esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Hybrid PETG Composites for 3D Printing
## 3 Results
### Testing of Prepared Samples by 3D Printing  
Prepared composite materials were tested with a commercial 3D printer, which uses FFF technology. Materials for testing were prepared as a filament (Figure 4A) and 3D printed spare parts are presented in Figure 4B.  
All prepared composite filaments were suitable for 3D printing using the FFF method. The produced filaments had excellent processing properties for 3D printing, except for the AG-10 filaments, which were very brittle and broke during printing. This break was due to overall worse mechanical properties and poor dispersion of filler compared to other EG/PETG composites.  
It is necessary to mention the positive effect of partially replacing non-environment friendly CFs for EG--a form of naturally occurring graphite. EG significantly eliminates surface roughness and therefore eliminates a nozzle abrasion, which is a very common problem during 3D printing filaments with CF composites. The most positive effect is reducing the final price of filament when commercial virgin PETG was replaced for cheaper recycled PETG. The final result is a cheaper filament with enhanced mechanical properties which is more suitable from the point of view of ecology.  
The new hybrid material is very suitable for 3D printing, ranging from small parts (Figure 4B) to larger parts (e.g., large printer pad in Figure 4C) to massive equipment with a total length of over 6 m. The prints are extremely strong, the layers adhere firmly, and the overall strength of the products is extraordinary. The individual layers adhere perfectly. It enables large product printing as well as several-day continuous 3D printing compared to competing materials. The resulting products look more beautiful, which improves subsequent work with products and saves time. As can be seen in Figure 4C, where the products printed under the same conditions are compared, the differences between commercial CF PETG filament (Figure 4C--left) and our hybrid filament from recycled PETG (Figure 4C--right) are clear. Last but not least, the final products are also lighter by about 25%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Contact Angle (\({}^{\circ}\))** & **Density (g\(\cdot\)cm\({}^{-3}\))** \\ \hline
**B** & \(83.3\pm 2.6\) & 1.32 \\
**BG-5** & \(87.8\pm 5.1\) & 1.33 \\
**BF-5** & \(87.5\pm 5.6\) & 1.29 \\
**BGF-5:5** & \(86.8\pm 3.6\) & 1.25 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Contact angles and densities of prepared composite materials base on recycled PETG.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **\(\mathbf{T_{10}}\) (\({}^{\circ}\)C)** & **\(\mathbf{T_{50}}\) (\({}^{\circ}\)C)** & **\(\mathbf{m_{550}}\) (\%)** & **\(\mathbf{\alpha}\) (10\({}^{-6}\) K\({}^{-1}\))** \\ \hline
**B** & 401.9 & 426.5 & 9.2 & 73.71 \\
**BG-5** & 402.1 & 429.5 & 10.1 & 61.24 \\
**BF-5** & 398.4 & 426.7 & 9.6 & 48.41 \\
**BGF-5:5** & 404.9 & 432.2 & 17.4 & 46.04 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Results of thermogravimetric analysis of the prepared composite materials based on recycled PETG.
# Structural Modifications in Epitaxial Graphene on SiC Following 10 keV Nitrogen Ion Implantation
## 1 Introduction
Defect differences appeared at 200 keV N and Ar ion irradiation of epitaxial graphene, along with a decrease in corrugation [16]. Previously we showed that ion beam implantation (30 keV) and ion beam irradiation (100 MeV) using silver ions acts as a tool to increase sensing capacity of EG on SiC [17; 18]. Earlier studies explored defect generation in graphene and SiC independently [19; 20; 21; 22]. Only some studies are about defect generation in both graphene and SiC. The substrate plays a crucial role in defect generation in EG on SiC, which shows the importance to study defect generation in both EG and SiC as defect generation in both graphene and SiC enhances their applicability in various interdisciplinary areas [6]. This study presents the effects of ion implantation using N ions on structural properties of EG and SiC. The EG/SiC was irradiated with 10 keV N\({}^{+}\) ions at \(1\times 10^{12}\) ions/cm\({}^{2}\) to \(1\times 10^{14}\) ions/cm\({}^{2}\) fluences. This range of energy of incident ions create defects both in graphene and SiC while other energy ranges (eV and MeV) influence either graphene or the SiC substrate. Further, doping of graphene with N is advantageous as it results in n-type doping of graphene due to the reason that C and N have comparable atomic radii and chemical bond lengths, which causes substitution of C atom by N atom [23]. Theory revealed that different N dopant configurations result in affecting the local density of states and local charge distribution, and thus changes of the electronic and sensing properties of graphene [24]. The N doping is a way to have n-type graphene material, and is an excellent platform for Li batteries, fuel cells, supercapacitors. This plays a critical role for developing graphene-based nanoelectronics and optronics. Although chemical methods also produce n-type graphene, but they have many disadvantages comparatively to ion implantation like lack of precise control on the concentration of the dopant, contamination and inferred secondary impurities and site selectivity. The ion implantation has revolutionized the semiconductor technology to have a significant economic and societal impact. Integration of ion implantation with EG/SiC will be a further and major step-up into technological progress. Ion implantation of graphene with trivalent and pentavalent impurities presents huge prospect for industrial scale, non-chemistry reliant functionalization and processing of 2D materials.
