esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results from Experiments Using the Platform
### Further Discussion and Real-World Effects
The experiment employed the second configuration of the platform (real mobility, nodes equipped with NEO-M8N GPS devices), and the communication range was set to 3 m (for a network density of 0.43 neighbors per node, on average). The mobile nodes could move in the free part of the terrain according to the same mobility model as before (Random Direction mobility with speed \(V=0.54\) m/s), but in this case, the nodes changed direction, not only when reaching the borders of the terrain but also when hitting the obstacle. The results are in Figure 16, which also displays the corresponding performance metrics for the free terrain (same values as those depicted in the left parts of Figure 13a,b). Clearly, the delay-related performance of all routing protocols is reduced in the blocked terrain. This is expected because the obstacle occupies the central part of the terrain and prevents mobile nodes from taking positions that would enable them to act as beneficial next-hops for other nearby nodes. Moreover, apart from deteriorating the effectiveness of the forward action, the obstacle has a negative impact on the carry action too, as it prevents mobile nodes from moving along trajectories proximal to the shortest path from source to destination. Between these two effects, the one having an influence on the forward action is the strongest. It can be observed that the MFR protocol, which relies exclusively on this action, suffers the worst degradation in the delay-related performance. The low network density featured in the experiment is also a contributing factor: MFR performs poorly in sparse environments, and the reduced effectiveness due to the obstacle of the rarely encountered forwarding opportunities has an adverse effect. The deterioration of performance is less severe for the other protocols, because they can leverage (exclusively or partly) the carry action, which is more suitable in sparse environments. These trends are also reflected in  
Figure 16: Configuration 2 (real mobility, GPS type NEO-M8N): (a) average end-to-end delay and (b) average number of hops in a terrain with and without obstacle (\(V=0.54\) m/s, \(r=3\) m). Figure 15: Modified experimentation terrain with blocked central part. the hops-related results. In the blocked terrain, MFR uses a significantly greater number of hops in its attempts to circumvent the obstacle and make progress towards the destination. Contrarily, the other protocols use a smaller number of hops because in the blocked terrain, it is more difficult to encounter better-directed nodes that would carry faster the message to the destination. Finally, it may be observed that the two versions of MAD exhibit the smallest performance differences between the free and blocked terrains (with respect to both metrics)because of the protocol's potential for dynamic adaptation and effective exploitation of both the forward and carry effects, as the local conditions vary.
## 4 Results and Discussion
### WBVE and Quality of Sleep  
Sleep disorders are a frequent finding in the post-COVID-19 syndrome [84]. Moreover, there are clear evidence indicating that disturbed sleep might be among the strongest risk factors for the subsequent development of neurodegenerative diseases. [PERSON] et al., 2020 [85] investigated the relation between the level of physical activity and sleep in quarantined subjects and concluded that, to maintain health during home confinement, physical-activity promotion and sleep hygiene education and support are strongly warranted.  
Therefore, considering the post-COVID-19 symptoms, it would be important to incorporate questions about sleep hygiene and quality into the routine clinical practice [85]. [PERSON] et al., 2019 [53] evaluated effects of WBV on quality of the sleep in MSy individuals, in a protocol lasting six weeks, twice a week. The fixed group frequency performed 60 s of intervention (10 s of vibration exposition and 50 s of non-vibration exposition) plus 60 s of non-vibration in each bout; and the varied group frequency performed 60 s of vibration and 60 s of non-vibration in each bout. The WBV exercise was performed by 3 bouts (from 1 to 4 week); 4 bouts (from 5 to 8 week); and 5 bouts (from 9 to 12 week). The total time was 18, 24 and 30 min respectively. Clearly, this study demonstrated that WBV promoted improvements in the quality of the sleep of these individuals. It was concluded that the WBV improved parameters such as waist circumference and HR), improving the sleep quality in MSy subjects. WBVE might be a relevant intervention to improve parameters related to poor quality of sleep (fixed frequency and varied frequency groups) and in the daytime sleepiness in MSy subjects that performed the protocol of varied frequency (from 5 to 16 Hz), as described in the Table 6.
