right leg. The skin at the limb EMG recording sites was shaved with a razor and cleaned with alcohol, while the skin below the eye and at the neck was only cleaned with alcohol. Electrode impedance was less than 20 k\(\Omega\) at all recording sites (Grass F-EZM5 impedance meter, Astro-Med, Inc., West Warwick, Rhode Island, USA). The EMG signals were amplified and band-pass filtered (10 Hz-1 kHz with a 60 Hz notch filter, Grass P511 amplifiers, Astro-Med, Inc., West Warwick, RI, USA) prior to digitization. Electrogniometers were placed across the right ankle (SG110A, Biometrics Ltd., Newport, UK) and elbow joints (SG110, Biometrics Ltd., Newport, UK). All analog signals were digitized at 2000 Hz (PCI-MIO-16E-4, National Instruments, Austin, TX, USA) and stored directly to hard drive using a custom-written LabView data acquisition routine.
## 2 Methodology
### Feature Extraction
#### 2.2.2 Root Mean Square (RMS)  
RMS is a critical feature that represents the energy or amplitude of the signal and is directly related to signal strength [18]:  
\[\text{RMS}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}x_{i}^{2}} \tag{3}\]  
High RMS values often indicate the presence of mechanical faults, making it a representative indicator for condition monitoring.
## 3 Results and Discussion
### Safety Factor Variations  
In LEM analyses, by assuming the critical slip surface to be the entire mobilized mass, the constant drawdown ratio is obtained from the all-movable mass under unique F.S. This assessment is considered the same for the movable mass regardless of drawdown rate and permeability coefficients. The reason for this is that the critical slip surfaces are shallow and the shell is large enough. Even seepage-induced downward forces acting on the upstream slope are not effective since the shell materials are permeable during LEM-based analysis. For instance, the critical slip surface for all analyses is considered L/H = 0.5 regardless of the drawdown rate and core permeability. All estimated safety factors for critical slip surfaces were obtained by the Morgenstern-Price method. Entire permeability coefficients versus drawdown ratios are estimated by LEM stability assessments and presented in Figure 6. In addition, by conducting the coupled analysis for the LEM and FEM procedures, the critical F.S was considered and monitored.  
Referring to the results of the stability analysis with SLOPE/W, the slip surface in the studied dam is a shallow surface, but in every analysis with different k and R the selected slip surfaces are different from each other, resulting in a variation in the F.S. For instance, the critical slip surface at the end of drawdown for various k and R variations is considered individually and different safety factors are estimated as well. To investigate the impact of these parameter variations on rapid drawdown, the trend of safety factors versus drawdown ratio is obtained, which is illustrated in Figure 7. By using this figure, results can be estimated of the drawdown rate and permeability under different R values. By looking at the comparative results presented in Figure 7, it can be mentioned that the drawdown rate from 0.2 to 0.6 is the most critical state for safety factors, which shows significant declines. A possible explanation for the observed minimum is the weight of the soil and the trade-off between the soil weight and soil shear strength as the drawdown level varies. In the initial stage of drawdown (L/H < 0.5) the increased weight of the slope has a proportionately greater destabilizing effect than the increased frictional strength and factor of safety falls. At a higher drawdown ratio (L/H > 0.5); however, the increased frictional strength starts to have a greater influence than the increased weight, and the F.S rises.  
Figure 6: F.S variation versus drawdown ratio regarding R by Morgenstern–Price (LEM) method (Note: R rate is m/day).
## 4 Experimental Results and Performance Evaluation
### Path Stretch  
Route path stretch is defined as the differences between the length of the longest path used for transmitted flows to the length of the best possible shortest path for those flows. Figure 5 shows the average route path stretch (RPS) for ATT network topology. RPSO outperforms FLS and RPF for all the number of flows. The curve shows that it can lead to a route path up to 37% smaller than the two-benchmarking works FLS, and RPF for 67 flows, respectively. To further evaluate the performance of the proposed solution, the size of the network was increased using different topologies. As expected, a similar trend was observed in Spain's topology as shown in Figure 6, where RPSO performs better than its counterpart by reducing path stretch 12% and 16% compared to FSL, and RPF, respectively. Intuitively, this implies that the computed RPS reflects one of the design objectives of RPSO to select a path with minimum critical switches and length. Conversely, the benchmarking works overlook RPS and focus on other features such as shared edges and path latency only, respectively. Therefore, the proposed solution has a better performance compared to the existing solutions.
