esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction
## 2 Proposed Methodology
### Condition Monitoring
#### 2.1.2 Performance Assessment  
Although the feature extraction has already obtained very useful information, the expert may not be able to understand the real-time condition because many features still need to be known at each time step. Therefore, the performance assessment task is designed to combine the features into a single health index that represents the condition of the equipment. In order to assess the condition of the whole run-to-failure degradation process, the Pearson correlation coefficient (PCC) was selected to compute degradation performance. It has been verified as a reasonable evaluation method for vibration data [42]. Generally, the PCC is decreasing with the state degradation. Details of how PCC is calculated are given below:  
1. The first step is normalizaing the extracted features: \[x_{t}=\frac{x_{t}-x_{min}}{x_{max}-X_{min}}\] (2) where \(x_{t}\) is the feature at the time \(t\), which will be normalize into \([0,1]\).
2. The second step is to calculate the PCC with the obtained features between normal state and the real-time state: \[\text{PCC}(x,y)=\frac{\sum_{i=1}^{n}(x_{i}-\bar{x})(y_{i}-\bar{y})}{\sqrt{\sum _{i=1}^{M}(x_{i}-\bar{x})^{2}\sum_{i=1}^{n}(y_{i}-\bar{y})}}\] (3) where \(x\) and \(y\) are two different feature vectors and \(n\) is the length of a feature vector.
# Jet Noise in Airframe Integration and Shielding
## 3 Options for Computation of Jet Noise  
Jet noise is produced by the unsteady turbulent fluctuations in the jet. [PERSON] averaged Navier-Stokes (RANS) can predict turbulence only by means of time-averaged quantiles; the time-averaged quantiles do not directly produce noise, it is the time varying that is required for the calculation of noise. Thus, prior to 1990, the industry relied on empirical formulas for the prediction of engine noise. The success of such an approach is limited, particularly when addressing new designs that include various options for propulsion-airframe integration. We discuss here how jet noise can be calculated with and without the airframe integration effects.  
In the 1980s, lacking full computational power, an attempt to calculate the jet noise produced by large eddies was given by [PERSON] and [PERSON] [40]. They modeled large eddies as a wave packet with a radial profile that follows that of the nonlinear stability theory. Thus, the unsteady Navier-Stokes equation (NSE) can be transformed into a set of ordinary differential equations (ODEs), which were easier to solve computationally. The solution was then plugged into [PERSON]'s equation to obtain the far field. Their solution shows that the large eddies are responsible for the peak noise, as demonstrated in Figure 9 in comparison with [PERSON]'s [41] experiment. This opened a new avenue, namely, instead of attempting to resolve all the turbulence scales, we resolve only the large scales, which was shown by  
Figure 8: Far-field acoustic directivity of the (**a**) reflected side and (**b**) shielded side. Reproduced from (Ref. [35]), with the permission of the Acoustical Society of America.  
Figure 7: Schlieren images of a jet plane (minor-axis plane). (**a**) Baseline, (**b**) with plate (\(h/D=1\)). Reproduced from (Ref. [35]), with the permission of the Acoustical Society of America.  
[PERSON] and [PERSON] [40] to be responsible for the peak noise. Thus, large-eddy simulation (LES) may be adequate for resolving the noise-efficient sources.  
In the early 1990s, computational aeroacoustics (CAA) were developed in which the aerodynamically-generated noise is predicted based on direct computation of the governing equations. The full, compressible, time-dependent Navier-Stokes equations govern the noise generation and propagation process. Conventional computational fluid dynamics (CFD) codes based on RANS are not appropriate for calculating sound. Noise is obtained via numerically capturing the unsteady small oscillations in the pressure signal. This is not a trivial task, as discussed below.
