esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Random forest was used for fault detection based on these existing features. Existing domain specific features are calculated from raw sensor data, but that requires expert knowledge of the domain and results in a loss of information to some extent. To avoid these implications, we have developed an algorithm for profile extraction from the raw sensor data rides including both acceleration and magnetic signals. In addition, a generic algorithm with deep autoencoder random forest approach for automated feature extraction from raw sensor data profiles for fault detection in elevator systems. Our off-line profile extraction algorithm is signal-based and deep autoencoder random forest method is model-based. First, it extracts profiles from time-series signal and then, calculates highly informative deep features from extracted profiles. It is better than other algorithms because it provides better results, dimensionality reduction and is robust against overfitting characteristics. We have proposed a reliable fault detection model with above 90% accuracy in fault detection, which will increase the safety of passengers. In addition, we have validated the efficacy of the pre-trained model in terms of false positives with the remaining healthy rides, which is helpful in detecting false alarms for elevator predictive maintenance strategies. It is extremely helpful in reducing unnecessary visits by maintenance personnel to installation sites. Figure 1 shows the fault detection approach used in this paper, which includes raw sensor data rides extracted based on time periods provided by the maintenance data from all floor patterns. Acceleration and magnetic signal rides collected from an elevator system are fed to the algorithm for profile extraction separately. These extracted profiles from all five traction elevators including both acceleration and magnetic signals are then fed to the deep autoencoder model for feature extraction, and then random forest performs the fault detection task based on extracted deep features. We only extract start and stop profiles from the both acceleration and magnetic signal rides because of the  
different lengths of rides for each floor combination due to the constant speed phase, which is longer when there is longer travel. This paper provides the following novelties. (1) We propose a new off-line profile extraction algorithm for extracting elevator start and stop events from time-series data. (2) In addition, we propose a new deep autoencoder model to automatically generate highly informative deep features from sensor data for fault detection. The rest of this paper is organized as follows. Section 2 presents the methodology of the paper including profile extraction, deep autoencoder, and random forest algorithms. Then, Section 3 includes the details of experiments performed, results, and discussion. Finally, Section 4 concludes the paper and presents the future work.
# G&G Attack: General and Geometry-Aware Adversarial Attack on the Point Cloud
## 5 Discussion
### Ablation Study
\begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline
**Target Model** & **Method** & **ASR** & **Q** & **CD** & **HD** & **L2** & **AT** \\ \hline \multirow{3}{*}{**PointNet++**} & G&\&G attack & **78.97\%** & 34.41 & 0.00127 & 0.00942 & 0.88586 & 11.18 \\  & w/o local geometry-aware attack & 61.51\% & 2.93 & 0.00131 & 0.00243 & 1.40282 & 2.27 \\  & w/o autoencoder & 13.82\% & 115.83 & 0.00097 & 0.00504 & 0.96031 & 33.01 \\ \hline \multirow{3}{*}{**DGCNN**} & G&\&G attack & **97.20\%** & 8.76 & 0.00136 & 0.04232 & 1.44489 & 10.04 \\  & w/o local geometry-aware attack & 66.61\% & 3.34 & 0.00129 & 0.00234 & 1.41377 & 0.24 \\  & w/o autoencoder & 33.87\% & 88.26 & 0.00072 & 0.00390 & 0.72971 & 7.34 \\ \hline \multirow{3}{*}{**CurveNet**} & G&\&G attack & **95.30**\% & 9.66 & 0.00141 & 0.00410 & 1.47170 & 9.68 \\  & w/o local geometry-aware attack & 63.05\% & 3.03 & 0.00133 & 0.00244 & 1.44300 & 1.51 \\  & w/o autoencoder & 35.70\% & 101.85 & 0.00085 & 0.00454 & 0.85685 & 23.90 \\ \hline \multirow{3}{*}{**PointCNN**} & G&\&G attack & **81.36**\% & 8.26 & 0.00149 & 0.00409 & 1.46869 & 13.33 \\  & w/o local geometry-aware attack & 78.16\% & 3.03 & 0.00142 & 0.00242 & 1.44440 & 0.44 \\ \cline{1-1}  & w/o autoencoder & 57.74\% & 116.18 & 0.00097 & 0.00502 & 0.96174 & 32.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiment of G&G attack.
