esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Path Tracking  
### Tracking Target  
As shown in Figure 3, kinematic path tracking can be regarded as the generation of a trajectory for the mobile robot to travel along, from its current pose to the target pose. Several types of curves can be used here to blend the trajectory with the \(Y\)-axis at points \(C\) and \(T_{k}\) of Figure 3. Compared with circular arcs and ellipses, _Lame_ curves can provide the full trajectory with \(G^{2}\)-continuity, meaning that position, tangent and curvature are all continuous along the trajectory, including the blending points.  
As shown in Figure 6, the trajectory, on which a robot moves toward the target pose on the prescribed path, can be obtained by curve transformation. C\({}_{1}\) is a standard Lame curve, intersecting the \(X\)- and \(Y\)-axes at points \(A_{1}\) and \(B_{1}\), respectively. The difference of the orientation angles at these two points is a right angle, namely, the angle formed by the tangents at point \(E_{1}\). Point \(M_{k}\) is the intersection of the path with the \(Y\)-axis. Moreover, \(\angle CM_{k}T_{k}=2\psi=\pi-e_{\theta}\), and \(l_{2}=l_{1}+b\), as shown in Figure 3 (\(l_{1}\) and \(b\)) and Figure 6 (\(l_{2}\)). Affine transformations are used to convert Lame curve C\({}_{1}\) into the trajectory, curve \(CT_{k}\), by mapping points \(B_{1}\), \(E_{1}\) and \(A_{1}\) into points \(C\), \(M_{k}\) and \(T_{k}\), respectively, while keeping the smoothness of the Lame curve.  
The cubic Lame curve, the lowest order of this curve family with a variable curvature, is chosen as the trajectory for the DWMR. This curve is defined as [26]  
\[\Big{(}\frac{x}{g_{x}}\Big{)}^{3}+\Big{(}\frac{y}{g_{y}}\Big{)}^{3}=1 \tag{20}\]  
where \(0\leq x\leq g_{x},0\leq y\leq g_{y}\), while \(g_{x}\) and \(g_{y}\) are one-half of the side lengths of the circumscribing rectangle.  
The explicit parametric equations of this curve can be expressed in terms of a parameter \(\phi\) (\(0\leq\phi<\pi/2\)) as  
\[x(\phi)=\frac{g_{x}}{(1+\tan^{3}\phi)^{1/3}},\quad y(\phi)=\frac{g_{y}\tan\phi}{( 1+\tan^{3}\phi)^{1/3}} \tag{21}\]  
where \(\phi\) is the angle that the position vector of an arbitrary point on the curve makes with the \(X\)-axis, as shown in Figure 6. In our case, we use a _symmetric_ Lame curve, which means \(g_{x}=g_{y}\).
## 3 Results
### Main ELV Factors Causing Vegetation Dynamics  
Aiming to untangle the most causal environmental variables for each VP variable, a global per-pixel maxima analysis was conducted incorporating all ELVs. Figure 6 depicts the strength of the driving effect of the ELVs compared to each other for different VPs.  
Figure 5: Box and whisker plots representing the F values grouped as per the five major Koppen-Geiger biomes. Additional heatmaps show the corresponding mean and standard deviation (StdDev) for each group.  
Figure 6: Maximum per-pixel F Statistic on a global scale per variable. Each pixel shows the environmental variable that is the dominant driver at the given location.  
Here, for each VP the most causal ELV was displayed on a per-pixel basis. All ELV\(\rightarrow\)VP maps for each VP were superimposed, and the per-pixel maxima of the F stat is mapped.  
SM strongly drives VPs over the northernmost latitudes, with regions over Northern Russia (for all VPs) and Northern Canada (the FAPAR and LAI) being the most dependent on SM for all variables. Water availability (SM in conjunction with P) proved to be the strongest causal driver in vast arid areas. SM and P are the main factors for driving vegetation anomalies over the arid areas in the African continent, with P solely being the dominant ELV for causality over the Iberian Peninsula, Northern Australia, and over scattered regions in Northern America for SIF.  
Solar radiation tends to be the driving variable over the higher latitudes, especially over Northern Russia, amongst all VPs. The LAI, SIF, and the NDVI are also strongly dependent on R in Northern Europe. Northern latitudes over the American continent (regions in Canada and Alaska) show more heterogeneity considering the primary driver of ELVs.  
