esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Comparison of the QoS of the Node Deployment Strategies
#### 4.1.2 QoS of the Node Deployment Strategies  
According to the calculation formulas of the coverage-based QoS metrics introduced in Section 2.3, we compare the coverage, the information integrity, the information validity, the information redundancy, and the number of sensor nodes of the four node deployment strategies above. The specific results are shown in Table 2.  
As can be seen from Table 2, in terms of coverage, the random node deployment strategy only reaches 94.233%, while the other three deterministic node deployment strategies reach 100%. In terms of information validity and redundancy, we find that the 1-fold coverage-based node deployment strategy in a cellular network achieves the best effect; that is, this strategy can make the obtained information have a high validity and a low redundancy, which is the main reason why it is widely used in the case of 1-fold coverage requirements. However, the strategy's information integrity under the 2-coverage requirement is only 59.833%, which obviously cannot meet the basic requirements of the project. In terms of information integrity, the uniform node deployment strategy and the node deployment strategy of this paper both reach 100%. However, compared with the 63.134% information validity, 36.866% information redundancy, and the 110 nodes required by the uniform node deployment strategy, the node deployment strategy in this paper can achieve 83.480% information validity and 16.520% information redundancy and only requires 91 sensor nodes. Obviously, the node deployment strategy in this paper is more advantageous than the uniform node deployment strategy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Strategy** & **Coverage** & **Integrity** & **Validity** & **Redundancy** & **Amount** \\ \hline Random & 94.233\% & 86.689\% & 58.854\% & 41.146\% & 91 \\ Uniform & 100\% & 100\% & 63.134\% & 36.866\% & 110 \\
1-Coverage & 100\% & 59.833\% & 99.889\% & 0.111\% & 46 \\ Cellular Grid & 100\% & 100\% & 83.480\% & 16.520\% & 91 \\ Our Strategy & 100\% & 100\% & 83.480\% & 16.520\% & 91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the quality of service (QoS) of the four node deployment strategies.  
Figure 14: The node distribution diagram of the node deployment strategy proposed in this paper.  
Figure 13: The node distribution diagram of the 1-fold coverage-based node deployment strategy in a cellular network.  
In summary, in the four node deployment strategies, the node deployment strategy proposed in this paper has a superior QoS and can meet the basic requirements of the project.
## 2 Problem Formulation  
### Signal Model  
The major purpose of this paper is to employ an evolution method to enhance the performance of GNSS signal acquisition. Under the scenario of ignoring data modulation and interference, the incoming signal is down-converted and then transferred to a digital Intermediate Frequency (IF) signal. The signal at sampling time, \(t_{k}\), can be described as:  
\[r(t_{k})=\sqrt{P}G(t_{k}-\tau)\exp(-j\,2\pi(f_{c}+f_{d})t_{k}+\theta)+w(t_{k}) \tag{1}\]  
where \(P\) is the power of the direct line-of-sight GNSS signal, \(f_{c}\) is the IF and \(j=\sqrt{-1}\). The variables \(f_{d}\), \(\theta\) and \(\tau\) denote the Doppler frequency, carrier phase, and code delay, respectively. The noise components \(w(t_{k})\) are referred to as white Gaussian noise distribution, in which the power spectrum density is \(\mathrm{N}_{0}/2\). The \(\mathrm{N}_{0}\) stands for single sideband power spectrum density of noise. \(G(\cdot)\) is the filtered code sequence expressed by C/A-code, P-code or binary offset carrier (BOC) signal [12,21,22].
# A Novel Method for Modeling the Electromagnetic Characteristics of Switched Reluctance Motors
## 4 Dynamic Simulation and Experiment
### Dynamic Simulation Model  
In order to simulate the actual SRM dynamics, a system simulation model is shown in Figure 8a, which is set up in MATLAB/Simulink software. The proportional-integral (PI) controller is used to  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Phase & 3 \\ Stator/rotor poles & 12/8 \\ Rated power & 1.5 kW \\ Rated torque & 9.55 N\(\cdot\)m \\ Speed range of constant torque & 100–1500 r/min \\ Maximum flux linkage & 0.986 Wb \\ Stator resistance & 0.9 \(\Omega\) \\ Moment of inertia & 0.01 kg\(\cdot\)m\({}^{2}\) \\ Friction coefficient & 0.005 N\(\cdot\)m\(\cdot\)s \\ Aligned inductance & 154 mH \\ Unaligned inductance & 23 mH \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of the switched reluctance motor (SRM).  