# Secure On-Off Transmission in UAV Relay-Assisted mmWave Networks
## 3 Secrecy Evaluation
### Performance Analysis with Perfect Beam Alignment  
In this subsection, considering the perfect beam alignment between the legitimate transmitter and receiver, the new closed-form expressions for the TP and SOP are derived in the on-off transmission scheme. For comparison, we then consider the conventional non-on-off transmission scheme and present the SOP.
# Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm
## 4 Modified Differential Evolution Algorithm
### De-Crossover Strategy  
In the planning process, in order to increase the convergence speed, it is necessary to design a de-crossover strategy to reduce cross paths. First of all, it is necessary to judge whether the two line segments \(ab\) and \(cd\) cross, \(a(x_{1},y_{1})\), \(b(x_{2},y_{2})\), \(c(x_{3},y_{3})\), \(d(x_{4},y_{4})\) and the two lines are:  
\[ab:\left\{\begin{array}{cc}x=x_{1}+m\cdot(x_{2}-x_{1})&\\ y=y_{2}+m\cdot(y_{2}-y_{1})&0\leq m\leq 1\end{array}\right. \tag{26}\]  
\[cd:\left\{\begin{array}{cc}x=x_{3}+n\cdot(x_{4}-x_{3})&\\ y=y_{3}+n\cdot(y_{4}-y_{3})&0\leq n\leq 1\end{array}\right. \tag{27}\]  
Set, \(D=\left|\begin{array}{cc}x_{2}-x_{1}&x_{3}-x_{4}\\ y_{2}-y_{1}&y_{3}-y_{4}\end{array}\right|\) when \(D=0\), two line segments \(ab\) and \(cd\) are parallel or coincide; when, \(D\
eq 0\) the calculation formula of \(m\) and \(n\) are:  
\[m=\frac{1}{D}\left|\begin{array}{cc}x_{3}-x_{1}&x_{3}-x_{4}\\ y_{3}-y_{1}&y_{3}-y_{4}\end{array}\right| \tag{28}\]  
\[n=\frac{1}{D}\left|\begin{array}{cc}x_{2}-x_{1}&x_{3}-x_{1}\\ y_{2}-y_{1}&y_{3}-y_{1}\end{array}\right| \tag{29}\]  
When \(0\leq m\leq 1\) and \(0\leq n\leq 1\), the paths \(ab\) and \(cd\) have an intersection, otherwise none.  
Five nodes are used to illustrate the crossover strategy, as shown in Figure 1. The specific process is as follows, starting from node \(A\), walk a closed loop along \(ABCDEA\), where \(AB\) and \(DE\)_cross_,then replace with \(AD\) and \(BE\) and chain node \(B\) to node \(D\) in reverse order, so the loop \(ADCBEA\) is formed. The specific operation is shown in the pseudocode Algorithm 1.  
```
0: path (A, B, C, D, E, A)
0: path_uncross (A, D, C, B, E, A)
1. for i\(\leftarrow\)1:length(path)
2. j\(\leftarrow\)i + 1
3. While j\(\leftarrow\)length(path)
4. ab\(\leftarrow\)path(i,i+1)
5. cd\(\leftarrow\)path(j,j+1)
6. flag\(\leftarrow\)Cross_judgement (ab, cd) % Judge whether cross
7. if flag=1
8. path(i+1:j)\(\leftarrow\)path(j-1:i+1)
