esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential of Sub-GHz Wireless for Future IoT Wearables and Design of Compact 915 MHz Antenna
## 2 Comparison of Wireless Performance in the 915 MHz and 2.45 GHz ISM Bands  
In this section, an analysis of potential wireless link performance for a wrist-worn wireless SpO2 sensor device is investigated by considering two frequency bands, namely the 915 MHz and 2.45 GHz ISM Bands. In order to compare potential performance in both the frequency bands under examination, several factors need to be investigated. These factors can be divided into two main groups: wireless link characteristics and DC power consumption. The wireless link characteristics comprise free-space path loss (FSPL), and antenna gain constraints due to device form factor. DC power consumption is related to the wireless protocol and to the RF Integrated Circuit transceivers that implements the specific protocol as well as the wireless channel coexistence with other systems using different wireless standards [24].
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 2 Conventional Interpretation Techniques for Bender Element Tests
### Frequency Domain Methods  
In order to avoid the uncertainties induced by direct measurements of the first arrival time using time domain methods, frequency domain approaches have been suggested [9; 10; 23; 26]. Three major methods are commonly applied, namely the discrete \(\pi\)-point method, the cross-spectrum method, and the continuous method. It is noted that frequency domain methods are also based on the assumption that input and output signals have similar shapes. As the only frequency domain method applicable to pulse (compact) input signals is the cross-spectrum method, this method is used to validate GeoHyTE's readings in Section 4.2 and described next.  
In the cross-spectrum method, fast Fourier transforms are applied to the input and output signals to obtain the phase angle between them. The travel time is calculated from the slope of the frequency-unwrapped phase plot \(\phi(f)\)[26]:  
\[t=\frac{1}{2\pi}\times\frac{d\phi(f)}{df} \tag{2}\]  
Several studies have reported that frequency domain methods tend to yield lower shear wave velocities compared to the time domain methods [12; 23]. The causes for this behaviour are still not fully understood.
# Equinoctial Asymmetry in Solar Quiet Fields along the 120\({}^{\circ}\) E Meridian Chain
## 2 Data and Calculations
Next, daily ranges of the monthly average of the SqH over 1957-2013 are calculated as the difference of the extremes: \(\rm A=H_{max}-H_{min}\), plotted with black curves in Figure 4. They show significant month-to-month variation and a noticeable 11-year variation corresponding to the solar cycle, as shown by the 10.7 cm solar radio flux (F107 index) (Figure 4, top). It is similar to the variation of the intensity and focus of the Sq current and the correlation with solar activity [1; 36; 37; 38; 39]. Figure 3: Diurnal variations of monthly (**left panel**), yearly (**right panel**) averaged SqH and associated standard deviations d (in red short lines) at each observatory, sorted by latitude from north (**top**) to south (**bottom**) of the Sq current focus. Then, the annual value should be calculated to remove the contribution of the solar cycle on A. As is well known, the method of moving average is a common low-pass filter technique. The effect of the moving window size could not be neglected [11; 40; 41; 42]. From their analysis, for a signal with period T, using 2T as the moving window was more effective in eliminating the periodic T signal than a 1T moving window. Therefore, we performed the analysis with both a 12-month and 24-month moving window, and we confirm that there is no essential difference between the results of these two windows. Thus, in this study, the 12-month moving window was chosen and derives the 12-month smoothed SqH range, noted with A\({}_{\rm annual}\) and shown by red curves in Figure 4, in good agreement with the results of the 12-month moving window. Figure 4: (**Top**): Yearly averaged F107 index (1957–2013). (**Bottom**): Month-to-month variations at each observatory of the SqH daily range (A, black curves); 12-month smoothing of the SqH daily range (A\({}_{\rm annual}\), red curves) and their variability (dA, green curves). with the F107 index. The month-to-month range variability is finally derived as a residual: \(\rm{dA}=A-A_{\rm{annual}}\), and it is plotted in Figure 4 with green curves, which show successful removal of the solar activity contribution.