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 1 Introduction
Over the past few decades, the FEM has emerged as a powerful computational tool for modeling and simulating fatigue crack growth behavior [22; 23; 24; 25; 26; 27; 28; 29; 30]. Compared to traditional analytical approaches based on fracture mechanics, FEM offers advantages by capturing complex geometries, loading conditions, and material behaviors that influence fatigue crack propagation [30; 31]. Additionally, FEM allows analysis of crack growth under multiaxial, variable amplitude, and other realistic service loading scenarios that are challenging to address using closed-form solutions [32; 33]. FEM-based fatigue crack propagation simulation aims to achieve accurate predictions of both the fatigue fracture mode and the lifespan of the relevant material or component. It takes into account microstructural effects under various service loading and environmental conditions, striving for the most realistic outcomes possible. This improves safety and reliability, optimizes designs (weight, cost, and strength), guides material selection and maintenance, and reduces the need for costly physical testing. The objective of this review article is to present a comprehensive overview of the recent advances in finite element modeling of fatigue crack propagation. It aims to summarize state-of-the-art techniques, methodologies, and developments in this field, highlighting their strengths and limitations. This review aims to assist readers in evaluating which finite element methods are most effective for simulating fatigue crack growth. By providing insights into quicker and more straightforward approaches, it addresses the challenges that need to be navigated and highlights unnecessary paths that can be avoided. This guidance will facilitate more efficient decision-making in the selection of methods. The article will discuss various aspects of FEM, including the Extended Finite Element Method, Cohesive Zone Modeling, Virtual Crack Closure Technique, Adaptive Mesh Refinement, Dual Boundary Element Method, Phase Field Modeling, Multi-Scale Modeling, Probabilistic Approaches, and Moving Mesh Techniques. Additionally, it will explore the challenges faced in FEM, such as computational complexity, material characterization, meshing issues, and model validation. Furthermore, the article will provide insights into the application areas where FEM has been successfully employed, including the aerospace, automotive, civil engineering, and biomechanical sectors.
## 4 Discussion  
### Width of Permissible Range with Respect to the Amount of Stimulation  
The permissible range of the difference in visual and tactile sensation increases as the width of the ridge/groove of the actual texture becomes wider. However, when we normalized the data by dividing \"the width of the permissible range\" (the difference between upper and lower thresholds) by \"the ridge/groove width of the real texture\", the result is as shown in Figure 7 (this value is referred to as the \"normalized threshold\"). We observe that this normalized threshold tends to decrease monotonically as the ridge/groove width increases in the range 0.6-2.2 mm. In other words, the allowable discrepancy between tactile and visual texture is relatively large at small textures and decreases as the texture becomes coarse. We must note that if the result of this experiment follows [PERSON]'s law, the normalized threshold should be constant regardless of the amount of tactile  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Tactile Texture (mm)** & **Visual Condition (px)** & **75\% Threshold (mm)** & **Permissible Range (mm)** & **S.D.** \\ \hline
0.2 & 38 (1.19 mm) & 0.473 & – & 0.114 \\
0.6 & 2 (0.0626 mm) & 0.404 & 0.429 & 0.184 \\
0.6 & 51 (1.60 mm) & 0.834 & 0.4  
stimulation. In addition, it also seems that from 2.2 mm, normalized threshold might become constant, which requires further investigation.  