# Fundamental Spacetime Representations of Quantum Antenna Systems
## Appendix A Classical Antenna Theory  
Let the source or current Green's function be the dyadic tensor \(\overline{\mathbf{F}}(\mathbf{x},\mathbf{x}^{\prime},t-t^{\prime})\)[28, 46, 47, 48, 89]. The forward Green's function is the standard retarded radiation Green's function \(\overline{\mathbf{G}}(\mathbf{x}-\mathbf{x}^{\prime},t-t^{\prime})\)[49]. Then, we have the following two fundamental relations [28, 90]:  
\[\mathbf{J}(\mathbf{x},t)=\int_{S}\mathrm{d}^{2}x^{\prime}\,\mathrm{d}t^{ \prime}\,\overline{\mathbf{F}}(\mathbf{x},\mathbf{x}^{\prime},t-t^{\prime}) \cdot\mathbf{E}^{\mathrm{ex}}(\mathbf{x}^{\prime},t^{\prime}), \tag{1}\]  
\[\mathbf{E}(\mathbf{x},t)=\int_{S}\mathrm{d}^{3}x^{\prime}\,\mathrm{d}t^{ \prime}\,\overline{\mathbf{G}}(\mathbf{x},\mathbf{x}^{\prime},t-t^{\prime}) \cdot\mathbf{J}(\mathbf{x}^{\prime},t^{\prime}), \tag{2}\]  
where \(\mathbf{E}^{\mathrm{ex}}(\mathbf{x},t)\) is the excitation electric field, \(\mathbf{J}(\mathbf{x},t)\) is the radiating antenna current on its compact surface \(S\) supporting the electromagnetic boundary conditions, and \(\mathbf{E}(\mathbf{x},t)\) is the radiated field. For simplicity, we assume perfect-electric conducting (PEC) antennas where the magnetic field does not contribute to the current Green's function. Another radiating Green's functions similar to \(\overline{\mathbf{G}}(\mathbf{x},\mathbf{x}^{\prime},t-t^{\prime})\) is needed in expressions such as (2) in order to obtain the magnetic field \(\mathbf{B}(\mathbf{x},t)\), which is always present in any radiation problem beside the electric field; see [89, 90, 91] for more details and applications.  
The relation (1) captures the antenna's fundamental Mode A, where a source field \(\mathbf{E}^{\mathrm{ex}}(\mathbf{x},t)\) produces a radiating current via the current Green's function \(\overline{\mathbf{F}}(\mathbf{x},\mathbf{x}^{\prime},t-t^{\prime})\), which in turn generates the radiated fields \(\mathbf{E}(\mathbf{x},t)\) and \(\mathbf{B}(\mathbf{x},t)\) throughout the region exterior to the surface \(S\). To complete the antenna-based wireless communication system, a third Mode C, where a receiving (Rx) antenna, placed at some distance from \(S\), interacts with the radiated field in order to produce an observable receive port current \(\mathbf{J}_{\mathrm{rx}}(\mathbf{x})\) by means of the formula  
\[\mathbf{J}_{\mathrm{rx}}(\mathbf{x},t)=\int_{S_{\mathrm{rx}}}\mathrm{d}^{2}x^ {\prime}\mathrm{d}t^{\prime}\,\overline{\mathbf{F}}_{\mathrm{rx}}(\mathbf{x},\mathbf{x}^{\prime},t-t^{\prime})\cdot\mathbf{E}(\mathbf{x}^{\prime},t^{ \prime}), \tag{3}\]where \(\overline{\mathbf{F}}_{\mathrm{rx}}(\mathbf{x},\mathbf{x}^{\prime},t-t^{\prime})\) is the receive current Green's function, which is generally different from the transmit (Tx) current [PERSON]'s function \(\overline{\mathbf{F}}(\mathbf{x},\mathbf{x}^{\prime},t-t^{\prime})\). The three relations and Green's functions in (A1)-(A3) fully describe the antenna system in classical electromagnetic theory [92], with obvious generalization to magnetic field interactions included in essentially the same logic [90].