## 4 The Dynamic Initial Weights Assignment in SLS
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Problems** & **nCls** & **MinCIS** & **\#MinClsS** & **MaxClsS** & **\#MaxClsS** & **AvgS** & **\#AvgS** \\ \hline
4 blocks & 47,820 & 2 & 9898 & 33 & 12 & 2 & 9896 \\ ais12 & 5666 & 2 & 4191 & 12 & 12 & 2 & 4189 \\ BMS-499 & 278 & 3 & 278 & 3 & 0 & 3 & 278 \\ bw-large.c & 50,457 & 2 & 37,493 & 16 & 17 & 2 & 37,491 \\ bw-large.d & 131,973 & 2 & 102,580 & 20 & 25 & 2 & 102,578 \\ CBS & 403 & 3 & 403 & 3 & 0 & 3 & 403 \\ f800-hard & 3440 & 3 & 3440 & 3 & 0 & 3 & 3440 \\ f1600-hard & 6880 & 3 & 6880 & 3 & 6880 & 3 & 6880 \\ f2000 & 8500 & 3 & 8500 & 3 & 8500 & 3 & 8500 \\ fla-barthel & 1720 & 3 & 1720 & 3 & 1720 & 3 & 1720 \\ flat200-har & 2237 & 2 & 2037 & 3 & 200 & 2 & 2037 \\ g125.17 & 66,272 & 2 & 66,147 & 17 & 125 & 2 & 66,145 \\ g125.18 & 70,163 & 2 & 70,038 & 18 & 125 & 2 & 70,036 \\ huge & 7054 & 2 & 5682 & 10 & 5 & 2 & 5681 \\ logistics.d & 21,991 & 2 & 19,647 & 16 & 7 & 2 & 19,645 \\ uf400-hard & 1700 & 3 & 1700 & 3 & 1700 & 3 & 1700 \\ unif-k5-v200 & 4223 & 5 & 4223 & 5 & 4223 & 5 & 4223 \\ unif-k7-v98 & 8603 & 7 & 8603 & 7 & 8603 & 7 & 8603 \\ unif-k7-v102 & 8955 & 7 & 8955 & 7 & 8955 & 7 & 8955 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Clause specific structural statistics. ```
1: randomly instantiate each literal in \(\mathcal{F}\);
2:if\(size(Cl)<=AvgClS\)then
3:if\(size(Neighbor(Cl))<=AvgN\)then
4: set the \(initWeight(Cl)\) to \(size(Neighbor(Cl))\);
5:else
6: set the \(initWeight(Cl)\) to \(AvgClS\);
7:endif
8:else
9:if\(size(Cl)>AvgClS\)then
10:if\(size(Neighbor(Cl))<=AvgN\)then
11: set the \(initWeight(Cl)\) to \(AvgClS\);
12:else
13: set the \(initWeight(Cl)\) to \(size(Cl)\);
14:endif
15:endif
16:endif
17:for each clause \(c_{a}\in\mathcal{F}\)do
18: let the weight \(w_{a}\) of each clause \(c_{a}=InitWeight(c_{a})\);
19:endfor
20: set the minimum \(m\) to the number of false clauses \(c_{f}\in F\);
21: set counter \(i\) to zero and boolean \(b\) to false;
22:while solution is not found and not timeout do
23: calculate the list \(\mathcal{L}\) of literals causing the greatest reduction in weighted cost \(\Delta w\) when flipped;
24:if(\(\Delta w<0\)) or (\(\Delta w=0\) and probability \(\leq 15\%\)) then
25: randomly flip a literal in \(\mathcal{L}\);
26:else
27:for each false clause \(c_{f}\in\mathcal{F}\)do
28: select a satisfied same sign neighboring clause \(c_{n}\) with maximum weight \(w_{n}\);
29:if\(w_{n}<2\)then
30: randomly select a clause \(c_{n}\) with weight \(w_{n}\geq 2\);
31:endif
32:if\(w_{n}>2\)then
33: transfer a weight of two from \(c_{n}\) to \(c_{f}\);
34:else
35: transfer a weight of one from \(c_{n}\) to \(c_{f}\);
