esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Frequency Regulation Strategy of WTG Based on VSG Control
### Frequency Regulation Control Model of WTG  
The WTG model controlled by the VSG includes the wind turbine model, shafting rotation model, MPPT control, and the VSG control system. If WTG works in MPPT mode, only the kinetic energy of the rotor can be used to support the system frequency. The frequency regulation control block diagram of the WTG based on VSG control is shown in Figure 2[26].  
When the WTG operates in MPPT mode, the output active power is only determined by the WTG rotor speed, and the output active power can be expressed as follows:  
\[P_{\text{MPPT}}=k_{\omega}\omega_{r}^{3} \tag{3}\]  
Since the frequency response expression of the system needs to be quantitatively derived in this paper, the least squares method is adopted to linearize the output active power of the MPPT control link to facilitate the subsequent calculation, which is shown in Figure 3. The minimum constraint value of the rotor speed is 0.7 pu.  
After linearization by the least squares method, the output reference power under MPPT control is expressed in Equation (4):  
\[P_{\text{MPPT}}=k\omega_{r}-b \tag{4}\]  
where \(k\) and \(b\) are the slope and intercept of the line after linear fitting by the least squares method, respectively.  
Figure 3: Least squares fitting process.  
Figure 2: Control block diagram of WTG.  
The output electromagnetic power of the WTG can be expressed as follows:  
\[P_{\text{WTGe}}=P_{\text{MPPT}}+\Delta P_{\text{d}} \tag{5}\]  
The rotor motion equation of the WTG is shown in (6):  
\[2H_{\text{WTGe}}\omega_{\text{r}}\frac{d\omega_{\text{r}}}{dt}=P_{\text{WTGm}}- P_{\text{WTGe}} \tag{6}\]  
When the WTG participates in the frequency regulation process, the rotor speed of the WTG will change, and on this time scale, the wind speed can be considered constant. Therefore, to simplify the analysis, the variation of \(P_{\text{WTGm}}\) can be ignored, and the mechanical power of the WTG is assumed to be equal to the output active power under MPPT control. Then, when the WTG participates in the frequency regulation, the small signal equation of Equation (6) can be expressed as in (7):  
\[2H_{\text{WTG}}\omega_{\text{r0}}\frac{d\Delta\omega_{\text{r}}}{dt}=-\Delta P _{\text{WTGe}} \tag{7}\]  
Equations (4) and (5) can be substituted into (7) as follows:  
\[2H_{\text{WTG}}\omega_{\text{r0}}\frac{d\Delta\omega_{\text{r}}}{dt}+k\Delta \omega_{\text{r}}=-\Delta P_{\text{d}} \tag{8}\]
# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 2 Wastewater Treatment Processes in an Industrial Wastewater Treatment Plant
### Challenges in Wastewater Treatment
Overall, the complex, dynamic, and highly nonlinear behavior of the wastewater treatment processes from an industrial plant imposes the usage of modern approaches to analyze, model, predict, and especially to control them, to optimize and to make more efficient the entire plant operating process [52; 63]. Multi-objective models have been proposed to deal with more complex trade-offs in water systems, such as maximizing resilience for given costs [3]. All these challenges necessitate continuous research and development in this field, particularly in model interpretability, data management, and the development of sustainable wastewater treatment technologies that ensure a performant wastewater treatment process. However, AI-based systems, once implemented in a WWTP, can significantly improve the efficiency of wastewater treatment processes, thereby contributing to environmental protection (by providing a quality effluent) and resource recovery. So, AI techniques (such as ML and DL) are a very useful tool for the optimization of wastewater treatment processes, due to their ability to process the data supplied by a WWTP, to achieve quality predictions for very important parameters, in discovering the hidden relationships between these parameters, and so on, despite the challenges that inherently arise, such as the need for a very large amount of data (very large databases, which must be very well structured) and the interpretability of complex models [27; 31; 62].