# Evolutionary Game for Content Cache in a mm-Wave-Based Vehicular Fog
## 3 mm-Wave-Based Vehicular Communication Model
### Directional Antenna and Coverage
We denote antenna gain of bore sight (i.e., main-lobe) and others (i.e., side-lobe) as \(G\) and \(g\), respectively; main-lobe gain is \(G=\gamma D\) and side-lobe gain is \(g=(1-\gamma)D\) when the front-to-back lobe ratio (FBR) is \(\gamma\) (\(0<\gamma\leq 1,1\) for omni-direction). As shown in Figure 2, Tx and Rx antennae of vehicles can steer toward different sectors and beam-forming gain can be varying according to the directivity of those antennae. In Equation (4), connectivity range can be determined by the directivity gain of the Tx and Rx antennae as illustrated in Figure 3. When both antennae face toward each other, coverage is \(d_{3}\) with gain \(G_{\mathrm{f}}G_{\mathrm{r}}\). Directivity gain from only one antenna leads to \(G_{\mathrm{f}}g_{\mathrm{r}}\) or \(g_{\mathrm{f}}G_{\mathrm{r}}\) and coverage \(d_{2}\). Otherwise, the gain can be only \(g_{\mathrm{f}}g_{\mathrm{r}}\) and its coverage is \(d_{1}\).
# A Lightweight Graph Neural Network Algorithm for Action Recognition Based on Self-Distillation
## 4 Experiments and Discussion
### Denser Representations
#### 4.3.3 Each Block's Feature Representation  
**The deeper blocks generate denser clusters.** When the blocks are deeper, the cluster densities (Figure 9) improve. This is illustrated by a smaller Davies-Bouldin index and larger Dunn Index and silhouette index. The features of eight actions from different blocks are also projected to visualize the density differences. In Figure 10, as the depth of the block deepens, the denser each cluster is, and the farther the cluster are from each other. This is reasonable considering the back propagation of training. The gradient flows are back propagated from the deeper blocks to the shallower blocks. The blocks that are close to the output layer are more nourished with informed gradients and therefore generated representations more similar to the true clusters. Furthermore, the shallower blocks first learn the coarse features, and gradually the fine features are learned by the deeper blocks.  
**Increasing the capacity of the model encourages the model to focus more on the detailed difference of each action.** In the eight selected actions, staggering and falling are anomalies that vary from other actions, if taking the moving speed and moving amplitude into consideration. ST-GCN 20 and ST-GCN 12 can both classify each action, while ST-GCN 8 can divide staggering and falling from the other six actions, but fails to further divide the other actions clearly. As demonstrated in Figure 11, although the clusters of actions 0, 10, 16, 27, 31 and 40 are mixed together, actions 41 and 42 (falling and staggering) are already separated from the others.  
When increasing the model capacity, the eight actions are classified more clearly. More layers contribute to more capability for the model to have enough parameters to record the difference of actions that differ slightly.  
Figure 10: The features from (**a**) the 1 st block; (**b**) the 2 nd block; (**c**) the 3 rd block and (**d**) the overall output are projected by the 20-nearest-neighbor-graph. The features are trained by ST-GCN 20 with \(pos\) as input data, performed on NTU xsub. The overall output includes the features from the output of the whole backbone. From (**a**,**b**), the classes \(0,16\) and \(27\) (marked as black, red and yellow) are more separated. From (**b**,**c**), the classes 0 and 27 are separated more clearly. From (**c**,**d**), the distance between actions 10 and 31 becomes larger.
