esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Innovative Calibration of Intradyne Coherent Receiver Using Polarization-Multiplexed Signal
As seen in Figure 3, an SMF connects the output of the polarization controller with the input of ICR. Since the SMF cannot maintain SoP, the drift of SoP will lead to errors in the measurement. We can eliminate this issue by using the polarization-multiplexed signal. The second way is to use a single TLS to generate the polarization-multiplexed signal. Compared with the first way, the second way removes the requirement for two TLSs and eliminates the efforts to match the frequency and power for two TLSs. First, a beam splitter (BS) splits the output of the TLS into  
Figure 8: Comparison between the frequency response calibrated by two different methods. (**a**) Results for XI tributary. (**b**) Results for YI tributary. (**c**) Results for XQ tributary. (**d**) Results for YQ tributary. two paths. The polarization in one path is rotated by 90 degrees using a half-wave plate. Then, these two paths are recombined using a PBC. Polarization maintaining fiber (PMF) is required for both paths. The length difference between two paths needs to be larger than the coherence length of TLS. A wide laser linewidth leads to a short coherent length. Thus, a low-cost TLS with large linewidth is well suited for this application. Figure 9 shows this configuration. The wavelength accuracy of low-cost TLS might be low. As demonstrated later in Section 4, we can measure the frequency difference between the TLS and the LO very accurately by performing Fourier transform on the output signal of the ICR. This will address the concern of using the low-cost TLS for ICR calibration.
## 1 Introduction  
The reliable operation of electric power plants has become increasingly critical, driven by technical demands and economic pressures. To remain economically viable, the equipment must demonstrate substantial resilience against potential failures.  
Power transformers represent crucial components within electrical power systems, and early identification of potential issues, especially during operation, is essential to reduce repair costs significantly. Maintaining the operational safety of oil-filled transformers requires thorough diagnostic assessments of essential insulating materials, including paper, barriers, and insulating oil. A variety of diagnostic techniques, such as insulation resistance measurement, capacitance testing, dissipation factor analysis, and partial discharge evaluation, are typically utilized to determine insulation condition and reliability [1, 2].  
Operational experience indicates the importance of measurement and diagnostic procedures for distribution transformers operating at voltage levels of 22 kV and 110 kV, in addition to transmission transformers. To prevent transformer failures, diverse measurement approaches are employed to precisely capture the actual state of equipment.  
Therefore, carefully selecting suitable diagnostic and measuring methods is vital for accurately forecasting transformer conditions [3,4].  
Applying appropriate combinations of measurement and diagnostic methods enables non-destructive identification of insulation aging, moisture presence, or structural defects in power transformers, applicable both during active operation and downtime, irrespective of whether assessments are conducted online or offline.  
Evaluating oil-paper-insulated power transformers, particularly regarding their moisture content, is gaining increased importance, both for managing aging transformers and for quality assurance of newly installed equipment. This heightened requirement for accurate and dependable diagnostic methods has spurred advancements in dielectric response techniques, while improvements in diagnostic procedures include the use of commercial technology. The growing need for advanced and dependable diagnostic approaches has driven the evolution of dielectric response techniques [5,6].  
One traditional and straightforward approach for assessing insulation condition involves measuring insulation resistance. However, this method's effectiveness is limited due to its dependency on insulation type and dimensions, as well as its inability to precisely identify whether insulation deterioration is primarily within the paper or the oil layers. Nonetheless, it remains capable of detecting minor local deteriorations traversing the oil-paper-insulation boundary [7,8].  
More advanced techniques, including polarization and depolarization of current measurements and frequency domain spectroscopy (FDS), have demonstrated their efficacy in transformer diagnostics and are extensively adopted. These modern methods provide more detailed and reliable information about insulation conditions, solidifying their value as essential tools in power transformer evaluations [9, 10, 11].
