esa-sceva/satcom-chunk-collection huggingface dataset:
# Adjustable Capacity Evaluation Method Based on Step-by-Step Power Mapping of Offshore Wind Farms
## 3 Step-by-Step Mapping of Offshore Wind Farm Power
### Mapping of Wind Turbine Power to Collector System Power
\begin{bmatrix}h_{1}&0&\cdots&0\\ 0&h_{2}&&\vdots\\ \vdots&&\ddots&0\\ 0&\cdots&0&h_{a}\end{bmatrix} _{a\times a},\] (7) where \(h_{i}\) is the sum of the elements in the \(i\)-th row of matrix \(P\), and the matrix \(H\) is a diagonal matrix. 3. Construct the Laplacian matrix \(L\), and normalize it. \[L=H-P,\] (8) \[L^{\prime}=H^{-0.5}LH^{-0.5},\] (9)
4. Determine the number of spectral clusters \(k\). Calculate the first \(k\) minimum eigenvalues of \(L^{\prime}\) and the corresponding eigenvectors, and normalize the eigenvectors to construct a new matrix \(U_{a\times k}\). 5. K-means clustering is performed on the row vector of the matrix \(U_{a\times k}\). Based on the distance between the calculated sample and the center point, the samples belonging to each cluster are summarized, and iteratively realizes the minimum distance between the sample and the center of the cluster to which it belongs. Firstly, \(k\) samples are randomly selected as the initial cluster centers. Secondly, for each sample, calculate its Euclidean distance to \(k\) cluster centers. And assign it to the cluster corresponding to the cluster center with the smallest distance. Then, for each cluster, recalculate its cluster center position. Finally, repeat the above two steps until the number of iterations reaches the upper limit or the position of the cluster center remains unchanged. The clustering process is shown in Figure 4, C1, C2 and C3 are cluster centers. The division of \(k\) clusters of wind turbines \(C_{1},C_{2},\cdots,C_{k}\) is obtained to realize the spectral clustering of wind turbines. Based on the clustering results of wind turbines, calculate the matrix \(Z\) of the total power of wind turbines in \(k\) clusters \[Z= \begin{bmatrix}z_{11}&\cdots&z_{1b}\\ \vdots&\ddots&\vdots\\ z_{k1}&\cdots&z_{kb}\end{bmatrix} _{k\times b},\] (10) where, the \(i\)th row vector of \(Z\) represents the time series of the total wind turbine power in the \(i\)th cluster. Different clustering results are obtained by setting different clustering numbers, and finally the one with the smallest error is selected. Taking the wind turbine clustering power as the input and the collector system power as the output, the mapping can be established based on the BP neural network. \[P_{G}=f_{1}(P_{Z}), \tag{11}\]  
where \(P_{G}\) is collector system power, \(P_{Z}\) is the wind turbine clustering power, \(f_{1}(\cdot)\) is the trained network model.
## 2 Optimal Design of the TID  
### Mathematical Modelling  
The first vibration mode of the base-isolated structure is represented by the SDOF structure, and its main dynamic characteristics are equivalent mass \(m_{s}\), viscous damping \(c_{b}\), and stiffness \(k_{b}\). A TID featured by inertial mass \(m_{d}\), viscous damping \(c_{d}\) and stiffness \(k_{d}\) is attached to the base-isolated structure as illustrated in Figure 1. The dynamic equilibrium equation of the structure equipped with a TID under the base acceleration excitation \(\ddot{x}_{g}\) is given by Equation (1):  
\[\left\{\begin{array}{l}m_{s}\ddot{x}_{s}+c_{b}\dot{x}_{s}+k_{b}x_{s}+c_{d} \big{(}\dot{x}_{s}-\dot{x}_{d}\big{)}+k_{d}(x_{s}-x_{d})=-m_{s}\ddot{x}_{g}\\ m_{d}\ddot{x}_{d}+c_{d}\big{(}\dot{x}_{d}-\dot{x}_{s}\big{)}+k_{d}(x_{d}-x_{s} )=0\end{array}\right. \tag{1}\]In Equation (1), \(x_{s}\), \(\dot{x}_{s}\), and \(\ddot{x}_{s}\) represent the displacement, velocity, and acceleration vectors of the base-isolated structure, respectively, while \(x_{d}\), \(\dot{x}_{d}\), and \(\ddot{x}_{d}\) represent the displacement, velocity, and acceleration vectors of the TID, all referenced to the ground.  