# Blind Deconvolution with Scale Ambiguity
## 3 The Proposed Approach
### Optimization
#### 3.2.2 Final Deblurring  
With the blur kernel \(k\) being determined, a variety of non-blind deconvolution methods can be used to estimate the latent image. We simply employ a hyper-Laplacian prior proposed by [PERSON] et al. [42] to recover the latent image. The formulation of this non-blind deconvolution method is  
\[\min_{u}\|u*k-f\|_{2}^{2}+\zeta\|\
abla u\|_{0.8}. \tag{10}\]  
Figure 2 illustrates the whole pipeline of our framework. Algorithm 1 presents the main steps for the whole deblurring process. Figure 3 shows the diagram of the proposed algorithm.  
Figure 2: The pipeline of the proposed algorithm. In this algorithm, \(u\) and \(k\) are solved iteratively for each image scale in a coarse-to-fine pyramid framework.  
```
0: Blurred image \(f\).  Initialize the intermediate image \(u\) and kernel \(k\) with the results from the coarser level. for\(l=1\to K_{pyramid}\)do  Estimate \(u\) according to (5);  Estimate \(k\) according to (8). endfor  Estimate the final latent image by solving (10). Output: Latent image \(u\) and blur kernel \(k\).
```  
**Algorithm 1** Proposed blind deblurring algorithm
###### Abstract
The pillars included (1) monitoring of information, (2) knowledge refinement and quality improvement processes (e.g., fact-checking), (3) the presentation of timely and accurate knowledge that minimizes or eliminates the influence of commercial and political influence, and (4) advocating for facts and science, which often have been overtaken by social media advertisements presenting \"inappropriate content\". Another effort that addresses the trustworthiness of online knowledge and information sources introduced a COVID-19 infodemic crowdsourcing framework [6]. The effort resulted in recommendations that also overlapped with the four pillars presented in [16] (e.g., knowledge refinement for fact-checking). The recommendations stated the importance of using computational methods such as artificial intelligence (AI) and machine learning (ML) to produce insights that enable decision making to manage the infodemic. From the wide spectrum of issues associated with the COVID-19 infodemic and assessments [17], and recommendations made by the scholars in the field, we believe that computational scientists have a significant role to play in the fight against this infodemic. Particularly, the use of artificial intelligence, natural language processing, and machine learning must demonstrate their full potential in this fight. As demonstrated by the DailyMail news article, disinformation thrives in major news networks. The impact of such articles is clearly magnified when it is also socialized on social media platforms such as (Twitter and Facebook). It is imperative to address misinformation and disinformation at the source (i.e., the news article) before it is socialized on social media and becomes viral. A step that is dire at this point is the development of a mechanism that analyzes the content of an article to assess how viable the content of a news article is from a linguistic point of view. We argue that each news article must pass a step of label-prediction; otherwise, it must be flagged as potentially untrustworthy. This has to be accomplished by measuring the quality of the linguistic aspects of the article. Noun-phrases, for example, are essential for making up the main facts of each article. Therefore, any computational mechanism must utilize the noun-phrases to decide if an article should pass the label-prediction process. The final outcome generates a COVID-19 safe/disputed label accordingly. In the past few months, Twitter started flagging socialized contents of political dispute \"this claim is disputed\". Twitter has also taken more advanced measures and applied filters to remove vaccine misinformation from the platform [18]. In the ideal scenario, we envision that our mechanism will theoretically be adopted by all major social media platforms and flag socialized news articles as safe or disputed COVID-19 articles.
# Revising the Observation Satellite Scheduling Problem Based on Deep Reinforcement Learning
## 3 Solving Method  
### Task Preprocess: Graph Clustering  
In Section 2.1, we proposed an undirected graph clustering model \(G=\langle V,E\rangle\). According to a previous analysis, clustering tasks can be selected by dividing the graph into independent cliques, aiming to minimize the number of clusters, which is known as the minimum clique partition algorithm [42]. [PERSON] et al. [38; 43] improved the clique partition algorithm by considering the priorities of vertices (original tasks) and adopted it in the task clustering phase. In this paper, an improved minimum clique partition algorithm is proposed. The maximum task priority and the minimum observation slewing angle of clustering tasks are taken into consideration simultaneously. This improvement could save energy to maintain a smaller observation slewing angle, which is significant in real engineering applications.
