esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Detailed Procedure for Fault Diagnosis
### Dimensionality Reduction  
To simplify the features of sensor data having a high-dimensional vector form, a process of reducing the dimension is required. Dimensional reduction of the sensor data is a data processing method by focusing on representing data with minimal number of dimensions while keeping the fundamental properties of the data from being lost. PCA is a method that is applied to sensor data analysis and processing of multiple data in order to reduce the data dimensions [26,27]. In this study, to minimize the dimensionality of the features and classify the fault condition clearly, two principal components having the high Eigen values are selected, as shown in Figure 4. The principal components can be represented by the following Equation (5).  
\[PC_{i}=a_{1}X_{1}+a_{2}X_{2}+\cdots+a_{n}X_{n} \tag{5}\]  
where \(PC_{i}\) denotes the principal component \(i\), \(X_{n}\) and \(a_{n}\) represent the original feature \(n\) and numerical coefficient for \(X_{n}\), respectively.
# Extraction of Periodic Terms in Satellite Clock Bias Based on Fourier Basis Pursuit Bandpass Filter
## 2 Fundamentals of Fourier Basis Pursuit  
The FT serves as a basic mathematical tool that transforms a time-domain signal into its frequency-domain representation. For a discrete time-domain signal \(x[n]\), its discrete Fourier transform (DFT) and inverse discrete Fourier transform (IDFT) are given by:  
\[X[k]=\sum_{n=0}^{N-1}x[n]\cdot e^{-j\frac{2\pi}{N}kn} \tag{1}\]\[x[n]=\frac{1}{N}\sum_{k=0}^{N-1}X[k].e^{\frac{1}{N}\pi kn} \tag{2}\]  
where \(k,n=0,\ 1,\dots,\ N-1\), and \(N\) is the data length. The index of \(k\) corresponds to the angular frequency \(\sigma_{k}\).
# Gas Foil Bearing Technology Enhanced with Smart Materials
## 4 Applications of Shape Memory Alloys to Gas Foil Bearings  
SMAs constitute the group of smart materials exhibiting one- and two-way memory effects and superelasticity (pseudoelasticity) [39; 40]. The above-mentioned effects are observed due to thermomechanical phenomena related to the reversible phase transformation (martenstic transformation) in SMAs. The unique thermally and mechanically induced SMA's responses may be easily exploited to construct solid state actuators and dampers. These devices make use of the memory effects and superelasticity, respectively.  
Both the memory effects and superelasticity may be employed in GFBs to effectively enhance their operational properties. The authors of the work [27] propose to substitute bump foils in a GFB with SMA wires (circumferentially distributed springs). Hence, active elasto-damping properties control becomes feasible via thermal activation. The thermally induced change of the resultant stiffness of the structural part of the GFBs' supporting layer allows for the desired adjustment of the clearance between the shaft's journal and top foil. Thermal control is performed using electric current supply and dedicated axially mounted pipes, respectively, for heating and cooling the SMA wires. Apart from the experimental study conducted, the authors of the referenced work also report a numerical model of a GFB composed by SMA springs employing the constitutive relation and the Reynolds equation. The simulated elasto-damping characteristics, as well as pressure profiles and load carrying capacity, are identified and discussed in the study. The presented work confirms the feasibility of the proposed solution.  
The concept of a passive SMA-based GFB is presented in the research [20]. Specifically, a direct installation of SMA wires on the outer surface of the top foil is proposed, e.g., via gluing or soldering. The authors of the cited work develop the nonlocal model for SMA materials, i.e., an adequate nonlocal formulation for constitutive relation, making use of peridynamics to conveniently deal with various types of nonlinearities for the planned numerical studies. The use of SMA wires is proposed to effectively modify the elasto-damping properties of the top foil, and, hence, reduce the mechanical vibrations of the shaft's journal during operation of the bearing. Thanks to the phenomenon of superelasticity the immanent mechanism of energy dissipation may be employed to enhance the GFBs' characteristics. The work [21] extends the scope of the above-mentioned SMA constitutive description adding the functionality of modeling non-isothermal phase transformations.
