esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Wearable Devices for Non-Invasive Sensing
## 7 Conclusions and Perspectives  
Unlike conventional wearable sensing systems, which generally have bulky, planar, and rigid components, current research has focused on constructing fully-wearable, non-invasive sensing systems. Such systems are aimed at providing improved convenience as well as accurate diagnosis and clinical insights. Remarkable progress has been made in wearable sensing devices based on multi-disciplinary research that included the design of materials, sensors, and wireless technologies as well as their integration. Herein, we have provided a comprehensive review of the progress that has been made concerning the primary factors that are required to construct fully-wearable and integrated sensors. Metal-based, polymeric, and ionic conductors were combined with various structural designs, such as serpentine, wavy, 3D structures to create novel wearable materials. Such materials have been incorporated in the fabrication of wearable sensors to maximize the capabilities of the sensors when subjected to various conditions. Attempts were made to utilize wireless technologies and construct smart integration systems.  
Despite such progress, there have been challenges in the operational reliability and durability of current wearable sensors. Developing multiplexed sensing systems could be an option to overcome the reliability issues since such systems allow a comprehensive analysis through the collective input of various parameters. In addition, efforts are needed to develop novel algorithms to properly and efficiently process raw data into vital and useful information. Meanwhile, efforts have been made to find alternative materials and structures to improve the durability of wearable sensors. However, this might result in reduced sensitivity and require complicated fabrication procedures and expensive materials. Therefore, another alternative strategy could be to develop structural designs that enable the simple replacement of components in the wearable sensor. This allows the long-term use of the sensors and benefits their economic perspective. With such improvements, we will get much closer to our ultimate goal of producing completely bio-integrated systems.  
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# Stabilization of Pickering Emulsions by Hairy Nanoparticles Bearing Polyanions
## 3 Results and Discussion
### Oil Phase  
Pickering emulsions consist of water phase, oil phase, and solid particles--these must be carefully controlled. [PERSON] et al. reported that PNIPAM-carrying particles preferentially formed oil-in-water (O/W)-type Pickering emulsions with a variety of oils such as hexadecane, heptane, and trichloroethylene [22]. Meanwhile, long carbon chains of alkane are the simplest hydrophobic structures. Both decane and hexadecane have this kind of structure, and they are non-volatile, cheap, and easily available. They cannot dissolve PS@PSS hairy nanoparticles, which was verified by experiments. In spite of a series of similarities, hexadecane is more hydrophobic than decane because of six extra -CH\({}_{2}\), which gives us a convenience to compare the effect of Pickering emulsion with different oil phases. Here, hexadecane/H\({}_{2}\)O and decane/H\({}_{2}\)O Pickering emulsions stabilized by PS@PSS were prepared with ultrasonic power of 150 W, ultrasonic time of 3 min, and water:oil ratio of 1:1. In the systems containing PS@PSS nanoparticles, the type of emulsions were always O/W reflecting the hydrophilic nature of this nanoparticle in oil-water systems.  
From microscope images (Figure 6), we could find that hexadecane/H\({}_{2}\)O Pickering emulsion droplets significantly outnumbered decane/H\({}_{2}\)O Pickering emulsion droplets. In terms of uniformity, hexadecane/H\({}_{2}\)O Pickering emulsion droplets were also more homogeneous than droplets in decane/H\({}_{2}\)O Pickering emulsion. Furthermore, statistical results in Figure 6c revealed the obvious difference in droplets size distribution. Hexadecane/H\({}_{2}\)O Pickering emulsion droplets size was distributed around 4.3 \(\upmu\)m but decane/H\({}_{2}\)O Pickering emulsion droplets size was much larger, which was up to 7.1 \(\upmu\)m. This distinction resulted from the structure difference between decane and hexadecane. Compared with decane, hexadecane had extra six -CH\({}_{2}\) and turned to be more hydrophobic. Accordingly, surface tensions (\(\gamma_{\text{ao}}\)) of decane and hexadecane were 24.3 mN/m and 27.2 mN/m, respectively. Higher surface tension made hexadecane less wettable by PS@PSS nanoparticles in water phase and then the curvature of droplets would be bigger. So droplet size of hexadecane/H\({}_{2}\)O Pickering emulsion would be much smaller than decane/H\({}_{2}\)O Pickering emulsion (Figure 6a). Since the total volume of hexadecane and decane was the same, the amount of hexadecane/H\({}_{2}\)O Pickering emulsion droplets was larger as shown in Figure 6c.  