regulate the speed of the SRM. Its parameters \(k_{p}\) and \(k_{i}\) are set to 0.1 and 0.05, respectively. The sampling frequencies of the speed loop and current loop in the simulation are set as 1 kHz and 10 kHz, respectively. In Figure 8b, \(T_{L}\) is the load torque, \(\omega\) is the rotor speed, \(T_{e}\) is the total electromagnetic torque, \(J\) is the moment of inertia and \(B\) is the friction coefficient. As can be seen from Figure 8b, two look-up tables including current-flux-position \(i(\psi,\theta)\) characteristics and torque-current-position \(T(i,\theta)\) characteristics are used to build the SRM model, which are obtained by Equations (9) and (15). Two operation modes, which included current chopping control (CCC) and pulse width modulation (PWM), were simulated to test the accuracy of the proposed analytical method. The simulation results are presented in Figures 9a and 10a.
# Continuous-Thrust Circular Orbit Phasing Optimization of Deep Space CubeSats
## 3 Spacecraft Dynamics and Trajectory Optimization
### Trajectory Optimization and Optimal Control Law
To this end, Equations (16) and (17) are more conveniently rewritten as  
\[\mathbf{a}=\frac{\tau\,T_{\max_{\oplus}}}{m}\left(\frac{N_{T}}{D_{T}}\right)\mathbf{ \dot{a}},\quad\frac{\mathrm{d}m}{\mathrm{d}t}=-\frac{\tau\,T_{\max_{\oplus}}}{ g_{0}\,I_{\mathrm{sp}_{\oplus}}}\left(\frac{N_{T}\,D_{I}}{N_{I}\,D_{T}}\right), \tag{24}\]  
where \(\{N_{T},\,D_{T},\,N_{I},\,D_{I}\}\) are defined as  
\[N_{T} \triangleq d_{N_{1}}(r/r_{\oplus})^{4}+d_{N_{2}}(r/r_{\oplus})^{3} +d_{N_{3}}(r/r_{\oplus})^{2}+d_{N_{4}}(r/r_{\oplus})+d_{N_{5}} \tag{25}\] \[D_{T} \triangleq(r/r_{\oplus})^{3}+d_{D_{1}}(r/r_{\oplus})^{2}+d_{D_{2} }(r/r_{\oplus})+d_{D_{3}}\] (26) \[N_{I} \triangleq e_{N_{1}}(r/r_{\oplus})^{4}+e_{N_{2}}(r/r_{\oplus})^{3}+e_{N_{ 3}}(r/r_{\oplus})^{2}+e_{N_{4}}(r/r_{\oplus})+e_{N_{5}}\] (27) \[D_{I} \triangleq(r/r_{\oplus})^{3}+e_{D_{1}}(r/r_{\oplus})^{2}+e_{D_{2} }(r/r_{\oplus})+e_{D_{3}} \tag{28}\]  
Taking Equations (22) and (24) into account, the part \(\mathcal{H}_{c}\) of the Hamiltonian function which depends on the control terms \(\{\tau,\,\mathbf{\dot{a}}\}\) is written as  
\[\mathcal{H}_{c}=\frac{\tau\,T_{\max_{\oplus}}\,N_{T}}{m\,D_{T}}\Bigg{(}\lambda _{u}\,\mathbf{\dot{a}}\cdot\mathbf{\dot{r}}+\lambda_{v}\,\mathbf{\dot{a}}\cdot\mathbf{\dot{ \theta}}-\frac{m\,\lambda_{m}\,D_{I}}{g_{0}\,I_{\mathrm{sp}_{\oplus}}\,N_{I}} \Bigg{)} \tag{29}\]  
where the dimensionless ratio \(N_{T}/D_{T}\) is always positive during the transfer according to Equation (16). The maximization of \(\mathcal{H}_{c}\) with respect to the thrust unit vector \(\mathbf{\dot{a}}\) gives the well-known result (the superscript \(\star\) indicates the optimal control law)  
\[\mathbf{\dot{a}}=\mathbf{\dot{a}}^{\star}\triangleq\frac{\lambda_{u}\,\mathbf{\dot{r}}+ \lambda_{v}\,\mathbf{\dot{\theta}}}{\sqrt{\lambda_{u}^{2}+\lambda_{v}^{2}}} \tag{30}\]  
Substituting the last equation in the expression of \(\mathcal{H}_{c}\) given by Equation (29), observing that \(\mathcal{H}_{c}\) is a linear function of \(\tau\) and neglecting the singular arcs during the transfer [41; 42], the optimal value of the engine throttle parameter \(\tau\) is given by  
\[\tau=\tau^{\star}\triangleq \begin{cases}1&\text{if}\quad 1-\frac{m\,\lambda_{m}\,D_{ I}}{g_{0}\,I_{\mathrm{sp}_{\oplus}}\,N_{I}}\geq 0\\ 0&\text{if}\quad 1-\frac{m\,\lambda_{m}\,D_{I}}{g_{0}\,I_{\mathrm{sp}_{ \oplus}}\,N_{I}}<0\end{cases}  \tag{31}\]  
Equations (30) and (31) are used to express the MTC control terms in both the equations of motion and the Euler-Lagrange equations during the numerical simulation of the optimal trajectory. The results of the optimization process are illustrated in the next section as a function of the value of the target orbit radius.