T shows an even F stat distribution over the globe, consequently being a primary driver ELV on vegetation, whereas other ELVs show no significant causality. As a driving variable, T is dominant for the FAPAR over Australia and in some regions in Southern America, as well as for SIF in North America and Southeast Asia.
# A Geographical Heuristic Routing Protocol for VANETs
## 5 Performance Evaluation
### Performance Comparison
Regarding GHR configurations, they have less idle time than default protocol configurations because GHR employs a higher number of hops to reach the destination, and a Tabu list is added to each data packet. In this section, we have compared the performance of two geographical routing protocols, i.e., MMMR and greedy-DTN, with and without the assistance of the heuristic implemented by our proposal Geographical Heuristic Routing (GHR). The results show that GHR contributes to the improvement in the performance of the geographical protocols. Furthermore, we have shown that geographical routing proposals can reach better results than classical topological routing protocols like i AODV with a reasonable bandwidth consumption.
## 2 The n-Dimensional Phase Portraits by State Combinations
### Foundation  
This section aims to obtain and model the overall possible perspectives that an observer/onlooker can have of a multidimensional object.  
In particular, a point \(x\
eq 0\) in a 2-dimensional Euclidean plane (with axis \(X_{1}\) and \(X_{2}\)), is viewed by an onlooker located at a distance \(r\), with a perpendicular sight to the plane in that point, univocally; that is, every two-dimensional characteristic is visible and measurable regardless of the sharpness. This approach is extended for a sufficiently small line and its centroid for n-dimensional and the pair plane-onlooker is named view.  
**Definition 1**: _An n-point or a small enough n-line (n-sline) in a n \(>2\) dimension can be determined univocally, if every n-dimensional characteristic is uniquely measurable by an onlooker._  
**Definition 2**: _A non-empty set of n-points and n-slines in conjunction with some Euclidean plane is a view._  
**Definition 3**: _The sharpness of a view is the capacity of the onlooker to differentiate two or more n-points and/or n-slines in a view._  
Clearly, most of humans can imagine and determine an object univocally for dimensions less or equal to 3. For higher order dimensions this is not a trivial task; for instance, to determine univocally an object in a dimension 6: although mathematics can easily handle such higher dimensions, it is unnatural for a human being to analyze such object.  
In the following, it is demonstrated that an onlooker for each plane (e.g., \(X_{1}-X_{2}\), \(X_{1}-X_{3}\), and \(X_{2}-X_{3}\) for \(\mathbb{R}^{3}\)) is enough to determine univocally the 3-dimensional characteristics of a set of n-points and n-slines, specifically:  
**Theorem 1**: _An n-point needs \(\
u=\frac{n!}{2(n-2)!}\) views to be determined univocally._  
The number of different 2D planes of an n-dimensional phase space is equal to the number of combinations to select different subsets (\(x_{i}\), \(x_{k}\)) out of the set \(\{x1,x2,...xn\}\), which is the binomial coefficient \(\mathcal{C}(\cdot,\cdot)\) of pairs of coordinate-axis. That is:  
\[\
u=\mathcal{C}\left(n,2\right)=\frac{n!}{2(n-2)!} \tag{1}\]  
For a nonempty set of n-points or n-slines, one can easily extend the previous result.  
**Lemma 1**: _A nonempty set of n-points or n-slines needs \(\
u=\frac{n!}{2(n-2)!}\) views with enough sharpness to be determined univocally._  
From the fact the onlooker can distinguish different n-points and n-slines (enough sharpness) in a view and Theorem 1, then the conclusion is obtained.
