esa-sceva/satcom-chunk-collection huggingface dataset:
# Considerations for Developing Robot-Assisted Crisis De-Escalation Practices
## 4 Integrating the De-Escalation Process into Human-Robot Interactions
### Assessment: Sensing the Environment and Knowing When to Intervene
checklists can provide a starting point for elements to consider in developing robots capable of de-escalation, but they fall short in offering the level of specificity required for training a robot in these practices. To be engaged in de-escalation, robots not only require the ability to identify individuals in crisis and determine their level of agitation, but they also need to be able to assess the impact of de-escalation interventions in real time to determine whether the intervention is working or whether adjustments are necessary. At a macro-level, indicators that de-escalation interventions are working include decreased agitation, evidenced by positive shifts in affect and behaviour. At a micro-level, robots will need to detect indicators that de-escalation is proceeding effectively. These indicators will vary for each task the robot carries out but may include assessing whether agitated individuals have been successfully engaged by the robot (e.g., verbal acknowledgment, eye contact), whether agitated individuals are receptive to de-escalation tasks (e.g., if offered a drink of water, do they accept it? If limits are set, do they abide by them? ), and whether an appropriate amount of personal space is being maintained.
# Kohonen Mapping of the Space of Vibration Parameters of an Intact and Damaged Wheel Rim Structure
## 2 Research Methodology
Neurons are arranged in two-dimensional topology, which allows the layer to create the representation of distribution and two-dimensional approximation of data set topology. Creating a single two-dimensional SOM map based on empirical data obtained for four rim positions is subject to some error. In such a solution, it is not possible to determine the position of the wheel rim for which a state deviation has been recorded. Combining the information from the four positions in a single cell may also contribute to a narrowing of the reading scale (with three correct system responses and one outlier, it may be difficult to discern the difference in the SOM maps). For this reason, the authors decided to create SOM maps for each of the proposed positions, in order to highlight the deviations. In this paper, SOM is used in mapping mode. Competitive learning is utilized in the training of the Kohonen network. When a learning example is implemented in the network, its Euclidean distance to all weight vectors is calculated. The neuron whose mass vector is the most similar to the input is and its neurons within the SOM mesh are adjusted towards the input vector. The update formula for \(v\) neuron with \(Wv(s)\) weight vector is:  
\[(s+1)=Wv(s)+\Theta(u,v,s)\cdot\alpha(s)\cdot(D(t)-Wv(s)), \tag{3}\]  
where: \(s\) is an incrementing index; \(t\) is a training sample index; \(u\) is BMU index for \(D(t)\) input vector; \(\alpha(s)\) is a monotonically decreasing learning coefficient; \(\Theta(u,v,s)\) is the neighbourhood function, which indicates the distance between the u neuron and the \(v\) neuron in the \(s\) increment. At the same time, the numerous shortcomings of Kohonen maps are well-known. SOM does not reproduce the genesis of an object (process). In other words, the model understands how data are created. Sometimes, generating SOM is very time-consuming. The training of Kohonen maps with slowly changing vectors of input data is problematic. Therefore, the next logical step in this paper is to compare the results of the employment of the identification method of a wheel's structural damage with various numbers of neurons, which, of course, influences the information characteristics of the SOM and the time of its generation. Figure 4: Dimensionality reduction with SOM. Further recognition algorithm of the wheel quality assumes a dimension reduction of the emerged space of data with its condition. To execute this, each point of a data sequence vector is recognized and competes for the possibility of representing it in two-dimensional space. SOM mapping steps start with the initialization of weight vectors. With a randomly selected input value, a weight coefficient that represents the position best is sought. The Figure 5 shows a flow chart of the proposed approach. An excerpt elaborating in detail on the FFT method used, marked '''' in the graphic, can be found in source [44].
# Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review
## 3 Wavelet Transformation for Non-Stationary Time-Series: State-of-the-Art
### Application of WT in Remote Sensing in Vegetation Analysis
[37] detected change using the MODIS NDVI TS from five different regions located in the Chinese Mainland from 2001 to 2010. While the inter-annual change was analyzed using the Morlet CWT, a detailed change in certain regions was detected using the combination between wavelet and region growing. As for [PERSON] et al. [38], they have applied WT to investigate the temporal variability of the scaling characteristic of Landsat NDVI TS and LST in a heterogeneous area in India. Using MRA DWT, three detail fields, i.e., horizontal, diagonal and vertical, have been produced for each level of decomposition. Then, the linear regression and the slope test have been calculated to analyze the different components. In addition, several mother wavelets, such as Haar, Symmelt and Daubechies, have been applied to understand how the scaling analysis depends on the chosen mother wavelet function. Additionally, [PERSON] et al. [39] have deployed the WT for land cover change and trend analysis using MODIS EVI coupled with quality assessment science data for the period from 2005 to 2014. In addition, the Daubechies DWT has been applied to detect trends in the EVI TS. The trend has been analyzed using the [PERSON]'s slope tests. Recently, [PERSON] et al. [40] have compared three satellite image time-series decomposition methods, namely Break for Additive Season and Trend (BFAST), MRA-WT, and Seasonal Trend Loess (STL), for vegetation change detection. Four simulated NDVI TS have been used to test the proposed approach. While the Meyer MRA has been selected to decompose the TS, the RMSE has been calculated to compare the three methods. Finally, the authors have proved that MRA-WT is more informative due to the multi-level decomposition.
# An Improved Bi-LSTM-Based Missing Value Imputation Approach for Pregnancy Examination Data
## 1 Introduction
### Related Work  
Nowadays, several scholars have realized the severity of the missing data problem and explored many approaches to reduce the negative effects of missing data. There are several methods now to process missing data. For instance, the most common method, mean filling, uses the mean value of the sample and prior value filling to fill the missing values with the support of medical experts. In addition, there exist some filling methods that have advantages in effect and applicable scenes. For instance, regression filling [8] develops a regression model on the basis of the relationship between the data of each indicator and the pregnancy week to make interpolations to fill the data. KNN (K-nearest neighbors) [9] filling selects K samples that are most similar to the sample to be filled based on a specific kind of similarity measurement algorithm and fills the data with the weighted mean of the full data of the K samples. The matrix completion algorithm [10] decomposes the original data into two low-rank matrices and then uses the gradient descent method to obtain an approximate value to fill missing data.  
Although classical methods of statistics, such as interpolation-like techniques, can be used to approximate the missing data in a time series, the recent developments in deep learning (DL) have given impetus to innovative and much more accurate forecasting techniques. Some researchers use a combination of LSTM and transfer learning to fill in missing data values for water quality, air quality, and energy [11; 12; 13]. [PERSON] et al. proposed LSTM-based missing data reconstruction for time-series Landsat images [14]. [PERSON] et al. combined LSTM with different activation functions to impute the missing data of a diabetic, breast cancer, and wine quality data set [15]. [PERSON] et al. combined a CNN and bi-LSTM to fill the missing data of a sensor [16].
# Synthesis and Characterization of Unsaturated Succin Acid Biobased Polyester Resins
## 3 Results and Discussion
### Synthesis and Characterization of Cross-Linked Resins
#### 3.3.2 Spectroscopies Studies  
In Figure 7, the FTIR spectra of the cured samples are presented. When compared with Figure 3, alternations of the signals in the areas of 3600-3500 cm\({}^{-1}\) and 1635 cm\({}^{-1}\) can be seen, suggesting differences in the structure of the materials after the incorporation of acrylic acid. These are direct indications supporting the proposed chemical structure for the cross-linked resins, as depicted in Figure 8. Nevertheless, a more detailed analysis is given in the following XPS section.  
In Figure 9, the high-resolution XPS spectra of the C-1s and O-1s orbitals are presented for two representative resins of the PESM and PPEGSM groups after thermal cross-linking. Regarding the C-1s orbital of PESM 25-75 (Figure 9a), the applied curve-fitting resulted in different carbonyl components. In particular, the peak at 284.6 eV was attributed to aliphatic C-C and C-H bonds, together with adventitious carbon attributed to pollution [51; 52; 53]. The peaks at 286.5 and 289.0 eV were attributed to C-O and O-C=O of carbonyl and ester, respectively [54; 55; 56]. The peak at 287.5 eV was assigned to the stretching vibration of C=O bonds of maleic acid [57]. Finally, the peak at 285.5 eV was assigned to the vibration C-C of carbon chain branching, which supports that the cross-linking was successful, according to similar results obtained for poly(acrylic acid) by [PERSON] et al. [58]. In the same direction, the curve-fitting of the C-1s spectrum of PPEGSM 25-75 (Figure 9c) presented similar results, differentiating only in the enhanced participation of the peak at 286.3 eV of C-O bonds because of the PEG incorporation [57]. In both cases, the two peaks at 285.5 and 287.5 eV of the carbon chain branching attributed to the incorporation of AA and the C=O bond of MA, respectively, prove that the resins were successfully cross-linked after the addition of AA [53; 55; 57; 58; 59].  
