esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characteristic Analysis of the Enrichment Factor of the Elements (EF)  
The enrichment factor has been widely used to evaluate the pollution degree of heavy metals in particulate matter and the influence of human factors in many studies [54; 55], and it was found that Fe, Pb, Cr, V, Ni, Zn, and other elements are highly enriched in particles, and most of them come from human activities.  
In this study, Mn was selected as the reference element in the EF calculation, and the pollution enrichment characteristic factors of Cr, Cu, Fe, Pb, and Zn were analyzed in the atmospheric dust before and after the epidemic occurrence. The EF values of the elements are shown in Table 3. The EF value of Fe in the samples of December 2019 is the highest (EF = 18.120), and it is obviously higher than that (EF = 4.302) in the samples of December 2020, indicating that Fe pollution is extremely significant, mainly due to the more intense human activities before the COVID-19 outbreak. In addition, the EF values of Zn in the dustfall samples of December 2019 and December 2020 are relatively high, reaching 4.732 and 1.560, respectively. The EF value of Zn in 2019 was more significant than that in 2020, indicating that the Zn pollution was significant in 2019, mainly coming from intense human activities, such as metal material manufacturing, fuel combustion, and transportation before the COVID-19 epidemic. The EF values of Cu, Ni, and Pb in the dustfall samples of 2019 and 2020 are all less than 1, indicating that the metal elements' pollution caused by human activities is extremely low.  
In summary, the enrichment levels of Fe and Zn elements in the dustfall of 2019 are significantly higher than those of 2020, indicating heavy Fe pollution and moderate Zn pollution before the COVID-19 outbreak, and moderate Fe pollution and mild Zn pollution after the COVID-19 outbreak. Through comparing the EF values of the samples in 2019 and 2020, it can be seen that the enrichment factor values of the heavy elements in 2019 are significantly greater than those in 2020, which is likely due to the execution of air pollution management regulations and the reduction in human activities during the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**EF Value** & **Mn** & **Cr** & **Cu** & **Fe** & **Ni** & **Pb** & **Zn** \\ \hline
2019 & 1.000 & 0.218 & 0.037 & 18.120 & 0.020 & 0.058 & 4.732 \\
2020 & 1.000 & 0.456 & 0.253 & 4.302 & 0.289 & 0.270 & 1.560 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Enrichment factor of metals.  
COVID-19 epidemic. The EF calculation results in 2019 are similar to the results of previous studies [56].
# Experimental Assessment of the Spatial and Temporal Distribution of Non-Contact Tonometer Airflows
## 2 Materials and Methods
### Outcome Measures and Analysis  
The primary outcome measure was the airflow pressure over time recorded for both devices. Spatial and temporal distributions were analysed and visualised.  
Environmental factors such as the temperature, humidity and atmospheric pressure were monitored using a conventional hygrometer and a barometer app with GPS tracking and height calibration and were kept constant throughout the experiment. To minimise the effects of background noise potentially contaminating sensor readings, data were collected over weekends or evenings after normal working hours.  
To evaluate the accuracy of the mapping, for each measurement the camera pictures were used to analyse the position of the sensor tube opening and the nozzle. Ten pictures for each measurement point were taken and averaged to describe the respective position.  
Standard statistical analysis (mean and standard deviation (SD)) was used to determine the airflow pressure for each measurement point. Covariance (CoV) was calculated to analyse the variability of the recorded pressure over time and for each point. For comparisons between pressure curves, the full width at half maximum (FWHM) was used.
## 5 Case Studies: Validating the CILS Framework  
### Case Study One: Busuu  
#### 5.1.1 Detailed Description of Busuu  
Busuu is an interactive language learning system that integrates social networking features with personalized language instructions to form an innovative learning environment. Based on community-based learning, Busuu allows users to communicate with native speakers, stimulating real-life language use and cultural exchange during communication [35]. This ability is very important not only for the development of language skills but also for intercultural competence among learners.  