# Locomotion of Self-Excited Vibrating and Rotating Objects in Granular Environments
## 2 Simulation Implementation Details
### The Simulated System  
The simulations took place in a container of dimensions \(0.5\) m \(\times\) 1.0 m \(\times\) 1.3 m. We used periodic boundary conditions in the \(x\) and \(y\) directions, with the dimensions in these direction deemed sufficiently large to avoid boundary effects, as well as interactions of the SO with its periodic image. The bottom of the container was fixed at \(z=-0.5\) m. Earth gravity was in the \(-z\) direction. We generated bed particles with diameter \(d_{0}=0.02\) m and density \(\rho_{0}=2500\) kg/m\({}^{3}\) at the top of the container and let them fall under gravity and fill the box up to \(z=0.0\) m. At this stage, a SO of diameter \(d_{i}=0.12\) m and density \(\rho_{i}=2500\) kg/m\({}^{3}\) was placed as close as possible to position \((0,0,0)\). Then, more bed grains were poured into the box up to the level of \(z=0.5\) m. In total, a bed of \(8*10^{5}\) particles was generated, filling a volume of \(0.5\) m \(\times\) 1.0 m \(\times\) 1.0 m, with the SO buried in its center (Figure 1). Before starting each formal simulation, we ran an initial equilibration simulation to stabilize the system structure. The system was considered in equilibrium when the kinetic energy dropped to \(10^{-4}\) of its initial value.  
Starting from the equilibrated initial state, we carried out two sets of simulations. In the first, the SO was driven by excitation in a composite mode of oscillation and constant speed velocity rotation. The oscillation is simulated by a periodic oscillation in the \(y\) direction, \(y=Asin(2\pi ft)\), with amplitude \(A=0.03\) m and frequency \(f=3\) Hz, using the values in [35]. We set the SO rotations around \(x\)-axis, \(y\)-axis, \(z\)-axis, and an \(xz\)-axis at an oscillating angular velocity of amplitude \(\omega=80\) rad/s, using the value in [36], which was shown to lead to a significant rise of SOs. In the second set of simulations, the SO was driven by both directional and rotational oscillations, \(y=A\sin(2\pi ft)\) and \(\omega=\Omega\sin(2\pi ft)\), respectively. We varied the three parameters, \(A\), \(\Omega\), and \(f\) in a series of DEM simulations, described in Section 2.1, and analyzed their effect on the motion of the SO. The time step in all the simulations was \(10^{-5}\) s. Altogether, we ran 58 simulations, each with different parameters, 56 of which for \(4\times 10^{5}\) time steps, corresponding to \(=4\) s physical time, and 2 simulations for \(5\times 10^{5}\) time steps, corresponding to \(=5\) s physical time. One simulation of 4 s physical time took about 2 h to run in CPU time.  
Figure 1: The simulated system: (**a**) the initial state, with \(h_{2}=1.3\) m the height of the container and \(h_{1}=1.0\) m the height of the bed medium; (**b**) a cross-section at \(x=0\), showing the initial SO at depth \(h_{0}\).
## 3 Notations and Problems
### Problem Definition  
Using the notation mentioned above, we define the problem of co-embedding in KG as follows.  
**Problem 1**.: _The Co-embedding Model for KG Embedding. Given a knowledge graph \(\mathcal{G}=(\mathcal{E},\mathcal{R},\mathcal{O})\), our goal is to learn the representations of KG components, including entities and relations, in the same semantic space as that of a transformation \(\Xi\)._  
\[\mathcal{G}\overset{\Xi}{\rightarrow}\mathbf{Z}^{\mathcal{E}},\mathbf{Z}^{ \mathcal{R}}, \tag{1}\]  
_where \(\mathbf{Z}^{\mathcal{E}}\in\mathbb{R}^{M\times D}\) and \(\mathbf{Z}^{\mathcal{R}}\in\mathbb{R}^{N\times D}\), respectively. The \(i\)-th row vector in \(\mathbf{Z}^{\mathcal{E}}\), written as \(\mathbf{z}_{i}^{\mathcal{E}}\), is denoted as the resulting embedding of the \(i\)-th entity, and the \(j\)-th row vector in \(\mathbf{Z}^{\mathcal{R}}\) written as \(\mathbf{z}_{j}^{\mathcal{R}}\) is denoted as the resulting embedding of the \(j\)-th relation._  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(\mathcal{G}\) & a knowledge graph \\ \(\mathcal{E}\) & set of entities \\ \(\mathcal{R}\) & set of relations \\ \(\mathcal{O}\) & set of triples \\ \(M=|\mathcal{E}|\) & size of entities \\ \(N=|\mathcal{R}|\) & size of relations \\ \(W=|\mathcal{O}|\) & size of triples \\ \(\mathbf{D}\) & dimension of latent variables \\ \(\mathbf{O}\in\mathbb{R}^{W\times 3}\) & observed data for triples \\ \(\mathbf{Z}^{\mathcal{E}}\in\mathbb{R}^{M\times D}\) & latent representation matrix for entities \\ \(\mathbf{Z}^{\mathcal{R}}\in\mathbb{R}^{N\times D}\) & latent representation matrix for relations \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main notations in our paper.
