esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Unified Modeling of LEO Satellite Constellation
### Model Implementation Based on Modelica
#### 2.3.3 System Control Center Model  
The system control center model belongs to the ground segment. The main function in the satellite constellation is to maintain the stability of the constellation structure. The input is the orbital factors of all satellites calculated by the constellation orbit model, and the output is the speed pulse that the GNC subsystem needs to generate the orbital maneuver control signal. In the satellite constellation structure, the two factors that have the greatest impact on the satellite coverage performance are the phase distribution and orbital plane distribution of the satellite, so phase control and orbital plane control are required.  
The orbital plane and phase control reference of each satellite in the constellation are determined according to the following formula [20]:  
\[\Omega_{0}^{s}=\sum_{j=0}^{p-1}\sum_{k=0}^{s-1}\lambda_{jk}(\Omega_{jk}-j\cdot \frac{2\pi}{P}+j\cdot\frac{2\pi}{P}) \tag{6}\]  
\[\begin{split} u_{00}^{*}=&\sum_{j=0}^{p-1}\sum_{k=0 }^{s-1}\lambda_{jk}(u_{jk}-j\cdot F\cdot\frac{2\pi}{T}-k\cdot\frac{2\pi}{S})\\ &+j\cdot F\cdot\frac{2\pi}{T}+k\cdot F\cdot\frac{2\pi}{S}\end{split} \tag{7}\]  
where \(\lambda_{jk}\) is the normalized weighting factor and the value is \(1/\text{T}\).  
RAAN tolerance \(\epsilon_{\Omega}\) and phase tolerance \(\epsilon_{u}\) are given in the form of model parameters. When \(|\Delta u+H_{u}\Delta i|>\epsilon_{u}\), a phase-holding maneuver is required, and the required tangential velocity pulse \(\Delta v_{u}\) is given by the following formula:  
\[\Delta i_{r}=\begin{cases}0,&|\Delta u|<\epsilon_{u}\\ K_{u}(\Delta u-\epsilon_{u}),&\Delta u>\epsilon_{u}\\ K_{u}(\Delta u+\epsilon_{u}),&\Delta u<-\epsilon_{u}\end{cases} \tag{8}\]\[\Delta v_{u}=\frac{a}{3}(\Delta\dot{u}-\Delta\dot{u}_{r}) \tag{9}\]  
where \(\Delta\dot{u}_{r}\) is the phase deviation change rate correction value. \(\Delta\dot{u}\) is the rate of change of the phase deviation, which is obtained by the difference. \(K_{u}=-1/H_{u}\), and \(H_{u}\) is the rate of change of the phase deviation, which is obtained by the difference.  
Similarly, when \(|\Delta\Omega+H_{\Omega}\Delta\dot{\Omega}|>\epsilon_{\Omega}\), the orbital maintenance maneuver is required, and the required normal velocity pulse \(\Delta v_{\Omega}\) is given by the following formula:  
\[\Delta i=-\frac{1}{3J_{2}\sin i}(\frac{a}{R_{E}})^{2}\sqrt{\frac{a^{3}}{\mu}} (\Delta\dot{\Omega}-\Delta\dot{\Omega}_{r}) \tag{10}\]  
\[\Delta v_{\Omega}=2\sqrt{\frac{\mu}{a}}\sin{(i/2)} \tag{11}\]  
where the calculation of \(\Delta\dot{\Omega}_{r}\) is similar to that of \(\Delta\dot{u}_{r}\). \(\Delta i\) is the required change in inclination. \(J2\) refers to the perturbation of the earth's oblateness.
# Defects in Silicon Carbide as Quantum Qubits: Recent Advances in Defect Engineering
## 3 Defects in Silicon Carbide
### Silicon Vacancies (V\({}_{Si}\))
High-energy particle irradiation and ion implantation also introduce electrically active defects into SiC [15; 44; 66; 67]. Among them is the silicon vacancy, which introduces two deep levels into the bandgap. Those levels are known as S1 and S2, and they have been assigned to different charge states of V\({}_{\text{Si}}\) at the h and k sites, (\(-3/\)=) and (\(=/0\)), respectively [15; 44]. [PERSON] et al. [15] have presented a very rare, if not unique study, where optically and electrically active defects (i.e., silicon vacancies) are compared, offering valuable insight into their fundamental properties and potential for quantum technology applications [15]. In their study, they successfully correlated the optical V1/V1\({}^{\prime}\) signals with the electrical S1/S2 signals. To summarize, once introduced in SiC, V\({}_{\text{Si}}\) has been comprehensively characterized using PL, EPR, and OMDR [10; 11; 12; 27; 28; 52; 53; 66; 68]. Readers are strongly encouraged to explore the referenced papers for further insights. Ongoing research continues to focus on optimizing spatial precision, charge state control, and process efficiency to meet the growing demands of quantum applications. It is important to note that, for the sake of clarity, numerous other equally valuable studies have not been included in this summary.