[PERSON] et al. [10] reported that tactile perception is better than visual perception for textures with a spatial period of less than 0.1 mm, and visual perception is better than tactile perception for textures with a spatial period of 1 mm. Therefore, our experimental setup covers the range over which visual sensation gradually becomes more dominant than tactile sensation, which may explain our finding that the normalized allowable discrepancy between tactile and visual perceptions gradually decreases. In future work, we will check whether the same tendency is observed at textures with intervals larger than 3.0 mm. We will also use textures other than one-dimensional textures.
## 4 Results and Discussion  
### Scattering Coefficient of Scattering Medium Sample Changing by Stirring Rate  
The scattering coefficients of each sample shown in Section 2 were measured. Figure 7a and 7b respectively shows the graphs of the stirring speed on the horizontal axis and the scattering coefficient on the vertical axis, for the particle component titanium  
Figure 6: Flow of the sensory test protocol.  
dioxide and zinc oxide. The results were obtained using an optical property measurement system developed by the authors [19; 20], and data are given at \(\lambda=550\) nm, the wavelength with the highest visual sensitivity [25] for human eyes, shown as a representative example.  
These results showed that the scattering coefficient increased for both particle samples as the stirrer speed was increased. This was consistent with the results of experiments conducted in our previous study [14]. Particles in a sample have a structure that consists of not only the original particle size of the particle material (primary particle size), but also agglomerated particles of a different size (secondary particle size). Increasing the stirring speed has the effect of making the agglomerated secondary particles finer, leading to an increase in the probability density of the primary particle size and an increase in the scattering coefficient of the sample. Therefore, Figure 7 revealed that the same effect occurs even if the particles are made of different materials and have different values of scattering coefficients.  
The scattering coefficients for all samples are then summarized in Figure 8. The horizontal axis is the sample number listed in Tables 1 and 3. Sample numbers 11 and 12, which differ greatly in composition, are not shown in Figure 7 and these samples have significantly higher scattering coefficients compared to the other samples used. The scattering coefficients of the samples used in this experiment fall into three groups, with zinc oxide having very small scattering coefficients (Nos. 6-10), Nos. 11 and 12 with large scattering coefficients of different compositions, and Nos. 1-5 in the intermediate band of the three groups. The scattering coefficients of Nos. 1-10 varied depending on the stirring speed. The following sensory test was conducted using 12 samples with these scattering coefficients.  
Figure 8: Measurement results for all samples used in the sensory test.  
Figure 7: Scattering coefficients at different stirring speeds: (**a**) titanium dioxide sample (Nos. 1–5) and (**b**) zinc oxide sample (Nos. 6–10).
# Research on Vehicle Trajectory Prediction and Warning Based on Mixed Neural Networks
## 5 Conclusions  
The motivation of this research is to improve the safety of drivers. Car detection, lane line detection, and car trajectory prediction are applied to construct our warning system. Car detection has the ability to detect cars precisely, reaching an accuracy as high as 0.91 and taking merely 0.132 s to detect. Lane line detection can detect the appropriate lane area, which assists drivers to drive more safely. Car trajectory prediction, receiving a loss of 0.00024 and taking only 12 milliseconds to predict, is capable of predicting the future trajectory accurately and warning drivers to be more cautious and reduce the speed of the car in advance. By means of the above methods, based on the results of the two verifications, the probability of vehicle accidents can be reduced, and the safety of driving can be increased.  
Conceptualization, C.-H.S.; data curation, T.-J.H.; investigation, C.-H.S. and T.-J.H.; methodology, T.-J.H.; project administration, C.-H.S.; resources, C.-H.S. and T.-J.H.; software, T.-J.H.; supervision, C.-H.S.; validation, C.-H.S. and T.-J.H.; visualization, T.-J.H.; writing--original draft, T.-J.H.; writing--review and editing, T.-J.H. 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.
## 4 Experiments and Results
### Results of the HMT Gesture Recognition
#### 4.2.2 Results of the Background Velocity  
According to the above results of continuous gesture segmentation and the calculation of background velocity based on the matching of the SURF keypoints, a group of the velocity curves of the 10 predefined gestures in this experiment is shown in Figure 9. The velocity curves of \(\mathrm{V_{X}}\) and \(\mathrm{V_{y}}\) are mapped from the background velocity to the gesture trajectory velocity. As seen from the figure, the simple movement between gesture 1 and 4 takes less time to make; the effective gesture action duration is about one second, and the complex movement between gestures 5 and 10 is about twice the simple duration of about three seconds. To reduce the influence of the different lengths of gesture time for classification, all of the hand gesture data were normalized by the method mentioned in Section 3.