## 5 Limitations  
The experiment only included bicycle exercise; other exercise methods, such as treadmills and stair climbing, were not considered. Cycling was chosen because a fixed point of motion must be selected to collect dynamic data. Moreover, participants' cycling experience was not investigated, and exercise habits were surveyed by questionnaire only. Exercise habits were distinguished as either aerobic or anaerobic exercise. The sample sizes of 9, 10, and 11 for the Sedentary, Normal, and Exercise groups are too small in this study. Finally, only SampEn data were used; other entropy methodologies could produce different results.
## 2 Materials and Methods
### Method  
#### 2.2.1 Seasonal and Trend Decomposition Using Loess  
Seasonal and Trend decomposition using Loess (STL), proposed by [PERSON] et al. in 1990, is a classical time series decomposition method capable of handling time series data with irregular seasonal patterns and missing values [79]. It is a versatile and robust approach for estimating nonlinear relationships. Compared with conventional decomposition methods such as Variational Mode Decomposition and Empirical Mode Decomposition, STL is based on loess, offering a more intuitive principle and robustness against data anomalies without inducing mode mixing or boundary effects. It employs robust locally weighted regression as its smoothing technique. The typical decompositions performed by STL include additive and multiplicative models, where the time series at time t is decomposed into trend component \(\mathrm{Tt}\), seasonal component \(\mathrm{St}\), and remainder component \(\mathrm{Et}\), as expressed in Equation (2):  
\[\mathrm{Yt}=\mathrm{Tt}+\mathrm{St}+\mathrm{Et}=1,2,\ldots\mathrm{N} \tag{2}\]  
The implementation of STL decomposition primarily consists of nested inner and outer loop recursive processes. The inner loop calculates the trend and seasonal components, while the outer loop computes the robustness weights used to adjust the neighborhood weights for the subsequent inner loop iteration based on the results of the previous inner loop. This study applies the STL method to decompose the NDVI time series of China's regions from 2001 to 2023. Using this method, this study decomposes the NDVI time series into three components: the trend component, which reflects the long-term directional changes; the seasonal component, which represents the periodic changes within a year; and the residual component, which indicates random disturbances that cannot be explained by the trend or seasonal components. The resulting trend component is used for subsequent trend analysis.
## 3 Results and Discussion
### Energetic Analysis
Optimizing the other three parameters for this less demanding climate control strategy, the following results were obtained: as regards the opaque envelope, the best result was obtained for the reference thermal transmittance, i.e., the improvement of the opaque envelope is not expected to improve the energy consumption of the model; as regards the windows' thermal transmittance, an interesting result is achieved--an optimum U\({}_{\rm w}\) of 5.85 W/m\({}^{2. }\)\({}^{\circ}\)C was obtained showing that even in buildings with windows with poor thermal response, their replacement from an exclusively energetic point of view can be not justified. Regarding the SHGC optimization, an optimal value of 0.1 was obtained for the two set-points. Despite the analysis performed for each of the parameters, the analysis of Figure 10 makes it obvious that by simultaneously optimizing more than one parameter, the results can be considerably different. The savings for all the possible combinations comparatively to the reference case, such as the optimal values for each parameter estimated from the application of the achieved mathematical model represented by equations 2 and 3, within the limits defined by Equation (4), can be seen in Table 4.  
This analysis led to the conclusion that only the scenarios involving the SHGC and/or the change in the climate control strategy provide considerable energy savings. Combining all the four parameters under analysis, a maximum saving of around 75% was obtained referring to the reference case, which corresponds in absolute terms to a saving of around 48 kWh/m\({}^{2}\).year. This result was obtained for the following combination: set-point 16(18)-25 \({}^{\circ}\)C and 40-60% RH; U-value equal to 0.30 W/m\({}^{2. }\)\({}^{\circ}\)C; U\({}_{\rm w}\)-value equal to 2.08 W/m\({}^{2. }\)\({}^{\circ}\)C and an SHGC equal to 0.10. This result shows that the analysis performed individually for each parameter does not reflect the way they interact together. However, it should be noted that the change in the climate control strategy plays a leading role in optimizing the energy consumption in museums. If the rehabilitation of the opaque envelope and windows were excluded, a reduction of 70% was obtained, representing an absolute saving of around 44.50 kWh/m\({}^{2}\).year. From an economic point of view, this second option can lead to a best result, since the initial investment would be much smaller.