# Impact Wear Behavior of the Valve Cone Surface after Plasma Alloying Treatment
## 3 Results and Discussion
### Impact Wear
#### 3.3.2 Wear Amount  
The wear surfaces of the valve substrate and CrN coating were scanned within a range of 0-270\({}^{\circ}\) at a step of 90\({}^{\circ}\) using a laser level. The contours of four wear surfaces were measured as depicted in Figure 10. Two horizontal lines denote the average depth of the contour before and after the test, with the difference representing the average wear depth. Figure 8a-d illustrate the valve profile before and after the contact between the valve base and its counterpart, while Figure 10e-h show the valve profile before and after the contact between the CrN coating and the seat pair. As can be seen from Figure 10, the contour curve before the test comprises two sections: a rapidly rising oblique contour and a horizontal contour. The horizontal contour corresponds to the cone area in contact with the seat, and the oblique contour is ascribed to the neck transition area of the valve, in which wear predominantly occurs. Initially, the horizontal contour of the valve substrate appears neat with minimal fluctuation, indicating low surface roughness; conversely, the horizontal contour of the CrN coating exhibits slight fluctuations, reflecting the microscopic surface morphology, which is consistent with the island growth regularity of the coating. During the test, the valve is subjected to the impact load from the spring and the burst pressure in the cylinder, leading to severe plastic deformation and radial flow. The acceleration of the circulating seat generates a shock load on the valve cone, causing periodic shear stress and contact stress, which leads to the wear and the notable increase in roughness of the valve surface. The wear depth of the valve substrate first increases and then gradually decreases, with the depth of the contour line significantly increasing after the test, which suggests a slow and stable deepening of the CrN wear depth of the coating below that of the valve substrate. This indicates the high hardness and good wear resistance of the coating [26].  
Figure 10: _Cont._Before and after the test, the valve neck was completely immersed in absolute ethanol, and an ultrasonic cleaning machine was used to remove oil and impurities from the surface. After drying, the mass of the valve was determined using an electronic analytical balance to calculate the difference in mass before and after the test, thus determining the wear amount. The depth and amount of wear marks are listed in Table 4. According to the data, the wear depth was similar at the four positions of the CrN coating, with the maximum difference of 15 \(\upmu\)m, indicating that the valve was evenly stressed during the bench impact wear test. After 2 million shocks, the average wear depth and the wear amount of the coating sample were 42.93 \(\upmu\)m and 23.49 mg, respectively, i.e., 63.7% and 74.6% lower than those of the valve substrate (118.23 \(\upmu\)m and 92.66 mg, respectively), demonstrating the effective protection provided by the CrN coating.
## 2 Characteristics of Satellite Signals and Framework of GNSS Receivers
### Framework of GNSS Receivers  
Primarily, the signal processing framework of GNSS receivers mainly includes 4 parts: data collection, signal acquisition, signal tracking, and navigation solution, as shown in Figure 1. Firstly,the original signal received by the antenna is converted from low-power high-frequency into intermediate-frequency, and then sampled as digital to send to different channels of receivers. Next, visible satellites are identified by signal acquisition. It is also obtained that estimated values of carrier frequency and code phase of acquired satellite signals. Then, based on these estimated values, carrier wave and pseudo-random noise (PRN) code are accurately removed from the received signal by applying phase locked loop (PLL) and delay locked loop (DLL) to signal tracking. Finally, the navigation message is extracted by bit and frame synchronization to calculate position and velocity of the receiver. In this way, satellite signal has been processed by GNSS receivers to accomplish the functions of positioning, navigating, and timing.  