## 3 Attacks on HWMP
### Internal Attacks
Confidentiality is another important security measure that protects the privacy of frames. Confidentiality is ensured by encrypting frames with an algorithm, such as RC4 or the Advanced Encryption Standard (AES) [35]. RC4 is a typical stream encryption algorithm and AES is a typical block encryption algorithm. After encryption, external attackers will not be able to read the transmitted frames. Table 7 lays out the external attacks, the required security services to protect them, and the relevant HWMP frames and fields involved. Note that only link-to-link based security services are required here. The security services required to prevent external attacks are quite simple and straightforward; the more interesting topic in WMN security is preventing internal attacks. Because an attacker cannot capture the correct contents of an encrypted message without knowing the key, encryption protects a passive attack. Digital signature or _MAC_ is a good tool to authenticate the communicating messages. A non-authenticated broadcasting message would not be delivered by the intermediate nodes along the network and thus the flooding attack will be protected. A well designed authentication protocol with the use of freshness identifiers such as nonce or time-stamp can prevent the replay attack. Any replay of old messages violates the freshness of the authentication protocol [36]. Because path diversion attack and impersonation attack in HWMP are launched by modifying some information in a message, the message integrity check service can protect these types of attack. Especially end-to-end integrity service is required for the NMFs because NMFs should not be changed from the source to the destination along the transmission path.
# Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation
## 3 Antenna Selection Schemes for FGSM
### ML Methods
#### 3.4.1 Svm  
An SVM model consists of a hyperplane containing representations of distinct classes in multidimensional space [22]. SVM will iteratively create the hyperplane in order to reduce errors. SVM divides datasets into classes in order to determine the best marginal hyperplane. TAS is a multiclass classification problem. The one-versus-one and one-versus-all (OVA) approaches are two common ways of creating multiclass SVM classifiers. This work looks at how OVA can be used to overcome the drawback of complexity in FD-TAS. It creates \(r\) binary SVMs for an \(R\)-class problem. Specifically, for TAS-assisted FGSM, \(R\) SVM models are generated as follows:  
* To address this two-class classification problem for the attribute label combinations \((\mathbf{f}^{n},c^{n})\), where \(n\)=\(1\),..., \(N\), \(\text{r}\in\{1,2,\ldots,R\}\), an \(R\) order SVM is constructed. In this case, \(\mathbf{f}^{n}\) represents the \(n\)th row of the normalized attribute matrix \(\hat{\mathbf{H}}_{T}\). Samples belonging to the \(r\)th class are characterized by positive labels, while those belonging to the other classes are characterized by negative labels.
# Effects of a Short-Term Supervised Exercise Program in Women with Breast Cancer
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & **Pre-Exercise** & **Post-Exercise** & **Post-Exercise** & **Post-Exercise–Pre-Exercise** \\  & **(M \(\pm\) SD, 95\% CI)** & **(M \(\pm\) SD, 95\% CI)** & **(\%)** & **(\%)** \\ \hline Global health status & Global health status & 39.3 \(\pm\) 21.7 (31.2–47.4) & 52.5 \(\pm\) 17.3 (46.1–59.0) & 0.001 * & 9.4\% \\ \hline \hline \multirow{4}{*}{Functional scales} & Physical functioning & 86.2 \(\pm\) 13.9 (81.0–91.4) & 85.5 \(\pm\) 13.0 (80.7–90.4) & 0.668 & \(-\)14\% \\  & & 67.8 \(\pm\) 30.9 (86.3–79.4) & 78.3 \(\pm\) 25.5 (68.8–87.9) & 0.008 * & 16.4\% \\ \cline{1-1} \cline{2-5}  & Emotional functioning & 68.6 \(\pm\) 29.2 (57.7–79.5) & 82.6 \(\pm\) 14.1 (77.4–87.9) & 0.004 * & 20.4\% \\ \cline{1-1} \cline{2-5}  & Cognitive functioning & 68.6 \(\pm\) 26.5 (58.7–78.5) & 87.2 \(\pm\) 17.3 (80.7–93.6) & \textless{}0.001 * & 27.1\% \\ \cline{1-1} \cline{2-5}  & Social functioning & 73.9 \(\pm\) 30.8 (62.4–85.4) & 78.9 \(\pm\) 24.3 (69.9–88.1) & 0.196 & 6.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of the different areas and items of the EORTC QLQ-C30 questionnaire before and after the implementation of the exercise program.