36:endif
37:endfor
38:endif
39:endwhile
```  
**Algorithm 1** DDFW(\(\mathcal{F}\)) + DynamicInitWeight.
# Minimum Variance Distortionless Response--Hanbury Brown and Twiss Sound Source Localization
## 1 Introduction
It then constructs the directional vector to calculate MVDR directional weights, which are used to weight the correlation function of HBT, optimizing the directionality of the HBT algorithm and thus improving the positioning accuracy. The MVDR-HBT algorithm is then extended from narrowband signals to broadband signals, enhancing the directionality of the HBT algorithm in broadband signals and further improving the positioning accuracy. This paper proposes a positioning method that combines MVDR with the HBT interference target positioning method based on the sound field, aiming to solve the problem of inaccurate positioning of HBT in low-SNR situations. The remaining sections of this paper are organized as follows: Section 2 provides a detailed explanation of the MVDR-HBT method for wideband and narrowband sound source localization. In Section 3, we present the simulation results of the MVDR-HBT algorithm under varying sound source parameters such as SNR, sound source position, and frequency, and we compare them with those of the HBT algorithm. We analyze the localization performance of both methods. In Section 4, we further validate the effectiveness and feasibility of the proposed method through a sound source detection experiment, where sound source parameters such as SNR, sound source position, and frequency are varied. Finally, conclusions are presented in Section 5.
## 2 Seismic Active Earth Pressure Analysis  
### Acceleration Response Characteristics of the Backfill Behind the Retaining Wall  
[PERSON] et al. [26] revealed through numerical simulations that, under seismic action, the peak acceleration at the top of a gravity retaining wall and in the backfill soil exhibited varying degrees of amplification compared to the input seismic peak acceleration when the peak ground acceleration (PGA) reached approximately 0.55 g. Beyond 0.55 g, however, the amplification weakened, with possible causes for this attenuation at higher accelerations (PGA > 0.55 g) including soil vibration densification and the formation of failure surfaces, as illustrated in Figure 1a. [PERSON] et al. [27], via large-scale shaking table model tests, observed that the peak seismic acceleration at the backfill surface significantly exceeded that which was output at the shaking table base, demonstrating pronounced acceleration amplification effects near free-face structures, as shown in Figure 2. Similarly, [PERSON] et al. [28; 29; 30], in their model tests, identified acceleration amplification during seismic wave propagation from the shaking table to the backfill. Notably, the acceleration amplification became markedly significant when the seismic vibration frequency approached the natural frequency of the soil. The observed patterns of acceleration amplification in backfill soil can be summarized as follows: (1) Threshold-dependent amplification: When the input PGA is below a threshold (0.55 g-0.6 g), the acceleration at the backfill surface exhibits clear amplification relative to the base acceleration. Beyond this threshold, the amplification diminishes and the surface acceleration attenuates. (2) Nonlinear vs. simplified distributions: The acceleration amplification coefficient along the backfill height typically follows a concave-shaped nonlinear distribution. For engineering applications, this is often simplified to a linear distribution, which may lead to conservative estimates of the seismic earth pressure. Conversely, this simplification provides a safe upper limit for seismic earth pressure calculations in retaining wall design.  
Figure 1: (a) Peak acceleration response at wall top and soil top for PGA = 0.053 g–1.2 g [26]; (b) typical acceleration distributions in the soil during active failure conditions [27]; (c) amplification of horizontal acceleration and estimation of shear wave velocity in backfill [28].  
Figure 2: Schematic of active earth pressure forces on a retaining wall subjected to seismic loading.