## 3 Discussion: Two Mechanisms of THz Resonance Excitation for the Proposed Detector  
### Interpretation of the THz Plasmon Resonances  
The model presented here includes the possibility to excite plasmon resonances in the detector of Figure 1. This phenomenon can be also observed in the simpler case where only one GNR is used to contact the two electrodes; therefore, in the following we will assume a single GNR. In the absence of tunneling, the circuit model in Section 2.2 reduces to that of single lossy TL, with p.u.l. parameters given by:  
\[R=\
u L_{k},\ \ \ L=L_{k}+L_{M},\ \ C=C_{{}_{E}} \tag{25}\]  
These results hold because it is usually \(L_{k}>>L_{M}\), \(C_{q}<<C_{{}_{E}}\), and the collision frequency is usually of the order of \(\
u=10^{12}\) Hz. A consequence of the huge value of the kinetic inductance is a low propagation velocity, \(c_{{\it GNR}}\), and a high characteristic impedance, \(Z_{{\it C},{\it GNR}}\): typical values of these two parameters normalized to an ideally-scaled copper interconnect of the same dimensions would be:  
\[\frac{c_{{\it GNR}}}{c}\approx\sqrt{\frac{L_{m}}{L_{k}}}\approx 10^{-2},\ \frac{Z_{{\it CNT}}}{Z_{{\it C}}}\approx\sqrt{\frac{L_{k}}{L_{m}}}\approx 1 0^{2} \tag{26}\]  
The plasmon resonances are then obtained by imposing the classical resonance condition along a transmission line, that is:\[\beta l=n\pi/2\,,\,\text{with $n=1,2,3$} \tag{27}\]  
Let us refer to the detector in Figure 1a, assuming a metallic A-GNR, of width \(w\!=\!2\,\text{nm}\) and length \(l\!=\!1\,\upmu\)m. The GNR lies on a dielectric layer of permittivity \(\varepsilon_{r}\!=\!2.2\) and thickness \(0.1\,\upmu\)m.  
In the absence of external field, the TL model with p.u.l. parameters (Equation (25)) provides the input admittance reported in Figure 4, which clearly shows the existence of a plasmon resonance in the THz range. If the contact would be realized by copper instead of graphene, the higher propagation velocity would shift such a resonance to a frequency about two orders of magnitude higher, hence outside the THz range. This confirms a well-known result in the literature: in carbon-based nanostructures, by exciting the propagation of plasmons, _i.e._, such slowed down surface waves, it is possible to obtain resonances in the THz range, with reasonable lengths of the detector device [36; 37; 38]. Note that the high length/width ratio used in this example makes this configuration hard to be realized. According to Equation (27), reducing the length by a factor \(X\,\)means increasing the plasmon resonance frequency by the same factor. Therefore, to have plasmon resonance in the THz range we should fabricate a GNR with a minimum length of the order of about 100 nm.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 1 Introduction
Therefore, the intelligent physical layer, which is more different from traditional wireless, could be the core breakthrough point in forthcoming years. In this paper, we will introduce the concepts of the intelligent physical layer and the intelligent network layer of IVLC. The underlying physical layer will be given great consideration. Among the existing machine learning algorithms, there are four main categories according to the purpose of implementation: regression [14], classification [15], clustering [16], and dimensionality reduction [17]. However, in IVLC, especially in the physical layer, the existing machine learning algorithm is not designed to achieve the above functions. For this reason, we redefine the categories of machine learning techniques in the physical layer of the IVLC based on the communication system framework, including optimal coding, channel emulator, MIMO, channel equalization, and optimal decision. As seen in Section 3, such categorization intersects with the traditional ML applications, which facilitates readers who are interested in investigating intelligent visible light communication. Each module of the communication framework is featured with unique issues and thus requires specially-designed machine learning algorithms. We will go through the major obstacles of visible light communication and discuss how AI-empowered IVLC could overcome them. It is possible that the newly emerging intelligent visible light communication may play a key role in the 6G communication network, enabling worldwide smart connectivity and the construction of air-space-ground-sea integrated networks.