## 4 Challenges in Rainfall Estimation by CMLs  
Despite extensive research within the past two decades, the CML-based rainfall estimation technique still confronts certain challenges (refer to Table 2), which will be described in detail in the remainder of this section.  
\begin{table}
\begin{tabular}{c c l} \hline \hline \multicolumn{2}{c}{**Challenges**} & \multicolumn{2}{c}{**Highlights**} \\ \hline \multirow{4}{*}{Data Acquisition} & 1. & Sampling strategy: usually low sampling frequency [70], limited quantization level [64] and \\  & 2. & Variation of TSL during rainfall due to ATPC [158]. \\  & 3. & Difficulty in sharing data [42]. \\  & 4. & Trend of fiber optics replacing CMLs. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Challenges faced by CML rainfall measurement techniques.
# Passive Intermodulation at Contacts of Rough Conductors
## 3 Thermal Nonlinearities at Contacts of Good Conductors
### Thermal Expansion of the Contact Area  
The heat, generated by the high RF power, is dissipated in conductors and their joints and causes the thermal expansion and deformation of contact surfaces. As the result, the contact areas and the conductor resistivity \(\rho_{c}(\mathbf{x})\) increase with temperature. The contact resistance \(R_{\mathit{cm}}\) of asperities also grows with temperature, whilst the pace of its growth depends on the relation between the temperature coefficient of resistivity \(\alpha\), the rate of the thermal expansion of the contact areas and asperity deformations as illustrated by Figure 1. An increase of the contact area is linked to the rate of heat flow from the contact spots, but  
Figure 6: Analytic model prediction of third order PIM products (solid lines) as compared with the measured IM3 products (triangles) for platinum at input power 12, 20, and 23 dBm versus carrier tone separation \(\Delta f=\Delta\omega/2\pi\). © 2008 IEEE. Reprinted with permission from [37].  
it progresses much slower. Therefore, the pace of establishing the thermal equilibrium is dictated by the lower speed of the asperity deformations.  
The thermal effects at joints of conductors with rough surfaces are amplified by the current constriction by the contact spots. For example, as the temperature of an asperity increases due to the current constriction, the thermal expansion of the compressed asperities somewhat reduces the constriction current density. But the growing contact pressure normally exceeds the yield stress of the heated asperities. As the result, they become susceptible to creep when subjected to compressive strain [71]. The effect of the contact pressure and the asperity deformations are dictated by the mechanical properties of the contact surfaces, which are discussed next.
# Integrating AIoT Technologies in Aquaculture: A Systematic Review
## 3 Components of AIoT in Aquaculture  
The successful implementation of AIoT in aquaculture hinges on two fundamental components: IoT-enabled sensing devices for continuous data acquisition and AI-driven analytics for intelligent decision-making. IoT sensors are crucial for monitoring biological and environmental factors, including fish behaviour, health metrics, and water quality indicators such as pH, temperature, and dissolved oxygen. These sensors are integrated with advanced communication technologies like LoRa, NB-IoT, and 5G to facilitate seamless data transmission, enabling efficient and reliable connectivity between remote aquaculture sites and centralised management systems. This interconnected framework (Figure 2) allows real-time monitoring, predictive analysis, and automation, optimising resource utilisation and improving farm productivity.
# An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs
## 5 Simulation and Evaluation
### Simulation Results
#### 5.2.2 Statistical Results on Average Residual Energy Based on Various Methods  
We define the average residual energy (ARE) as the difference between the average initial energy (AIE) and the average energy consumed (AEC) by all nodes. The total average residual energy (ARE) is expressed as follows:  
\[\text{ARE}=\text{AIE}-\text{ASEC} \tag{20}\]  
\[\text{AIE}= \frac{1}{\text{N}}\sum_{\text{i}=0}^{\text{N}}\text{IE} \tag{21}\]  
\[\text{AEC}= \frac{1}{\text{N}}(\sum_{\text{i}=1}^{\text{N}}\text{E}_{\text{T(i)}} +\text{E}_{\text{R(i)}}) \tag{22}\]  
where IE is the initial energy of node i, and \(\text{E}_{\text{T(i)}}\) and \(\text{E}_{\text{R(i)}}\) are the consumption energy for transmission and reception by node i, respectively.  