## 5 Case Study Analysis
### Analysis of Energy-Saving Potential for Different Demand Response Types
As a result, the reduction in purchasing energy costs and overall costs in Scenario 3 is not as significant as in Scenario 2. Scenario 4, which simultaneously considers both price-responsive and incentive-based demand response, operates with the combined effects of price mechanisms and incentive measures. During peak load periods, the electricity price increases due to the simultaneous action of pricing mechanisms and incentives. As a result, some users shift their high-demand periods to the low-price periods when wind power is abundant, further increasing the proportion of wind power. This leads to an 11.20% reduction in wind curtailment costs. Moreover, during peak load periods, CCHP and wind power generation can meet more demand, reducing the proportion of purchased energy and corresponding costs by 8.34%. The associated carbon emissions from purchased energy decrease, resulting in a 15.60% reduction in the overall system carbon emissions. Carbon trading costs also decrease, contributing to a 6.16% reduction in the overall system costs. Therefore, simultaneously considering both types of demand response can further promote peak shaving, accommodate new energy sources, and significantly reduce carbon emissions.
# Fault Diagnosis Method Based on Time Series in Autonomous Unmanned System
## 4 Fault Diagnosis Method Using Improved Clustering Algorithm
### Time Series Feature Engineering Using the Improved Markov Chain Model
In order to facilitate similarity measurement, the transition probability matrix is converted into a one-dimensional vector \(\mathrm{X}\), and the transformation method is as follows:  
\[\mathrm{x_{i*k+j}}=\mathrm{p_{i,j}} \tag{7}\]  
Since the one-dimensional vector \(\mathrm{X}\) can describe each time series \(\mathrm{S}\), the similarity of the two time series can be described as the Euclidean distance of the corresponding one-dimensional vector. Because the time series data will be in a state for quite a long time when a time series is directly modeled using a Markov chain, the transition from one state to its own state will account for the majority. The proportion to other states is much smaller than this value, which leads to this method describing the proportion of different states and ignoring the changes between states. Therefore, this paper improves the time series feature engineering based on Markov chain model. First, the state transition is divided into two part: maintaining the current state and transitioning to other states. For the part that keeps the current state, the proportion of the state in the whole time series is measured, and the state transition probability after the state transition to other states is measured by removing the state transition to itself. The specific process is as follows:  
* Input: Symbolized time series \(\mathrm{S}=\{\mathrm{s_{1},s_{2},\ldots,s_{m}}\}\), symbol set \(\mathrm{C}\)
* Output: feature matrix corresponding to the time series \(\mathrm{M}\)
* Process: Traverse the symbolized time series \(\mathrm{S}\), count the number of transitions between states, and construct a matrix of times as follows:  
\[\mathrm{Count_{i,j}}=\sum_{\mathrm{S_{i+1}=i}\;\wedge\mathrm{S_{i+1}=j}\; \wedge\mathrm{i,j\in\mathrm{C}}}1 \tag{8}\]  
Traverse the matrix and calculate the total number of times that the time series maintains its own state, as follows:  
\[\mathrm{Sum_{keep}}=\sum_{i\in\mathrm{C}}\mathrm{Count_{i,i}} \tag{9}\]  
Traverse the matrix and calculate the total number of jumps to other states in the time series as follows:  
\[\mathrm{Sum_{change}}=\sum_{i\in\mathrm{C},j\in\mathrm{C},i\
eq j}\mathrm{ Count_{i,j}} \tag{10}\]  
Use the following formula to obtain the feature matrix corresponding to the time series \(\mathrm{M}\)  
\[\mathrm{M_{i,j}}= \begin{cases}\mathrm{Count_{i,j}}/\mathrm{Sum_{keep}}&\mathrm{ i}\in\mathrm{C},j\in\mathrm{C},i=j\\ \mathrm{Count_{i,j}}/\mathrm{Sum_{change}}&\mathrm{i}\in\mathrm{C},j\in\mathrm{ C},i\
eq j\end{cases}  \tag{11}\]  
In Section 5.2, we will compare these two different feature extraction methods.
## 5 Experiment Result
### First Scenario: Tracking Multiple Patients at Similar Locations  
This section conducts trial scenarios to verify the effectiveness of the system and simulate two transmitters representing two different patients. Figure 9 shows the setup of the OCC-IoMT system featuring two transmitters (TX) and a receiver (RX) specially designed for continuous and real-time patient health monitoring.  