## 1 Introduction  
Atopic dermatitis (AD) is a common chronic inflammatory skin disease associated with a significant patient and social burden [1; 2]. Although it was previously considered a predominantly pediatric condition, recent epidemiological studies have demonstrated that atopic dermatitis is both common and burdensome among adults as well [1; 2; 3; 4]. One-year prevalence estimates in adults range between 1.2 and 8.7% in Europe [3] and between 1.7 and 6.4% in Greece [4].  
The etiopathogenesis of AD is multifactorial, involving genetic, immunologic, and environmental factors [5; 6; 7]. Furthermore, it is associated with both atopic (e.g., asthma, allergic rhinitis, and food allergy) and diverse non-atopic comorbidities, including infections, depression, and anxiety [1; 2; 8; 9; 10; 11].  
AD is characterized by dry skin, erythema, induration, lichenification, and excorizations, while patients suffer from intense purritus and skin pain, which is an increasingly recognized symptom [1; 12; 13; 14]. Patients also experience sleep disturbance and significant effects on their quality of life (QoL), which worsens with increasing disease severity [1; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. Moderate-to-severe (M2S) AD poses a substantial socioeconomic burden driven by workplace challenges and increased healthcare resource utilization (HCRU) [2; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32].  
Guideline-recommended pharmacologic treatments for M2S-AD include basic compliant therapy, topical corticosteroids (TCSs), topical calcineurin inhibitors (TCIs), and systemic treatment (ST) [33; 34]; current European Medicines Agency-approved systemic agents include cyclospor, glucocorticoids, biologic agents, and Janus kinase inhibitors (JAKis), which constitute a novel addition to the therapeutic armamentarium [34; 35]. Interestingly, the definition of candidates for systemic treatment has expanded and includes patients unable to participate in normal daily life activities whilst following an adequate treatment regimen, as per the social definition for candidates for systemic treatment, accentuating the patient experience and burden [34]. It is also highlighted that assessing only the signs of the disease is not an adequate tool for optimal treatment choices.  
Given this changing treatment landscape of M2S-AD [36], it is crucial to understand the complex disease burden. Limited data exist for Greek patients, and the majority have been sourced from patients with self-reported disease [4; 17; 37]. This study aims to be the first to detail patient and disease characteristics, patient-perceived burden, and treatment patterns of M2S-AD patients following confirmed clinical diagnosis in expert centers in Greece and associated HCRU. Moreover, the results are stratified by disease severity (moderate and severe AD), allowing for further understanding of the multifaceted impact of AD on these subpopulations and the most suitable treatment selection, as per the approach described in recent guidelines.
# Carbon Dioxide Uptake by Brazilian Cement-Based Materials
## 2 The Brazilian Cement Sector
### The Brazilian Cement Sector Roadmap  
Brazilian cement production expanded at rates well above 273 per cent between 1990 and 2014, i.e., from 26 million to 71 million tons. By contrast, global carbon dioxide emissions increased by 223% over this period. This result was mostly due to the 18% reduction in carbon dioxide emissions intensity from 700 kg CO\({}_{2}\)/ton cement to 564 kg CO\({}_{2}\)/ton cement over this period. Accordingly, the Brazilian cement industry has one of the lowest worldwide levels of specific carbon dioxide emissions, on account of mitigation measures implemented by the Brazilian industry over recent decades [36].  
One of the main mitigation measures is the increase in cement constituents, such as limestone and calcined clay, to partially replace the clinker in the final blended cement. It is expected there will be a sharp clinker/cement ratio reduction from 67% (2014) to 52% in 2050, allowing for significant CO\({}_{2}\) emissions reduction (290 million tons in the period). Therefore, the carbon dioxide mitigation due to the clinker substitution will represent a 69% decrease.  
