esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Amplifiers for Access and Passive Optical Networks: A Tutorial
## 3 Optical Amplifiers in Telecommunications Networks
### Semiconductor Optical Amplifiers--SOAs
The energy of an incident photon must be sufficient to overcome the forbidden band of the semiconductor. * Population inversion. In a pro-polarized P-N transition, it is possible to achieve population inversion by molecular excitation to a higher energetic layer. The state of population inversion means that the quantity of electrons in the valence band is higher than the quantity of electrons in lower-energy bands. * Gain generation. New photons are released. The resonator is reduced in comparison with semiconductor lasers. The newly generated photon stimulates recombination of electrons and holes. The result of this recombination is the generation of coherent photons with the same wavelength, polarization and phase as the incident photon. * Leaving the edge of the chip. Stimulated emission is dependent on the intensity of the incident radiation. SOAs are manufactured as a chip situated in a standard housing with the capability of temperature control, which allows wavelength stability and the possibility of achieving maximal gain. A high concentration of carriers in an active area causes an increase in the refractive index, which is higher than that in the coating. This region serves as a lightweight circuit for newly generated photons [60]. Advantages of SOAs:  
* large range of wavelengths 1280-1600 nm,
* large bandwidth,
* maximal gain up to 30 dB,
* small size, possibility for integration on chips with lasers and semiconductor components,
* appropriate for all-optical systems,
* no optical pump is needed (electrically pumped),
* very good gain dynamics in comparison with fiber amplifiers,
* low cost,
* suitable for PONs. Disadvantages of SOAs:
* high insertion loss of the SOA amplifier (approximately 5 dB), which increases if the amplifier is switched off,
* low gain in commercial amplifiers (15-25 dB),
* residual polarization sensitivity,
* higher noise figure and cross-talk levels due to nonlinear phenomena such as 4-wave mixing (7-12 dB),
* requires temperature stabilization,
* cross-gain modulation of multiple signals via carrier depletion. Saturation of an SOA is achieved by a strong input signal, which depletes free carriers in an active area. The gain decreases with increasing input power. Saturated power is achieved by a 3 dB increase in the maximal value (see Figure 9). The influence of carrier depletion can be partially limited by so-called holding beam injection (optical copumping) [69].
# Fault Diagnosis of Wind Turbine Planetary Gear Based on a Digital Twin
## 5 Discussion  
This paper applies digital twinning technology to the real-time condition monitoring and fault diagnosis of wind turbine planetary gear. In the monitoring and diagnosing planetary gear of wind turbines, the shortcomings of traditional monitoring and diagnosis systems lacking 3D visualization and online real-time diagnosis are solved. This study aims to establish a digital twin fault diagnosis system by integrating the digital twin model and EMD-ASO-SVM fault diagnosis model to realize the condition monitoring and real-time diagnosis of planetary gear of wind turbines. The feasibility of the digital twin system and the accuracy of fault diagnosis are observed in experimental examples. The results show that, compared with the traditional monitoring and diagnosis system, the proposed method can realize the fault diagnosis and 3D visual monitoring of the planetary gear of wind turbines, more intuitively reflect the operating status and data changes of the equipment, quickly locate the fault location, shorten the equipment maintenance time, and improve the economic benefits. Moreover, experiments show that the diagnostic accuracy of the EMD-ASO-SVM diagnosis model is 94%, which is 6.67% higher than that of the traditional SVM fault diagnosis model. Moreover, the advantages of a small sample size and short time highlight the diagnostic efficiency of the system. Meanwhile, compared with the traditional digital twinning system, the proposed method has the characteristics of low delay and high universality. In future studies, the proposed method may facilitate the study of digital twin fault diagnosis systems for more wind turbine components to complete the comprehensive monitoring of wind turbine operating conditions and online fault diagnosis.
# Mixed Method for Isogeometric Analysis of Coupled Flow and Deformation in Poroelastic Media
## 3 Isogeometric Analysis
### Fundamentals on B-Splines and NURBS  
We start the discussion on B-Splines and NURBS by first defining a knot vector. A knot vector in one dimension is a non-decreasing set of coordinates in the parameter space, written \(\Xi=\{\xi_{1},\xi_{2},\ldots,\xi_{n+p+1}\}\), where \(\xi_{i}\in\mathbb{R}\) is the \(i\)th knot, \(i\) is the knot index, \(i=1,2,\ldots,n+p+1\), \(p\) is the polynomial order, and \(n\) is the number of basis functions. Knot vectors may be uniform or non-uniform depending on whether the knots are equally spaced in the parameter space or not.  