Figure 8: Chemical structure of the cross-linked materials, after the procedure of curing UPRs with AA.  
In the case of the O-1s spectra of both the cross-linked experimental resins, their curve-fitting was performed conservatively, as there were expected participations of the same bonds attributed to different monomers. In this direction, the curve-fitting of the O-1s high-resolution core level spectrum of PESM 25-75 (Figure 9b) resulted in three separate peaks: at 531.8 eV of ester and carboxylic C=O bonds, at 532.9 of ester C-O-C bonds, and finally, at 533.8 eV, attributed to carboxylic C-OH bonds [53; 55; 58; 59]. Similarly for the PPEGSM 25-75 (Figure 9d), the same attributions applied for the peaks at 531.9, 533.2, and 533.8 eV, with an additional peak at 532.5 eV because of the additional ether C-O-C bonds of PEG [53; 58].
## 3 Current State of Acellular TEVG
### Electrospinning
[PERSON] _et al._ (2019) used a fabrication method similar to that used by [PERSON], mentioned above. They used heparin- and neuropeptide substance P (SP)-conjugated poly(L-lactide-co-\(\varepsilon\)-caprolactone) (PLCL) copolymers as the scaffold material because SP has been shown to accelerate tissue repair by endogenous cell recruitment and mobilization. [PERSON] et al. [PERSON] _et al._ (2019) also adjusted the material of the scaffold to improve its mechanical properties. They blended low-molecular-weight (LMW) elastic polymers during the electrospinning process. LMW elastic polymers increased the elasticity of the scaffold and greatly increased the amount of heparin conjugated to the surface of the scaffold, which led to an increase in the load capacity of vascular endothelial growth factor (VEGF) and a prolonged the release of VEGF. Figure 2: Schematic of a bilayer graft fabrication and images of a real sample at different stages. Reprinted with permission from ([PERSON] _et al._, 2019), Copyright Elsevier, 2019. Electrospinning is the most popular method for fabricating acellular in situ vascular grafts. However, the degradation of scaffolds has not been examined in many studies. The degradation of the material has been analyzed in vitro using simulated bodily fluid, but the degradation of the vascular grafts in in vivo tests has not been analyzed. In addition, most recent case studies do not consider mechanical properties such as radial compliance, burst pressure, and suture retention strength, which are important factors for the long-time patency of a vascular graft. Furthermore, most cases only involve short-term in vivo testing for durations such as two weeks or one month, and these results do not shed light on the properties of vascular grafts in long-term use. The failure of long-term patency remains an unsolved problem for vascular grafts.
# The Elasto-Plastic Contact Models of Cylinder-Based and Sphere-Based Fractal Rough Surfaces
## 3 The Revised Model of a Single Fractal Asperity Contact
### The Existing Conditions for Asperity Elasto-Plastic Deformation  
Under the contact load, the interference is greater than the elastic critical interference of asperity, namely \(\omega_{n}>\omega_{nec}\), and the asperity of frequency index \(n\) is in elasto-plastic deformation. [PERSON] and [PERSON] obtained relations among contact area, contact load of a single asperity, and interference, which ranges from \(\omega_{nec}\) to \(76.8\omega_{nec}\) using the finite element method [8]. The contact area and contact load can be expressed as  
\[a_{nep}=a_{nec}\!\left(\frac{\omega_{n}}{\omega_{nec}}\right)^{1.1597}\text{, for }\omega_{nec}<\omega_{n}\leq 76.8\omega_{nec} \tag{23}\]  
\[F_{nep}=\frac{2}{3}KHa_{nec}\!\left(\frac{\omega_{n}}{\omega_{nec}}\right)^{1. 3801}\text{, for }\omega_{nec}<\omega_{n}\leq 76.8\omega_{nec} \tag{24}\]  
Substituting Equation (23) into Equation (24), the relation between the contact load and contact area can be written as  
\[F_{nep}=\frac{2}{3}KHa_{nec}^{-0.19}a_{nep}^{1.19}\text{, }a_{nec}<a_{nep}\leq a _{nepc}\text{, }a_{nepc}=153.6a_{nec} \tag{25}\]  
where \(a_{nepc}\) is the elasto-plastic critical contact area.  