# Bioavailability of Liposomal Vitamin C in Powder Form: A Randomized, Double-Blind, Cross-Over Trial
###### Abstract
Undoubtedly, the growing demand for supplements containing vitamin C translates into their market supply. The development of an effective ascorbic acid-containing formula requires that the vulnerability of this active ingredient to degradation during the manufacturing and storage process be taken into account [2; 10]. Ascorbic acid is highly reactive and unstable. When exposed to light, elevated temperature, air, oxygen, and alkaline environments [2; 21; 22], it undergoes reversible oxidation to dehydroloxacin acid (DHA), which then irreversibly hydrolys to 2,3-diketo-L-gulonic acid [1; 4; 21]. Vitamin C decomposition reactions accelerate with increasing water activity (a\({}_{w}\)) [23] and in the presence of transition metal ions, particularly iron (Fe\({}^{3+}\)) [24; 25] and copper (Cu\({}^{2+}\)) [25]. The breakdown of vitamin C significantly reduces its effectiveness in the human body. Accordingly, in recent decades, researchers' interest has focused on the possibility of protecting ascorbic acid from the destabilizing effects of environmental conditions by encapsulating it in a variety of micro- and nanocarriers [2; 21; 26]. The results of available scientific studies indicate numerous attempts to encapsulate vitamin C in liposomes [27; 28; 29; 30; 31; 32; 33; 34]. These lipid carriers are composed of biocompatible, biodegradable, and non-immunogenic components [35; 36; 37]. They also exhibit, among other things, the ability to protect sensitive active substances from degradation and increase their bioavailability [35; 38; 39]. Unfortunately, the widespread use of most traditional liposomal technologies in the industrial production of dietary supplements, nutraceuticals, and drugs is limited by the instability of aqueous liposomal dispersions [36; 40; 41; 42], their high polydispersibility, and the significant amounts of organic solvents (ethanol, methanol, chloroform, ether, etc.) used to dissolve lipids that remain in the final product [42; 43]. These solvents can contribute to the degradation of the encapsulated active ingredients and destabilization of the carriers themselves. They also pose a real threat to human health and the environment [42]. With this in mind, liposomal vitamin C in powder form was obtained in a procedure devoid of organic solvents. The aim of the study presented here was the evaluation of the properties of the resulting product taking into account the parameters that could significantly affect its effectiveness in vivo, as well as determination of its bioavailability compared to its non-liposomal counterpart in an in vitro study (human colon adenocarcinoma cell line Caco-2) and a randomized, double-blind, single-dose, cross-over clinical trial involving healthy volunteers.
## 3 Wireless Body Area Network (WBAN)
### Available Medical Sensors  
Healthcare 4.0 requires integrating new and emerging technologies, including IoMT and WBANs, into healthcare systems. It is an era of big data systems comprising massive data from various sources. These data types are summarized as follows [29; 30]. 1. Patient data: This includes personal information of the patient, their medical history, vital signs, and any other relevant information related to their health. 2. Clinical data: This includes data from medical devices, vital signs monitoring systems, and other clinical instruments used in patient care. 3. Electronic health records (EHRs): EHRs store digital versions of a patient's medical record, which includes information about their medical history, laboratory test results, and any other information related to their health. 4. Wearable data: Wearable devices, such as smartwatches, fitness trackers, and health monitors, provide data on a patient's activity levels, sleep patterns, and overall health. 5. Social determinants of health: This includes factors such as a patient's socioeconomic status, living conditions, and lifestyle choices that can impact their health. These data are generated from various sources. This section introduces the main available wireless medical sensors deployed for WBANs, IoMT, and Healthcare 4.0. A plurality of available medical sensors is used to monitor patients' vitals in real-time and measure a wide range of biological variables [31]. These sensors can be categorized as follows [31; 32; 33]. 1. Wearable health trackers include activity, heart rate, and blood pressure monitors. Table 2 presents the features of the common market available wearable watches. 2. Connected health sensors and monitors include blood glucose meters, pulse oximeters, and electrocardiography (ECG). These sensors enable real-time monitoring of medical conditions, such as cardiac disease, diabetes, and respiratory disease. 3. Smart pill sensors monitor medication adherence and provide feedback to healthcare providers. 4. Telehealth and virtual care devices that enable remote consultations, diagnosis, and treatment of patients. 5. Endoscopic pills are small, swallowable devices used in medical diagnosing.