A univariate B-Spline curve is parametrized by a linear combination of \(n\) B-Spline basis functions, \(\{N_{i,p}\}_{i=1}^{n}\). The coefficients corresponding to these functions, \(\{\mathbf{X}_{i}\}_{i=1}^{n}\), are referred to as control points. The B-Spline basis functions are recursively defined starting with piecewise constants (\(p=0\)):  
\[N_{i,0}(\xi)=\begin{cases}1&\text{if }\xi_{i}\leq\xi<\xi_{i+1}\\ 0&\text{otherwise}\end{cases} \tag{35}\]  
For higher-order polynomial degrees (\(p\geq 1\)), the basis functions are defined by the Cox-de Boor recursion formula:  
\[N_{i,p}(\xi)=\frac{\xi-\xi_{i}}{\xi_{i+p}-\xi_{i}}N_{i,p-1}(\xi)+\frac{\xi_{i+ p+1}-\xi}{\xi_{i+p+1}-\xi_{i+1}}N_{i+1,p-1}(\xi) \tag{36}\]  
B-Spline geometries, curves, surfaces and solids, are constructed from a linear combination of B-Spline basis functions. Given \(n\) basis functions \(N_{i,p}\) and corresponding control points \(\mathbf{P}_{i}\in\mathbb{R}^{d}\), \(i=1,2,\ldots,n\), a piecewise polynomial B-Spline curve is given by:  
\[\mathbf{C}(\xi)=\sum_{i=1}^{n}N_{i,p}(\xi)\mathbf{P}_{i} \tag{37}\]  
Similarly, for a given control net \(\mathbf{P}_{i,j}\), \(i=1,2,\ldots,n,j=1,2,\ldots,m\), polynomial orders \(p\) and \(q\), and knot vectors \(\Xi=\{\xi_{1},\xi_{2},\ldots,\xi_{n+p+1}\}\), and \(\mathcal{H}=\{\eta_{1},\eta_{2},\ldots,\eta_{m+q+1}\}\), a tensor product B-Spline surface is defined by:  
\[\mathbf{S}(\xi,\eta)=\sum_{i=1}^{n}\sum_{j=1}^{m}N_{i,p}(\xi)M_{j,q}(\eta)\mathbf{P}_{ i,j} \tag{38}\]  
B-Spline solids are defined in a similar way as B-Spline surfaces from tensor products over a control lattice.  
NURBS are built from B-Splines to represent a wide array of objects that cannot be exactly represented by polynomials. A NURBS entity in \(\mathbb{R}^{d}\) is obtained by projective transformation of a B-Spline entity in \(\mathbb{R}^{d+1}\). The control points for the NURBS geometry are found by performing exactly the same projective transformation to the control points of the B-Spline curve. A detailed treatment of B-Splines and NURBS can be referred from [PERSON] et al. [45].
## 3 Results
### Evaluation of plaque Load and Size Using Multicontrast OCT  
The diameters of the plaques segmented in intensity and retardation data was evaluated. The size distributions of all data sets revealed by the two OCT contrast channels are shown in the histogram in Figure 5a. In the intensity images, the diameters ranged from 11 to 85 \(\upmu\)m with a mean value of 42 \(\upmu\)m. The plaque size observed in the retardation data ranged from 14 to 148 \(\upmu\)m in diameter (mean 54 \(\upmu\)m). Next, the observed plaque volume density (plaques per mm\({}^{3}\)) was analyzed and is plotted in Figure 5b. The plaque density in intensity data (median of 224 [Q1-Q3: 112-332 p/mm\({}^{3}\)]) and retardation data (median of 156 [Q1-Q3: 124-208 p/mm\({}^{3}\)]) was rather similar. Lastly, the mean plaque area (in \(\upmu\)m\({}^{2}\)) was calculated for each data set. Results are displayed in box plots in Figure 5c. In intensity data, plaque areas ranged between 1250 and 1450 \(\upmu\)m\({}^{2}\) (median of 1394 [Q1-Q3: 1256-1458 \(\upmu\)m\({}^{2}\)]). This is significantly smaller than those measured in retardation images, where the plaque area was clustered between 1400 and 1750 \(\upmu\)m\({}^{2}\) (median of 1518 [Q1-Q3: 1430-1727 \(\upmu\)m\({}^{2}\)]). Q1 and Q3 represent the first and third quartile.  