In this section, we study the performance of the algorithms by measuring their average residual energy. In Figure 7, we evaluate the average residual energy performance of four algorithms with the following number of sensors (N): 200, 400, 600, 800, 1000, and 1500. In Figure 8a,b, we evaluate the network-lifetime performance of four algorithms by fixing the number of sensors and varying the number of sinks. Figure 8a depicts a scenario in which the number of sensors is N = 500 and the number of sinks ranges from 5 to 15; Figure 8b depicts a scenario in which the number of sensors is N = 1000 and the number of sinks ranges from 5 to 15. As a result, the outcomes of Figures 7 and 8a,b validate the observation shown in Figure 6a.  
In Figure 7, as the total number of sensors grows, the average residual energy of the previous four methods increases significantly. However, the network lifetime generated by our proposed approach remains the longest; thus, it produces the highest average residual energy out of all of the methods.  
The scenarios with varying total numbers of sinks are depicted in Figure 8a,b. The average residual energy produced by the four algorithms falls significantly as the number of sensors grows. Similarly, when the overall number of sinks increases, the overall number of  
Figure 7: Average residual energy with a wide-ranging number of sensors N.  
SNs reaching the CH directly in each cluster increases. Consequently, the average residual energy increases as well. Compared to other methods, our approach achieved a higher average residual energy.
# AUV Adaptive Sampling Methods: A Review
## 4 Mission Objectives of Sampling
### Source Localisation  
The common targets of a source localisation mission by AUV(s) are chemical features, since physical or biological features do not usually have a point-source feeding phenomenon. Examples include a single point source, such as a pipe leakage, or multiple numbers of sources, such as hydrothermal vents. For this particular objective, searching behaviours and mechanisms naturally evolved by animals have inspired several searching algorithms. A wide variety of bio-mimetic techniques from unicellular organisms (bacterium), small-size insects (months) to relatively larger and slower aquatic creatures (lobsters and crabs) are discussed in this section. They may also be classified by a dominant flow field at different Reynolds number ranges. For example, the behaviour and the movement by bacterium cause a flow with low Reynolds number, while those by aquatic creatures or insects occur at a medium to high Reynolds number [75]. Further, if the source is stationary, the localisation strategy is dominated by the attributes of the current flow in the area of influence. Generally, laminar flows occur at low Reynolds number, while turbulent flows occur at higher Reynolds number. Gradient methods are assumed to be less effective in turbulent flows [75], although the scale of the turbulence is important in making this assessment.  
[PERSON] et al. [59] reviewed four types of chemotaxis algorithms, including three bio-mimetic methods (E. coli bacterium, the silkworm moth and the dung beetle) and a plain gradient method. The chemotaxis approach is basically twofold: detection and tracking. [PERSON] [54] summarised the essence of detection and tracking strategies through four algorithms based on the reviews performed by [PERSON] et al. [59], as given in Table 9.  
[PERSON] et al. [76] demonstrated another simple bio-mimetic gradient-following technique for two targets using the ABE AUV: one was to localise the deepest spot of a pond utilising a single beam sonar altimeter, and the other was to search for hydrothermal vents using the temperature gradient. In the first part of the demonstration, they presented the gradient of water depth along the AUV trajectory by executing a straight line. They also further characterised the horizontal slope of the bathymetry by performing a circular manoeuvre. In the second part for hydrothermal vents, a hybrid search technique was applied, employing a straight line and circular manoeuvre interchangeably.  
[PERSON] et al. [77] developed a sequential least-squares field estimation algorithm for a REMUS AUV to seek the source of a hydrothermal plume. The algorithm generated the path plan of the vehicle with the first waypoint p1 based on the field gradient. Once measurements are collected, the path plan is controlled by a nonlinear model predictive control (NMPC) scheme. When the waypoint p1 is reached, the field estimation is updated, then a new waypoint, p2, is planned. In simulation, they demonstrated that the NMPC algorithm was more effective than the circle-manoeuvre proposed by [PERSON] et al. [76] to find the source.
## 2 Materials and Methods  
### Data Acquisition and Processing  
The CT data were chosen for a suspected coronary artery disease (CAD) male patient aged 46, for the development of 3D-left coronary artery (LCA) models. A specific procedure was followed to obtain clear CT scanned images of the patient. The reorganization period was set 0.6 mm, and the beam collimation of 0.6 with a pitch of 1.4, a tube voltage of 100 kVp, and a current span between 300 and 650 mAs. The images were reconstructed axially with a slice thickness of 0.6 mm and an accumulative distance of 0.75 mm. Morethan 400 slices were used to create images in all directions of sagittal, coronal, and axial planes. The data were acquired in standard DICOM formats, and a realistic 3D left coronary artery model was reconstructed using CT volume data and image processing software MIMICS (Materialise HQ Technologielaan 15, Leuven, Belgium), also used in earlier studies [20,28,29].  