9. end if
10. j\(\leftarrow\)j+1
11. end While
12. end for
13. path_uncross\(\leftarrow\)path
14. return path_uncross
```  
**Algorithm 1** The pseudo-code of uncrossed strategy.
## 3 Results and Discussion
### Electrical Properties  
Recently, bone-implantable wireless devices have been actively investigated [29; 30]. Bone mimetic platforms are necessary for evaluating the performance of such devices. PCL/HA composite filaments can be potentially used to build such platforms, and the dielectric properties of the composite structure is one of the key parameters that must be known. However, the dielectric properties of PCL/HA composite structures have yet to be reported. To find the dielectric constants and losses in structures fabricated with PCL/HA composite filaments, structures with dimensions of 15 mm(L) \(\times\) 15 mm(W) \(\times\) 5 mm(T) were printed. Each structure was printed with 100% filling density, because a porous structure may reduce the dielectric constant. Figure 6a shows the measured dielectric constants of the PCL/HA composite structures with varying HA content. As the wt.% of HA increased, the dielectric constant of each structure increased proportionally, although difference was not significant in the measurement frequency, since the molecules in the structure cannot respond to frequency changes in the GHz range. The dielectric loss was small, close to zero, as shown in Figure 6b.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**PCL/HA Composite**} & \multicolumn{2}{c}{**Filament**} & \multicolumn{2}{c}{**Thin Film**} \\ \cline{2-5}  & **Tensile Strength** & \multirow{2}{*}{**Fracture Strain (\%)**} & **Tensile Strength** & \multirow{2}{*}{**Fracture Strain (\%)**} \\  & **(MPa)** & & **(MPa)** & \\ \hline PCL/HA 5 wt.\% & 25.91 & 7.5 & 15.40 & 21.7 \\ PCL/HA 10 wt.\% & 22.90 & 6.3 & 14.31 & 16.7 \\ PCL/HA 15 wt.\% & 18.24 & 5.6 & 13.83 & 13.3 \\ PCL/HA 20 wt.\% & 17.67 & 5.0 & 11.69 & 11.7 \\ PCL/HA 25 wt.\% & 15.15 & 4.4 & 9.44 & 10.0 \\ PCL/HA 30 wt.\% & 10.25 & 2.5 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of the PCL/HA composite filaments and 3D-printed PCL/HA composite thin films.  
Figure 5: Tensile stress–strain curve of (**a**) fabricated filaments. (**b**) 3D-printed specimen.
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 4 Self-Mixing Signal Processing
### Multiple Target Case  
Including an additional target involves small modifications to Equation (7). Adding in terms for an additional magnitude and Doppler shift will result in Equation (12), given by  
\[s_{Rx}(t)=A_{dir}\text{cos}(\omega_{0}t)+A_{ref,1}\text{sin}\left((\omega_{0 }+\omega_{D,1})t\right)+A_{ref,2}\text{sin}((\omega_{0}+\omega_{D,2})t) \tag{12}\]  
where \(A_{ref,1}\), \(A_{ref,2}\), \(\omega_{D,1}\), and \(\omega_{D,2}\) represent the signal amplitudes and respective Doppler shifts for targets 1 and 2. Following similar processing steps as in Section 4.1, the result is described in Equation (13), given by  
\[S(t)=A_{ref,1}A_{ref,2}\text{cos}((\omega_{D,1}-\omega_{D,2})t)+A_{dir}\Big{[} A_{ref,1}\text{sin}(\omega_{D,1}t)+A_{ref,2}\text{sin}(\omega_{D,2}t)\Big{]} \tag{13}\]Like the single target case, there are Doppler expressions scaled by the direct and reflected signal amplitudes. However, an additional sinusoid is also created with an oscillation frequency derived from the difference between the Doppler shifts of both targets. This signal is scaled by the magnitudes of the reflected signals. Therefore, it can be expected that this signal is extremely weak in comparison to the Doppler expressions and may be related to the distance between the targets [24]. However, since GPS signals contain Pseudorandom Noise (PRN) sequences that are modulated onto a continuous-wave carrier rather than a chirp signal [40], this may not be the case.  