In elasto-plastic deformation, for \(R_{n}\gg\omega_{n}\), the asperity truncated area can be expressed as  
\[a^{\prime}{}_{nep}=2\pi R_{n}\omega_{n}\Rightarrow a_{nep}=\frac{1}{2}\big{(} a^{\prime}{}_{nec}\big{)}^{-0.1597}\big{(}a^{\prime}{}_{nep}\big{)}^{1.1597} \tag{26}\]  
where \(a^{\prime}{}_{nepc}\) is the elasto-plastic critical truncated area. Substituting Equation (26) into Equation (24), the relation between the contact load and truncated area can be written as  
\[F_{nep}=\frac{1}{3}KH\big{(}a^{\prime}{}_{nec}\big{)}^{-0.3801}\big{(}a^{ \prime}{}_{nep}\big{)}^{1.3801}\text{, }a^{\prime}{}_{nec}<a^{\prime}{}_{nep}\leq a^{\prime}{}_{nepc}\text{, }a^{\prime}{}_{nepc}=76.8a^{\prime}{}_{nec} \tag{27}\]As the asperity height is less than or equal to its own elasto-plastic critical interference, namely \(\delta_{n}\leq\omega_{nepc}=76.8\omega_{nec}\), elastic deformation or elasto-plastic deformation can take place in the asperity under contact load. The elasto-plastic critical frequency index can be expressed as  
\[n_{epc}=\text{int}\left[\frac{\log\left(\frac{76.8}{4}\frac{L^{2(D-2)}}{G^{2(D-2 )}\ln\gamma}\left(\frac{KH}{2E}\right)^{2}\right)}{2(D-2)\log\gamma}\right] \tag{28}\]
## 3 Results and Analysis
### Setting Time  
Figure 7 shows the cement mortar with different TPSW substitution amounts (0%, 5%, 10%, and 15%) and W/C ratios (0.4, 0.5 and 0.6). In the 0% control group, the W/C ratios were 0.4, 0.5, and 0.6, and the initial setting times were 251 min, 341 min, and 448 min, respectively; the final setting times were 598 min, 685 min, and 926 min, respectively. In the 5% TPSW substitution group, the W/C ratios were 0.4, 0.5, and 0.6, and the initial setting times were 238 min, 316 min, and 419 min, respectively; the final setting times were 556 min, 644 min, and 901 min, respectively. In the 10% TPSW substitution group, the W/C ratios were 0.4, 0.5, and 0.6, and the initial setting times were 231 min, 301 min, and 406 min, respectively; the final setting times were 542 min, 626 min, and 885 min, respectively. In the 15% waste PS substitution group, the W/C ratios were 0.4, 0.5, and 0.6, and the initial setting times were 226 min, 292 min, and 397 min, respectively; the final setting times were 509 min, 575 min, and 842 min, respectively. An increase in the overall water content led  
Figure 6: Slump flows of cement mortars with different TPSW amounts and W/C ratios.  
to partial secretion of the cement paste, while the hydration heat reaction slowed, which prolonged the setting time.  
The research results also indicate that as the water-cement ratio increased, the setting time increased correspondingly. This occurred because the higher water-cement ratio led to an increased overall water content, which caused the cement slurry to bleed. As a result, the rate of the hydration reaction slowed down, which prolonged the setting time.
## 3 Optimum Parameters Selection
### RFI mitigation performance vs. Interference-to-Noise Ratio (INR)  
Figures 6 and 7 show the RFI mitigation performance using the Haar transform or the optimum wavelet transform respectively, as a function of the threshold method and _INR_, for a sequence length of \(2^{16}\) and a decomposition level equal to 12. As it can be appreciated, using the soft heuristic SURE thresholding the rejection is very good (\(\sim\)40 dB) for high \(INR=100\), decreasing with decreasing \(INR\). Below \(INR\sim 2\cdot 10^{-4}\) the algorithm is no longer able to estimate the RFI signal (Figure 6).  
Figure 5: RFI mitigation performance using the Haar transform as a function of the threshold method and decomposition level for four different types of interferent signals, and sequence length \(2^{16}\). Threshold method: squares = fixed threshold with single level noise estimation, circles = soft SURE, diamonds = soft heuristics SURE, and triangles = minimax. RFI signal: solid line = sine, dashed line = Doppler, dotted = chirp, and dash-dot = pseudo-random noise.  
The optimum wavelets for each signal type have found to be the symlet 3 (sym 3) for the sinusoidal RFI, the reverse biorthogonal wavelets 1.5 (rbio1.5) for the Doppler signal, the discrete approximation of Meyer (dmey) wavelet for the chirp signal, and the reverse biorthogonal wavelets 1.3 (rbio1.3) for  
Figure 6: RFI mitigation performance using the Haar transform as a function of the threshold method and _INR_ for four different types of interferent signals, sequence length \(2^{16}\) and decomposition level = 12. Threshold method: squares = fixed threshold with single level noise estimation, circles = soft SURE, diamonds = soft heuristics SURE, and triangles = minimax. RFI signal: solid line = sine, dashed line = Doppler, dotted = chirp, and dash-dot = pseudo-random noise.  