## 4 Data Processing
### Processing the Available Data  
In order to calculate the transmission rates we will make some assumptions:  
* During the lockdown stages adopted in the different studied countries, the social interaction of an exposed individual is similar to a diagnosed infectious one.
* The exposed transition rate during the lockdown stage will be equal to the infectious transition rate \(\beta_{E_{0}}=\beta_{I_{0}}\).
* The diagnosis methods and the prophylactic measures around these diagnosed infectious individuals will improve after the beginning of the epidemic.
* The value of \(\beta_{I}\) will be reduced partially as the awareness of the disease and the risk around infected individuals are reduced because of the social distancing strategies adopted in the country.
* The value of the transmission rate of the diagnosed infectious \(\beta_{I}\) after the initial lockdown will remain constant as the official approach to a diagnosed individual will remain the same independently of the lockdown stage.
* The bias in the influence of the non-measured infected individuals will be constant through all the time.  
The \(\beta_{I}\) and \(\beta_{E}\) will be calculated then in two steps. First, we will take the available data from the lockdown stages applied on a population, considering equal the initial transmission rates \(\beta_{E_{0}}=\beta_{I_{0}}\). The value of the total cumulative cases, which will include all the infected subpopulations, is the same independently of the SEIR model chosen from Equations (1)-(4) or (26)-(29), namely, it is \(CC_{i}=E_{i}+I_{i}+R_{i}\). We obtain that  
\[\Delta CC_{i}=\Delta E_{i}+\Delta I_{i}+\Delta R_{i}=S_{i}(\beta_{I}I_{i}+ \beta_{E}E_{i})=\beta_{I_{0}}S_{i}(I_{i}+E_{i}) \tag{46}\]  
Since \(\beta_{E}=\beta_{I}\) in this first step Then, as the value for the susceptible at any day \(i\) would be \(S_{i}=1-CC_{i}\), we will get the equation for \(\beta_{I}\) and \(\beta_{E}\) for the lockdown time  
\[\beta_{E_{0}}=\beta_{I_{0}}=\frac{\Delta CC_{i}}{(I_{i}+E_{i})(1-CC_{i})} \tag{47}\]  
A linear regression will be used to obtain the \(\beta_{I_{0}}\) at the initial total lockdown stage. Then, the transmission rate for the infectious at the next stages of the lockdown \(\beta_{I}\) will be reduced to a fraction of the original value as the prophylactic measures and the awareness of the disease increases. Then, the \(\beta_{I}\) for the rest of the lockdown stages will be \(\beta_{I}=k\beta_{I_{0}}\) with the factor \(k\in[0,1]\) set to a point as the transmission rate of the exposed \(\beta_{E}\) is calculated. The \(\beta_{E}\) will be obtained applying this \(k\) to the rest of the stages and calculating again through linear regression using the data of the days of each stage as  
\[\beta_{E}=\frac{\Delta CC_{i}}{E_{i}(1-CC_{i})}-\frac{\beta_{I}I_{i}}{E_{I}} \tag{48}\]  
Different \(k\) will be studied in order to fit the most appropriate \(\beta_{E}\).
## 2 Description of the Partially Shaded PV Energy System
### Description of UI, PS, and CPS under Study  
Uniform irradiance case has the same irradiance values on the PV panels. In the partially shaded condition, it observes the various irradiance level. Due to the PS, a series of PV modules linked together may experience a low current and do not give high efficiency. Many PV modules are connected in series to increase voltages and it is called a string. Multiple strings are connected with a single inverter.  
However, due to different irradiance levels, it may result in the non-uniform current that causes the mismatching and hot-spot effects. These effects can be reduced by connecting a bypass diode that gives an extra path to the flow of the high current of other PV modules. PV array of 4 \(\times\) 1 of same and different irradiance levels is shown in Figure 3a,b respectively. PS faces the problem of MPPT that results in low efficiency of PV energy system. This problem is solved by conventional algorithms such as P&O and InC as their decisions are based on negative and positive gradients [5]. These techniques may not find any difference between a single GM and LM that operates on one point only. However, PS produces more than one peak on the curve, and in this way, by using these conventional algorithms the performance is compromised. Bio-inspired algorithms can locate GM successfully for PV energy systems.  