Figure 6: Microscope images of Pickering emulsion stabilized by PS@PSS with different oil phase (**a**) hexadecane; (**b**) decane. The magnification is 500 times. The bar is 10 \(\upmu\)m. (**c**) Droplets size distribution in number corresponding to microscope images.
## 3 Numerical Results and Discussions
### Evaluation of Positions for Piezoelectric Patches  
The effect of positions is studied for the piezoelectric patches on the transient behaviors of the stiffened model under blast load. To investigate the effect of sensors and actuators locations on the control performances, the stiffened model is performed for two different positions of piezoelectric pairs that are attached on top and bottom surfaces, as shown in Figure 11. Each model has eight piezoelectric patches, and the dimensions of each sensor and actuator patch are 0.05 m \(\times\) 0.05 m. The center points of the Models I and II are 0.05 m and 0.35 m away from the clamped edge, respectively. To investigate the control effects, two parameters are chosen. One parameter is settling time that oscillation does not exceed the limit value of each transient responses. The limit values of the vertical displacement, the transverse bending, and the lateral twisting are 0.4 mm, 0.002, and 0.002, respectively. For other parameter, the limit is the maximum response of the models.  
Figure 10: Lateral twisting responses with two locations of stiffener using LQR control: (**a**) Stiffener Case 1; (**b**) Stiffener Case 2.  
Figure 9: Transverse bending responses with two locations of stiffener using LQR control: (**a**) Stiffener Case 1; (**b**) Stiffener Case 2.  
on the panel surface. The linear quadratic regulator (LQR) method is chosen as a control algorithm. As piezoelectric patches are distributed on the whole surfaces of the model, the effect of stiffener's location is investigated. Furthermore, the effect of piezoelectric patches position is also studied on the transient responses of the stiffened structure under blast load. The most effective position of patches can be determined with settling time and maximum responses. Consequently, this presented analysis could be easily applied to various types of models. In order to get the best control performances, the research aimed to find the optimum position of sensor and actuator pairs that is most effective under blast load environments.  
[PERSON]-[PERSON] participated in the design, computational analysis and writing. [PERSON]-[PERSON] played an important role in initial overall research. [PERSON]-[PERSON] participated in the supervision of analysis and paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms
## 4 Algorithms
### Packing Problems
#### 4.1.2 Vertex Coloring
This problem is FPT parameterized by the treewidth [110], but the related Min Sum Edge Coloring problem is NP-hard [111] on graphs of treewidth 2 (while being polynomial time solvable on trees [112]). For this problem the edges need to be colored with integer values, so that no two edges sharing a vertex have the same color, and the aim again is to minimize the total sum of colors. Despite being APX-hard [111] and also paraNP-hard for parameter treewidth, Min Sum Edge Coloring admits a PAS for this parameter [113]. Theorem 15 ([113]): _For the Min Sum Edge Coloring problem a \((1+\varepsilon)\)-approximation can be computed in \(f(k,\varepsilon)n\) time for any \(\varepsilon>0\), where \(k\) is the treewidth of the input graph._
## 2 Overall Structure and Working Principle
### Operational Principle  
When the mobile platform works in the field, it will be affected by the field's topography, resulting in the tilt of the mobile platform, and the tilt of the mobile platform posture will reduce the accuracy of safflower picking. At this time, the automatic levelling control system is needed to adjust the position and posture of the mobile platform to the horizontal state. The automatic levelling control system is an electric drive system based on multi-sensor fusion. The sensors are used to receive the tilt angle of the mobile platform and the pressure value of the electric push rod and transmit it to the controller in real time. According to the principle of levelling control, the designed automatic levelling control system is shown in Figure 2. The inclination sensor is installed in the center of the mobile platform in order to detect the inclination information when the mobile platform is tilted.  