# Effects of the 12 May 2021 Geomagnetic Storm on Georeferencing Precision
## 4 Discussion and Main Conclusions
### 12 May 2021 Geomagnetic Storm  
The geomagnetic storm under study occurred in 12 May 2021. It can be classified as moderated in terms of Dst and strong in terms of Kp (Dst = \(-\)61 nT, Kp = 7, and AE = \(\sim\)1500 nT). This storm is the first strong storm, in terms of Kp, of the solar cycle 25. The main phase of this storm lasted one hour from 13 UT to 14 UT, with the Dst peak at 14 UT (see Figure 2). Since the storm occurred in May the southern and northern hemispheres were in fall and spring seasons, respectively. During the main phase of the storm the Sun moved from the (18.27\({}^{\circ}\)N, 15.91\({}^{\circ}\)W) to the (18.28\({}^{\circ}\)N, 30.91\({}^{\circ}\)W) coordinates. This storm caused important effects on the GNSS stations in South America (see Figures 3 and 4g plot), which are rare for this type of storm.
## 2 Materials and Methods
### Remote Sensing Data
#### 2.3.2 Sentinel-2 Data  
The Sentinel-2 mission, developed by ESA in 2015, is based on a two-satellite constellation (S2-A and S2-B) that orbits in space for Earth observation. Each satellite is equipped with a multispectral sensor that acquires images in 13 bands in the visible, near infrared and short-wave infrared. In GEE, the \"COPERNICUS/S2_SR\" collection was used to select 218 images acquired from April to September 2019. The images in the collection are radiometrically and atmospherically corrected products representing surface reflectance. The entire dataset was further processed using the QA60 and SLC bands to mask out cirrus, clouds, and shadows. Finally, the Normalized Difference Vegetation Index (NDVI) [46], Normalized Difference Water Index (NDWI) [47], and Normalized Difference Moisture Index (NDMI) [48] were calculated using Equations (2)-(4):  
\[\text{NDVI}\,=\frac{\text{b}_{\text{8}}-\,\text{b}_{\text{4}}}{\text{b}_{\text {8}}+\,\text{b}_{\text{4}}} \tag{2}\]  
\[\text{NDWI}\,=\frac{\text{b}_{\text{3}}-\,\text{b}_{\text{8}}}{\text{b}_{\text {3}}+\,\text{b}_{\text{8}}} \tag{3}\]  
\[\text{NDMI}\,=\frac{\text{b}_{\text{8}}-\,\text{b}_{\text{11}}}{\text{b}_{\text {8}}+\,\text{b}_{\text{11}}} \tag{4}\]  
where b\({}_{\text{3}}\), b\({}_{\text{4}}\), b\({}_{\text{8}}\), and b\({}_{\text{11}}\) are green, red, near-infrared, and short-wave infrared bands centered at approximately 560, 665, 842, and 1610, respectively. We generated the time series of each vegetation index by calculating the median values and subsequently extracted the mean value of each index within each field plot in the Geographic Information System (GIS).
## 3 In Situ Sensors Used in the L-PBF Processes
### Vision Sensors  
The choice of imaging sensors to be integrated with the L-PBF process depends on the monitored process signature. For example, high-speed cameras can be employed to monitor the meltpool size and spatter formation during the L-PBF printing process [82]. To ensure proper data acquisition is collected along the path where the laser melts the powder, the camera can be connected to a dichroic mirror, a galvanometer scanner, and an F-theta lens, as illustrated in Figure 7a. In this case, the camera is considered on-axis, as it follows the laser path and only a small region is captured (meltpool region). In another example of monitoring the meltpool and spatter signature, the high-speed camera was installed off-axis, as illustrated in Figure 7b [83], which is relatively an easier setup. However, more image processing would be required to rectify the captured image and select the region of interest. To monitor scanned layer and powder bed signatures, digital single-lens reflex (DSLR) cameras are usually integrated with the L-PBF process. The DSLR is known to achieve the highest possible image quality [84]. It is common to use external light modules with DSLR cameras and acquire images before and after laser melting to increase the amount of data and, consequently, information obtained from the process [85].