Busuu provides courses in twelve key languages using various multimedia content, including dialogues, written exercises, and speaking and pronunciation practice. The platform utilizes AI-driven adaptive learning technologies that evaluate a learner's progress and adjust the learning experience accordingly. This guarantees that each user's educational path is well rewarding and adjusted to the particular learning objectives and level of proficiency in the language [36].  
The platform's structure is aimed at multiple learning styles and preferences. It contains self-paced modules for people who want to engage at their own pace and structured lessons that capitalize on previous knowledge. All language courses are organized around thematic lessons, including all the necessary vocabulary and structural grammar, which are contextual with cultural and practical living examples [37].  
Busuu's social learning integration adds a dynamic aspect. Learners can submit speaking and writing exercises that will be reviewed by native speakers from the community. The peer-to-peer feedback system, which helps in correcting practice, actually also mirrors social interaction in the educational process, thus promoting language learning, similar to what happens in immersive cultural environments.
## 3 Adjustment Strategy  
Ideally, in the global coordinate system of the assembly (Figure 2), the incident and the reflected beams (red dash line) only propagate in the plane of the interferometer (the designated X-Y plane) and parallel to the bonding surface, and establishes the \"in-plane\" [17]. Hence, if we adjust the \"in-plane\" angle of propagation, we only need to adjust angle \(\varphi\). In accordance with the principle of kinematic constraint, the adjustment of the three degrees of freedom (x, y, \(\varphi\)) in the \"in-plane\" can be accomplished using three points (Figure 2). For high adjustment precision, a large-stroke hexapod (Model P84, Core Tomorrow, Harbin, China) is used to manipulate the position of the target. The displacement and angular precision of the piezoelectric actuators are \(\pm 7\) nm and \(\pm 0.5\)\(\upmu\)rad, respectively.  
Figure 1: Schematic representation of the layout of the interferometer used in demonstrations.  
However, the bottom plane of the optical components and the baseplate are not always ideal planes. In this situation (Figure 2), \"out-of-plane\" refers to the beam position evolution in the \(z\) and \(\theta\) degrees of freedom, which is mainly caused by the following three parts:  
(1) The surface shape of the baseplate exhibits a shape of an approximate sinusoidal curve [18] with a flatness of \(\lambda\)/4 (peak-to-valley, \(\sim\)160 nm, \(\lambda\) = 632.8 nm). For an optical element with 10 mm thickness, the maximum value \(\gamma\) of the angular error introduced thereby is about \(\pm\)5 \(\upmu\)rad.  
(2) The thickness of the glue layer introduced by the hydroxide-catalysis bonding process is about a hundred nanometers order. The non-uniform distribution of the adhesive will cause the unnecessary surface tilt \(\upalpha\). The value is about \(\pm\)10 \(\upmu\)rad.  
(3) For the limitation of the optical lens processing, the parallelism between two optical surfaces is about 10 \(\upmu\)rad.  
Therefore, the \(\theta\) are obtained using the manufacturing tolerances (\(\beta\) and \(\gamma\)) together with the uniform bond layer thickness (\(\upalpha\)), and this degree of freedom is uncontrolled in the assembly process. For those values, an additional \"out-of-plane\" error is actually present of about 5-30 \(\upmu\)rad.
# An Overview of Machine Learning and 5G for People with Disabilities
## 2 Machine Learning for People with Disabilities
### Hearing Impaired
#### Sound Recognition Systems  
UbiEar [28] is a _smartphone-based sound awareness system_ for the hearing impaired. The deaf person subscribes to one or multiple acoustic events (e.g., doorbell ring), described with a short introduction and a picture on the smartphone interface. A light-weight deep convolutional neural network enables location-independent acoustic event recognition. Event reminder includes, as options, a graphic view with the name of the acoustic event, followed by screen flickering, phone vibration, or flashing of the phone flashlight.  