Figure 1: The overall framework of the mobile platform: (1) Pressure sensor, (2) Linear actuator, (3) Levelling outrigger, (4) Displacement sensor, (5) Levelling platform, (6) Microcontroller and other control devices, and (7) IMU.  
The inclination information is transmitted to the controller through CAN communication. After receiving the information, the controller begins to calculate the height difference between each electric push rod and then controls the driver to drive the electric push rod for lifting action. The lifting and contraction of each electric push rod are realized through the start [8], stop, and positive and negative movements of the motor to accomplish automatic levelling of the mobile levelling table. When the position and posture of the mobile platform are horizontal, the height position of the electric push rod is obtained by observing the value of the displacement sensor installed on the first electric push rod. After levelling, the pressure sensor installed above the electric push rod detects the pressure value of each electric push rod and feeds back the pressure value to the controller. When the pressure value of the electric push rod is zero or significantly less than that of the rest of the electric push rod, the controller will execute the \"virtual leg\" elimination program. The reasons for designing the \"virtual leg\" elimination program are: when four-point support is adopted, one outrigger will not be stressed due to deformation and other problems, and the weight of the platform will be supported by the other three outriggers [9], resulting in virtual legs. When virtual legs occur in the levelling process, the mobile platform will shake due to external interference, and overturn will occur in serious cases. Therefore, it is necessary to avoid the imaginary appearance of empty legs.
## 3 Results of Numerical Simulations and Case Studies  
The mathematical model illustrated in Section 2 is employed to evaluate the phasing maneuver's performance of a Sun-facing diffractive sailcraft of assigned characteristic acceleration. In this section, three mission scenarios are investigated, namely, (1) a Mercury-based case in which the reference elliptical orbit has a semimajor axis \(a_{0}=0.3870\,\text{AU}\) and an eccentricity \(e_{0}=0.2056\), so that the semilatus rectum is \(p_{0}=0.3706\,\text{AU}\); (2) an Earth-based case in which \(a_{0}=1\,\text{AU}\), \(e_{0}=0.0167\), and \(p_{0}=0.9997\,\text{AU}\); and (3) a circular orbit case where \(e_{0}=0\) and \(a_{0}\equiv p_{0}=1\,\text{AU}\). The latter case is consistent, for example, with a simplified form of the Earth-based mission scenario.
# Improving Air Pollution Prediction System through Multimodal Deep Learning Model Optimization
## 4 Deep Learning Models
### Basic CNN (Convolutional Neural Networks)  
We first use the basic CNN [13] to handle satellite images to compare the performance of sophisticated CNN models, such as VGGNet. The CNN model solves the problem of common neural network inputs without spatial/topological information and determines the association between each pixel of the image and the surrounding pixels by processing it in part rather than the entire image. In this way, the CNN can learn images while maintaining spatial information of the images. Thus, it is widely used for image processing, and the feature of the image is extracted through convolution layers and pooling layers. The basic CNN experimented with in this paper is shown in Figure 4 and has a total of 5 layers consisting of 3 convolution/pooling layers and 2 fully connected layers.
## 3 Classification
### Mode of Communication
#### 3.3.2 Indirect  
_Indirect_ mode of communication involves vehicles and VRUs communicating with each other _indirectly_ i.e., through infrastructure. References [7; 20; 32; 37; 38; 41] are a few examples of the V2P systems for VRUs that use the indirect mode of communication. The devices may be equipped with same technology, such as cellular [20], or with different technologies, such as 802.11p and Wi-Fi [37]. As the exchange of messages happens through the infrastructure, the system may first process the messages in infrastructure nodes before forwarding them to other nodes. This may require the infrastructure nodes to have high computing power. Also, the exchange of messages through infrastructure nodes may cause higher communication latency. This imposes the requirement that the latency constraint of the target V2P application must be assessed against the infrastructure latency.  