Figure 4: Overview of plaques segmented in the OCT data sets of eleven patients in 500 \(\upmu\)m \(\times\) 500 \(\upmu\)m \(\times\) 500 \(\upmu\)m fields of view (FOV). (**a**) Plaque count in retardation and intensity data including the overlapping plaques (i.e., plaques observed both in intensity and retardation images) plotted for each patient. (**b**) Relative plaque count in retardation and intensity data including the overlapping plaques plotted with age. Patients with two data sets were averaged and mean values are plotted.  
Figure 5: Statistical evaluation of segmented data sets. (**a**) Histogram of plaque diameters measured for intensity and retardation data sets. (**b**) Absolute numbers of plaques per mm\({}^{3}\) in intensity and retardation images. The amount of plaques visualized in intensity data (median of 224 [Q1–Q3: 112–332 p/mm\({}^{3}\)]), as compared to retardation data (median of 156 [Q1–Q3: 124–208 p/mm\({}^{3}\)]) was rather similar. (**c**) Mean plaque size in \(\upmu\)m\({}^{2}\) calculated for intensity (mean 1394 \(\pm\) 294 \(\upmu\)m\({}^{2}\)) and retardation (mean 1750 \(\pm\) 608 \(\upmu\)m\({}^{2}\)) data. For boxplots, mean values of each data set were computed.
## 3 Results and Discussion
### Breaking Strength of Conductive Wrapped Yarns  
Figure 4 shows the breaking strength of conductive wrapped yarns as related to the twist counts. The breaking strength is correlated with the level of twisting. Increasing the twist counts first increases and then decreases the breaking strength. As for the wrapped yarns composed of one metallic wire, the optimal twist counts is 60 turns/10 cm with a maximum breaking strength of 1.67 cN/dtex. As for the wrapped yarns composed of two metallic wires, the optimal twist counts is 60 turns/10 cm with a maximum breaking strength of 1.02 cN/dtex. A low twist count renders the wrapped yarns with uneven strength lengthwise, which generates a greater number of rounds for weak ring arrangement.  
Figure 3: **(a,b)** The scanning electron microscope (SEM) image of Core yarn with different number of wires; (**c**) SEM image of the warp yarn is PSA yarn and the weft is a conductive yarn wrapped with a wire (PSA\(-\)SS-W1); (**d**) SEM image of PSA-SS-W1’; (**e,f**) SEM image after burning of PSA-SS-W1’conductive fabric (**g,h**): SEM image after burning of PSA-SS-W1 conductive fabric; (**i**) Photograph displaying water droplets with different pH values on the superhydrophobic fabric surface; (**j,k**) Thermal properties of fabrics before and after superhydrophobic treatment.  
Hence, the wrapped yarns have a higher strength where the weak rings are situated rather than other parts. An increase in the twist counts causes the fibers to incline and decreases the axial force of the core yarns. Therefore, the wrapped yarns demonstrate a lower strength. In addition, the twisting process exerts prestress onto the fibers, attenuating the ability of fibers to withstand a force. To sum up, the core and the sheath of the wrapped yarns jointly form a compact structure and contribute to a greater cohesion when the twist counts increase. Meanwhile, the amount of PSA roving per unit length also increases, which improves the strength and elongation of the wrapped yarns. Conversely, excessive twist counts have a negative influence on the axial force of the wrapped yarns which, in turn, interferes with the mechanical properties.
## 3 Overview of the Matrix-DSP  
The Matrix-DSP integrates 24 DSP cores with a peak double precision performance of 4.608 TFLOPS per second at 2.0 GHz. The Matrix-DSP uses the very long instruction word (VLIW), which can launch five scalar instructions and six vector instructions simultaneously,  
Figure 3: ELL format based on 5-point discrete format on a \(4\times 4\) grid.  
for a total of eleven instructions. The overall architecture of the Matrix-DSP, shown in Figure 4, consists of a scalar processing unit (SPU) for scalar computation and flow control, as well as a vector processing unit (VPU) for vector computation. The VPU consists of 16 64-bit vector processing elements (VPEs) that support vector computation with a total length of 1024 bits. Each VPE consists of six functional components, including three floating point multiply accumulators (FMAC), two vector load/store units, and one bit process (BP). The SPU and VPU can exchange data through the scalar-vector-shared register (SVR), which supports broadcasting data from scalar registers to vector registers.  
