esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Validation of a Cascading Vector Tracking Loop in High Dynamic Environments
## 4 Discussion  
For the traditional FLL-PLL, the different dynamic components of the signal are tracked by different orders, the high-order loop (more than third order) is unstable, and the noise bandwidth limits the dynamic tracking range. However, for a sequential filter, the current state is based on the last moment, so locking can be completed within the local range. Thus, the dynamic range and accuracy will be greatly improved. Furthermore, if the adaptive noise matrix adjustment strategy is adopted, the performance can be further improved. The vector loop is not the end goal but a good platform for deep combination with inertial navigation, and a high degree of integration and robust tracking is very advantageous in certain environments.  
We find that the vector tracking loop can work perfectly in highly dynamic conditions, and the vector carrier loop with a carrier-assisting pseudo-code method is sufficient for this signal tracking, indicating that we can achieve vector tracking with as few loop changes as possible. However, we notice that a switching relationship exists between the FLL-PLL and the vector loop because the vector and scalar loops are not completely separated. To realize vector tracking under high dynamics, the preprocessing part of the vector loop and the scalar tracking must be unified, as in this study, to reduce the transient disturbance caused by loop switching.  
In the future, in addition to the applications in weak signal tracking, real-time vector tracking and the research on vector deep integration navigation, we must focus on combining vector tracking and unlocked recapture. The theory is that after the loss of tracking loop lock, if we can obtain the corresponding almanac and vehicle state information, the carrier and code loop can still work. However, when the number of obscured satellites is more than or equal to one, how long the vector loop can maintain the high level of confidence of the local carrier and pseudo code is unknown, and considering the potential channel contamination, the problem of satellites selection on this kind of vector structure is worth exploring.
# Design and Analysis of the Composite Stability Control of the Reflective Optoelectronic Platform
## 2 Problem Description
### The Preparation of System Motion
Furthermore, the geometric relationship formula \(\theta_{E2}=\frac{\theta_{E1}}{\lambda}-\theta_{err}\) can be utilized, where \(\theta_{E1}\) and \(\theta_{E2}\) denote the real-time angular velocities of the coarse-stable frames of driving and driven, respectively, \(\lambda\) represents the transmission ratio, and \(\theta_{err}\) represents the transmission angle error (which is also negligible). This simplifies Equations (3) and (4). Finally, Equations (5) and (6) were derived from Equation (4). \[\bar{\omega}_{Le}= \begin{bmatrix}cos(\lambda\theta_{E2})&0&-sin(\lambda\theta _{E2})&0\\ 0&1&0&\lambda\dot{\theta}_{E2}+\lambda\dot{\theta}_{e}\\ sin(\lambda\theta_{E2})&0&cos(\lambda\theta_{E2})&\dot{\theta}_{a}\\ 0&0&0&1\end{bmatrix} \cdot\bar{\omega}_{La} \tag{3}\]  
\[\bar{\omega}_{Le}= \begin{bmatrix}cos(\theta_{E1})&0&-sin(\theta_{E1})&0\\ 0&1&0&\dot{\theta}_{E1}+\lambda\left(\theta_{e}-\theta_{err}\right)\\ sin(\theta_{E1})&0&cos(\theta_{E1})&\dot{\theta}_{a}\\ 0&0&0&1\end{bmatrix} \cdot\bar{\omega}_{La} \tag{4}\]  
Furthermore, due to  
\[\bar{\omega}_{E1}= \begin{bmatrix}cos(\theta_{E1})&0&-sin(\theta_{E1})&0\\ 0&1&0&\dot{\theta}_{E1}\\ sin(\theta_{E1})&0&cos(\theta_{E1})&0\\ 0&0&0&1\end{bmatrix} \cdot\bar{\omega}_{A} \tag{5}\]  
The kinematic models of the line-of-sight (LOS) can be represented as follows:  
\[\bar{\omega}_{Le}= \begin{bmatrix}\omega_{ex}\\ \omega_{ey}\\ \omega_{ez}\\ 1\end{bmatrix} = \begin{bmatrix}\omega_{E1x}\\ \omega_{E1y}\\ \omega_{E1z}\\ 1\end{bmatrix} + \begin{bmatrix}0\\ \lambda(\dot{\theta}_{e}-\dot{\theta}_{err})\\ \dot{\theta}_{a}\\ 0\end{bmatrix}  \tag{6}\]
## 4 Discussion and Conclusions
The constructed prototypes are displayed in Figure 6. However, a number of questions remain open and this implies the need of reliability tests, clinical trials as well as fulfilling regulatory and legal formalities. Furthermore, significant commitment from companies that can manufacture them, or even military industry, will be needed if these concepts are to be converted intoreal solutions within a reasonable timescale. An important aspect is that, perhaps for the first time a global collection of efforts appeared spontaneously in a very short period, and not only to address the mechanical ventilator availability problem, but many other aspects, yet most of these efforts were disorganized and the convergence of the emergency-development, emergency-manufacture and emergency-legal/regulatory-clearance do not seem to be converging at appropriate speeds to deal with the problem. These aspects will need to be addressed in order to better respond to future crises.