Whenever PV modules in large numbers are combined in series and give the output power, then CPS takes place [10]. Multiple close peaks are found on the P-V and I-V characteristics curves. The collection of these number of different peaks on power and voltage curves is known as a cluster. The CPS block diagram is demonstrated in Figure 4.  
Figure 3: (**a**) Uniform Irradiance Case (**b**) Partial Shading Case.  
There is a single GM in all clusters of CPS cases. This single maximum is also known as cluster head maxima. However, in a string, the number of partially shaded PV modules have an equal number of peaks obtained on a PV curve from the PV energy system. P-V characteristics curve of unique type is shown if the shading of the PV energy systems is broadly distributed. Moreover, this curve has multiple close peaks points. In CPS, finding GM is a big problem.
## 3 Results
### Data Decompostion  
Data decomposition was performed with three methods: principle component analysis (PCA), PLS-regression (PLS), and t-distributed stochastic neighbor embedding (TSNE). Figure 3 illustrated the score plots of PCA, TSNE, and PLS, and corresponding violin plots of PCA (5 illustrated), TSNE (2 illustrated), and PLS (5 illustrated) components. Clearly, PLS decomposition could provide better separation for the malignant and benign samples.  
Figure 3: Score plots of PCA (**a**), TSNE (**c**), and PLS (**e**), and violin plots of PCA (**b**), TSNE (**d**), and PLS (**f**) components.  
The contribution of each ion ratio to the separation of malignant and benign nodules can be approximated with \(Y=\) A\(X\). Figure 4 illustrates 10 major features that contribute the most to the separation of malignant and benign nodules by PLS decomposition.
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
###### Abstract  
Ultra-wideband technology has the merits of high temporal resolution and stability, and it has been widely used for high-accuracy localization and tracking. However, most ultra-wideband localization systems need multiple anchors for trilateration, which results in high system cost, large messages overhead, and insufficient extraction of information. In this paper, we propose a single-anchor localization (SAL) mehtod that achieves high-accuracy multi-agent localization with high efficiency. In the proposed method, the anchor broadcasts the first two messages and then each agent responds one message to the anchor (quasi-)simultaneously. Based on the received message with superpositioned agent responses, the time-of-flight and angle-of-arrival information from all agents to the anchor can be extracted altogether. We implement the localization system in two indoor environments, and show that the proposed method can achieve decimeter-level accuracy for multiple agents using three messages. Our method provides design guidelines for high-accuracy and high-efficiency multi-agent localization systems.  
multi-agent localization; ultra-wideband; single anchor; antenna array Article
# Distribution of Operating Costs Along the Value Chain of an Open-Pit Copper Mine
## 2 Materials and Methods
### Estimation of Operating Costs
#### 2.4.2 Blasting  
The blasting operation used Emulsion S100 explosives with a density of 1.2 g/cm\({}^{3}\), a relative weight strength (RWS) of 85% (with ANFO = 100%), and a velocity of detonation (VOD) of 5000 m/s. The powder factor ranged from 0.4 to 1.2 kg/m\({}^{3}\) for a rock mass of specific gravity of 2.5 g/cm\({}^{3}\). Drill cuttings were used as stemming material with depths ranging between 3 and 4 m. An average of 55,440,000 tons of material was blasted annually, representing a 25% increase over the annual volume of material drilled due to swelling during blasting. This translated into 18,480 tons of explosives required per year.  
The blasting operating cost was estimated using the following equations:  
\[K_{Bt}=K_{Bms}+K_{Blab}+K_{Bf}+K_{Bprg}+K_{BO} \tag{3}\]  
\[OPEX_{B}=\frac{K_{Btc}}{M_{B}} \tag{4}\]  
where \(K_{Bt}\) is the total cost of blasting (USD/year); \(K_{Bms}\) is the cost of the material and supplies, including explosives and blasting accessories (USD/year); \(K_{Blab}\) is the labor cost inclusive of salaries and wages for blast operators and technicians (USD/year); \(K_{Bf}\) is the cost of fuel associated with blasting (USD/year); \(K_{Bmq}\) is the regulatory cost associated with permits, blasting licenses, insurances, and disposal of blasting consumables (USD/year); \(K_{BO}\) is the other costs (USD/year); \(OPEX_{B}\) is the blasting operating cost (USD/ton); \(K_{Btc}\) is the total cost inclusive of the 10% contingency (USD/year); and \(M_{B}\) is the annual volume of material blasted (tons/year).