## 2 Data Acquisition and Processing
### VHR Satellite Series
GCPs were visualized first on panchromatic images (to increase the precision), and then applied to the multi-spectral images. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Acquisition Date** & **Satellite** & **Product Level** & **Product Type** & **DEM Correction** & **Correction Method** \\ \hline
1 May 2014 & GE01 & LV2A & Standard & Coarse DEM & 4 \\
22 May 2014 & WV02 & LV3D & Orthorectified & Fine DEM & 4 \\
30 May 2014 & WV02 & LV3D & Orthorectified & Fine DEM & 4 \\
18 June 2014 & QB02 & LV2A & Ortho ready & Base Elevation & 2 \\
24 June 2014 & GE01 & LV3D & Orthorectified & Fine DEM & 4 \\
26 June 2014 & WV02 & LV2A & Ortho ready & Base Elevation & 2 \\
8 July 2014 & GE01 & LV2A & Standard & Coarse DEM & 4 \\
11 July 2014 & QB02 & LV2A & Ortho ready & Base Elevation & 2 \\
29 July 2014 & WV02 & LV2A & Ortho ready & Base Elevation & 2 \\
7 August 2014 & GE01 & LV3D & Orthorectified & Fine DEM & 4 \\
26 August 2014 & GE01 & LV2A & Standard & Coarse DEM & 3 \\
4 October 2014 & QB02 & LV2A & Standard & Coarse DEM & 3 \\
18 October 2014 & WV02 & LV2A & Ortho ready & Base Elevation & 1 \\
14 November 2014 & WV02 & LV2A & Ortho ready & Base Elevation & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Available Digital Globe images series acquired over the Sukumba site, Mali in 2014 including GeoEye1 (GE01), WorldView2 (WV02) and QuickBird2 (QB02). The right column (Correction Method) refers to the specific type of processing applied to the images as described below.
## 4 Precise Orbit Determination (POD)
### Satellite Clock Offset Validation  
In this study, satellite clock offsets were estimated from the undifferenced ionosphere-free code and phase combination of the B1I and B3I signals, together with orbit parameters. Because there was no external reference for BeiDou-3e satellites, differences in the clock offset series after removing reference clock data in overlapping arcs, in the same way as for the orbit overlaps, were used to assess the clock quality. Figure 7 shows the STDs for one-day clock offset overlaps between two adjacent arcs, where results for BeiDou-2 GEO (C03), BeiDou-2 IGSO (C10), BeiDou-2 MEO (C14), BeiDou-3e IGSO (C32), and BeiDou-3e MEO (C34) are illustrated, together with the mean values for all BeiDou-2 and BeiDou-3e satellites.  
Figure 6: SLR residuals for BeiDou-2 satellites (top) and BeiDou-3e satellites (bottom).  
Figure 7: STD values for BeiDou clock products using one-day overlaps between two adjacent arcs with the middle day in the first arc as a reference.  
Note that, for MEOs, there is a substantial reduction in clock offsets due to the data gap outside the Asia-Pacific region. Thus, the statistics in Figure 7 and Table 8 only include clock offset within Asia-Pacific region for MEOs. As seen in Figure 7, MEOs displayed the best performance among all three types of satellites, with an average STD of 0.16 ns, followed by IGSOs (0.17 ns) and GEOs (0.22 ns), as listed in Table 8. As our analytical results show, the precision of the satellite clock offsets is consistent with that of the radial component of the orbit listed in Table 5, which is in line with theoretical expectations.
## 3 The KLSVM Model
### Linearly Non-Separable Data
#### 3.6.2 Standard Linear Classifier Optimization  
For positive data point \(w\cdot x+b\rightarrow+1\), the linear response is already greater than \(+1\) and has no cost \(J=0\). On the other hand, if it is less than \(+1\), the cost will increase linearly with its distance from the margin. This kind of loss is called Hinge Loss. Its analytical form \(J_{i}\) as shown in  
Figure 4: KLSVM: Linearly non-separable data points between the _Very Good Link_ and the _Good Link_ regions. The retransmission count patterns of these data points are inconsistent with the other data points of their classes.  
Equation (17) is piecewise linear, and is either a zero or positive course that increases away from the margin. Our overall optimization in Equation (18) is then a margin term \(\sum_{j}w_{j}^{2}\) plus our stack variable \(R\) times the Hinge Loss \(J_{j}\) and a balance between the margin term and stack variables, also defined as  
\[w^{*}=arg\min_{w}\frac{1}{R}\sum_{j}w_{j}^{2}+\sum_{i}J_{i}(y^{(i)},w\cdot x^{( i)}+b) \tag{19}\]  
Dividing Equation (19) by \(R\), we get that the optimal parameter minimizes the sum of the data term and hinge loss, that is, \(\frac{1}{R}\) times an \(l_{2}\) regularization terms of their weights. This equation forms as a standard linear classifier optimization, but the difference is the Hinge Loss and not regularizing the coefficient \(b\).  