In this paper, the resampling strategy is proposed to decrease the data size of the sampled signal without obvious loss of signal quality, so as to greatly reduce computational complexity and time consumption of signal acquisition. Currently, signal acquisition is fundamentally realized by the serial search algorithm, parallel frequency space search algorithm, and parallel code phase search algorithm. By contrast, the parallel code phase search algorithm is the most efficient one that searches all code phases at the same time for each frequency bin. Hence, we chose the parallel code phase search algorithm on behalf of conventional acquisition algorithms to perform the resampling strategy. Besides, signal acquisition is improved by variable circular correlation time.
## 1 Introduction
Therefore, it should be concluded that the filter structures presented in [1; 2; 1; 3; 4; 5; 6; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 40; 41] suffer from one or more of the following disadvantages: (i) inability to realize various filtering responses in all four possible modes; (ii) use of a greater number of active components; (iii) use of an excessive number of passive components; (iv) use of ungrounded passive elements; (v) need of some external switches; (vi) lack of built-in tuning capability; (vii) inability to control \(\omega_{o}\) and \(Q\) independently; (viii) use of a complex active building block; (ix) need for inverting-type or double-type input signals. Due to its simple structure, versatility, and CMOS integrability, the voltage differencing buffered amplifier (VDBA) is an alternative and suitable active building block for biquad after solutions [47]. Interestingly, the internal circuit architecture of the VDBA block consists of an operational transconductance amplifier (OTA) and a voltage follower (VF) [48; 49]. This simple circuitry implementation leads to low power consumption and small chip area requirements. Therefore, in this work we adopt the advantages provided by the VDBA device to design a mixed-mode universal biquad filter. The designed filter topology possesses the following salient properties: (i) use of a reasonable number of active and passive elements (i.e., two VDBAs, two resistors, and two capacitors); (ii) capability of realizing universal biquadratic filter functions in all four modes; (iii) employment of all grounded passive elements, except for VM; (iv) exhibits inbuilt tuning capability; (v) noninteractive control of \(Q_{i}\)(vi) low-output impedance for VM and TIM operations. The functionality of the circuit has been evaluated through simulation results based upon TSMC 0.18-\(\mu\)m 1P6M CMOS technology parameters, and furthermore through the experimental measurements of the commercially available integrated circuit (IC), LT1228. Additionally, all properties of the proposed mixed-mode filter are mentioned and compared with the previous related works in Table 1. Furthermore, Table 2 presents a comprehensive comparison illustrating the superiority of the proposed mixed-mode universal filter over the earlier reported relevant VDBA-based biquad configurations [50; 51; 52; 53; 54; 55; 56; 57; 58]. As can be observed, no earlier VDBA-based filter realization can be operated in all four modes of operation. The passive components used for their realizations are all floating. Although the works proposed in [54; 56] use a single VDBA as an active element, they suffer from operating in only a single mode and using at least four floating passive components.
# An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine
## 2 Feature Engineering Based on Circle Model
### Circle Model-Based Feature Engineering
#### 2.2.2 Feature Construction  
Using the circle model, it is easy to acquire the characteristic fault voltage curves of the simulated elements quickly. Under the presence of tolerances, however, some elements belong to fuzzy groups, due to the similarity between their characteristic curves. The elements in the same fuzzy group are indistinguishable. Direct positioning of such elements will lead to misclassification, reducing the accuracy of diagnostic positioning. The classification accuracy will also be suppressed, if the selected features for classification are not distinguishable. For example, points _X1_, _X2_ and _X3_ (Figure 4) are too close to each other at a certain frequency. These points and their tolerance areas will greatly affect the classification accuracy. Therefore, the fuzzy groups must be handled effectively before diagnosing circuit faults [17; 18].  
\begin{table}
\begin{tabular}{c c} \hline
**Fuzzy Groups** & **Fault Elements** \\ \hline
1 & R1 \\ \hline
2 & R2 \\ \hline
3 & R3 \\ \hline
4 & R4 \\ \hline
5 & R5 \\ \hline
6 & R6, C2 \\ \hline
7 & R7, R13 \\ \hline
8 & R8, R9, R10, R12 \\ \hline
9 & R11 \\ \hline
10 & C1 \\ \hline
11 & C3 \\ \hline
12 & C4 \\ \hline \end{tabular}
\end{table}
Table 2: Fuzzy groups of the Leapfrog circuit.  
Figure 4: Sketch map of fuzzy groups.  