Equations (14) and (15) include the received and output expressions in a scenario involving three moving targets.  
\[\begin{array}{rl}s_{Rx}(t)&=A_{dir}\text{cos}((\omega_{0})t)+A_{ref,1}\text {sin}((\omega_{0}+\omega_{D,1})t)+A_{ref,2}\text{sin}((\omega_{0}+\omega_{D,2} )t)\\ &\qquad\qquad+A_{ref,3}\text{sin}((\omega_{0}+\omega_{D,3})t)\\ S(t)&=A_{dir}\Big{(}A_{ref,1}\text{sin}(\omega_{D,1}t)+A_{ref,2}\text{sin}( \omega_{D,2}t)+A_{ref,3}\text{sin}(\omega_{D,3}t)\Big{)}\\ &\qquad\qquad+A_{ref,1}A_{ref,2}\text{cos}((\omega_{D,1}-\omega_{D,2})t)+A_{ ref,1}A_{ref,3}\text{cos}((\omega_{D,1}-\omega_{D,3})t)\\ &\qquad\qquad+A_{ref,2}A_{ref,3}\text{cos}((\omega_{D,2}-\omega_{D,3})t)\\ \end{array} \tag{14}\]  
Equation (15) shows three (3) Doppler signals scaled by the direct and respective reflected amplitudes. In addition, there are three pairs of Doppler-difference signals scaled by the respective reflected amplitudes. Observing Equations (11), (13) and (15), it is apparent that, for \(N\) targets, the signal processing chain will produce an expression containing \((N+\sum_{k=N}^{1}k-1)\) terms--of which, \(N\) are Doppler sinusoids and \({}^{N}C_{2}\) are Doppler-difference sinusoids.
## 3 Improved Multi-Group Ant Colony Optimization Algorithms Based on Monitoring Overdue Time
### Greedy Strategy for Node Selection  
This section proposes a greedy strategy, which can help UAV select the optimal node among its neighbors. The strategy is motivated by the idea that the greater the overdue time of the ant's adjacent node \(j\) is, the greater the probability that node \(j\) will be selected by the ants in the next step. First calculate the overdue time of all adjacent nodes. Since some adjacent nodes may not be overdue, the calculated overdue time by \(f_{k-1,j}+c-T_{j}\) is possibly negative and inconvenient to compute the transition probability. Therefore, this research constructs a pseudo-overdue time, \(R_{j}(t)\), which is guaranteed to be positive.  
\[R_{j}(t)=f_{k-1,j}+c-T_{j}+T_{0},\forall k\in P \tag{18}\]  
where \(j\) represents the adjacent node of the current node. \(T_{0}\) represents the upper bound of the expected period of all monitoring nodes. Usually, it can be calculated by \(T_{0}=\max_{i\in V}\{T_{i}\}\) offline.  
The transition probability is not only related to the overdue time of its neighbor node, but also related to the adjacency constraints, exclusive flags, and pheromone distribution of the ants' current adjacent nodes. The improved ant transition probability \(p_{ij}^{z}\) is as follows:  
\[p_{ij}^{z}=\left\{\begin{array}{ll}\frac{\tau_{ij}^{z}(t)\eta_{ij}^{B}(t)R_{ j}(t)a_{ij}\big{(}1-a_{j}\big{)}}{s\sum\limits_{i\in all\omega_{z}}\tau_{is}^{z}(t) \eta_{is}^{B}(t)R_{s}(t)a_{is}(1-a_{s})},&j\in allow_{z}\\ 0\,,&\text{other}\end{array}\right. \tag{19}\]  
where \(i\) is the current node of the ant whose adjacent node is denoted by \(j\). \(\alpha\) and \(\beta\) stand for the importance factor of the pheromone and the heuristic function, respectively, \(\tau_{ij}(t)\) represents the pheromone concentration on the edge \(e_{ij}\) after the optimization of each ant at the \(t\)-th iteration. \(a_{ij}\) stands for the adjacency relationship between node \(i\) and \(j\), \(o_{j}\) represents the exclusive state of the node \(j\), \(z\in\{1,2,3,\ldots,Z\}\) represents the ant number, \(z\) is the ant quantity, and \(allow_{z}\) represents the set of nodes that the ant \(z\) can visit next time. After the transition probability of the ants is calculated, the roulette method is used to select the next node according to the maximum probability.