We can optimize \(\sum_{j}J_{i}(y^{(i)},w\cdot x^{(i)}+b)\) in whatever manner we like such as any standard Stochastic Gradient algorithm from the linear classifier. If we take the dual of the soft margin quadratic program, we obtain a quadratic program similar to before over only the Lagrange multiplier \(\lambda\).  
\[\max_{0\leq\lambda\leq R}\sum_{i}\lambda_{i}-\frac{1}{2}\sum\lambda_{i} \lambda_{i}y^{(i)}y^{(j)}(x^{(i)}\cdot x^{(j)}) \tag{20}\]  
subject to \(\sum_{i}\lambda_{i}y^{(i)}=0\). The \(\lambda_{i}\) is bounded from above as well by \(R\). Intuitively, the equation says that, if the data point violates the margin constraints, the margin will penalize the Lagrange multiplier \(\lambda_{i}\) of that data point. Then \(\lambda\) will increase until it is at most \(R\) times the violated distance, defined as  
\[w^{*}=\sum_{i}\lambda_{i}y^{(i)}x^{(i)}\quad\forall\lambda\in(0,R) \tag{21}\]  
Complimentary slackness tells that \(\lambda\) is non-zero only on data that are at the margin or on the wrong side, for positive data at any point where the linear response is less than equals to \(+1\).
# Inverse Design of Distributed Bragg Reflectors Using Deep Learning
## 2 Materials and Methods  
The reflector design has been simplified into a six-layer structure composed of alternating layers of SiO\({}_{2}\) and Si\({}_{3}\)N\({}_{4}\), with each layer ranging from 10 to 60 nm. An example of this is shown in Figure 2.  
These are a traditional choice of dielectrics, and for good reason. SiO\({}_{2}\) is a low-refractive-index material (\(n\) in the range of 1.45-1.47 within the visible spectrum), and  
Figure 1: A simplified tandem network showing the pretrained SNN network as connected to the inverse network. The hidden layers are represented by the grey nodes, where, in practice, there would be multiple layers.  
Figure 2: A 6-layer thin-film structure of alternating layers of SiO\({}_{2}\) and Si\({}_{3}\)N\({}_{4}\), all of some depth \(d_{l}\). The transmission and reflectance media surrounding the structure are both half-infinite free space.  
is usually used alongside a high-index material. In this case, this is Si\({}_{3}\)N\({}_{4}\), which has a corresponding refractive index in the range of 1.99-2.08 [18]. Together, they are able to produce highly reflective surfaces when in the right combination. Furthermore, several methods have been developed for the stable production of both SiO\({}_{2}\) and Si\({}_{3}\)N\({}_{4}\) films, making them a practical choice [19].  
The following methodology analyses a range of six-layer thin-film structures, bounded on each side by free space and subject to normal incident light waves.
# Improved Chaff-Based CMIX for Solving Location Privacy Issues in VANETs
## 6 Security Analysis
### Unlinkability  
Our scheme preserves privacy by signing the messages with different pseudo-identities. The real identity is secretly hidden in these messages because \(PS_{j}=(s_{1}^{a_{j}},H_{1}(\)\(Cert_{VID_{j}},a_{j})\)\(X^{a_{j}},Y^{a_{j}}Z^{a_{j}})\) is computed by the authentication key \(a_{j}\) where \(\theta=H_{4}(s_{1}^{a_{j}},H_{1}(\)\(Cert_{VID_{j}},a_{j})X^{a_{j}})\). Note that this key is only accessible by \(VID_{j}\). For accountability reasons, \(RA\) can compute \(PS_{j}=(A_{j},H_{j}A_{j}^{x},A_{j}^{y+\theta z})\) for the vehicle \(VID_{j}\) where \(\theta=H_{4}(A_{j},H_{j}A_{j}^{x})\) because the public parameters \(x,y,z\in_{R}Z_{q}^{*}\) are known by all the certificate authorities \(LTCA\), \(RA\), and \(PCA\). Hence, no adversaries can obtain the real identity from the ciphertext \(H_{1}(\)\(Cert_{VID_{j}},a_{j})\). Moreover, we also prevent information leakage by providing mutual authentication and sending Chaff messages on the road to mitigate the risk of linkability attacks by eavesdrop dropping adversaries due to the low-density traffic. Therefore, our scheme provides unlinkability.