###### Abstract  
Predictive maintenance allows industries to keep their production systems available as much as possible. Reducing unforeseen shutdowns to a level that is close to zero has numerous advantages, including production cost savings, a high quality level of both products and processes, and a high safety level. Studies in this field have focused on a novel approach, prognostic health management (PHM), which relies on condition monitoring (CM) for predicting the remaining useful life (RUL) of a system. However, several issues remain in its application to real industrial contexts, e.g., the difficulties in conducting tests simulating each fault condition, the dynamic nature of industrial environments, and the need to handle large amounts of data collected from machinery. In this paper, a data-driven methodology for PHM implementation is proposed, which has the following characteristics: it is unsupervised, i.e., it does not require any prior knowledge regarding fault behaviors and it does not rely on pre-trained classification models, i.e., it can be applied \"from scratch\"; it can be applied online due to its low computational effort, which makes it suitable for edge computing; and, it includes all of the steps that are involved in a prognostic program, i.e., feature extraction, health indicator (HI) construction, health stage (HS) division, degradation modelling, and RUL prediction. Finally, the proposed methodology is applied in this study to a rotating component. The study results, in terms of the ability of the proposed approach to make a timely prediction of component fault conditions, are promising.  
predictive maintenance; Prognostic Health Management; streaming analysis 2020
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## 3 Geodetic Measurement Description
### GNSS Data  
Surface displacements of Piton de la Fournaise volcano are continuously monitored by a permanent GNSS network consisting of 24 stations, operated by the Piton de la Fournaise Volcanological Observatory (OVPF). In this study, we used only the 10 stations located in the summit part of the Enclos Fouque area, which were close enough to the intrusion to capture the unrest associated with the eruption (see Figure 1).  
Processing was carried out with the GAMIT/GLOBK software package [43] to determine a daily position for each station. The station REUN, located 17 km away from the volcano was used as a reference to correct for Earth plate motion. Time evolution of two GNSS baselines located at the summit is shown on Figure 2. No significant deformation was recorded during the period spanned by InSAR data prior to and after 25-26 May 2016. Only a very subtle inflation signal (less than 1 cm) can be inferred during the 10 days preceding the eruption (see top part of Figure 2).  
In order to analyze surface displacements due to magma propagation during the crisis, an epoch-by-epoch processing in differential mode was also performed with the TRACK software. The station GITg on the upper edge of the Enclos Fouque caldera was used as a reference. The result is a time series sampled every 30 s. Because of the limited elevation difference between the stations, we assumed that there were no differences in the atmospheric delay for the area (\(6\,\mathrm{km}\times 4\,\mathrm{km}\)). The automatic tropospheric correction was removed during the processing. Then, to correct sidereal effects, GNSS measurements from the 15 days preceding the crisis were cut into slices spanning 23 h 56 min 4 s (the revolution period for GNSS satellites) and stacked. The average signal resulting from multipath reflections on the antenna was removed from the time series. At last, a principal component analysis (PCA) was also conducted to remove correlated noise between stations. Smoothing was applied using a 30 minutes window filtering. More details can be found in [PERSON] et al. [16].  
A network of 80 points was also measured after each eruption during field campaigns (Figure 1). It provides a dense map of the 3D-displacements that occurred between 28 August 2015 and 30 May 2016. This data was included in the inversion to better constrain the north-south component of the surface displacements which is not always easy to retrieve with InSAR data alone [38; 44].
## Materials and Methods  
### Reagents, Chemicals and Analytical Tools  
Tri-butyl-phosphate (TBP) (99% purity, Acros, Geel, Belgium), di-(2-ethylhexyl)phosphoric acid (HDEHP) (99% purity, Acros, Geel, Belgium) and a mixture of quaternary ammonium bases (Aliquat 336) (99% purity, Acros, Geel, Belgium) were used as extractants. TBP was used without a solvent, and HDEHP was dissolved in dodecane (99%, Himmed company, Moscow, Russia). A relatively stable emulsion (stratification time was about 30 minutes without application of centrifugation) formed when dodecane was used as a solvent for Aliquat 336. The addition of 10 vol. % 1-octanol (99% purity, Acros, Geel, Belgium) suppressed the formation of emulsion [34]. The formation of the stable emulsion was not observed in the case of use of toluene as a solvent. However, due to the high volatility of toluene, a mixture of dodecane and 1-octanol was used in all the experiments.  
The commercially available Aliquat 336 is a mixture of quaternary ammonium bases with chloride as a counter-ion. Its use in a nitrate medium requires conversion it to a nitrate form. For this, 0.5 mol-L\({}^{-1}\) solution of Aliquat 336 was contacted with a solution of 1 mol-L\({}^{-1}\) of calcium nitrate twice. According to argentometry [35], the degree of conversion was 99.7% after two contacts.  