By setting \(\mathbf{U}=\begin{bmatrix}x_{s}&x_{d}\end{bmatrix}^{T}\), Equation (2) is derived from Equation (1):  
\[\mathbf{M}\ddot{\mathbf{U}}+\mathbf{C}\dot{\mathbf{U}}+\mathbf{K}\mathbf{U}= \begin{bmatrix}m_{s}\\ 0\end{bmatrix}\ddot{x}_{g} \tag{2}\]  
in which  
\[\mathbf{M}=\begin{bmatrix}m_{s}&0\\ 0&b\end{bmatrix},\mathbf{C}=\begin{bmatrix}c_{b}+c_{d}&-c_{d}\\ -c_{d}&c_{d}\end{bmatrix},\mathbf{K}=\begin{bmatrix}k_{b}+k_{d}&-k_{d}\\ -k_{d}&k_{d}\end{bmatrix} \tag{3}\]  
Upon solving Equation (2), the frequency transfer function \(\mathbf{H}(\omega)\) between displacement response to the base acceleration excitation \(\ddot{x}_{g}\) is expressed in Equation (4):  
\[\mathbf{H}(\omega)=\begin{pmatrix}-\omega^{2}\mathbf{M}+i\omega\mathbf{C}+ \mathbf{K}\end{pmatrix}^{-1}\begin{bmatrix}m_{s}\\ 0\end{bmatrix} \tag{4}\]  
in which \(\omega\) is the frequency of the base acceleration excitation \(\ddot{x}_{g}\).  
Based on Equations (3) and (4), the frequency transfer function \(H_{1}(\omega)\) relating the structural displacement \(x_{s}\) to \(\ddot{x}_{g}\) can be derived as shown in Equation (5):  
\[H_{1}(\omega)=\frac{-\begin{pmatrix}-\omega^{2}+2i\xi_{d}\omega\omega_{d}+ \omega_{d}^{2}\end{pmatrix}}{\begin{pmatrix}-\omega^{2}+2i\xi_{b}\omega\omega \omega_{b}+\omega_{b}^{2}\end{pmatrix}\begin{pmatrix}-\omega^{2}+2i\xi_{d} \omega\omega_{d}+\omega_{d}^{2}\end{pmatrix}-\mu\omega^{2}\begin{pmatrix}2i \xi_{d}\omega\omega_{d}+\omega_{d}^{2}\end{pmatrix}} \tag{5}\]  
where  
\[\omega_{b}=\sqrt{k_{b}/m_{s}},\;\xi_{b}=c_{b}/2m_{s}\omega_{b},\;\omega_{d}= \sqrt{k_{d}/m_{d}},\;\xi_{d}=c_{d}/2m_{d}\omega_{d},\mu=m_{d}/m_{s} \tag{6}\]  
in which \(\omega_{b}\) and \(\xi_{b}\) represent the frequency and damping ratio of the base-isolated structure, respectively; \(\omega_{d}\), \(\xi_{d}\), and \(\mu\) represent the frequency, damping ratio, and mass ratio of the TID, respectively.