## 2 System Model  
As shown in Figure 1, Let us consider a secure transmission in cognitive wiretap networks with FD receiver consisting of a secondary user (SU) transmitter (Alice), a legitimate SU receiver (Bob), a relay (Relay), a primary user receiver (PU) and a passive eavesdropper (Eve). A transmitter Alice, an eavesdropper Eve and a primary user receiver only have a single antenna, while a secondary Relay and Bob are equipped with multiple antennas, which are denoted by \(N_{R}\) and \(N_{B}\), respectively. As in, it is assumed that in this paper the Alice, Relay, Bob and Eve nodes are located in the same cluster far away from the primary user transmitter, and hence there is no direct link from the Alice-Bob and the link security information can only be transmitted through the Relay node. In this paper, we assume that both main and wiretap channels experience quasi-static independent block Rayleigh fading. The malicious eavesdropper has strong eavesdropping ability. The channel coefficient between \(M\) and \(N\) nodes, termed as \(h_{MN}\), is an distributed random variable (RV) with zero mean and variance \(\lambda_{MN}\) as denoted by \(\mathcal{CN}\left(0,\lambda_{MN}\right)\). As in [20; 32; 33; 34], legitimate channel state information (CSI) is assumed available at the Relay and Bob, namely the CSI of Alice-Relay and Relay-Bob. Recently, with advances on self-interference (SI) cancellation technology, we assume that the SI can be completely suppressed of the relay-relay link [11; 20]. For investigating the advantages of FD Bob, we explore two distinct secure transmission schemes, namely MRC/MRT-SC scheme of HD operation and MRC-ZFB/MRT-SC-ZFB scheme of FD operation. For MRC-ZFB/MRT-SC-ZFB scheme, the CSI of the eavesdropping links is available for Bob, i.e., the CSI of Bob-Eve link. According to [12; 33; 35] and the mutual reciprocity of the channels, the Bob can obtain a reliable feedback CSI from the Eve in the condition of time-division duplex transmission.
# Mapping Mountain Landforms and Its Dynamics: Study Cases in Tropical Environments
## 5 Discussion
### Downscaling Fluxial Geomorphology in Challenging Environments  
The Use of low-cost UAV technology can allow the acquisition of large volumes of high-resolution data in a short time for the fluvial geomorphology analysis of reaches. In this case, the RbAMB has pristine primary forest coverage, which makes it impossible for satellite and airborne sensors to acquire quality elevation data and imagery [84]. Drones can be exceedingly useful in acquiring data for fluvial geomorphological purposes, while they give high-resolution elevation data and imagery that can be used for multiple purposes, such as channel morphology classification, river dynamics and surface processes analysis, hazard assessment, riverine vegetation analysis, morphometrics, hydraulic modelling and further purposes related to geosciences [11; 31; 85; 86]. In mountainous steep river systems, especially relating to the humid tropics, remote sensing of rivers can be challenging, due to environmental conditions related to vegetation and cloudiness [87]. We presented in this research paper a low-cost alternative for mounting the mapping of streams and rivers with these characteristics, considering the rapid deployment ability of the technology and the quality of the data, which allows to perceive high-resolution changes in the channel. As mentioned by [PERSON] et al. [11], UAV technology is rapidly evolving and becoming more accessible to geoscientists, and in the future decade will increasingly become a more utilized tool in geomorphology analysis; due also to its low-cost characteristics, it makes an excellent alternative for developing countries for surveying geomorphological features. When comparing UAV-derived photogrammetric products with other surveying methods such as piloted airborne missions, satellite imagery and, topographical stations, there is a clear difference in terms of the benefits related to spatial resolution of the elevation data. High-resolution terrain and surface models are key inputs when quantifying fluvialmorphodynamic changes, especially in narrow streams such as the San Lorencito River [88]. Regarding the acquisition of RGB imagery, satellite remote sensors do not provide quality data for analyzing the morphodynamic changes in this environment, due to (1) dense vegetation coverage, and (2) high cloudiness. The experimental observations performed with the UAV proved to be a strong asset for analyzing the fluvial processes.
# Voltage Distribution on Transformer Windings Subjected to Lightning Strike Using State-Space Method
## 2 Network Elements
### Modeling of Power Transformer Winding  
Various winding models are utilized for the transient analysis of power transformers, including Multi-Conductor Transmission Lines (MTLs), Circular Multi-Conductor Transmission Lines (CMTLs), ladder networks, and hybrid models (MTL-ladder networks) [22]. MTLs and CMTLs are turn-based models, while this study employs the disc-based ladder network model. Figure 3 illustrates the double-ladder network model, which incorporates both primary (high-voltage side) and secondary (low-voltage side) windings. In contrast, the single-ladder network model represents only the high-voltage or low-voltage windings of the transformer. For this study, a single-ladder network model was utilized. The ladder network is commonly used in frequency response analysis studies of transformers and is also referred to as the high-frequency power transformer model.  