# VAE-Based Real-Time Anomaly Detection Approach for Enhanced V2X Communication Security
## 3 Proposed Methodology  
This section outlines the proposed method for real-time anomaly detection in V2X communication systems. Starting with the attack model, we will detail the proposed VAE-based anomaly detection model.
# Structural Particularities, Prediction, and Synthesis Methods in High-Entropy Alloys
## 2 Structural Particularities of HEAs, Prediction and Estimation Concepts
### Microstructural Stability
#### 2.2.4 Cocktail Effect  
The concept of \"cocktail effect\" is used for the first time by Prof. [PERSON] [9]. Unlike the other \"base effects\", the \"cocktail\" effect is not a hypothesis and does not need to be proven. The \"cocktail effect\" [19] highlights that the extraordinary properties of materials often emerge from unexpected synergies. Similar unpredictable, synergistic outcomes can be observed in other material behaviors, including physical properties like the near-zero thermal coefficient of expansion; functional properties such as thermoelectric response or photovoltaic conversion; unique combinations of structural properties, like ultra-high hardness paired with good fracture resistance, fatigue resistance, or ductility. In all these instances, the properties are influenced by the material's composition, microstructure, and electronic structure. The unusual combinations of elements and microstructures, featured by HEA, determine associations of properties that present a high grade of novelty [12; 13].  
This effect could be interpreted for alloys as a composite characteristic. The composite aspect arises from the notion of solid solution mixture formed by multiple primary components. Basically, this phenomenon refers to the specific property gain, acquired upon the intimate mixture of more than 5 elements within a single sample. The characteristic behavior of each phase present within HEA impacts its structural characteristics, such as morphology, grain size distribution, grains integrity, etc. Also, every individual element contributes to a specific property improvement. For example, this effect can alter material magnetization, flexibility, strength, coercivity, and electrical strength [20; 21; 22]. For example, the addition of Cr may promote the formation of BCC phase, thus raising the plasticity and lowering both hardness and strength. Also a list of different added elements and their influence with the cocktail effect was presented.
# Three-Dimensional Guidance Laws for Spacecraft Propelled by a SWIFT Propulsion System
## 3 Optimal Guidance Laws
### Spacecraft Dynamics in a Heliocentric Spherical Reference Frame
Bearing in mind Equations (3) and (26) and introducing the auxiliary vector \(\mathbf{\lambda}_{S}\in\mathbb{R}^{3}\) defined as  
\[\mathbf{\lambda}_{S}\triangleq[\lambda_{\varphi_{\tau}},\,\lambda_{\varphi_{\theta}},\,\lambda_{\varphi_{\gamma}}]^{\mathrm{ T}} \tag{28}\]  
with  
\[\lambda_{S}\triangleq\sqrt{\lambda_{\varphi_{\tau}}^{2}+\lambda_{\varphi_{ \theta}}^{2}+\lambda_{\varphi_{\gamma}}^{2}}\quad\text{and}\quad\hat{\mathbf{ \lambda}}_{S}\triangleq\frac{\mathbf{\lambda}_{S}}{\lambda_{S}} \tag{29}\]  
one obtains that the function \(\mathcal{H}_{c}\) can be written as  
\[\mathcal{H}_{c}=\mathbf{a}\cdot\hat{\mathbf{\lambda}}_{S} \tag{30}\]  
which is substantially coincident with Equation (21) with the formal substitution \(\hat{\mathbf{\lambda}}_{S}\to\hat{\mathbf{\lambda}}_{\varphi}\). Therefore, according to Equations (22) and (23), the optimal guidance law is given by  