# Evaluation of Hydrogen Generation with Hybrid Renewable Energy Sources
## 3 Results and Discussion  
In this section, the performance of the hydrogen system is thoroughly evaluated through energy analysis, providing a comprehensive understanding of how the system operates. To achieve this, a dynamic simulation of the system was carried out using TRNSYS software, and it covered various hours and months throughout the year. The simulation was conducted with a time step of 1 h, and the time scale used spanned around a year, running from 1 January to 31 August 2023. This extensive simulation allows for a detailed assessment of the system's behaviour over different seasons and under varying conditions. The weather data in this context pertain to the geographical location of Oshawa City, within the Markham zone, which is situated in Ontario, Canada. These data are crucial for understanding the climatic conditions and environmental parameters specific to this region, and they play a significant role in simulations and analyses related to energy systems and environmental modelling in this area.  
The simulation was conducted using two distinct power sources: the first one is the wind turbine, and the second is the PV (photovoltaic) panel. Each of these power sources will be described in detail in the next section, including their characteristics with the respective data input and output, to provide a comprehensive understanding of their performance and contribution to the energy system. This detailed description will help analyze the operation and efficiency of the wind turbine and PV panel within the simulation. The price estimation of the hydrogen and electric energy can be conducted using the capacity factor ratio. The capacity factor (CF) is a unitless ratio of the actual electrical energy output over a given period to the theoretical maximum electrical energy output over that period [40]:  
\[\text{CF}=\frac{\text{Output Power Hour from the Turbine}}{\text{MAX. Turbine Power available}\times 365\times 24} \tag{6}\]  
Figure 4 illustrates the connection between electricity prices, capacity factors, and the resulting levelized hydrogen cost, which is calculated using the H\({}_{2}\) model for different capital costs related to LTE (low-temperature electrolysis) [41].
# Upscaling Customer Access Network Using Spectrum Conversion-Slicing-Duplication Technique
## 1 Introduction
### AWG-Based Technique  
Arrayed waveguide gratings (AWGs) do not require DWDM filters on each ONT, as shown in Figure 2. However, conceptually, using AWG outside of the plant comes with a host of issues. Since AWG is extremely sensitive to temperature variations, channel spacing is severely constrained. Furthermore, it is important to tune the DWDM source and keep an eye on the AWG path band's wavelength [13]. In order to create several WDM-based lightweight tree PONs on the same physical fiber, aggregated downstream traffic modulated at different wavelengths from the OLT is transmitted to the NxN AWG and the wavelengths are decompressed to its respective output ports. All ONUCs in the light tree receive the wavelength from the AWG output port via broadcast using a 1 \(\times\) N optical divider. The ONU is then set to a particular fiber wavelength. By making use of the AWG FSR, an endless number of wavelengths can be transported from a certain input port to a particular output port. Service providers have the freedom to launch new services with little to no impact on their existing legacy systems thanks to the presence of many light trees in the same physical fiber link. Future scaling will be endless due to the physical PON's ability to support an infinite number of wavelengths. Furthermore, it utilizes passive NxN AWG and takes advantage of wavelength cycle routing made feasible by the device's free spectral range (FSR) at 2.5 Gbps. The unique characteristic of WDM-PON is the foundation for the suggested architecture, which includes the potential to simultaneously provide switch and broadcast services as well as additional benefits in terms of signal privacy, vulnerable location, and live capacity enhancement. It may also be possible to provide various types of services on physical networks. Optical transmission experiments reveal error-free transmission has been achieved using a 25 km passive optical link with 16 \(\times\) 16 AWG and a 1 \(\times\) 32 divider added, despite the additional AWG in the network having an additional 5 dB power penalty [14].