# Multi-Region OpenFOAM Solver Development for Compact Toroid Transport in Drift Tube
## 3 Program Verification
### Magnetic Diffusion Solver Verification  
To verify the accuracy of the magnetic diffusion solver, this study selects the conductive cylinder model with analytical solutions proposed by [PERSON] [32] for numerical testing. This model considers an axisymmetric 2D cylindrical conductor, where the current source is applied axially, and the magnetic field is distributed only in the azimuthal direction and is a function of radius \(r\) and time \(t\), assuming the electrical conductivity \(\sigma\) is a spatially uniform constant and both ends of the cylinder apply zero normal flux boundary conditions. If the total current is \(S(t)\), the magnetic field at the surface of the cylindrical conductor can be calculated using the integral form of [PERSON]'s law:  
\[B(r,t)_{\text{exterior}}=\frac{2S(t)}{r} \tag{21}\]  
When the total current is a constant \(S(t)=S_{0}\), the analytical solutions for magnetic flux density and current density are  
\[B(r,t)=\frac{2 rS_{0}}{R_{w}^{2}}\left[1+2\sum_{n=1}^{\infty}\frac{R_{w}}{ry_{ n}}\frac{J_{1}\left(\frac{r}{R_{w}}y_{n}\right)}{J_{0}(y_{n})}e^{-t\frac{y_{n}^{2} }{b^{2}}}\right] \tag{22}\]  
\[j(r,t)=\frac{S_{0}}{\pi R_{w}^{2}}\left[1+\sum_{n=1}^{\infty}\frac{J_{0}\left( \frac{r}{R_{w}}y_{n}\right)}{J_{0}(y_{n})}e^{-t\frac{y_{n}^{2}}{b^{2}}}\right] \tag{23}\]  
where \(b=R_{w}\sqrt{\mu_{0}\sigma}\) and \(y_{n}\) are the n-th non-zero roots of \(J_{1}(y)=0\).  
Figure 5: Time evolution of density error under different grid resolutions for the Brio–Wu problem.  
Numerical simulation parameters are set as follows: total current \(S_{0}=1.0A\), cylinder radius \(R_{w}=1.0\) m, vacuum permeability \(\mu_{0}=1.256\times 10^{-6}\)\(\mathrm{N/A^{2}}\), electrical conductivity \(\sigma=1.0\)\(\mathrm{S/m}\). The initial value of magnetic flux density is  
\[B(r,0)=\frac{2 rS_{0}}{R_{w}^{2}}\left[1+2\frac{R_{w}}{ry_{1}}\frac{J_{1}\left( \frac{r}{R_{w}}y_{1}\right)}{J_{0}(y_{1})}\right] \tag{24}\]  
The magnetic diffusion solver is used to compute and compare the numerical solution with the analytical solution at \(t=0,\ 0.05,\ 0.1\) s. As shown in Figure 6, the blue solid lines represent the numerical solutions, and the red dashed lines denote the analytical solutions. The numerical and analytical solutions match closely in the distribution of magnetic flux density and current density, indicating that the magnetic diffusion solver can accurately capture the dynamic process of magnetic field diffusion.
## 3 Overview of OCC
### Encoding Principle  
The basic encoding principle in rolling shutter-based OCC remains the same, i.e., encoding data in stripes. Thus, depending upon the modulation schemes, the properties of stripes will change. For example, in direct OOK modulation, the width of stripes will change based on the bit patterns. Whereas for FSK, the modulated output would vary in the number of stripes, and for CSK, it would be colored bands. Although the number of stripes required to represent a bit determines the efficiency of the modulation technique, it is also dependent on the size of the transmitter [22]. Figure 3 shows sample frames from each modulation scheme. In FSK, the data is transmitted by modulating the signals in-terms of shifting frequencies, while in Pulse Duration Modulation(PDM), it is transmitted by changing the duration of pulse. The proposed modulation technique combine these two techniques to transmit the data by modulating duration of frequencies.  
Figure 2: The schematic block diagram of an OCC system.