# Numerical Evaluation of Ultrasonic Detection of Solid Occlusions in CSP Molten Salt Piping
## 4 Results and Discussion
#### Statistical Analysis  
After examining the various scenarios associated with the formation of solid occlusions within the piping system, the representative curve shown in Figure 7 was obtained. To make this result operationally useful, it must be noted that experimental analysis provides an attenuation value, while the position and degree of solid occlusion remain unknown. To prevent clogging of the system during restart and fluid initiation phases, an estimated percentage of solid occlusion is considered acceptable to mitigate the risk of clogging. Underestimating this percentage would lead to overly frequent activation of intervention mechanisms to transition the phase from solid to liquid, resulting in significant energy consumption. Using a probabilistic approach, it was possible to determine the probability of the section porosity \(\varphi\) (or, equivalently, the degree of solid inclusion), given the attenuation value. This analysis resulted in the probability map shown in Figure 8. For example, if an attenuation value of \(\bar{\alpha}=10\) dB is measured and a section porosity between 0.2 and 0.3 is considered acceptable, the graph in Figure 8 allows us to identify the probability range (between \(P_{\text{min}}=40\%\) and \(P_{\text{max}}=\)50%) that this attenuation of 10 dB is caused by an inclusion corresponding to that acceptable \(\varphi\) range \((\varphi_{\text{min}},\ \varphi_{\text{max}})\). If the minimum probability \(P_{\text{min}}\) of a solid inclusion within this range is deemed acceptable, the intervention mechanisms remain inactive; otherwise, they are triggered. The procedure is illustrated in Figure 9. As the measured attenuation value increases, it is evident that the probability range progressively narrows. This is because, conversely, the probability of more significant solid inclusions increases, which further reduces the section porosity below the acceptable threshold values. This graph serves as the decision-making framework for triggering intervention mechanisms. Once the attenuation level is measured via the ultrasonic signal at the emitter and receiver, the probability map allows determining the likelihood that a given section porosity corresponds to the observed attenuation. Consequently, to optimize the energy and timing of the intervention mechanisms and prevent clogging, it is necessary to define a range of section porosity values considered critical. A corresponding threshold probability of inclusion can then be established, below which the intervention mechanism is triggered.  
Figure 7: Surface plot illustrating the dimensionless attenuation \(\bar{\alpha}\) as a function of section porosity \(\varphi\) and the dimensionless position of the solid inclusion \(x_{p}\). The graph provides a comprehensive view of how both the location and size of the solid inclusion impacted signal attenuation. Peaks occurred near the emitter and receiver positions, with attenuation increasing as the section porosity decreased.
# Evaluating the High Frequency Behavior of the Modified Grounding Scheme in Wind Farms
## 3 Proposed Modified Grounding Scheme for a Wind Turbine  
Typically, a grounding system consists of electrical conductors, ring electrodes, which are tied to the turbine tower around the wind turbine foundation to reach a specific resistance value by coupling vertical rods with them. However, this model fails to calculate the simultaneous impact of soil ionization and high frequency. This section presents a scheme that not only considers these two effects, but also noticeably reduces touch and step voltages.  
The main factor behind this improvement is the addition of three additional electrodes and two rectangular rings, with the longer conductors connected to the local transformer grounding as shown in Figure 1. Thanks to this new scheme, the need to repair and replace in the case of a fault occurrence is decreased. To examine the role of the PMGS, we assume that it is 1 m deep and further that the soil resistivity, relative permeability and permittivity are 100 \(\Omega\)m, 1 and 10, respectively.  
In the following, the advantages obtained from the PMGS are explained through measuring the voltages of the wind turbine and the pair of turbines during lightning in diverse conditions.
## 2 Experimental Background  
In the main loop for the boiling process examination, Fluorinert FC-72 was used as the working fluid. Experiments were carried out on the research stand described in detail in [5; 6; 18]. The main flow loop of the set-up, in addition to the essential part, i.e., the test section, comprises of the following elements: a circulating pump, a heat exchanger and a pressure regulator (a compensating tank). The following devices for control and measurement were used: Coriolis mass flow meter (type: Proline Promass A 100, manufacturer: Endress + Hauser), gauge pressure meters (type: PMP71, Cerabar S, manufacturer: Endress + Hauser) and K-type thermocouples (manufacturer: Czaki-Thermo Product, Raszyn-Rybie, Poland). An infrared camera (type: A655 SC, manufacturer: FLIR) helped in determining the temperature of the heated wall. Flow structures were observed through the glass wall and recorded by a high-speed camera (type: SP-5000M-CXP2, manufacturer: JAI).  