## 4 Discussion  
### Effect of Spatial Resolutions on the Relationships between SIF and MODIS Products  
Scale dependency is an important issue in landscape ecology, and researchers have shown this for several remote sensor-derived parameters, such as land surface temperature and evapotranspiration [43; 44; 45]. The influencing factors and functions of ecological processes operate differently at varying spatial scales [46]. Scale heterogeneity between SIF and other data products posed great challenges for fully exploring the relationships that might exist between the datasets [16].  
At the OCO-2 footprint level, [PERSON] et al. [16], in their study of croplands in the United States, found significant correlations between SIF and EVI (R\({}^{2}\) = 0.476, \(p\) < 0.001) and also between SIF and FPAR (R\({}^{2}\) = 0.490, \(p\) < 0.001). Our study obtained a similar but slightly lower correlation coefficient (R\({}^{2}\) = 0.39, \(p\) < 0.001 and R\({}^{2}\) = 0.26, \(p\) < 0.001).  
From Figures 4 and 5, and Table 2, we found that SIF-EVI, SIF-FPAR, and SIF-LAI have close relationships at a regional scale. However, at the footprint level, the correlations between these variable pairs are weaker. Similar results were produced in our previous research, which showed that with a decrease in spatial resolution, the R\({}^{2}\) value of the relationship between SIF and EVI increases [12]. Generally, averaging large areas of vegetation with heterogeneous physiological characteristics increases the SIF-GPP relationship discernable from satellite imagery more so than that at the footprint or leaf level [7; 17]. The number of sampling points can also affect the R\({}^{2}\) value of regression models. For the regional scale, within 100 samples were used to calculate the relationship between SIF and MODIS indices, and the number for the footprint scale is tens of thousands.  
Each ecosystem has a characteristic scale; hence, we must study it at appropriate scales to obtain the right laws and features. Some researchers have argued that ecosystems are highly heterogeneous at spatial scales larger than 5-10 km, and some scholars believe that the characteristic scale of boreal forests is 30 km [12; 26; 47]. This suggests that it is  
Figure 9: The accuracy of GOSIF and HRGCSIF for cropland, forest, and grassland at regional scale (\(p\) < 0.001).  
better to simulate vegetation photosynthesis for different ecosystems using remotely sensed vegetation indices at different scales.
# Bioremediation of Hydrocarbon-Polluted Soil: Evaluation of Different Operative Parameters
## 2 Materials and Methods
### Hydrocarbon Removal
#### 2.5.2 Kinetics Analysis  
The kinetic of the hydrocarbon degradation can be described with the model:  
\[R=\partial C/\partial t=-k\cdot C^{n} \tag{1}\]  
where \(R\) is the reaction rate, \(C\) is the oily residual concentration in soil, \(k\) is the reaction rate constant, and \(n\) is the reaction order.  
In this study, the first (\(n=1\)) and the second-order (\(n=2\)) models were considered, due to their good fitting of experimental data, already demonstrated in previous studies [33; 47].  
Considering the first-order model (\(n=1\)), Equation (1) becomes:  
\[R=\partial C/\partial t=-k\cdot C \tag{2}\]  
and the residual can be achieved by integrating Equation (2) between \(t=0\) and \(t\):  
\[Ct=C_{0}\cdot e^{-kt} \tag{3}\]  
where \(C_{0}\) and \(C_{t}\) are, respectively, the oily residual concentration at \(t=0\) and \(t\), and \(k\) is the reaction rate constant.  