The multiple stenoses were conceived as the main objective of the present study. MIMICS 18 software used to export the CT scan images obtained from the patient. The left coronary artery model reconstructed was accomplished by identifying appropriate thresholds ranging from 84 to 630 HU. The left coronary artery was positioned, and the segmentation procedure was performed as shown in Figure 1a.  
Furthermore, a pulsatile inlet (Figure 2) was enforced at the model inlet. The pulsatile inlet velocity is applied at the inlet of the model, the physiology of blood flow in the left coronary artery was chosen from previously published articles [14,20,30]. Furthermore, an \"outflow\" boundary condition was imposed at the respective outlets of the LCA model. Moreover, the inlet diameter was 3.186 mm, while the left main (LM) length considered in simulations was 19.11 mm which was sufficient (i.e., six diameters) for the flow to become fully developed prior to stenosis. Numerical simulations were carried out in a LCA model for a total of 750 steps. A constant time step of 0.005 s was chosen with 20 iterations in each time-step. Total calculation time amounted to 3.75 s. The convergence of continuity  
Figure 1: (**a**) Computed Tomography (CT) images left, right, posterior, interior, and volume rendering image with actual location of the left coronary artery (LCA). (**b**) Stereo-lithography (.stl) model obtained from MIMICS software. (**c**) Artery wall structure developed in ANSYS.  
and momentum was considered as \(10^{-4}\). The results presented in the manuscript were revealed at the end of the third simulation cycle.
## 3 Setting and Theoretical Prediction
### Experiment Validations on Pressure Fluctuation  
In order to prove the reliability of the numerical calculation of pressure fluctuation, this paper compared and analyzed the calculation and test results of the monitoring points near the bottom ring in the bladeless region under the rated conditions. The degree of pressure fluctuation is expressed by the dimensionless number \(dH/H\) (where \(dH\) is the peak of pressure fluctuation and \(H\) is the head). Table 4 shows the protruding frequency and corresponding amplitude of pressure fluctuation in the bladeless region after fast Fourier transform (FFT) transformation and the comparison with the experimental results. It can be seen from the table that the protruding frequencies of the pressure fluctuation in the bladeless region were 10 f\({}_{\rm n}\), 20 f\({}_{\rm n}\), and 5 f\({}_{\rm n}\), in descending order according to the amplitude, which was the same as the theoretical prediction of the rotor-stator interaction frequency above, indicating that the pressure fluctuation in the bladeless region is mainly caused by rotor-stator interaction. It can be seen from the relative error of the corresponding amplitude that the unsteady numerical calculation overpredicted the amplitude of 10 f\({}_{\rm n}\) and 20 f\({}_{\rm n}\) at the node-diameter number k\({}_{1}=-\)6 and 4, but underpredicted the amplitude of 5 f\({}_{\rm n}\). This is because the compressibility of the fluid was ignored in the numerical calculation, the flow difference near the long and short blades was not sufficiently captured, and the influence of the flow solid coupling was not considered in the actual  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**n**} & \multirow{2}{*}{**m**} & \multirow{2}{*}{**k\({}_{1}\)**} & \multicolumn{2}{c}{**Stationary System**} & \multicolumn{2}{c}{**Rotating System**} \\ \cline{5-8}  & & & \(\omega_{\rm{1}}\)/\(\omega\) & \(\mathbf{f_{\rm s}}\)/\(\mathbf{f_{\rm n}}\) & \(\omega_{\rm{1}}\)/\(\omega\) & \(\mathbf{f_{\rm s}}\)/\(\mathbf{f_{\rm n}}\) \\ \hline
1 & 1 & \(-\)6 & \(-\)1.67 & 10 & \(-\)2.67 & 16 \\
1 & 2 & 4 & 5 & 20 & 4 & 16 \\
2 & 3 situation. In general, unsteady pressure fluctuation calculation can accurately capture the rotor-stator interaction frequency, and the error of the corresponding amplitude is also within an acceptable range. Therefore, the reliability of the numerical calculation of pressure fluctuation in this paper can be proven.