The essential part of the stand involved the test section with a group of five minichannels, Figure 1. Each of the minichannels had a pre-set depth of 1 mm, width of 6 mm, and length of 36 mm. The test section was vertically positioned with an upward flow. The heating element for FC-72, flowing through the minichannels, was a foil of 0.1 mm thickness (3, Figure 1b), designated as Haynes-230 (manufacturer: Haynes Int., Kokomo, IN, USA). This alloy is made of high temperature Ni-Cr-W-Mo alloy. The foil was powered by a heat source. As mentioned above, the two-dimensional temperature distribution on the heated foil outer surface was possible as a result of infrared thermography.  
During the experimental series, at fixed mass flow rate, with the gauge pressure and temperature of the subcooled liquid at the inlet to the minichannels, there was a gradual increase in the electrical power supplied to the heated wall, and consequently the heat flux delivered to the working fluid. The main experimental parameters were captured by the data acquisition system. Thermograms were also recorded on the heated wall surface that contacted the ambient air. The measurements were taken in 1 s time intervals. The basic data and parameters of the experimental series are shown in Table 1.  
Figure 1: The test section: (**a**) assembly drawing; (**b**) main elements: 1—top cover, 2, 4—Teflon spacer, 3—heated foil, 5—glass panel.
# Resonant Laser Ionization and Fine-Structure Study of Silver in an Ablation Plume
## 2 Theory and Motivation
### Best Fit of Experimental Resonance Profiles  
When an atomic line is scanned with a laser having a linewidth larger than that of the atomic line, the laser profile is effectively reproduced in the resonance profile. In a scenario where Doppler broadening is present, the resonance profile becomes wider with additional Gaussian contribution.  
Centered Lorentzian distribution L(\(\chi\gamma\)) and Gaussian distribution G(x\(\sigma\)) with profile parameters \(\gamma\) and \(\sigma\), respectively, are given by  
\[L(x;\gamma)=\frac{\gamma}{\pi(x^{2}+\gamma^{2})} \tag{2}\]  
\[G(x;\sigma)=\frac{e^{-x^{2}/2\sigma^{2}}}{\sigma\sqrt{2\pi}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**First Transition** & **First Excited Level** & **Second Transition** & **Second Excited Level** \\ \(\lambda\) **(nm)** & **(Term; J )** & **\(\lambda\) (nm)** & **(Term; J)** \\ \hline \multirow{4}{*}{328.163} & & 362.514 & 4d\({}^{10}\)8d (\({}^{2}\)D; 5/2) \\  & & 371.026 & 4d\({}^{10}\)9s (\({}^{2}\)S; 1/2) \\  & 4d\({}^{10}\)5p (\({}^{2}\)P\({}^{o}\); 3/2) & 381.205 & 4d\({}^{10}\)7d (\({}^{2}\)D; 5/2) \\  & & 421.402 & 4d\({}^{10}\)6d (\({}^{2}\)D; 3/2) \\  & & 466.983 & 4d\({}^{10}\)7s (\({}^{2}\)S; 1/2) \\  & & 546.707 & 4d\({}^{10}\)5d (\({}^{2}\)D; 5/2) \\ \hline \multirow{4}{*}{338.387} & \multirow{4}{*}{4d\({}^{10}\)5p (\({}^{2}\)P\({}^{o}\); 1/2)} & 368.357 & 4d\({}^{10}\)7d (\({}^{2}\)D; 3/2) \\  & & 384.186 & 4d\({}^{10}\)8s (\({}^{2}\)S; 1/2) \\ \cline{1-1}  & & 405.666 & 4d\({}^{10}\)6d (\({}^{2}\)D; 3/2) \\ \cline{1-1}  & & 447.734 & 4d\({}^{10}\)7s (\({}^{2}\)S; 1/2) \\ \cline{1-1}  & & 521.059 & 4d\({}^{10}\)5d (\({}^{2}\)D; 3/2) \\ \hline \end{tabular}
\end{table}
Table 1: Possible resonant photo-ionization schemes of silver; ground state configuration of Ag is 4d\({}^{10}\)5s \({}^{2}\)S\({}_{1/2}\).  
Full width at half maximum (FWHM) of a Lorentzian profile is \(f_{L}=2\gamma\) and of a Gaussian profile is \(f_{G}\approx 2.355\sigma\).  