## 2 Motivation
### Major Issues  
Some of the limitations experienced by traditional models of real-world object recognition in federated learning are listed below:  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Method** & **Advantages** & **Disadvantages** \\ \hline [PERSON], [PERSON] et al. [1] & Federated object-detection algorithms & It is able to mitigate non-IID issues. & It was not able to augment the dataset. \\ \hline [PERSON], [PERSON] et al. [9] & FedCV & It is able to perform various computer vision tasks. & Increasing the effectiveness of federated learning was difficult. \\ \hline [PERSON] et al. [19] & DRFL & It provided better performance. & The implementation was not achieved using high-scale datasets. \\ \hline [PERSON], [PERSON] et al. [8] & FedVision & It reduced the communication overhead. & It failed to obtain sustainable mechanism. \\ \hline [PERSON], [PERSON] et al. [20] & Active learning & It increased the precision level. & It did not work well with non-homogeneous data. \\ \hline [PERSON], [PERSON] and [PERSON], [21] & FedAVg & It reduced the divergences. & It leads to reduction in mapping. \\ \hline [PERSON], [PERSON] et al. [22] & ICM & It effectively worked on decentralized data. & It was incapable of enhancing the communication effectiveness of system factors. \\ \hline [PERSON], [PERSON] et al. [23] & Adaptive model communication approach & It effectively deals with future congestion scenario. & It failed to compute the offloading decisions. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Review of literature survey.  
* Achieving real-time detection in crowded areas becomes a challenging issue in the existing models.
* Imbalanced data handling is another major issue in the existing object-detection models.
* Owing to network slicing in resolution image sensing, it was unable to update the needs of resource allocation, computation offloading resolutions, and service caching.
* The communication overhead of the conventional models is high.
# A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation
###### Abstract  
Pumps are one of the most critical machines in the petrochemical process. Condition monitoring of such parts and detecting faults at an early stage are crucial for reducing downtime in the production line and improving plant safety, efficiency and reliability. This paper develops a fault detection and isolation scheme based on an unsupervised machine learning method, sparse autoencoder (SAE), and evaluates the model on industrial multivariate data. The Mahalanobis distance (MD) is employed to calculate the statistical difference of the residual outputs between monitoring and normal states and is used as a system-wide health indicator. Furthermore, fault isolation is achieved by a reconstruction-based two-dimensional contribution map, in which the variables with larger contributions are responsible for the detected fault. To demonstrate the effectiveness of the proposed scheme, two case studies are carried out based on a multivariate data set from a pump system in an oil and petrochemical factory. The classical principal component analysis (PCA) method is compared with the proposed method and results show that SAE performs better in terms of fault detection than PCA, and can effectively isolate the abnormal variables, which can hence help effectively trace the root cause of the detected fault.  
sparse autoencoders; unsupervised learning; multivariate data; fault detection; pump +
Footnote †: journal: Applied Sciences  
Article
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 2 Related Work
### Internal Events  
In this section, we summarize research on reconstructing model data lost through internal events. [PERSON] et al. [21] introduced a framework to identify and address sensor faults, which are missing, spiky, random, and drifting. Initially, a CNN was employed to determine the existence of a fault and classify its specific type. Subsequently, a collection of distinct CAE networks, each specifically trained to correspond to a particular type of fault, was utilized for the purpose of reconstruction. [PERSON] et al. [18] introduced a data-centric approach involving a bidirectional recurrent neural network (BRNN) to reconstruct sensor data. This approach takes into account the spatiotemporal correlations present among the sensor data.  
Researchers have investigated data reconstruction techniques based on compressive sensing in order to mitigate the impact of data packet losses in wireless sensor networks [19]. This system uses a compressive sensing technique to rebuild data after a packet loss. The algorithm under consideration demonstrates significant promise within the domain of structural health monitoring. [PERSON] et al. [20] used a recursive graph median filter to simultaneously address impulsive and Gaussian noise. The proposed filter is ideal for sensor networks with low resources because it can be implemented with distributed processing.  
The data reconstruction scheme proposed by [PERSON] et al. [17] utilizes matrix completion and temporal stability as its foundation. The problem of data reconstruction was initially conceptualized as a matrix completion task, wherein structural noise was taken into account. This approach relied on the identification and utilization of low-rank characteristics inherent in the sensory environmental data. The matrix completion issue was then constrained to achieve short-term stability to further reduce the reconstruction error. To overcome this issue, an algorithm based on the operator splitting technique and the block coordinate descent method was developed.