# Cybersecurity Requirements for Industrial Machine Control Systems
## 5 Case Study--Examples of Determining SLs of Safety Functions Implemented in Machine Safety Systems
### Example 7--Machine Control System: Category 4, PLe; Cybersecurity SL-T 3  
Let us consider a hydraulic press. The movement of the press actuator is fast, which implies that danger cannot be avoided (parameter P2). The sliding motion can crush a  
Figure 16: Safety system consisting of laser scanner (PLd/Cat.3) and robot controller (PLd/Cat.3) to achieve PLd/Cat.3 level (own elaboration).  
Figure 15: Hazardous zone in a robotic cell protected by a laser scanner (own elaboration).  
person or a body part. So, the potential injury is assumed to be severe (parameter S2). The operator inserts and removes the workpiece from the press every few minutes, violating the protective zone monitored by the light curtain type 4 according to IEC 61496-1. This implies parameter F2 is used. Based on the graph, the level PLr = e is required. Hydraulic presses should be designed in accordance with the requirements of ISO 16092-3 [29], which requires a PLr = e level and category 4 for the SF under consideration.  
The light curtain (Type 4, PLe/Kat.4) is connected to a programmable safety unit, the outputs of which are connected to redundant electromechanical components with fault monitoring (electrohydraulic valves or contactors) that stop the dangerous movement of the actuator (Figure 17).  
For the circuit implementing the depicted function of detecting the violation of the danger zone from Figure 17 with the assumptions described above, protection against cyber-attack is required. The required protection level is assumed to be SL-T 3 (high level).
## 2 Materials and Methods
### Strains  
_Chlorella_ sp. (CHLO_UFPS010), and _Scenedesmus_ sp. (SCEN_UFPS015) from INNOValgae collection (UFPS, Colombia) were used in this study. The strains were maintained in a 2 L glass flask containing 1.2 L of sterile Bold Basal [39]. Each flask was mixed through the injection of filtered air with 0.5% (\(v/v\)) CO\({}_{2}\) at a flow rate of 0.78 L min\({}^{-1}\)(Resun, LP-100), 25 \({}^{\circ}\)C, and light-dark cycle of 12:12 h at 100 umol m\({}^{-2}\) s\({}^{-1}\) for 30 days.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Strain**} & **Leachate Concentration** & **Biomass Concentration** & \multirow{2}{*}{**Reference**} \\  & **(\% \(v/v\))** & **(g-dm\({}^{-3}\))** & \\ \hline Algal consortium & 10 & 2.4 & [20] \\ _Chlamydomonas_ sp. & & & \\ SW15 aRL & 30 & 2.99 & [21] \\ _Ch. snowiae_ & & n/a * & [22] \\  & 10 & 8.2 & [23] \\ _Chlorella_ sp. & & n/a * & [29] \\  & 15 & 0.34 & [28] \\ _Chlorella minutissima_ 26a & 10 & 1.1 & [25] \\  & 20 & 4 & [26] \\ _C. vulgaris_ & 5 & 0.45 & [27] \\  & 50 & 1.92 & [32] \\ _C. vulgaris_ FACHB-31 & 10 & 1.64 & [31] \\ _C. pyrenoidosa_ & 100 & 2.13 & [33] \\ _C. pyrenoidosa_ & 10 & n/a * & [22] \\ _C. pyrenoidosa_ (FACHB-9) & 20 & 1.58 & [1] \\ _Desmodesmus_ sp. & 7 & 1.3 & [34] \\ _Desmodesmus subspicatus_ & & n/a * & [24] \\ ([PERSON] 1953/SAG) & & 8.1 & [23] \\ _Microcytsis_ sp. & 10 & 8.0 & [23] \\ _Oscillatoria_ sp. & 20 & 0.8 & [28] \\ _Picochlorum ocultatum_ & & 1.9 & [35] \\  & 8.12 & [23] \\ _Scenessdesmus_ sp. & & n/a * & [29] \\  & & 0.16 & [28] \\ _S. obliquus_ & 7 & 1.2 & [34] \\ _Stigeolonium_ sp. & 10 & 8.1 & [23] \\ _Tetradesmus obliquus_ & 15 & 0.56 & [26] \\ \hline n/a *: Data non-available. & & & \\ \end{tabular}
\end{table}
Table 1: Strains of algae and cyanobacteria cultured on different concentrations of landfill leachate.