# A Multi-Physics Modeling-Based Vibration Prediction Method for Switched Reluctance Motors
## 3 The Proposed Multi-Physics Modelling-Based Method
### The Mechanical Module  
The mechanical module includes two sub-modules: modal identification and vibration prediction. The modal identification integrates the constrained modal simulation and takes into account the effects of stator, rotor, shell winding, end cover, bearing, gravity, and assembly stress on the stiffness of the installed motor. The modal simulation of SRM is developed based on static pre-calculation, and the dynamic equilibrium equation of its vibration system can be given by  
\[\left(\left(\begin{array}{cc}K_{ff}&K_{fr}\\ K_{rf}&K_{rr}\end{array}\right)_{n\times n}+\left(\begin{array}{cc}S_{ff}& S_{fr}\\ S_{rf}&S_{rr}\end{array}\right)_{n\times n}-\omega^{2}\left(\begin{array}{ cc}M_{ff}&M_{fr}\\ M_{rf}&M_{rr}\end{array}\right)_{n\times n}\right)\left(\begin{array}{c}\{ u_{f}\}\\ 0\end{array}\right)_{n\times 1}=\left(\begin{array}{c}0\\ \{R_{r}\}\end{array}\right)_{n\times 1}, \tag{9}\]  
where \(\left(\begin{array}{cc}K_{ff}&K_{fr}\\ K_{rf}&K_{rr}\end{array}\right)_{n\times n}\) is the prestress matrix, \(\left(\begin{array}{cc}S_{ff}&S_{fr}\\ S_{rf}&S_{rr}\end{array}\right)_{n\times n}\) is the stress stiffening matrix, \(\left(\begin{array}{cc}M_{ff}&M_{fr}\\ M_{rf}&M_{rr}\end{array}\right)_{n\times n}\) is the mass matrix, and \(R_{r}\) is the binding force vector. The characteristic equation in Equation (9) can be expressed as  
\[\left(K_{ff}+S_{ff}-\omega^{2}M_{ff}\right)\left\{u_{f}\right\}=0 \tag{10}\]Solving Equations (9) and (10) yields the eigenfrequencies \(\omega_{i}\) and eigenvectors \(\{u_{fi}\}\) of each order modes, which respectively correspond to the natural frequencies and modal modes of the vibration system. The SRM can be simplified to a second-order system, and its input and output are the radial force \(F_{r}\) and the motor surface displacement \(x\), respectively. The motor surface displacement is the displacement of the measuring point, which is located on the outer surface of the stator core and directly above the salient pole of any phase (e.g., phase A), as shown in Figure 4. The generic modal superposition method [25] is used to predict the system output \(x\), namely  
\[x(s)=\sum_{i=1}^{n}Q_{i}\frac{1}{s^{2}\zeta_{i}\omega_{ni}s+\omega_{ni}{}^{2}}, \tag{11}\]  
where \(\omega_{ni}\) is the characteristic frequency, \(\zeta_{i}=c/2m\omega_{n}\) is the damping ratio, and \(Q_{i}\) is the gain coefficient of the \(i_{th}\) order mode.
## 3 Results and Discussion
### Characterization of LON Motifs Patterned on 2H-TaS\({}_{2}\)
#### 3.2.2 Electrical Properties of the Ultrathin TaS\({}_{2}\) Layers  
To probe the conducting behavior of the deposited flakes and the material growth on the generated motifs, conducting tip AFM (CT-AFM) was performed on oxidized and non-oxidized TaS\({}_{2}\) flakes.  
First, the resistance map of a flake with different thicknesses deposited on doped silicon was measured. When a 2 V bias was applied between the tip and the surface during contact scanning,  
Figure 5: **(a)** Etching on a thick TaS\({}_{2}\) layer (bulk). 3D topography image of an array of three oxide dots created at \(-\)22.5 V and 45 ms (top) and the same three HF etched oxide dots (bottom). Images size: 1.7 \(\upmu\)m \(\times\) 480 nm. Below, the corresponding profiles for both images pristine (blue) and etched (green) oxide mounds are shown; **(b)** Etching on a thin layer (9 nm). AFM topography image of an etched oxide rippled dot on a TaS\({}_{2}\) layer. Inset: AFM topography image of the oxidized dot fabricated at \(-\)27 V and 100 ms. Bellow, the corresponding profiles for both, oxidized (blue) and etched (green) motifs is shown.  