## 2 Materials and Methods
### Calibration of RHT Model Parameters
#### 2.2.2 \(p-\alpha\) Compaction Equation of State  
In the \(p-\alpha\) equation of state, the shear and pressure components are coupled. Pressure is expressed using the Mie-Gruneisen form, incorporating a polynomial Hugoniot curve and an A-B compaction relationship [24].  
\[P_{R}=\frac{1}{\alpha_{0}}\Big{(}\big{(}B_{0}+B_{1}\mu\big{)}\alpha_{0}\rho_{ 0}e+A_{1}\mu+A_{2}\mu^{2}+A_{3}\mu^{3}\Big{)} \tag{1}\]  
where \(P_{R}\) is the EOS pressure in the RHT model; \(\alpha_{0}\) represents the initial porosity parameter of the ore rock; A\({}_{1}\), A\({}_{2}\), and A\({}_{3}\) are the polynomial coefficients; B\({}_{0}\) and B\({}_{1}\) are material constants; \(\mu\) is volumetric strain; and \(\rho_{0}\) and \(e\) are the initial density and the internal energy per unit mass of the rock, respectively. When T\({}_{2}\) = 0, B\({}_{0}\), B\({}_{1}\), A\({}_{1}\), A\({}_{2}\), and A\({}_{3}\) are derived from the Rankine-Hugoniot and Mie-Gruneisen equation of state [25].  
\[B_{0}=B_{1}=2s-1, \tag{2}\]  
\[A_{3}=\rho_{0}v_{0}^{2}, \tag{3}\]  
\[A_{3}=\rho_{0}v_{0}^{2}(2s-1), \tag{4}\]  
\[A_{3}=\rho_{0}v_{0}^{2}\Big{(}3s^{2}-4s+1\Big{)}, \tag{5}\]  
where s is an empirical constant specific to the rock. [PERSON], in his monograph [26], provides values for the material parameter s. For the magmatic intrusion mine, the value of s = 1.14.
## 3 Methodology and Implementation
### Average of Clustering Coefficients per Degree and Fit  
Inspired by [54], we study the linking possibility between neighbouring nodes by calculating the clustering coefficient of every node in these four networks. We plot the average of the clustering coefficients per degree. After that, we assess their fit with a power law function with the _curve_fit_ Python module.
## 3 Results and Discussion
### High Temperature Properties  
#### 3.2.1 Hot Compression Test  
Figure 5 shows the stress-strain curve according to the high-temperature compression test. As shown in Figure 5, the strain rate hardening effect and the flow softening curve, which increase in strength according to increases in the strain rate, and the strength tend to decrease as the temperature increases. The high-temperature compression strength represented the lowest value of 14 MPa at 1100 \({}^{\circ}\)C and \(1\times 10^{-2}\)/s, and the highest strength value of 232 MPa at 800 \({}^{\circ}\)C and \(1\times 10^{0}\)/s. In addition, rapid stress reduction and vibration are observed under low temperature conditions in the early high-temperature compression stage. It is known to occur when the plastic deformation of materials is unstable or locally concentrated [29]. The change in microstructures that occurs during a high-temperature forming process directly affects the behavior of high-temperature deformation, and optimal forming conditions can be derived by presenting a process capable of controlling the final microstructure.