A variation of the _Indirect_ mode may be _multi-hop_ communication. In case of Non-Line-of-Sight (NLOS) crash scenarios, multi-hop communication may be useful. A vehicle may re-broadcast the safety message that it receives from a VRU to the surrounding vehicles. For example, a public transport bus, at the bus-stop, may re-broadcast the safety messages from the VRUs crossing the street in front of the bus.
## 1 Introduction
Accordingly, Figure 1b shows that some of the original nanoparticles are retained in the microstructure of the coating, and these particles can effectively impede dislocation movement and improve the hardness and creep resistance of the coating [28]. However, detailed studies regarding the influence of microstructural features on the thermal control properties of nano-alumina coatings are limited in the literature. In general, the thermal control properties (solar absorptance (\(\alpha\)s) and infrared emissivity (\(\epsilon\))) of coatings are determined by their microstructure and composition [29; 30]. [PERSON] et al. [29] optimized the scattering and reflective properties of porous alumina by controlling the spray parameters in order to optimize porosity. The absorption rate of the coating was less than 0.1 within 300-800 nm. In addition, [PERSON] et al. [30] found a significant increase in the reflectivity of an alumina plasma-sprayed coating caused by the conversion of \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) to \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) after an annealing treatment. Therefore, in this study, we used an atmospheric plasma-spraying method to prepare conventional alumina coatings and nanostructured alumina coatings in order to characterize the microstructures, thermal control properties, and mechanical properties of different  
Figure 1: Schematic of formation process of plasma-sprayed nano Al\({}_{2}\)O\({}_{3}\) coating. (**a**) Melting state of particles; (**b**) nano-sized Al\({}_{2}\)O\({}_{3}\) particle-based coating models [25]. alumina coatings and to relate their microstructures and composition with their thermal control and mechanical properties.
## 4 Discussion
### Number of Extractions on the Same Material  
The second extraction managed to extract another 5% resveratrol content compared to the first extraction, and the third only 0.67%. The next four extracts did not contain resveratrol (Figure 6).  
From an economical point of view, further extractions on the same material are not advantageous. The use of solvents, time and manufacture practice are not economical for the second and third extraction, which together add up to only 5.67% out of the total content of trans-resveratrol from vine shoots. This attempt to increase the amount of trans-resveratrol extracted from the same material highlights the fact that the optimal parameters of the first extraction are well chosen, thus extracting 94.33% of the amount of resveratrol available.  
[PERSON] et al. (2015) conducted multiple Soxhlet extractions with methanol on vine shoots and compared the extraction yield of stilbene for each extraction. The yield of stilbene for the second extraction is under 5%, and the yields of the third and further extractions were under 1% [39]. These results are the same with the result obtained by conventional maceration with diethyl ether-ethanol 1:4 ratio.
## 1 Introduction
Taking the CMM combined with a fifth-order CR as an example, in this case, 2190-14,600 sigma-samples are to be integrated, which is hundreds of times larger than the conventional single-Gaussian propagation using fifth-order CR [16]. Thus, it is necessary and promising to investigate approaches to enhance the efficiency of GMM. In recent, the use of virtual sample generation (VSG) has been well discussed. The VSG is a kind of technique that aims at providing artificial samples to enlarge the current data set, which was first introduced in the field of machine learning [43; 44]. Apart from the field of machine learning, the VSG technique has also been successfully applicated to engineering optimization problems. [PERSON] et al. proposed a VSG-based optimization method to reduce the required expensive evaluation [45]. Motivated by the concept of the VSG, it is possible to improve the efficiency of the GMM by incorporating visual samples in propagation. In this paper, an adaptive Gaussian mixture model using virtual sample generation (AGMM-VSG) is proposed. The virtual sample generation (VSG) technique [45; 46] is employed to reduce the required computation for propagating each Gaussian component. Furthermore, a novel Gaussian component splitting strategy based on the virtual sample is employed for efficiently updating weights. The paper is organized as follows. In Section 2, the orbital uncertainty propagation problem with a single Gaussian model and a GMM is described. Section 3 presents details of the proposed AGMM-VSG, followed by detailed discussions of the adaptively splitting strategy for Gaussian components and the VSG-based propagation method. The proposed AGMM-VSG is applied to three cases of uncertainty propagation for uncertain orbital dynamical systems in Section 4. Finally, Section 5 gives the conclusions.