The Voigt profile is a probability distribution given by a convolution of a Lorentzian distribution and a Gaussian distribution. It is often used in analyzing data from spectroscopy where different contributions to the line profiles are present.  
\[V(x;\sigma,\gamma)=\int_{-\infty}^{\infty}G(x^{\prime};\sigma)L(x-x^{\prime}; \gamma)dx^{\prime} \tag{4}\]  
where \(x^{\prime}\) is the shift from the line center.  
The FWHM of a Voigt profile can be calculated to an accuracy of 0.02% using the following relation [21]:  
\[f_{V}\approx 0.5346f_{L}+\sqrt{0.2166f_{L}^{2}+f_{G}^{2}} \tag{5}\]  
The \(\gamma\) and \(\sigma\) fit parameters are reported for different resonance profiles with the corresponding fit curves in Section 4. The resulting peak amplitude and the FWHM values of the fitted profiles are derived with a high level of confidence and a relative error of less than 7%, mainly due to the noisy character of the data.
## 6 Results, Analysis and Discussion
### Analysis Spatial Distribution of Performance Metrics
#### 6.2.6 Energy  
In Figure 11, the total energy at each node is shown in the case of reactive jamming. This is analyzed for the three positions of the jammer node: near, in the middle of the topology and far from the coordinator. The model of the energy is shown in Table 8, used for the Texas Instruments CC2530 chip. We can see that when there is jamming, the concentration of power is in the next coordinator nodes, because these nodes generate traffic and also carry traffic from other network nodes. However, the MPH protocol saves about 46% of the energy of these nodes closest to the coordinator. Furthermore, when we analyzed the total energy for the case when there was no jamming, the MPH protocol represents a 42% energy savings compared to AODV and 37% compared to DSR. Using Table 9, we have the quantization of the total energy for each of the protocols in each of the positions of the jammer node. We can see that when the jammer node is in the center of the attack, the topology appears to be more aggressive with respect to energy consumption, that is at the cost of a greater number of nodes. We note that the MPH protocol consumes less energy in all cases regarding AODV and DSR. For the close position, MPH has a savings of 44% compared to 41% AODV and DSR. For the center position, MPH has a savings of 41% compared to 38% AODV and DSR. For the far position, MPH has a savings of 43% compared to 42% AODV and DSR.  
Figure 11: 2D representation of energy for the no jamming and reactive cases when the jammer node is near to, in the middle of and far from the coordinator.
# Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector
## Materials and Methods
### Methodology
#### 3.2.3 Proposed Method
To make the target of the data to be detected prominently, the training data should be pre-processed before training the model. The brightness, contrast, hue, and saturation of the image must be randomly adjusted. The appropriate optical noise must be added to the image. Thereafter, the image was randomly cropped to substantially train the data of the small target. The SDD model was trained using the Windows platform. The loss function setting is as discussed in reference [11]. The optimizer and hyperparameters that need to be set before training are shown in Table 1.  
After 80 k iterations, the learning rate dropped to \(1\times 10^{-4}\), and after 100 k iterations to \(1\times 10^{-4}\). The learning rate decay ensures that the model does not fluctuate substantially in the latter stages of training, thereby approximating the optimal solution.