The Deaf Assistance Digital System (DADS) [29] performs speech and _sound recognition_ as well; the speech engine can recognize critical cautionary words, e.g., to prevent car or fire accidents using the Soundex algorithm; the sound engine can recognize four sound categories (vehicle, baby, normal, and loud sounds) using machine learning. Three different sound engine classifiers were tested: K-Nearest Neighbor (KNN), Neural Network (NN), and Decision Tree (DT); a three-layer NN with 60 nodes achieved the best results. Furthermore, a smart hearing aid _detects and makes audible important noises (e.g., fire alarm, car horn)_[30]. The designed deep neural network can treat input noise differently according to its classification (important, or not important).
## 4 Results and Discussion
The computations show that for every 1% of round-trip efficiency improvement, the needed solar cell area decreases by approximately 70 m\({}^{2}\) (16.1 kW rated capacity) and the annually dissipated energy by 122 MWh. The needed storage capacity is also significantly decreased with better design and technological advances of the system that lead to improved round-trip efficiency, and Figure 6 shows this effect. It is observed that for every 1% efficiency improvement, the required energy storage capacity is reduced by approximately 28 m\({}^{3}\), which is equivalent to 29 MWh. The effect of the number of wind turbines used for the generation of electric power was also considered. Figure 7 depicts this effect on the required storage capacity and the PV cells area. It was observed that the needed area of PV cells drops by approximately 11,800 m\({}^{2}\) (2.71 MW rated capacity) with every additional turbine, indicating that wind turbines generate a great deal of power in the area. However, the needed energy storage increases by approximately 368 MWh for every additional wind turbine. This happens because solar energy is better correlated with the energy demand in the region, which is dominated by the summer usage of air-conditioning. Figure 4: Hourly variation of energy storage levels throughout the year, in MWh. Figure 5: Effect of storage component efficiency on area needed for solar arrays, in m\({}^{2}\), and energy dissipated in the system, in MWh. A powerful option in the design of this and similar microgrid systems is the reduction of the electric energy demand using energy conservation measures [8]. With relatively modest investment, improvements can be made in the cluster of buildings that would significantly reduce their energy demand [26]. For the case when two wind turbines are employed, Figure 8 depicts the effect of demand reduction and shows that energy conservation measures in the buildings has a salutary effect on both the required PV cell area and storage capacity; for every 1% of energy reduction in the buildings, the PV area decreases by approximately 530 m\({}^{2}\) (121.7 kW rated capacity) and the required storage capacity by 46 MWh. Figure 6: Effect of round-trip efficiency on the required storage capacity, in m\({}^{3}\) and MWh. Figure 7: Effect of the number of wind turbines employed in the system on the area needed for PV cells and required storage capacity.
## 4 Natural Antioxidants and Their Role in Cosmetics
### Peptides, Amino Acids, Enzymes
#### 4.5.3 Superoxide Dismutases  
A group of enzymes present in humans, including zinc-copper superoxide dismutase (SOD 1), manganese superoxide dismutase (SOD 2), and extracellular superoxide dismutase (SOD 3). They are activated as defense mechanisms during the interaction of free radicals. These mechanisms include the following: preventive--the first line of defense, which consists in preventing the reaction of ROS and their derivatives with biological substances; repair--the interruption of free radical and non-radical oxidation reactions, removing the products of the ROS reaction and their derivatives, by repairing or eliminating damaged molecules [145; 146].  
The reaction of superoxide anion radical dismutase to molecular oxygen and hydrogen peroxide is a two-stage process. The first stage is the reduction of the metal ion with the simultaneous release of an oxygen molecule. The second step is the oxidation of the metal ion by a superoxide anion radical and hydrogen to form hydrogen peroxide [146; 147].  