## Materials and Methods
### Matched Filters and Template Matching
The correlation \(r_{\text{ts}}\) at \((n_{0}+N/2,m_{0}+M/2)\) is obtained by summing all the components in \(st[n,m]\) (Equation (9))
4. By letting \(n_{0}\) and \(m_{0}\) vary in the range \(n_{0}=1,\ldots,J-N+1\;\;\vee\;m_{0}=1,\ldots,J-M+1\), the whole cross-correlation matrix is computed. \[s[j,k]\quad 1<j<J\;\vee 1<k<K\] \[\left\{ \begin{array}{ll}t[n,m]=\hat{t}[n,m]&n=n_{0},\ldots,n_{0}+N \;<J\;;m=m_{0},\;\ldots,m_{0}+M<K\\ t[n,m]=0&1<n<n_{0}\lor n_{0}+N<n<J\;;1<m<m_{0}\lor m_{0}+M<m<K\\ n_{0}=1,\ldots,J-N+1\;\;\vee m_{0}=1,\ldots,J-M+1\end{array} \right.\] (7) \[st[n,m]=s[n,m]\circ t[n,m]\] (8) \[r_{\text{ts}}[n_{0}+N/2,m_{0}+M/2]=\sum\
olimits_{j=1}^{J}\sum \
olimits_{k=1}^{K}st[j,k]\] (9)  
If the template is present in the search image, the cross-correlation features a maximum. The template detection becomes then a search for the maximum. This implies that the cross-correlation can be used as an effective similarity measure. Nevertheless, this basic approach to TM is effective only when the template is a crop from an acquired reference image, and the search image is acquired under the same conditions (i.e., illumination, scale, orientation, fixed background, etc.). That is why in [35] TM is considered only for quality inspection of precisely aligned integrated circuits. The limitations of traditional TM, as already introduced in Section 1, are well known [34; 35; 36] and are here summarized:  
1. noise, illumination changes, and occlusions in the search image,
2. background changes and clutter,
3. rigid and non-rigid transformations, rotations, and scale changes (i.e., images are a projection of a 3D scene onto a 2D plane),
4. high computational cost. Nevertheless, in this particular application (i.e., the SURVISHNO 2019 fan), as introduced in the previous sub-section, it was proposed to solve the issue of aliasing by focusing on the fan-locking screw head region. This reduction of the search space performed by cropping the image around the center of rotation is beneficial also according to points (b) and (d), as the background is effectively removed, while the computational burden is lightened. The overall pre-processing is described in the next subsection, which treats about the edge detection performed on the basis of the monochromatic image obtained from the brightness of the original RGB24 image. This helps in relieving the issues in point (a). Finally, point (c) is addressed by exploiting the GA for dealing with rigid transformations and scale variations of the template to obtain a better match.
# Automotive Cybersecurity Application Based on CARDIAN
## 2 Background on Cybersecurity in Automotives: Methods and Related Works
### Intrusion Detection Systems  
To overcome the problems of the protection systems described previously, intrusion detection systems (IDSs) can be used [10]. The installation of an IDS does not compromise data traffic on the bus nor does it require the modification of the various CAN controllers, necessary for the use of encryption and authentication techniques as well as the structure of the network itself. The main intrusion detection methods can be divided into two categories:  
* Signature-based: the system identifies the presence of attacks through the use of databases in which different types of attacks are present. It is clear that this method is not particularly effective if the type of attack has not been previously described.
* Anomaly-based: the system is responsible for analyzing the behavior of the network and is able to recognize any deviations from normal behavior. Unlike signature-based approaches, it can easily identify attacks that are not yet known. In the next section the main anomaly-based methods will be described.
## 2 Methodology
### Algorithm of Extracting \(t_{B}\)  
Due to the gradual change in the distance between the satellite and the ground receiver, the amplitude envelope of the SAR pulse signal recorded by the ground receiver is slightly different from the antenna pattern. In the area near the main lobe peak of the antenna beam, the change of the distance is very small (only a few range cells in the entire synthetic aperture time), whose influence could be ignored. In addition, the ground receiver's own antenna pattern also modulates the received SAR pulse signal, but since the ground receiver beamwidth is several tens of times greater the SAR beamwidth (generally 50 times), in the case of a near side looking, the flattest portion of the main lobe of the receiver antenna is used to measure the SAR antenna pattern, which means the effect of the modulation is very small and could be ignored. Therefore, we can calculate the energy center of the main lobe with the received data envelope instead of the antenna azimuth pattern.  