a clear contrast in the conductance between TaS\({}_{2}\) flakes and the surface was observed (Figure 6). The entire TaS\({}_{2}\) surface appeared to have the same conducting behavior and therefore, no effect of the flake thickness was observed (Figure 6b). The electrical properties of the oxide motifs patterned via LON on top of TaS\({}_{2}\) layers were also probed. As expected, a clear contrast was observed between conductive pristine and insulating oxidized regions (Figure 6d). The big difference in conductivity makes these TaS\({}_{2}\)/Ta\({}_{2}\)O\({}_{5}\) heterostructures surfaces excellent candidates for the fabrication of circuitry at the nanoscale and also for more advanced lower temperature applications profiting from the superconducting properties of TaS\({}_{2}\) layers.
## 3 LPBF Sensing Techniques
### Optical Techniques
#### 3.1.4 Techniques Using Other Sources
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Material** & **OM** & **CLSM** & **DSLR** & **DIC** \\ \hline \multicolumn{6}{c}{Stainless-Steel-Based Alloys} \\ \hline SS316L [41; 42; 53; 92; 128], 17-4 PH SS. [129], A800H & & & \\
[130], RAFM steel [93], CLF-1 steel [131], 17-4 PH SS & ✓ & & \\
[13], SS316L-SS431 [132], AISI 420 Steel [59; 96] & & & \\ \hline GP-1 Steel [133], SS316L [134; 135; 136] & & ✓ & ✓ \\ \hline SS316L [40; 51; 124] & & ✓ & \\ \hline \multicolumn{6}{c}{Titanium-Based Alloys} \\ \hline Ti6 Al4V [137; 138; 139], TiB [140], Ti [141], NiTi [62], & ✓ & & \\ TiB2-B4C [142], Ti6 Al7 Nb [106] & & & \\ \hline TiNi [61] & & ✓ & \\ \hline Ti–43 Al–9V-0.5Y [64] & & & ✓ & \\ \hline Ti6 Al4V [91; 143] & ✓ & ✓ & & \\ \hline \multicolumn{6}{c}{Nickel-Based Alloys} \\ \hline Haynes 282 [144], Ni10 Cr6W1 Fe9 Ti1 [112], & & & \\ Nickel-based superalloy [113] & & & \\ \hline IN718 [111; 114] & & & ✓ & \\ \hline IN718 [110] & ✓ & & ✓ & \\ \hline \multicolumn{6}{c}{Aluminum-Based Alloys} \\ \hline AlMgScZr [115] & & ✓ & ✓ & \\ \hline AlSi10 Mg [8; 77; 81; 102], AlSi7 Mg0.3 [73], Al2024 [74] & ✓ & ✓ & \\ \hline \multicolumn{6}{c}{Other alloys/elements} \\ \hline Ta [82], Nb [84], Ag [85], Cu [145], CoCrMo [121], & ✓ & ✓ & \\ Cu10 Zn [122] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Combinations of optical techniques using a visible light source for various customized or less investigated LPBF-printing powder materials. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **OES** & **AFM** \\ \hline SS316L [128] & ✓ & \\ \hline SS316L [95,146] & & ✓ \\ \hline IN718 [147] & & ✓ \\ \hline IN718 [148] & ✓ & \\ \hline Ta [82] & & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Combinations of optical techniques using other sources for investigating LPBF-printed parts. Figure 25: Example provided by [56] showing the APT analysis results for the austenitic phase of a 2205 duplex stainless-steel LPBF-printed part. (**a**) Atom maps of Cr species viewed at two different angles, (**b**) overlayed atom map of Cr and Fe species, (**c**) overlayed atom map of Cr and Ni species, (**d**) 1D concentration profiles of Fe and Cr, (**e**) 1D concentration profiles of Ni, Si, Mn, Mo, and N.