The Matrix-DSP [9; 10; 11; 12; 13] has a multi-level memory architecture with one 64 KB scalar memory (SM) for scalar access and one 768 KB array memory (AM) for vector access on each DSP core. All 24 cores share a 6 MB of global shared memory (GSM), which provides up to 307 GB/s of on-chip read and write bandwidth, as well as supports up to 128 GB of external shared memory. The Matrix-DSP integrates a direct memory access (DMA) engine, which enables fast data transfer between different memory structures(SM, AM, GSM, and external memory). The DMA provides the point-to-point transfer instruction DMA_p2p, which can transfer contiguous memory data with a fixed offset, as well as the indirect indexed address transfer instruction DMA_SG, which can transfer data from the GSM or external memory through an index array. The 24 DSP cores can only exchange and share data through the GSM or external memory. The external memory used for the Matrix-DSP in this paper is a dual-channel DDR4 with a theoretical bandwidth of 42.6 GB/s.
## 3 The Motion State Estimation Method
### Moving Object Segmentation
#### 3.3.1 Range Image Projection and Differential Image Generation  
In usual use, Lidar is rotated to generate a point cloud centered on itself. In this paper, the point cloud data are projected into an information-tight range image by spherical projection. The schematic of the spherical projection is shown in Figure 3.  
Figure 3: The diagram of the spherical projection.  
Figure 2: The structure of the motion segmentation model.  
The spherical coordinate system is mainly based on the following equation [14],  
\[\theta= \arctan(y/x)\] \[\varphi= \arctan(z/\sqrt{x^{2}+y^{2}}) \tag{5}\]  
where \((x,y,z)\) are the coordinates of the point in the Lidar coordinate system. The pitch and yaw angles of the point can then be calculated by Equation (6)  
\[yaw= (\theta+\pi)/2\pi \tag{6}\] \[pitch= (up-\varphi)/(up-down)\]  
where \(up\) and \(down\) are the Lidar vertical viewing angle upper and lower limits. By setting the range image resolution to \((W,H)\), for a point \(p(x,y,z)\), the range image coordinate \(r(row,col)\) is,  
\[col= yaw*W \tag{7}\] \[row= pitch*H\]  
In this way each point \(p(x,y,z)\) of the point cloud can be projected to a pixel coordinate \(r(row,col)\) in the range image \(r\). The corresponding pixel value of the image is a four-dimensional vector of \((x,y,z)\) and the distance of the point \(p(x,y,z)\) to the Lidar transmitting source, as shown in Equation (8).  
\[range=\sqrt{(x^{2}+y^{2}+z^{2})} \tag{8}\] \[r(row,col)=(x,y,z,range)\]  
The differential image \(d\) can then obtained by Equation (9),  
\[d_{i}(row,col)=\frac{\left|r(row,col)^{t}-r(row,col)^{t+i}\right|}{r(row,col)^ {t}} \tag{9}\]  
where \(r(row,col)^{t}\) is the range image of the source point cloud \(P_{t}\), \(r(row,col)^{t+i}\) is the range image of the ego-motion compensated point cloud \(P^{\prime}_{t+i}\), and \(d_{i}\) is the differential image. The range image feature \(f(row,col)\) can be obtained by concatenating \(r(row,col)\) and \(d(row,col)\), that is, \(f(row,col)=(x,y,z,range,d)\).
## 3 Review Results
### Technology Acceptance Model and Its Extensions
#### 3.1.3 Extended TAM with Trust and Perceived Risk  
Trust has been added as an essential construct in the extended Technology Acceptance Model (TAM) to account for the reliability and security concerns associated with using ChatGPT. This extension highlights the critical role of trust in shaping users' attitudes and intentions. When users trust that ChatGPT is reliable and secure, they are more likely to develop positive attitudes toward its adoption and use. Although perceived risk is sometimes considered to be insignificant, it still plays a role in understanding users' apprehensions about adopting new technologies. By incorporating trust into the extended TAM, researchers and developers can better address the concerns users may have regarding the dependability and safety of ChatGPT, thereby fostering greater acceptance and utilization of the technology in educational settings [24]. This approach underscores the importance of building and maintaining user trust to ensure the successful integration of AI tools in education.  
The Technology Acceptance Model (TAM) and its extensions provide a robust framework for understanding the adoption of ChatGPT in higher education. Confirmed factors such as perceived ease of use, perceived usefulness, attitude, feedback quality, trust, and personal innovativeness play significant roles in influencing adoption. However, unconfirmed factors like extrinsic motivation, perceived enjoyment, technicality, cost, privacy concerns, anxiety, and technology readiness highlight the complexity and variability among the technology adoption processes. By incorporating additional constructs and extending the original TAM, researchers can capture a more comprehensive picture of the determinants that facilitate or hinder the adoption of ChatGPT in educational settings. Future research should continue to explore these extensions and validate their relevance in diverse educational contexts.