The pulse compression peak of the range migration curve obtained above corresponds to the amplitude envelope of the data received by the ground receiver, which also reflects the azimuth pattern of the antenna of the spaceborne SAR. Record the pulse azimuth position and pulse compression peak of the range migration curve, and obtain the azimuth pattern function \(P(t)\), then construct the fitted function \(P_{f}(t)\). Using the antenna main lobe symmetry property, we could obtain the abscissa \(t_{0}\) of the antenna main lobe's maximum value with the same method of obtaining \(t_{A}\).  
The symmetry of the energy of the main lobe could be used to verify and correct \(t_{0}\). The method is formula (9), find a series of \(t_{i}\) near \(t_{0}\), calculate the integral of the left M points, and then calculate the integral of the right M points, then the left integral subtract the right integral and find the modulo value \(\Delta S(t_{i},M)\). The best estimation of the center of the antenna beam corresponds to the smallest modulus \(\Delta S(t_{i},M)\), so as to obtain \(t_{B}\). In the main lobe range, and the data allowable conditions, the larger the value of M, the more accurate the center of antenna pattern energy obtained. The process is shown in Figure 6.  
\[\Delta S(t_{i},M)=\left|\int_{t_{i}-M}^{t_{i}}P_{f}(t)dt-\int_{t_{i}}^{t_{i}+ M}P_{f}(t)dt\right| \tag{9}\]  
Figure 6: Antenna pattern energy center solution.
## 2 Methods
### United CF-based Beamforming Framework  
The sSCF and aSCF method both use the signal coherence of the RF data to weight the coherent sum output. Omitting the same'sign coherence factor', their differences are reflected in the'spatial' framework and 'angular' framework shown in Figure 2b,c.  
The spatial method calculates the coherence factor within every row of the matrix X(k) and the angular method obtains the factor after adding the entries in each row along the remaining column. Ultrasound backscattered signals contain coherence information in both the spatial and angular dimension, so the spatial framework and the angular framework could be combined to produce a united adaptive beamforming framework. We propose a new framework utilizing both the spatial coherence and angular coherence. The new framework unites the two kinds of coherence together. In the framework, all the elements of the matrix represent the information of the corresponding point (\(x\),\(z\)), and the coherence among them should be evaluated directly. So, the united CF-based beamforming framework acts as a united 2D filter in the spatial and angular dimension. Its schematic diagram is shown as Figure 2d.  
By the new united framework, the beamforming weight factor \(w_{u}\) could be calculated by the postsummed data matrix before DAS of all angles and applied to the additional result of the DAS.  
Based on the united framework, the factor \(w_{u}\)worked as:  
\[Y_{u}(k)=\frac{1}{N}w_{u}(k)\sum_{i=1}^{N}X_{c}^{i}(k), \tag{16}\]  
where \(w_{u}\) is the coherence factor of the matrix of all information (both in spatial and angular dimension) from the point.
## 9 Future Outlook toward 6G-Era
### NTNs in 3 GPP Releases 18 and 19
Furthermore, leveraging AI and ML has emerged as a pivotal strategy for enhancing energy efficiency in NTNs. AI and ML algorithms are employed to dynamically adjust critical parameters such as beamforming, power levels, and network topology, leading to significant energy savings [152]. As recognized by 3 GPP, energy efficiency will be a major focus of Rel-19, with its importance reflected in its consideration as a service criterion. Telecommunications operators are actively pursuing strategies to reduce energy usage in network infrastructure and wireless devices. Optimizing power consumption in the network equipment is paramount for achieving cost efficiency and reducing operating costs [153]. However, it is crucial to strike a balance between power reduction and maintaining QoS for end-users.
# Hierarchical Gradient Similarity Based Video Quality Assessment Metric
## 2 Hierarchical Video Quality Assessment
### Overall Score  
In the above subsections, degradations in different visual areas are modeled with the corresponding similarities. The final quality index of each frame can be calculated by combining thesesimilarities. For the prediction similarity, the pixel-level similarity, the block level-similarity, and the visual attention similarity are multiplied to evaluate the prediction similarity as  
\[S_{pre}(\mathbf{P}_{r},\mathbf{P}_{t})=S_{va}\cdot\underset{x\in\{\mathbf{C}_{r} \bigcup\mathbf{C}_{t}\}}{\text{Avg}}S_{dp}(x)\cdot\ S_{vp}(x), \tag{6}\]  
where \(S_{pre}(\mathbf{P}_{r},\mathbf{P}_{t})\) denotes the quality score of the prediction part which is decoupled from the distorted frame \(\mathbf{F}_{t}\). The parameter \(x\in\{\mathbf{C}_{r}\bigcup\mathbf{C}_{t}\}\) denotes the salient pixel in the union set (i.e., only the salient pixels are considered in assessing video quality). The terms \(S_{dp}(x)\) and \(S_{vp}(x)\) denote the pixel-level similarity and the block-level similarity of the pixel \(x\), respectively. The term \(S_{va}\) denotes the visual attention similarity.  