Superoxide dismutase (SOD) has remarkable properties that make it a valuable ingredient in skincare and dermatological treatments. SOD effectively neutralizes oxygen-free radicals. It reduces inflammation, helping alleviate redness and swelling in the skin. Whencombined with cell-penetrating peptides, SOD can penetrate deeper into the skin and provide significant protection against UVB-induced damage. Moreover, research has consistently demonstrated that SOD is non-toxic and safe for cosmetic use. By scavenging free radicals, SOD prevents oxidative damage, stabilizes collagen and elastin, and promotes their moderate crosslinking, which helps maintain skin elasticity and smoothness. It keeps skin soft and supple. It helps mitigate UV-induced damage, making it a valuable addition to sunscreens as well as a topical agent for reducing redness and swelling associated with conditions like rosacea or sunburn. SOD's dual role as a cosmetic and therapeutic agent underscores its versatility and significance in modern skincare formulations [148,149].  
The production of superoxide dismutase for cosmetic applications encompasses several methodologies, each aiming to achieve high yield, purity, and bioactivity. SOD can be isolated from various plant sources, including marine phytoplankton, horseradish, and canaloupe. However, this method often faces challenges such as low yield and variability in enzyme activity due to environmental factors affecting plant growth. Innovative approaches involve cultivating microalgae to produce extracts with SOD-like activity. These extracts can be utilized in cosmetic formulations to harness their antioxidant properties. Yeasts, particularly _Saccharomyces cerevisiae_, have been employed to produce SOD through fermentation processes. Advancements in biotechnology have enabled the production of recombinant SOD by inserting the SOD gene into host organisms such as _Escherichia coli_. This method allows for large-scale production with consistent quality and activity [150, 151, 152].
## 3 Optimal Service Provisioning Framework
By balancing delivery speed and throughput across mist, edge, and cloud layers, the framework effectively fulfills QoS requirements for both time-sensitive and bandwidth-intensive smart healthcare services.
## Appendix A The LoRaWAN Communication Solutions
### Experimental Validation of LoRaWAN solution
The numbers above each red balloon indicate the order which the locations were visited. The light blue area indicates the covering range which the packets sent by the LoRaWAN more reached the gateway. For each chosen location, the test procedure described above was repeated fifty times, recording the timestamps at both ends of the LoRaWAN link. In addition to its timestamp, each packet has its signal-to-noise ratio (SNR) and its received signal strength indicator (RSSI) recorded as provided by the LoRaWAN. By analyzing the records it is possible to determine the latency, SNR and RSSI according to device location. The results of the experimental validation are shown in Tables A1 and A2. The real operating distance is up to 250 m from the gateway when the campus scenario is considered. This distance is compatible with the usual LoRaWAN ranges when operating with SF7. The covered area is not symmetrical, a typical situation when real deployment scenarios are considered. The presence of tall buildings and complex terrain will reflect in the necessity to install more gateways to cover the entire campus area. The expected latency for the communication is shown in Table A2. The maximum latency is always under 400 ms with a very reduced jitter (defined as maximum minus minimum) of 20 ms. Such a latency is uniform across the campus. The low value of the latency is fully compatible with the Smart Campus application described in the use case, confirming the suitability of LoRaWAN for the proposed architecture.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 2 Rubber Toughening
### Factors Influencing Rubber Toughening
#### 2.2.3 Properties of Epoxy Resins  
The toughening effect is closely related to the inherent properties of the epoxy resin, with crosslink density being a particularly important factor. [PERSON] et al. [27] investigated the impact of crosslink density on the toughening of epoxy resins by using diglycidyl ether of bisphenol A (DGEBA) epoxy resins with varying monomer molecular weights to alter network density. Their study revealed that the fracture toughness of the pure resin is almost independent of network density. However, the toughness of rubber-toughened epoxy resins is highly dependent on the resin structure. Epoxy resins with lower crosslink densities exhibit greater ductility and consequently higher toughness.