The average thermal consumption and electricity consumption in Brazil is 3.5 GJ/t of clinker and 113 kWh/t of cement, respectively (Table 2). Although significant changes in cement production energy intensity are not expected by 2030, the replacement of obsolete installations with new lines according to the best available technologies (BATs) might start after 2030 in order to reach figures of 3.2 GJ/t of clinker and 91 kWh/t cement by 2050. These measures will lead to a cumulative carbon dioxide depletion of 38 million tons by 2050 (9% of the Brazilian industry mitigation).  
Figure 4: Comparison of the carbon dioxide emissions per ton of cement in some countries (kgCO\({}_{2}\)/ton of cement) +source: author original.  
With regard to the substitution rate with alternative fuels (% of thermal substitution), some regional differences have been observed in Brazil. Therefore, the range of substitution could be 30-40% and 50-60% for 2030 and 2050, respectively. Consequently, the carbon dioxide mitigation due to the increase in alternative fuel's usage is about 13%.  
Finally, Carbon Capture, Usage, and Storage (CCUS) technologies will play a key role in decarbonization in the Brazilian cement sector and in addressing the challenge of global climate change. It is expected to reach a cumulative reduction of 38 million tons of carbon dioxide (9% of the Brazilian industry mitigation).
# Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry
## 3 CF of the Aggregated Interference in LOS
### Analysis of AP Height  
To gain an insight into the effect of the height of the antenna, it is useful to evaluate the mean aggregated interference that, for simplicity, is for UCA. The mean \(\mathbb{E}[I]\) follows from the CF properties (check Appendix B for derivation):  
\[\mathbb{E}[I]=\frac{\pi\lambda\alpha^{2}}{(1-\alpha)}h^{2(1-b)}\ \tilde{\beta}^{2}\tilde{G}_{c}^{2}. \tag{26}\]where \(\tilde{G}_{\mathcal{E}}^{2}=\frac{1}{2\pi}\int_{0}^{2\pi}G_{\mathcal{E}}^{2}(\phi)d\phi\) is the mean power gain. The first and second derivatives of the mean interference power with respect to the AP height are  
\[\frac{d\mathbb{E}[I]}{dh} =-2\pi\lambda\alpha\tilde{\beta}^{2}\tilde{G}_{\mathcal{E}}^{2}h^ {1-2b}<0, \tag{27}\] \[\frac{d^{2}\mathbb{E}[I]}{dh^{2}} =-2\pi\lambda(\alpha-2)\tilde{\beta}^{2}\tilde{G}_{\mathcal{E}}^{ 2}h^{-2b}>0. \tag{28}\]  
The aggregated interference power decreases vs. h but increases with UE density \(\lambda\), with the second derivative always being positive. On the other hand, the received signal power \(P_{rx}\) from a target UE located at distance \(R_{0}\) is  
\[P_{rx}(h)=\frac{|\beta_{0}|^{2}}{(R_{0}^{2}+h^{2})^{b}}. \tag{29}\]  
The first and second derivatives with respect to the AP height are  
\[\frac{dP_{rx}(h)}{dh} =\frac{-2b|\beta_{0}|^{2}h}{(R_{0}^{2}+h^{2})^{b+1}}<0, \tag{30}\] \[\frac{d^{2}P_{rx}(h)}{dh^{2}} =\frac{2b|\beta_{0}|^{2}\big{(}(2b+1)h^{2}-R_{0}^{2}\big{)}}{(R_{ 0}^{2}+h^{2})^{b+2}}. \tag{31}\]  
The first derivative is always negative. The second derivative is negative for \(R_{0}>h\sqrt{(2b+1)}\) and positive for \(R_{0}<h\sqrt{(2b+1)}\). Comparing the first and second derivatives of the mean aggregated interference power and received target power, given a specific set of parameters, such as the target user distance \(R_{0}\), the signal-to-interference ratio (SIR) would have a concave behavior vs. the AP height \(h\). This is numerically evaluated in the next section.
# Automated Processing of fNIRS Data--A Visual Guide to the Pitfalls and Consequences
## 1 Introduction
### Filtering and De-Noising for Removal of Systemic Physiology
Removing the global average (GloAvg) signal has also been shown to reduce the influence of systemic physiology; however, it remains a highly debated topic among BOLD and fNIRS researchers due to concerns that it may introduce spurious correlations between spatially distributed signals [33]. More recently, the method used by [PERSON] and colleagues that accounts for differences in phase between spatially distributed channels appears to effectively remove global covariance without raising concerns about spuriously increasing correlations [34].