## 3 Results and Discussion
### Temperature Profiles during the Cool Storage Phase-Change Process  
Figure 4 shows the temperature profiles during the cool storage phase-change process test cycles. Figure 4a shows the curves repeat well in all cycles. So, we enlarge the 3 rd cycle into Figure 4b to see the process clearly. The curves for all samples are quite similar. So, we take the blank sample to describe the process. When the temperature setpoint for the constant-temperature sink was set to \(-20\)\({}^{\circ}\)C, the temperature of the sample decreased rapidly. But the water did not immediately solidify at 0 \({}^{\circ}\)C. With a decrease in temperature to some value (approximately \(-11\)\({}^{\circ}\)C for a blank sample, point B), the temperature suddenly increased to 0 \({}^{\circ}\)C (point C) and remained at that value for dozens of seconds (point D). Therefore, water was in the solidification process. When all the water was solidified, the temperature decreased again. Since the temperature of the blank sample needs to be \(11\)\({}^{\circ}\)C lower than the freezing point of water (0 \({}^{\circ}\)C), the supercooling for the blank sample is \(11\)\({}^{\circ}\)C.  
When the temperature setpoint for the constant temperature sink was set to 20 \({}^{\circ}\)C, the temperature rapidly increased. When it reached 0 \({}^{\circ}\)C (point F), the temperature remained for dozens of seconds (to point G). This means that water enters the melting process without significant supercooling.  
Figure 3: Microscopy images of samples (**a**) CF#0s, (**b**) CF#195s, (**c**) CF#255s, and (**d**) CF#300s.  
All other samples showed similar curves. All samples had significant supercooling during the solidification process, while there was no considerable supercooling during the melting process. Thus, supercooling during the solidification process will be discussed in detail.
## 5 Climatology (2002-2022) of Longitudinal Structures in the Ionosphere  
We mentioned in the Introduction that with the advent of new satellite missions, recently there has been considerable interest in studying the coupling processes between the lower atmosphere and the IT system. Further, it was noted that the atmospheric tides, and particularly the nonmigrating ones, are the primary source for the ionosphere variability forced from below. The previous two sections of the present study clearly revealed that not only some nonmigrating tides, but also the longitudinal structures in LT they produced have large climatological (2002-2022) magnitudes in the lower thermosphere. There are quite many reports, mentioned in the Introduction, for the presence of such longitudinal structures, particularly the WN4 and WN3 ones, in the ionosphere as well.  
Therefore, the next step in our analysis is to investigate the main climatological features of the longitudinal structures WN4, WN3, and WN2 in a way as they are present in the global TEC maps. The main reason is to quickly recognize these structures among the other ionospheric variations. This means that the absolute TEC magnitudes of the mentioned longitudinal structures will be considered. We underline that this is to some extent a weak point of the present paper because both the SC and semiannual variability of the TEC will not be removed in advance. However, we have to keep in mind that the TEC dependsmainly on the electron density of the \(F\)-region maximum, but the bottom ionosphere has significantly weaker contribution than the topside one; this difference is considerable during night conditions. We note that although there are some already proposed methods to estimate the relative tidal amplitudes in the ionosphere [89; 90], we would like to present a new approach for assessing the relative amplitudes and that will be the subject of a future study.  
We remind that the TEC data: (i) cover the same time interval as that of the SABER temperature data, January 2002-December 2022; (ii) both the SABER temperatures and the TEC data are analyzed by using the same method, and (iii) while the SABER temperature results are presented in geographical coordinates those of the TEC are shown in modify latitude and geographic longitude. The longitudinal TEC structures are considered only at latitudes of \(\pm 50^{\circ}\) not only because to compare them with the SABER temperature ones but also because at higher latitudes they quickly subside. Similarly to Section 4 here also the color scales at all latitudes will be different to distinguish right away the magnitude of the considered TEC longitudinal structure.