# Analysis of Motion Characteristics of Bionic Morphing Wing Based on Sarrus Linkages
## 3 Results
### Folding and Unfolding Experimental Analysis of Morphing Wing
The experimental data were then analyzed to determine how long it took the morphing wing to complete a single action, that is, the time it took for a steering gear to turn through its entire range. Recording the first experiment, it can be seen from several key relative time points in the experimental data that the morphing wing took 0.437 s to complete the folding motion and 0.3355 s to complete the unfolding motion. Recording the second experiment, it can be seen that the morphing wing took 0.431 s to complete the folding motion and 0.3535 s to complete the unfolding motion. Through the two groups of experiments, it was found that the folding time of the wings is about 0.434 s, the unfolding time is about 0.3445 s, and the theoretical time of the folding or unfolding action is 0.33 s. The relative errors between the theoretical time (0.33 s) and experimental data of the folding and unfolding actions are 31.5% and 4.39%. This is because the morphing wing is affected by gravity, and the outer wing part sags slightly \begin{table}
\begin{tabular}{c c c c c} \hline
**Time/s** & **0** & **0.4378** & **0.6915** & **1.027** \\ \hline Folding angle/\({}^{\circ}\) & \(-1.1755\) & 86.1909 & 86.2036 & \(-1.1646\) \\ \hline \end{tabular}
\end{table} 
Table 5: Folding angle of the morphing wing in the first experiment. \begin{table}
\begin{tabular}{c c c c c} \hline
**Time/s** & **0** & **0.4378** & **0.6915** & **1.027** \\ \hline Folding angle/\({}^{\circ}\) & \(-1.1755\) & 86.1909 & 86.2036 & \(-1.1646\) \\ \hline \end{tabular}
\end{table} 
Table 6: Folding angle of the morphing wing in the second experiment. compared with the inner wing part during the experiment. This is also why the time of the folding action is about 0.09 s more than that of unfolding. In addition, friction and other factors are ignored in the simulation process, so the experimental time will be more than the theoretical time, and the time error is within the allowable range of the actual situation. Therefore, the validity of the simulation calculation and the rationality of the scheme design are verified to some extent by the unfolding and folding experiment of the morphing wing.
_Article_  
A Novel Adaptive Mode Decomposition Method  
Based on Reassignment Vector and Its Application to Fault Diagnosis of Rolling Bearing  
[PERSON] 1,2,*, [PERSON] 1,2, [PERSON] 3 and [PERSON] 4  
Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China; [EMAIL_ADDRESS]  
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China  
Lishui Special Equipment Testing Institute, Lishui 323000, China; [PERSON]RESS]  
Taizhou Special Equipment Inspection and Testing Institute, Taizhou 318000, China; [PERSON]DRESS]  
Correspondence: [EMAIL_ADDRESS]; Tel.: +86-027-6886-2857; Fax: +86-027-6886-2212  
Received: 21 June 2020; Accepted: 5 August 2020; Published: 7 August 2020  
To solve the problem that the random distribution of noise in the time-frequency (TF) plane largely affects the readability of TF representations, a novel signal adaptive decomposition algorithm processed in TF domain, which provides adequate information about the time-varying instantaneous frequency, is presented in this paper. The theoretical basis of this algorithm is short-time Fourier transform (STFT). The research into the algorithm comprises two steps: the TF plane denoising takes sparse low-rank matrix estimation as a priority and then achieves signal decomposition based on reassignment vector (RV). A low-rank matrix approximation scheme, which exploits the sparse properties of the TF transformation coefficient and uses non-convex penalty, is put forward to obtain clean STFT. Then, a new approach called RV, which is different from the traditional mode decomposition methods such as Empirical Mode Decomposition (EMD), is used to estimate the characteristic curve corresponding to the TF ridges of the interested modes. Based on the classical reassignment method, RV has a solid theory foundation. Moreover, it can identify different signal components such as stationary signal, modulating signal and impulse characteristic. Combining the advantages of low-rank matrix approximation approach and those of RV defined in TF plane, a novel signal adaptive decomposition method is proposed in this paper to identify fault characteristics. To illustrate the effectiveness of the method, fault signals of rolling bearing under stationary condition and time-varying speed are respectively analyzed.  
Keywords:signal decomposition; reassignment vector; low-rank matrix approximation; time-frequency analysis; fault diagnosis +
Footnote †: journal: Applied Mechanics and Physics Letters