Considering the indistinguishability between 2D voltage features in the complex domain, this paper puts forward a 3D voltage feature construction method. First, all the points that are indistinguishable in 2D and distinguishable in 3D were identified based on the circle model of the original frequency. Then, the frequency was adjusted to find the second frequency making the originally indistinguishable voltage points distinguishable. The second frequency was inputted into the signal to compute the second set of complex domain features. In this way, a new feature was constructed as \((x_{f_{1}},y_{f_{1}},x_{f_{2}},y_{f_{2}})\), which can be compressed into a 3D feature \((x_{f_{1}},y_{f_{1}},x_{f_{2}})\).  
As shown in Figure 5, the 3D map with frequency feature helps to differentiate between the voltage output points that are indistinguishable in 2D complex domain, in the light of their variation with frequencies in 3D space.
## 5 Symptoms of NMOSD
### Metabolomic Studies to Differentiate NMOSD and Other Related Disorders  
Barring the discovery of specific biomarkers for early diagnosis of AQP4-IgG\({}^{+}\), AQP4-IgG\({}^{-}\), MOG-IgG\({}^{+}\), and MOG-IgG\({}^{-}\), the differentiation of NMOSD and other related disorders remains impassable. Although AQP4-IgG antibody titers represent the clinical presentation and immune response associated with NMOSD, similar AQP4-IgG titers values may occur due to complex immunoinflammatory processes [92; 93]. This could complicate the similarities and variations between disease and non-disease conditions. Studies evaluated serum samples of MS patients using metabolomic profiling to differentiate the pathophysiology of MS from healthy subjects. Mass spectrometry and nuclear magnetic resonance (NMR) spectroscopic studies revealed increased levels of sphingomyelin, phosphatidylinositol, phosphatidylinositol, phosphatidylinositol, phosphatidylinositol-ethanolamine, glutamate, and other amino acids [94; 95; 96; 97]. Further studies with ultra-high-performance liquid chromatography-mass spectrometry and ultra-high-performance liquid chromatography-time of flight-mass spec trometry determined the possible biomarkers of MS, which help differentiate MS from healthy conditions and other diseases such as NMOSD [30]. A lipid profiling study in MS patients revealed increased serum lysine and histidine and decreased HDL particles, unsaturated lipid, and alanine, which helps to differentiate MS from NMOSD [98].  
A serological comparative study in NMOSD and RRMS patients using a proton high-resolution magic angle spinning NMR spectroscopy (\({}^{1}\)H-HRMAS-NMR) evidenced that the astrocytic metabolites scyllo-inositol and acetate are promising biomarkers of MS and NMO, respectively, with more than 90% sensitivity and specificity [99]. The NMR analysis of urine samples of MS patients and healthy subjects revealed that 27 metabolites were altered in MS patients and were associated with fatty acid metabolism, mitochondrial activity, and the gut microbiome [100]. CSF metabolites of MS, NMO, and idiopathic transverse myelitis patients and healthy controls were reported. The study specified that CSF metabolomic profiling could enhance the accuracy of CNS inflammatory demyelinating diseases. The lipids (1-monopalmitin and 1-monostearin) were common in MS, NMO, and idiopathic transverse myelitis cases [101]. Coherently, the metabolomic studies of blood, plasma, serum, urine, and CSF showed that several metabolites were distinctive and that such changes directly describe their invincible role in disease progression or regression.
## 3 Research Methods and Data
### Analysis of Influencing Factors Based on Geographic Detector  
Compared with the previous regression analysis, the advantage of using geographical detectors to detect spatial differentiation and the driving force behind it is that it can achieve a certain statistical accuracy with a sample size of less than 30. Additionally, for collinearity immunity among multiple independent variables, space continuity is not required in the geographical detectors-based analysis [33; 34]. Based on this, the factor detection function is used to calculate the interpretation degree of the influencing factors of water conservation in Yunnan Province, and judge whether the interaction of the two factors is stronger or weaker than that of a single factor based on the interactive detection. Among them, the equation for calculating the detection force value \(q\) of influence factors on regional differences is as follows:  
\[q=1-\frac{1}{n\sigma}\sum_{i=1}^{M}n_{i}\sigma_{i}^{2}(i=1,2,3\ldots) \tag{10}\]  
In Equation (10), the value of \(q\) is between 0 and 1. The greater the value of \(q\), the greater the impact of this factor on regional water conservation. \(M\) is the stratification of influence factor \(x\). \(n\) and \(n_{i}\) are the number of samples in the study area and layer \(i\), respectively. \(\sigma^{2}\) and \(\sigma_{i}^{2}\) are the variances of the study area and layer \(i\), respectively.