\[\hat{\mathbf{t}}^{\star}= \begin{cases}\hat{\mathbf{\lambda}}_{S}&\text{if}\quad \alpha_{\lambda_{S}}\leq\alpha_{\max}\\ \frac{\sin(\alpha_{\lambda_{S}}-\alpha_{\max})}{\sin\alpha_{\lambda_{S}}}\, \hat{\mathbf{r}}+\frac{\sin\alpha_{\max}}{\sin\alpha_{\lambda_{S}}}\,\hat{\mathbf{ \lambda}}_{S}&\text{if}\quad\alpha_{\lambda_{S}}\in(\alpha_{\max},\,180\, \text{deg})\end{cases}  \tag{31}\]  
where \(\alpha_{\lambda_{S}}\in[0,\,180)\) deg is another auxiliary angle defined as  
\[\alpha_{\lambda_{S}}=\arccos\left(\hat{\mathbf{\lambda}}_{S}\cdot\hat{\mathbf{r}}\right) \tag{32}\]
## 2 Materials and Methods
### Objective Function  
The objective function assesses the discrepancy between forward modeling and the actual observed data, typically represented by the square of their difference. The residual \(R_{i}(\mathbf{u})\) between the true observed values and the forward modeling at the \(i\)th sampling points can be expressed by converting the inversion problem into a least squares form as follows:  
\[R_{i}(\mathbf{u})=F(\mathbf{u}|g_{i}(\mathbf{u})-d_{obsi}) \tag{13}\]  
where \(d_{obsi}\) represents the actual data observed at the sampling points, \(\mathbf{u}=[x_{0},\,y_{0},z_{0},m,I_{0},A_{0}]\), and \(F\) is the forward model.  
The objective function is defined as the residual sum of squares and is established as:  
\[f(\mathbf{u})=\frac{1}{2}\underset{i=1}{\overset{M}{\sum}}\,\parallel F(\mathbf{u}) \parallel^{2} \tag{14}\]  
where \(F(\mathbf{u})\) is the normalized form of the residuals, \(M\) is the total number of observation points, and \(\parallel\cdot\parallel\) is the 2-norm.  
Figure 3: (**a–e**) The coefficients \(\alpha_{1-5}\) of the simulated magnetic dipole source. (**f**) The sum of the squares of the five coefficients \(E\).
# NESO-Based Path Following Control for
## 5 Conclusions  
A safe control strategy has been presented for underactuated hovercrafts subject to nonlinear time-varying safety limit constraints and nonlinear time-varying uncertainties. A NESO is first proposed to estimate and compensate the nonlinear time-varying uncertainties of underactuated hovercrafts. The nonlinear time-varying turning safety limit is first considered in the controller design to improve the safety of underactuated hovercrafts. Theoretical analyses have indicated that the approximation errors of the NESO and the tacking errors of the closed-loop in the designed control system are bounded. The designed safe control strategy is applied on a certain underactuated hovercraft to verify the effectiveness of the designed control law. Theories and the application case have shown that the disturbances rejection safe control strategy greatly improves the safety and maneuverability of the underactuated hovercraft subjects to nonlinear time-varying uncertainties and the safety limit constraint. Even though this novel control methodology was described and validated in terms of an underactuated hovercraft, it also can be used to control other underactuated marine surface vehicles at constant altitudes.  
**Author Contributions**: Conceptualization, H.T.; methodology, H.T.; software, H.T.; validation, H.T.; formal analysis, H.T.; writing--original draft preparation, H.T.; writing--review and editing, H.T. and H.R.; supervision, Y.W. and H.R.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the China Scholarship Council (CSC201906680026), the National Natural Science Foundation of China under Grant No.51879049, and the National Natural Science Foundation of Heilongjiang Province under Grant No. LH2019E039, and the Fundamental Research Funds for the Central Universities under Grant No. 3072020 CF0403.  