In this setup, the two transmitters are represented by patients One and Two. Each patient is equipped with a matrix LED that transmits health information, such as oxygen saturation, heart rate, and body temperature as well as the value of the panic button. The receiver in this system is a camera functioning similar to a CCTV, continuously monitoring and recording data sent by both transmitters. This camera not only captures data but also processes it for transmission to the edge server. The edge server, which is shaped like a personal computer, plays a vital role in this system. It is responsible for managing data received from the receiver, processing it, and ensuring its availability to the medical team. This system operates patient health data, which are sent through the matrix LED on each transmitter. The camera, acting as the receiver, is equipped with the ability to detect and differentiate both patients (two transmitters) simultaneously. This approach is achieved using the YOLO algorithm and the SN displayed on the LED matrix to identify each patient. Furthermore, the system can determine the patient's location based on the camera ID. This allows monitoring of the location and duration of the patient's presence within the camera's range. Data obtained from both patients are then stored in the database via a REST API, ensuring the security and availability of information for further analysis. Figure 10 illustrates the results of implementing the OCC-IoMT system. Figure 10a shows the detection performed by the YOLO algorithm, depicting how the system can accurately identify and monitor two patients simultaneously. Figure 10b displays the database containing data received from both patients. Figure 10c,d shows the patient-accessible web interface. This web interface provides real-time information about the patient's condition and their location based on the last camera that detected them.  
Figure 10: The results of implementing the OCC-IoMT system. (**a**) The detection performed by the YOLO algorithm, depicting how the system can accurately identify and monitor two patients simultaneously; (**b**) the database containing data received from both patients; (**c,d**) the patient-accessible web interface (the first scenario).  
Figure 9: Setup of the OCC-IoMT system featuring two TXs and one RX (the first scenario).
## 3 Experiment Procedure
### Central Composite Design (CCD)  
A response surface methodology was applied to optimize the grinding conditions of 45 steel. A three-level central composite design (CCD) with three factors was selected to examine the comprehensive goal, and an experimental design can be seen in Table 4. The factors of abrasive size (\(X_{1}\)), grinding contact force (\(X_{2}\)), belt linear speed (\(X_{3}\)), and feed speed (\(X_{4}\)) were chosen as independent variables. When the value of weighting factor \(\lambda_{a}\) and \(\lambda_{w}\) were selected as 0.6 and 0.4, respectively, the coded and real values of abrasive size (\(X_{1}\)) were 0 for 240#, 1 for 320#, and 2 for 400#, those for contact force (\(X_{2}\)) were 0 for 15 N, 1 for 20 N, 2 for 25 N, those for belt linear speed (\(X_{3}\)) were 0 for 6 m/s, 1 for 7 m/s, 2 for 8 m/s, and those for feed speed (\(X_{4}\)) were 0 for 2 mm/s, 1 for 3 mm/s, 2 for 4 mm/s. We obtained similar interpretations referring to the above when the weighting factor \(\lambda_{a}\) and \(\lambda_{w}\) were selected as other values. Regression analysis was done on the data obtained by triplicate analysis for each dependent variable using design-expert software (Table 5). Response surface analysis was also applied to the data from CCD for modeling and prediction of optimum grinding contact force, belt linear speed, and feed speed.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Unit**} & \multirow{2}{*}{**Code**} & \multicolumn{6}{c}{**Level**} \\ \cline{3-10}  & & & & & & **0** & **1** & **2** \\ \hline Abrasive size (\(P\)) & \# & \(X_{1}\) & 240\# & 320\# & 400\# \\ Contact force (\(F\)) & N & \(X_{2}\) & 15 & 20 & 25 \\ Belt linear speed (\(V\)s) & m/s & \(X_{3}\) & 6 & 7 & 8 \\ Feed speed (\(Vw\)) & mm/s & \(X_{4}\) & 2 & 3 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Level of factors distribution of grinding (\(\lambda_{a}=0.6\); \(\lambda_{a}=0.7\); \(\lambda_{a}=0.8\); \(\lambda_{a}=0.9\)).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{4}{c}{**Parameter**} & \multicolumn{6}{c}{**Results**} & \multicolumn{3}{c}{\(\lambda_{a}=0.6\)} & \(\lambda_{a}=0.7\) & \(\lambda_{a}=0.8\) & \(\lambda_{a}=0.9\) \\ \cline{2-10}  & \(P\) & \(F\) & \(V_{S}\) & \(V_{W}\) & \(R_{a}\) & \(\hat{R}_{a}\) & \(Z_{w}\) & \(\hat{Z}_{w}\) & \(\hat{Y}_{1}\) & \(\hat{Y}_{2}\) & \(\hat{Y}_{3}\) & \(\hat{Y}_{4}\) \\ \hline