## 2 Materials and Methods
### Necessary and Sufficient Conditions of Bale Dimensions Equilibrium and Near-Equilibrium  
#### 2.3.1 Near-Equal Optimal Dimensions  
By Definition 2, the optimal bale dimensions are such that Equation (4) holds and the optimality condition expressed by Equation (1) is satisfied. For a given bale volume \(V_{b0}\) Equations (2) and (4) yield  
\[D_{b}^{*}=\sqrt[3]{\frac{4(1+\varepsilon_{0})V_{b0}}{\pi}} \tag{5}\]  
and  
\[H_{b}^{*}=\sqrt[3]{\frac{4V_{b0}}{\pi(1+\varepsilon_{0})^{2}}}. \tag{6}\]  
The necessary and sufficient conditions of near-equal optimal dimensions are given by the following proposition, which is proved in Appendix B.  
**Proposition 1**.: _For given: bale volume \(V_{b0}\), width \(b_{f}\) of the film and its mechanical parameters \(v_{f}\), \(\varepsilon_{lf}\), numbers of film layers \(p_{b}\), \(p_{u}\) and the overlaps \(\delta\), \(k_{fb}\), \(k_{f}\), such that the applicability condition expressed by Equation (A4) holds, the optimal bale diameter \(D_{b}^{*}\) is near-equal to its optimal height \(H_{b}^{*}\) with order \(\varepsilon_{0}\) in the sense of Definition 2 if and only if one (and only one) of the conditions is satisfied:_  
Figure 1: Schematic framework for the tasks of designing optimal equal and near-equal bale dimensions.  
_order_ \(\varepsilon_{0}\) _and the bale, film, and wrapping parameters are related by the equation_  
\[\frac{p_{b}\Omega\Big{(}k_{f}\Big{)}\left(2\delta-b_{fr}k_{fb}\right)\big{(} \sqrt[3]{1+\varepsilon_{0}}\big{)}^{2}}{p_{b}\Omega\Big{(}k_{f}\Big{)}-p_{u} \Big{(}1-k_{fb}\Big{)}-2p_{u}\Big{(}1-k_{fb}\Big{)}\varepsilon_{0}}=\sqrt[3]{ \frac{4V_{b0}}{\pi}},\] (7) _the equations_ \[p_{b}\Omega\Big{(}k_{f}\Big{)}=p_{u}\Big{(}1-k_{fb}\Big{)}+2p_{u}\Big{(}1-k_{ fb}\Big{)}\varepsilon_{0}\] (8) _and_ \[2\delta-b_{fr}k_{fb}=0\] (9) _are satisfied, simultaneously. The optimal bale diameter_ \(D_{b}^{*}\) _and height_ \(H_{b}^{*}\) _are described by Equations (_5_) and (_6_), respectively; in case (i) for a given bale volume, in case (ii) for an arbitrary volume_ \(V_{b0}\)_._  
The necessary and sufficient conditions for near-equal optimal bale dimensions, case (ii), are independent of \(V_{b0}\); they are volume-free. However, those from case (i) hold for given \(V_{b0}\), i.e., they are volume-fixed.  
Note, that the inequality  
\[\frac{2\delta-b_{fr}k_{fb}}{p_{b}\Omega\Big{(}k_{f}\Big{)}-p_{u}\Big{(}1-k_{ fb}\Big{)}-2p_{u}\Big{(}1-k_{fb}\Big{)}\varepsilon_{0}}>0 \tag{10}\]  
must hold to satisfy Equation (7); however, the expression \(\Big{(}2\delta-b_{fr}k_{fb}\Big{)}\) may be positive or negative, depending on the sign of the denominator. The necessary condition expressed by Equation (10) is independent on \(V_{b0}\), while Equation (7) depends on the particular value of bale volume \(V_{b0}\). Both conditions depend on \(\varepsilon_{0}\).