The ladder network model of a power transformer consists of a cascade connection of a large number of sections with the parameters \(R\), \(L\), and \(C\). Each section, which consists of lumped parameters, represents a disc in the equivalent circuit. Consequently, the number of discs in the transformer windings determines the number of sections used in the model. The lumped parameters in the sections \(R_{s}\), \(L_{s}\), \(C_{s}\), \(C_{g}\), and \(C_{HL}\) represent  
Figure 2: Distributed-parameter transmission line equivalent circuit.  
the series resistance, self-inductance, series capacitance, shunt (ground) capacitance, and the capacitance between the high-voltage and low-voltage windings of the transformer, respectively. By utilizing winding design details, such as the geometry and physical properties of the power transformer, or previously measured data, the parameters of the transformer ladder network model can be estimated or calculated. The Finite Element Method, analytical approximation, and terminal-based measurement are some of the methods used to calculate the model parameters [23].  
Transformer parameters are distributed as transmission lines. However, in [24; 25; 26], ladder circuits composed of lumped parameters are proposed for transformer modeling. As a distributed model of the transformer windings suitable for state-space analysis does not exist, a lumped-parameter model composed of ladder circuits is used for both state-space representation and ATP-EMTP simulations of the transformer winding.
## 2 Theory and Formulations  
An isotropic homogenous beam supported with viscoelastic axially FG bars is considered as shown in Figure 1. The beam is subjected to a sinusoidal force \(F(t)\) at the midpoint. It is assumed that the material properties of the bar vary longitudinally according to the power law distribution. The analysis of the harmonic forced vibration of a beam with the viscoelastic FG bar supports is proposed based on the equivalent complex stiffness coefficients. The beam has a length of \(L\), thickness of h and width of b. The bar has a length of \(L_{br}\), thickness of \(h_{br}\) and width of \(b_{br}\). The beam is connected to the bars with pin joints to acquire pure axial deformation in the bars. Each structural element has a rectangular section. The Euler-Bernoulli beam theory governs the dynamic behaviour of the beam, with small deflections occurring in the x-z plane. The origin is chosen at the left end of the beam as shown in Figure 1. The dynamic behaviour of the bar is governed by the bar theory. The deflections of the bars and beam in the \(z\) and \(x\) direction are defined with \(u(z,t)\) and \(w(x,t)\), respectively. The bars are connected to the end points of a beam by rotary joints to ensure that they work only in the axial direction. The other ends of the bars are clamped. Kinetic energy and strain energy expressions are generated for the axial FG viscoelastic bar and beam. Element matrices obtained using Lagrange equations are assembled for the system. The force value and frequency parameters of the global system transmitted to the ground under harmonic force are analysed according to the material change coefficient of the axial element.  
Figure 1: A beam supported with axially functional graded viscoelastic bars.  
The material properties of bars at the two ends are define as follows. \(E_{La}\): Young module of left surface material, \(E_{R}\): Young module of right surface material, \(\rho_{La}\): density of left surface material, \(\rho_{R}\): density of right surface material, \(\eta_{L}\): material loss factor of left surface material, \(\eta_{R}\): material loss factor of right surface material, \(P_{La}\): Young module of left surface material and \(P_{R}\): Young module of right surface material.
## 3 QSAR Today
### Applicability Domain and Methods for Its Identification  
The applicability domain (AD) defines a theoretical region within the chemical space determined by the descriptors used to build a QSAR model. It indicates where the model can be reliably applied. Predictions for new compounds are trustworthy only if their chemical space falls within the AD established by the model's training set.  
Several methods are used to determine a model's AD [163]. One common approach involves checking whether the compound being predicted is an outlier relative to the training set. Regression methods like PLS have built-in tools for outlier detection [122,164]. A PLS regression model can identify outliers in two ways: by evaluating the compound's position in the PLS score plot and by analyzing the residuals of its descriptors. Algorithms have been developed for both methods to calculate the statistical probability of a compound being an outlier [122,164]. Another approach is based on chemical similarity, assuming that a compound's prediction will be reliable if it resembles one or more compounds from the training set. Similarity can be quantified using metrics such as Euclidean or Manhattan distances and linkage methods like single, average, or complete linkage [165].  
A more advanced method is conformal prediction, which addresses limitations in the predictive accuracy of traditional statistical methods like regression and classification [166,167]. This approach has two main components--(1) prediction regions or intervals, used for regression problems, while classification tasks involve assigning sets of labels; and (2) nonconformity score, which measures how \"unusual\" a new observation is compared to examples in the training set.