1 & 240 & 15 & 6 & 2 & 0.709 & 0.769 & 0.02 & 0.333 & 0.328 & 0.438 & 0.548 & 0.658 \\
2 & 400 & 15 & 6 & 2 & 0.480 & 0.217 & 0.018 & 0.250 & 0.030 & 0.077 & 0.123 & 0.170 \\
3 & 240 & 25 & 6 & 2 & 0.623 & 0.561 & 0.024 & 0.500 & 0.137 & 0.243 & 0.349 & 0.455 \\
4 & 400 & 25 & 6 & 2 & 0.433 & 0.104 & 0.019 & 0.292 & \(-0.054\) & \(-0.015\) & 0.025 & 0.064 \\
5 & 240 & 15 & 8 & 2 & 0.657 & 0.643 & 0.022 & 0.417 & 0.219 & 0.325 & 0.431 & 0.537 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Central experiment design table and experiment results.
# Thermodynamics of Liquid Uranium from Atomistic and Ab Initio Modeling
## 4 Results
Figure 6 shows the total specific heats of solid \(\gamma\)-U, FPLMTO [1], and liquid uranium, LAMMPS. The lattice (solid) and ionic (liquid) contributions to the total specific heat are also shown separately in Figure 6. As was reported in Ref. [1], the calculations for \(\gamma\)-U were performed up to \(T=2000\) K, which is well above the experimental melting point, \(T_{m}=1408\) K, The electron contribution to the total specific heat is significant for both \(\gamma\)-U and liquid uranium, but the total specific heat of liquid uranium increases slower with the temperature that is the total specific heat of \(\gamma\)-U because (1) the ionic specific heat of liquid uranium is constant (it does not depend on temperature) while the lattice specific heat of \(\gamma\)-U increases with temperature [1]; and (2) the electronic contribution to the specific heat of liquid uranium is lower than the electronic contribution to the specific heat of solid \(\gamma\)-U. Figure 7 shows the temperature dependence of the atomic energy of the supercooled liquid uranium from 1400 K to 600 K. One can see the drop of the energy vs. the temperature curve at \(T_{sc}\approx 800\) K that manifests the solidification of supercooled (remaining liquid below the melting temperature) uranium. Figure 6: Specific heat of \(\gamma\)-U and liquid uranium. Figure 7: The atomic energy of uranium as a function of temperature during cooling below 1400 K.  
One should mention that the energy released during solidification, \(\Delta H_{S}\) = 73.13 meV/atom \(\approx\) 5.38 mRy/atom \(\approx\) 7.06 kJ/mole, is about 22% smaller than the melting enthalpy, \(\Delta H_{M}\approx\) 9.07 kJ/mole, as calculated above. We will explain this phenomenon in the Discussion section. We also calculated the self-diffusion coefficient for liquid uranium through the Stokes-Einstein relation [48]:  
Figure 1:0  
where  
Figure 1:1  
is the atomic mean-square displacement (MSD). The results of these calculations are shown in Figure 1:2 Although the supercooled liquid uranium solidifies at \(T_{sc}\approx\) 800 K, the self-diffusion coefficient approaches zero at \(T_{D}\approx\) 700 K, which is about 100 K below the thermodynamical solidification temperature \(T_{sc}\). The calculated (LAMMPS, EAM [4]) self-diffusion coefficient of liquid uranium is in good agreement with the results of QMD (VASP) calculations [10] and experimental data [49]. The experimental data of the self-diffusion coefficient of liquid uranium at the experimental melting point, \(D_{m}\) = 0.192 A\({}^{2}\)/ps =1.92 \(\times\) 10\({}^{-9}\) m\({}^{2}\)/s, are also shown in Figure 1:3 This value was estimated in Ref. [10] on the measurements of the viscosity of liquid uranium at the melting point [49], \(\eta\) = 6.59 cP, and its relationship with diffusivity through the Stoke-Einstein relation; see Equation (14) from Ref. [3] for details.