Aqueous solutions of REE(III) were prepared by dissolving nitrates of praseodymium and neodymium with the declared purity of 99.9% in deionized MiliQ water. Calcium nitrate (chemical grade purity, Himmed company, Moscow, Russia) was used as the salting-out agent. Acidity of aqueous solutions was adjusted by nitric acid (chemical grade purity, Himmed company, Moscow, Russia). All reagents were used as received without additional purification.  
The mixed aqueous solutions of neodymium and praseodymium nitrates with total content of 80-590 g\(\cdot\)L\({}^{-1}\) (as a REE(NO3)3) were prepared by dissolution of rare earth element nitrates in volumetric flasks with adding of a required amount of a standardized nitric acid solution and a certain mass of calcium nitrate. The final concentration of HNO3 was 0.3 mol-L\({}^{-1}\), and the concentration of Ca(NO3)2 varied from 0 to 300 g\(\cdot\)L\({}^{-1}\).  
Solutions were analyzed by ICP-OES technique (Agilent ICP-OES 720, Santa Clara, CA, US), but preliminarily were checked by UV-Vis analysis (Shimadzu UV 1800, Kyoto, Japan) for rough estimation of a metal concentration range.
# Classic Discrete Control Technique and 3D-SVPWM Applied to a Dual Unified Power Quality Conditioner
## 3 Design of Controllers
### Current, DC bus, and DC bus Unbalance Controllers  
The current controller of the series APF is given by Equation (21), using zero-order hold technique on the input; where the term \(Hi\) represents the current sensor gain.  
\[H_{i}(z)=\frac{2.735z-2.629}{z^{2}-1.73z+0.73}. \tag{21}\]  
Starting from the proportional action, the controller will raise the zero-cross frequency of the system to the required \(5\) kHz (one-quarter of the switching frequency); the integral action reduces the error in the system and also improves the rejection of disturbances. However, the speed of response is reduced with respect to the proportional action. A pole is added to the PI controller to reduce high-frequency noise. The bode diagram in Figure 13 presents the result of applying the controller to the system.  
Figure 12: Shunt active power filter single-phase equivalent circuit.  
Figure 13: Frequency response in the current loop with and without the controller.  
The DC bus controller and DC bus unbalance controllers are described in Equations (22) and (23); where \(H_{DC}\) is the transfer function of the DC bus controller and \(H_{unb}\) is the transfer function of the DC bus unbalance controller.  
\[H_{DC}(z)=\frac{15.2*10^{-3}\mathrm{z}-15.2*10^{-3}}{z^{2}-1.99z+0.995} \tag{22}\]  
\[H_{unb}(z)=0.12. \tag{23}\]  
The DC bus voltage unbalance control is a proportional gain chosen from the maximum current value required to compensate unbalances during transient events, starting from the maximum time in which it must be compensated.
## 4 Discussion
### Limitations of the Approach  
The primary limitation of the presented approach and other studies using test lungs or lung simulators is the principal inability of capturing the full complexity of patient-ventilator interactions [38; 42]. This raises the question of medical relevance. The ventilation parameters such as flow, average peak pressure and plateau pressure, observed at bed-site,can be higher than was simulated in this work. Nevertheless, the presented results serve as proof of concept and set the basis for scaleable experiments.  
Typically, an ASL500 breathing simulator (IngMar Medical, USA) or Michigan Instruments double compartment test lung (Michigan instruments, USA) is used to represent the behavior of a patient [36; 37; 38; 39; 40; 41; 42]. However, such models capture lung properties and expiratory efforts only to a limited extent [37; 38; 39; 40; 41; 42]. The lung simulator xPULM(tm) used in this paper can represent various lung properties (compliance, volume, inner structure) by the inclusion of different lung equivalents. In this study, two 3L latex bags have been used and have to be seen as limiting realistic measurements due to their missing inner structure and their specific elasticity characteristics. Moreover, this study is limited by strictly regular, sinusoidal simulation of the patient's breathing and the use of only one mechanical ventilator operated in two ventilation modes.