## 2 Methods
### The Proposed Hybrid Early Warning Method
#### 2.1.2 Critical Sliding Early Warning  
Refs. [13; 14] proposed an improved tangent angle method for critical sliding warning. Tangential angles measured at the same time would be different if different coordinate scales are adopted to map the displacement-time curve, which leads to uncertainty in the early warning. In order to solve this uncertainty, they normalized the total deformation with the speed \(v_{m}\) of the CDS, which is:  
\[T(i)=\frac{S(i)}{v_{m}} \tag{10}\]  
where \(S(i)\) is the \(i-\) th cumulative displacement, and \(T(i)\) is the normalized value of \(S(i)\). In the monitoring station of a landslide, the velocity \(v_{m}\) at the CDS is a constant value. By Equation (10), the horizontal axis and the vertical axis can be transformed to the time dimension to eliminate the uncertainty in calculating the tangent angle. After the dimensional transformation, the normalized tangent angle can be calculated by the following equation:  
\[\alpha_{i}=\arctan\frac{T(i)-T(i-1)}{t_{i}-t_{i-1}}=\frac{\Delta T}{\Delta t} \tag{11}\]  
where \(\alpha_{i}\) is the normalized tangent angle.  
Due to the characteristics of the automated monitoring data, using Equation (11) to calculate the normalized tangent angle will cause a large number of errors, resulting in false early warnings. Similarly, the normalized tangent angle is calculated using the same combination of time windows and the least-squares method as in the previous problem. The following equation is fitted using the least-squares method for the normalized \(T(i)\) sequence of Equation (10):  
\[T(t)=b_{0}+b_{1}t \tag{12}\]  
The normalized tangent angle can then be determined using the equation below:  
\[\alpha_{i}=\arctan b_{1} \tag{13}\]  
As Equation (13) shows, when the normalized tangent angle \(\alpha_{i}\) is smaller than \(45^{\circ}\), the landslide is in the initial deformation stage; when \(\alpha_{i}\) is near \(45^{\circ}\), it is in the CDS; and when \(\alpha_{i}\) is greater than \(45^{\circ}\), it is in the accelerated deformation stage.
# Progress in Remote Sensing of Photosynthetic Activity over the Amazon Basin
## 4 Case Study of Seasonal Variation in Light-Use Efficiency and SIF across the Amazon Basin
### Discussion
#### 4.3.1 Photochemical Reflectance Index and Sun-Induced Fluorescence
Regional estimates of net changes in water storage from GRACE are valuable, but should be supplemented at selected places with more direct measurements of water stress on trees and the interactions of other variables on photosynthesis [64].
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 2 Materials and Methods
### Study Area  
The study area was the Admine forest located in the Souss region, near the city of Agadir in southern Morocco (Figure 2). It covers approximately 128 square kilometers. This forest is part of the UNESCO-declared biosphere reserve dedicated to the protection of the organ tree. From a geological perspective, the area is situated in the alluvial basin of the Souss valley, which separates the Sahara Desert from the Anti-Atlas Mountains [55]. The region is characterized by hot summers and mild winters, with a dry and semi-arid  
Figure 1: Flowchart of the methodology followed in this study: (**1**) Data acquisition and preprocessing, (**2**) Argan trees detection and (**3**) Change detection.  
climate. In recent years, it has experienced a reduction in rainfall, leading to long-term environmental changes in the area. The predominant natural vegetation in the Soussi region consists of savanna landscapes, with the organ tree being the dominant species [20]. The height of organ trees ranges from 2 to 10 m, and the canopy area varies from tree to tree, ranging from less than one square meter for small trees to more than 50 square meters for very large trees [56; 57]. The study area also comprises buildings, roads, farms, and greenhouses. It is surrounded on all sides by residential areas belonging to Greater Agadir, with Agadir International Airport located centrally. The area was chosen because it has unfortunately experienced significant deforestation in recent years, which may threaten the future disappearance of organ in the region.