As in [11], the noise similarity is used as the exponent of the prediction similarity; i.e., the overall quality score of the frame is calculated as  
\[S_{overall}(\mathbf{F}_{r},\mathbf{F}_{t})=(S_{pre}(\mathbf{P}_{r},\mathbf{P}_ {t}))^{S_{nii}(\mathbf{N}_{r},\mathbf{N}_{t})}, \tag{7}\]  
where \(S_{pre}\) and \(S_{nii}\) denote the quality score of the prediction part and the noise part, respectively. Finally, all of the frame scores are averaged to give the final video quality index.
# Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
## 3 Methodology
### Dataset Preprocessing
#### 3.1.1. Oversampling Techniques  
Random Over-Sampling (ROS)  
The first and simplest method in this field is random oversampling (ROS), which aims to help the distribution of datasets by increasing the number of samples in the majority class until the class distributions tend to balance. This approach is non-heuristic, meaning that it does not boost any intelligent decision boundaries. Random oversampling is usually applied to the level that excludes the imbalance. By merely regenerating samples from the minority class, ROS tackles a balance in the training model. However, duplicating similar samples may lead to the problem of overfitting, particularly for the samples belonging to the minority classes [26; 48]. Figure 3 shows an illustration of the oversampling technique.  
Synthetic Minority Oversampling Technique (SMOTE) [14; 49; 50; 51] is another resampling technique that aims to increase the amount of minority class samples by creating synthetic samples in the minority class and is applied for balancing datasets with a highly unbalanced ratio. In order to avoid the issue of overfitting, the synthetic generation of new samples differed from the multiplication algorithm.  
The main idea behind SMOTE is to generate new samples of data in the minority class by interpolation between samples of this class that are in close vicinity of each other [4; 52]. Thus, SMOTE increases the number of minority class examples within an imbalanced dataset and consequently enables the classifier to achieve better generalizability. The formal procedure for SMOTE can be explained as follows: Firstly, \(N\), which is the desired amount of oversampling, should be set to an integer number. This number can be opted for in that the dataset becomes balanced with a ratio of 1:1 within the different classes. Then, three main steps should be taken iteratively. These steps are 1: Randomly selecting a sample that belongs to the minority class, 2: The \(K\) (default 5) nearest neighbors of this sample should be selected, 3: \(N\) of these \(K\) neighbors are selected randomly for interpolation and generating new samples [53]. An intuition of how SMOTE works is shown in Figure 4.
## 5 Results and Discussion
### Impact of Stress-Sensitivity  
The mechanical compression of a shale matrix after gas extraction led to losses in porosity, permeability, and effective pore diameter. Mass flux in the continuum regime, the slip flow regime, Knudsen diffusion, and the total mass flux would have been overestimated without considering the impact of stress-sensitivity (Figures 13b, 14b, 15b and 16b), with maximum deviations of \(-44.57\%\), \(-97.09\%\), \(-17.65\%\), and \(-31.78\%\), respectively (Figures 13a, 14a, 15a and 16a). For the continuum and slip flow regimes, the mass flux deviations caused by stress-sensitivity increased with decreasing reservoir pressure. In addition, the mass flux deviations in Knudsen diffusion and total mass flux caused by stress-sensitivity decreased and then increased with decreasing reservoir pressure, with maximum deviations occurring at reservoir pressures of 25 MPa and 15 MPa, respectively (Figures 15a and 16a).  
Figure 12: Effect of adsorption/desorption on total mass flux. (**a**) Mass flux deviation with respect to pressure; (**b**) mass flux evolution with respect to pressure.  
Figure 13: Effect of stress-sensitivity on continuum regime. (**a**) Mass flux deviation with respect to pressure; (**b**) mass flux evolution with respect to pressure.