**Acknowledgments:** I would like to express my gratitude to all those who helped me during the writing of this paper, especially my supervisors [PERSON] and [PERSON] who have offered me valuable suggestions in the academic studies. I am also very grateful to the reviewers for their comments that are of great help to improve the quality of this paper.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 3 Results and Discussion
### Structural Characterization of MPNTS
#### 3.2.2 \({}^{1}\)H NMR  
The \({}^{1}\)H NMR spectra of MPNT, 1,1,3,5,5,5-heptathyl trisiloxane and methyl propenyl polyether are shown in Figure 3. In the spectrum of heptamethylltrisiloxane (Figure 3c), The signals corresponding to Si-CH\({}_{3}\) are observed at about \(\delta\) 0.1 ppm, and the signal  
Figure 1: Synthesis mechanism diagram of MPNTS (MD\({}^{\rm H}\)M, EO(7), and MPNTS represent 1,1,1,3,5,5,5-heptamethylltrisiloxane, methylpropenyl polyether, and methylpropenyl polyether modified nonionic silicone surfactant, respectively).  
Figure 2: Infrared spectra of MPNTS (I) 1,1,3,5,5,5-heptathyltrisiloxane, (**II**) methyl propenyl polyether, and (**III**) methylpropenyl polyether modified nonionic silicone surfactants, (a,b) represents the characteristic peak of Si–H.  
for the Si-H proton is observed at 8 4.53 ppm. In the methallyl polyether spectrum (Figure 3b), the peak at 8 5.87 ppm is attributed to the C-CH\({}_{3}\) protons, and the peaks at 8 5.24 ppm are assigned to C=CH\({}_{2}\) protons. The characteristic peak of the Si-H proton and the characteristic peaks of the C=CH\({}_{2}\) protons do not appear in the spectrum of MPNTS (Figure 3a), and two new peaks appeared at 8 0.34 ppm and 8 1.50 ppm, which are attributed to the Si-CH\({}_{2}\) protons and the Si-C-CH proton, respectively. These proved, again, that the C=C of methyl propenyl polyether has been grafted onto the main chain of heptathyltrisiloxane to form MPNTS.  
According to Figure 1, the mole ratio of MD\({}^{\text{H}}\)M to methyl propenyl polyether is 1:1. However, the real mole ratio of MD\({}^{\text{H}}\)M to methyl propenyl polyether in the experiment was 1:1.05. The excess polyether made the active hydrogen of MD\({}^{\text{H}}\)M (Si-H) react completely [26]. According to the performance test results to be described later, the excess polyether hardly affects the surface properties and stability of MPNTS. The precursor of the reaction, heptamethyltrisiloxane, was a hydrophobic substance, so the changes in hydrophobicity can be observed to initially judge the changes in the above structure. Specifically, the whole system was in a clear state before the addition of polyether, but with the addition of polyether, the reaction system would become cloudy. Finally, under the action of the catalyst, the whole system would become a clear and transparent solution again [27]. This is also an easy way to see if the reaction was complete. According to these results, we successfully synthesized our expected target product MPNTS through a simple process, which proved that the synthesis scheme was feasible.
## 2 Materials and Methods
### Reasons for Using EMD Algorithm  
EMD can be used to detect fault signals generated when stones are mixed into coffee beans. The grinder grinds coffee beans by rotating blades and burrs. When faulty components such as stones participate, the interactions between components produce different vibration patterns and transient features. EMD is a method that decomposes a complex signal sequence into Intrinsic Mode Functions (IMFs) and residuals [12]. Each decomposed IMF can characterize a certain periodic and stationary mode of the fault, making it easier for signal processing techniques such as Fourier Transform and cross-correlation to capture the desired information. When a fault occurs, the IMFs often contain signals that characterize the fault model [13; 14]. The residual also contains transient features that may be excited by the fault [15]. By analyzing the IMFs and residuals, features that reveal whether the grinder is operating normally can be identified. Another advantage of EMD is its adaptability. Unlike wavelet transform-based or autoregressive methods, EMD does not require preset basis functions and can handle non-stationary signals [16], nor does EMD require noise whitening before decomposition [17]. This makes EMD highly suitable for analyzing fault signals in complex mechanical systems [18]. Fault signals in complex mechanical systems are often significantly weaker than the inherent modes of the system during normal operation and tend to undergo intermodulation with the system's inherent modes [19]. EMD can remove the influence of these strong inherent modes, thereby better separating the desired fault modes and transient features [20].