It can be useful to evaluate the half-life time. This parameter, \(t_{1/2}\), can be calculated as:  
\[t_{1/2}=ln\ 2/k=0.693/k \tag{4}\]  
In the first-order reaction rate model, \(t_{1/2}\) does not depend on the initial oil concentration. In the second-order model (\(n=2\)), Equation (1) becomes:  
\[R=\partial C/\partial t=-k\cdot C^{2} \tag{5}\]  
Analogously, the oily residue can be expressed by integrating Equation (5) between \(t=0\) and \(t\):  
\[1/C_{t}=(1/C_{0})+k\cdot t \tag{6}\]  
where \(k\) is the reaction rate constant.  
In this case, the half-life time \(t_{1/2}\) depends on the initial oil concentration:  
\[t_{1/2}=1/k\cdot C_{0} \tag{7}\]  
Experimental data fitting can give the most suitable order for the process kinetics.
# Flash Floods Hazard to the Settlement Network versus Land Use Planning (Lublin Upland, East Poland)
###### Abstract  
There has been an increase in the frequency of hazards associated with meteorological and hydrological phenomena. One of them is flash floods occurring episodically in areas of concentrated runoff--valleys without permanent drainage. In the opinion of residents and local authorities, these are potentially safe areas--they are not threatened by floods and are therefore often occupied by buildings. The importance of addressing flash floods in land use planning is essential for sustainable development and disaster risk reduction. The objective of this research was to assess the level of the hazard and to evaluate its presence in land use planning activities. This manuscript fills a research gap, as to date flash flood threats have not been analyzed for individual buildings located in catchments of dry valleys in temperate climates. More than 12,000 first-order catchments were analyzed. The study covered an upland area located in East Poland, which is characterized by high population density and dispersed rural settlement. Within the 10 municipalities, buildings located on potential episodic runoff lines were identified. Qualitative assessment was applied to ascertain the susceptibility of catchments to flash floods. Such criteria as slopes, size, shape of the catchment area, and land cover, among others, were used. Between 10 and 20% of the buildings were located on episodic runoff lines, and about 900 sub-catchments were highly or very highly susceptible to flash floods. The way to reduce the negative effects of these phenomena is to undertake proper land use planning based on knowledge of geohazards, including flash floods. However, an analysis of available planning documents shows that phenomena of this type are not completely taken into account in spatial management processes.  
geohazards; GIS; land management; episodic overland flow; rural areas Article
## 2 Proposed Methodology
### Time-Frequency Transformation  
Numerous signal processing techniques have been developed to obtain a time-frequency demonstration of a time domain signal. Among these techniques, CWT is widely recognized for its effectiveness in generating a time-frequency representation of transient signals. Due to the transient nature of most PQDs, CWT is particularly beneficial for PQD studies.  
Let us assume that \(\phi\)\((t)\) is the mother wavelet, a set of wavelet basis functions can be constructed as Equation (1) [35].  
\[\phi_{a,b}=\frac{1}{\sqrt{a}}\phi\bigg{(}\frac{t-b}{a}\bigg{)} \tag{1}\]  
In this context, \(a\) represents the scaling factor and \(b\) denotes the translation time, with \(\phi\) serving as the mother wavelet. The CWT for a given continuous signal \(x(t)\) is expressed by Equation (2) [35].  
\[CT(a,b))=\frac{1}{\sqrt{a}}\left[\int\limits_{-\infty}^{\infty}x(t)\phi\bigg{(} \frac{t-b}{a}\bigg{)}dt\right] \tag{2}\]  
Figure 3: An example of PQDs with 20 dB noise: (**a**) flicker; (**b**) flicker + harmonics; (**c**) flicker + sag; (**d**) flicker + swell; (**e**) harmonics; (**f**) impulsive transient; (**g**) interruption; (**h**) interruption + harmonics; (**i**) normal; (**j**) notch; (**k**) oscillatory transient; (**l**) sag; (**m**) sag + harmonics; (**n**) spike; (**o**) swell; and (**p**) swell + harmonics.  
After the transformation, the amplitude scale of the wavelet coefficients is calculated by Equation (3) [35].  
\[A(a,b)=|C(a,b)| \tag{3}\]  
After applying CWT, the time-frequency representations of the signals for Figure 3 are illustrated in Figure 4.