Figure 7: RFI mitigation performance using the optimum wavelet type for each signal as a function of the _INR_ for the heuristic SURE threshold method for four different types of interferent signals, sequence length \(2^{16}\) and decomposition level = 12. RFI signal: solid line = sine, dashed line = Doppler, dotted = chirp, and dash-dot = pseudo-random noise.  
the PRN signals. When using the optimum wavelet types for each RFI signal the performance is significantly improved for high _INR_s (Figure 7), but remains stable as _INR_ decreases so below \(\textit{INR}\sim 2\cdot 10^{-4}\), as in the case of the Haar wavelet, no improvement is seen.
# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 3 Methodology
### Optimization Algorithms  
Intelligence optimization algorithms, such as PSO and GA, have been widely applied in path planning optimization problems. In this study, we used PSO, GA, and a combination of PSO and GA due to their advantages, such as strong robustness, simulation evolution, and notable exploration and exploitation capability. The following describe the algorithms and their adjustments in detail.
## 2 Methodology  
### MEMD  
For an n-dimensional signal \(\mathbf{X}(t)=[x_{1}(t),\,x_{2}(t),\ldots,x_{n}(t)]\), each component \(x_{i}(t)\) can be easily decomposed into several IMFs by the EMD method individually. The main drawback here is that the IMFs derived from the different components are non-uniform in the number of IMFs or cannot match in frequency scale, which is also called mode misalignment. To decompose the signal synchronously and uniformly, the standard EMD method is developed to handle the complex signals.  
In the standard EMD method [7, 8], the envelope is generated with a spline by interpolating the local maximums and minimums, whereas for the multi-dimensional signal \(\mathbf{X}(t)\), it is difficult to define the local extreme values directly. To overcome the problem, the rotational mode is developed as an extension of the oscillatory mode, based on the assumption that the original signal consists of the slow and fast rotation components. The envelopes are generated by projecting the signal along multiple directions in n-dimensional space and interpolating the local maximums and minimums in splines. Then, the n-dimensional local mean can be obtained by averaging the multiple envelopes [14, 21]. Owing to the fact that the local mean is the approximation of the integration of all multiple envelopes, the choice of directional vectors in the n-dimensional space has a significant impact on the accuracy of the local mean values, especially when the number of direction vectors is quite limited. To yield a more uniform distribution of directional vectors, the low-discrepancy sequences [22], including the Halton and Hammersley sequences are adapted to generate the directional vectors. The main procedures of the MEMD method can be listed in the following steps [14, 23, 24, 25]:  
1. Generate n-point low-discrepancy Hammersley sequences to sample on an \((n-1)\) dimensional sphere.
2. Project the original signal \(\mathbf{X}(t)\) along each directional vector \(v^{\theta_{k}},k=1,2,\ldots,K\), where \(K\) is the number of directional vectors, denoted as \(y^{\theta_{k}}(t)\) of each vector.
3. Pick up the time instants \(\left\{t^{\theta_{k}}\right\}\) of the maximums of every projection \(y^{\theta_{k}}(t)\).
4. Interpolate \(\left[t^{\theta_{k}}_{i},\mathbf{X}\left(t^{\theta_{k}}_{i}\right)\right]\) to generate the multiple envelope \(e^{\theta_{k}}(t)\) by a spline.
5. Calculate the mean curve \(\bar{e}(t)\) for all \(K\) envelopes \[\bar{e}(t)=\sum_{k=1}^{K}e^{\theta_{k}}(t)/K\] (1)
6. Subtract the mean curve from the original signal \(\mathbf{d}(t)=\mathbf{X}(t)-\bar{e}(t)\). If the detail \(\mathbf{d}(t)\) satisfies the stoppage criterion, it can be regarded as the first IMF, otherwise, repeat step 2 to step 5 to regenerate \(\mathbf{d}(t)\). Then, apply the above steps with \(\mathbf{X}(t)-\mathbf{d}(t)\) to obtain other IMFs.  
One should be pointed out that the stoppage criterion adopted in MEMD is adopted from the standard EMD method [8]. Due to the unavailable extreme values of multi-dimensional signals, the equality of the number of extremes and zero crossing is ignored.