## 4 Survey of Existing Works
### Hybrid Approaches  
Hybrid approaches are also becoming popular nowadays. In [126], the authors propose a hybrid of PSO and GA for optimization in TDMA scheduling. The performance of this method is compared with PSO, max-degree-first coloring algorithm, and node-based scheduling algorithm. The results show that hybrid algorithm is marginally better (644 ml of energy) than the schedules determined by max-degree-first coloring algorithm and node-based scheduling algorithm, which consume 740 mJ and 666 mJ, respectively. Moreover, the proposed method can easily make tradeoffs between the time and energy objectives by a proper weight factor.  
Another hybrid approach was presented in [127] called LEACH-GA, which has basically the same set-up and steady-state phases of LEACH for each round, with the addition of a preparation phase. In preparation phase optimization is done by GA genetic algorithm-based adaptive clustering protocol with an optimal probability prediction to achieve good performance in terms of lifetime of network in wireless sensor networks. The preparation phase is performed only once before the set-up phase of the first round. This LEACH-GA hybrid method showed almost 40% better lifetime compared to LEACH, almost 400% better lifetime compared to minimum transmission energy (MTE), and nearly 600% better lifetime compared to direct transmission (DT).
# A Multi-Model Proposal for Classification and Detection of DDoS Attacks on SCADA Systems
## 4 Materials and Method
### LSTM-Based Models  
In this model, analyses were performed on the LSTM algorithm. The LSTM algorithm is an iterative neural network and has been used frequently recently. Due to its structure, it is very effective in catching long-term addictions. It can store information for a long time with its special memory cell architecture. LSTM consists of repetitive sequential blocks known as memory blocks.  
In this algorithm, there are input, output, and forget gates that enter and exit between cells and regulate the flow of information. For the iteration process, the input is generated, the predicted output value is obtained according to the current situation, and the next output vector is generated. Figure 5 shows the architecture of the LSTM-based deep learning models.  
The first proposed model was based on deep learning using the LSTM algorithm. LSTM networks contain a sequential input layer. In the proposed LSTM network architecture, the LSTM layer was placed after the input layer. Next came a smoothing layer and, finally, the fully connected classification and output layers. In the study, two LSTM models with 200 epoch and 300 epoch parameters were prepared (LSTM1a and LSTM1b). Other parameters selected for the models were mentioned at the beginning of the chapter.  
CNN-Based Models  
The CNN algorithm was studied in this model. The CNN algorithm is a variant of feed forward neural network. The architecture of the CNN algorithm is similar to the multilayer perceptron and consists of three layers. These are the convolution layer, pooling layer, and fully connected layer [34]. Multiple filters are included in this algorithm to extract or retrieve hidden features from the dataset. Figure 6 shows the architecture of the CNN-based deep learning models.  
Deep learning was performed with the CNN1a model using 200 epochs and the CNN1b model using 300 epochs. Both models employed a 1-D CNN layer and pooling layer followed by normalization and flattening. Finally, connected, classification, and output layers were used. Other parameters selected for these models were explained at the beginning of the chapter.
## 5 Research on the Frequency-Domain Characteristics of AE Signals
### Discussion on AE Signal Distribution and Peak Stress Precursor
#### 5.1.2 Time-Frequency Domain of the P-Signals  
In Section 5.1.1, P-signals, with large peak frequency, frequency centroid, and amplitude, are defined as the precursor signals for rock instability and failure. Then, the P-signals in each confining pressure test were extracted and filtered, and the detailed frequency-domain information of the P-signals was obtained according to the fast Fourier transform (FFT). The time and frequency domain waveforms of the P-signals under different confining pressures are shown in Figures 9 and 10, respectively. The P-signals under each confining pressure show extremely similar characteristics; that is, the primary dominant frequency of the P-signal is concentrated near 375 kHz, while the secondary dominant frequency is mostly concentrated at 310 kHz. The frequency-domain signal shows the phenomenon of \"multi-peak coexistence,\" dominated by the primary frequency.  
This phenomenon shows that the selection of the P-signals is not strongly affected by the test environment but is mainly determined by the physical properties of rock. This means that the P-signals defined in this paper have strong representativeness and applicability.