# Electric Drive with an Adaptive Controller and Wireless Communication System
## 1 Introduction
### Problem Formulation
The learning coefficient can be optimized using swarm inspired algorithms. Effective optimization of the initial parameters of a neural network can significantly improve the drive's performance. This is especially important because no training is performed prior to starting the drive. With a wrong initiation point, the control structure can struggle to quickly adapt to the current state of the control structure. Incorrect setting of the weights and the learning rate can cause an oscillatory response [30]. The selection of more appropriate values can be facilitated by using nature-inspired metaheuristic optimization algorithms [31]. The major advantage of such algorithms lies in the fact that they do not need information about the cost function derivative to find better values. As a trade-off, reaching the global minimum is never guaranteed. One of the most common algorithms from this group is Particle Swarm Optimization (PSO) [32]. Other examples include Cuckoo Search (CS) [33; 34], the Grey Wolf Optimizer (GWO) [35; 36], Ant Colony Optimization (ACO) [37; 38; 39], or the Bat algorithm [40]. In this work, the Artificial Bee Colony (ABC) algorithm [41] was implemented to facilitate the proper selection of the parameters responsible for scaling the input of the proposed neural network. Distributed measurement systems are developed to establish a connection between multiple units via the internet [42; 43; 44]. Remote access to information from a vastly distant instrument is often required. To obtain the measurements, virtual instruments (VIs) are often implemented. The VIs are usually created with the use of software dedicated to instrument control, such as LabVIEW(tm), LabWindows(tm)/CVI, and Keysight VEE(tm). Using these environments forces the user to install the required software and may cause VI compatibility problems. To resolve this issue, a web-based Human-Machine Interface was created to collect and visualize the data acquired during the drive operation.
# Multi-User Linear Equalizer and Precoder Scheme for Hybrid Sub-Connected Wideband Systems
## 2 System Model  
In this section, we describe the transmitter, the channel model, and the receiver for the considered uplink massive MIMO mmW SC-FDMA system.
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 3 Micromegas as a Tracker in CLAS12 and COMPASS
#### 3.1.1 Clas12  
The CLAS12 experiment [39] at Jefferson Lab was designed to study the structure of protons and neutrons using 11 GeV electrons impinging on a fixed target at luminosities of up to 10\({}^{35}\) s\({}^{-1}\) cm\({}^{-2}\). CLAS12 is composed of two tracking systems: the central detector, which is placed inside a narrow 5 T solenoid magnet and surrounds the target covering the angular range between 35\({}^{\circ}\) and 125\({}^{\circ}\) with respect to the beam direction; and the forward detector, which covers the region between 5\({}^{\circ}\) and 35\({}^{\circ}\), is composed of six identical sectors of drift chambers embedded in a toroidal magnetic field.  
The very forward region, between 2\({}^{\circ}\) and 5\({}^{\circ}\), is equipped with a calorimeter based system to detect electrons and photons. Micromegas are used close to the target to improve on the baseline tracking systems [40] and in the very forward region to provide the pointing direction of the scattered electron [41].  
In the central detector, the three inner double-layers of silicon strip detectors are complemented by the Barrel Micromegas Tracker (BMT). The BMT consists of six cylindrical layers of resistive Micromegas tiles (Figure 8a,b: the inner most layer sits at a radius of  
Figure 7: Typical readout with 1D zigzag geometry and 2D zigzag geometry taken with an optical microscope. The holes in the 2D pads are via connections. Etching of the pads was done with a laser. 2D patterns are currently under study.  
146 mm while the outer most at 221 mm, making it a very compact design. A three sectors carbon structure supports a total of 18 detectors. Three layers have strips along the beam axis, thus, providing \(r\cdot\phi\) measurements.  
The strips of the other three layers are perpendicular to the beam axis and, therefore, circular. Each BMT detector is made of a thin PCB (0.2 mm) with a stainless steel micromesh attached via the bulking process that has been curved on custom made mandrels. The structure made of 3 mm thick square carbon beams holds the drift electrode made of a Kapton foil (0.2 mm) with a metallic coating. The BMT detectors use a gas mixture of argon(90%)-isobutane(10%) that minimizes the Lorentz angle effect as the Micromegas are operated in a 5 T magnetic field.  
The Forward Micromegas Tracker (FMT) consists of a stack of six disks perpendicular to the beam axis positioned downstream at 30 cm from the target center. Each FMT disk has a diameter of 450 mm with 1024 readout strips in one projection. The strip direction of a disk relative to the previous one in the stack is rotated of 60\({}^{\circ}\). Since the magnetic field is parallel to the drift field and the primary electrons are not affected by the Lorentz angle effect, the FMT uses a gas mixture of argon (80%)/isobutane (10%)/CF\({}_{4}\) (10%) that provides a better timing resolution. The Forward Tagger Tracker [41] (Figure 8c) consists of four disks made of the same FMT technology.