esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Blockchain Technologies for Maritime Applications/Use Cases
### Blockchain for Maritime Sustainability/Decarbonization: BunkerTrace  
The maritime industry pursues environmental sustainability through decarbonization. In 2019, the shipping industry and the financial sector enacted the Poseidon Principles to address greenhouse gas emissions. These principles require financial institutions to consider climate-related factors when making lending decisions for the shipping industry. Annually, the shipping industry reports its climate-adjusted status, which allows financial institutions to influence loan terms based on the environmental performance of their portfolios. Currently, 35 signatories are participating in the Poseidon Principles.  
Additionally, companies are putting efforts into achieving decarbonization through technological innovations that reduce unnecessary energy consumption in maritime transport. Blockchain technology can contribute to decarbonization by enhancing trace, tracking, and transparency of sustainable fuel use within the maritime sector. Netherlands-based BunkerTrace uses synthetic DNA and blockchain technology to clarify the origin, composition, and quality of fuels. The company provides detailed information about fuel origins, quality, and tracking to ensure security and protection. By applying non-invasive synthetic DNA tagging to fuels and recording this information on the blockchain, BunkerTrace enables clients to continuously track fuel data, mitigating concerns about fuel adulteration. This tracking capability helps ensure the correct use of fuel, prevents the trading of low-quality illegal fuels, and promotes the transparency of fuel transactions, ultimately fostering the use of sustainable fuels [62].  
BunkerTrace demonstrated the efficacy of its technology through a 2020 pilot project with Cargill, which tracked compliance with environmental regulations for Cargill's marine fuels. Since then, BunkerTrace has collaborated with various organizations, including S&P Global Platts, Singapore Shipping Association (SSA), and DNV GL, to enhance fuel transparency.
# Energy Efficient Underlaid D2D Communication for 5G Applications
## 2 System Model
\[\max_{\mathrm{D_{p,i}}}\mathrm{EE_{d}}\ =\ \sum_{i=1}^{k}\mathrm{EE_{d,i}} \tag{7}\]  
\[\mathrm{D_{p,i,inf}}\ \leq\ \mathrm{D_{p,i}}\ \leq\ \mathrm{D_{p,i,sup}}\]  
\[\sum_{i=1}^{k}\mathrm{D_{p,i}}=\mathrm{D_{p}} \tag{8}\]  
The goal function of our optimal solution is the total EE of D2D communication (\(\mathrm{EE_{d}}\)). EE of the ith band D2D communication is \(\mathrm{EE_{d,i}}\). It achieves the description of the optimization process to maximize the EE across the entire cellular network using D2D communication.
## 2 State of the Art  
### Data Collection  
#### 2.1.1 Citizen Science and Crowdsourcing  
The creation of user-generated content is strongly supported by the availability of mobile phones and open-source 3D modeling tools. In terms of the level of participation, most citizen science projects use crowdsourcing to involve \"non-scientists to help to analyze or collect data as part of a researcher-led project\" ([57], p. 259). Examples include collecting and processing images [58] and crowd-based creation of 3D models [59; 60]. Co-design \"involves citizens into the research process from its beginnings, or the stimulus for the research project originates from the citizens\" ([61], p. 4). Although most prominent in text- or image-based research in humanities [62], co-design is frequently used for 3D content and experience design for museums [63; 64] or (serious) history games (e.g., [65]). Besides the challenges of participatory processes such as user activation and management, task definition, and quality control [66], citizen science tasks in humanities must handle complex, non-standardized, and knowledge-intensive tasks and therefore are hard to assess with regard to the scientific quality of processes and outcomes [67; 68]. Finally, citizen science approaches have found their way into research and teaching in various university disciplines in recent years [69], including digital heritage (e.g., [70]). In this context, knowledge transfer to citizen scientists creates particular didactic challenges [71]. This is not least because the target or interested group for cultural heritage projects are often not digital natives and has to be addressed via other channels [72]. Knowledge transfer via digital citizen science activities nevertheless appears to be a promising approach, as public involvement in generating knowledge increases the willingness to engage with cultural heritage.
## 4 Satellite Synchronization
It must be noted that, as mentioned before, the proposed model works for a short time, and it is not possible to use the estimated parameters \(a_{0},a_{1},a_{2}\) for long periods: the parameter estimation must be repeated, after a given time, for example, every 2 or 3 h.  
It follows that the Time Synchronization and Orbit Determination processes, also if based on the same set of measurements, may have the same renewal time (\(\Delta t\)) but different time spans. In Figure 4, a schematic representation of the whole algorithm is reported. Every \(\Delta t\), a set of TOA and FOA measurements, is taken and collected from the main spacecraft. The measurements are used to derive the range, range rate, and clock biases between spacecraft. At the time \(t_{k}\), the range and range rate accumulated from the last epochs in \(T_{spun}\) are used to derive the state deviation and the measurement residuals. Having all the epochs of the selected time span, the normal equation can be solved and the state of the spacecraft at time \(t_{0}\) can be estimated. In the same way, the clock parameters for the epoch \(t_{1}\) are estimated with the last \(M_{K}\) epochs. After that, the orbits and clocks parameters can be used to propagate the satellite's position and time to the future epochs, and the sliding windows can be moved to the epoch \(t_{k+1}\). Figure 4: Schematic representation of the Orbit Determination and synchronization algorithms.
## 4 Results and Discussion  
The mathematical model includes variables that are installation-dependent, such as battery and thermal storage size. A parametric study is conducted in Sections 4.1 and 4.2 in order to define optimal values.  
Figure 7: Daily cost for different iterations. Climate Zone 5, multiple family house, 3 families, setting WSB, battery 2 kWh, tank volume 800 L.
# Fabricating Sustainable All-Cellulose Composites
###### Abstract  
Climate change, waste disposal challenges, and emissions generated by the manufacture of non-renewable materials are driving forces behind the production of more sustainable composite materials. All-cellulose composites (ACCs) originate from renewable biomass, such as trees and other plants, and are considered fully biodegradable. Dissolving cellulose is a common part of manufacturing ACCs, and currently there is a lot of research focused on effective, but also more environmentally friendly cellulose solvents. There are several beneficial properties of ACC materials that make them competitive: light weight, recyclability, low toxicity, good optical, mechanical, and gas barrier properties, and abundance of renewable plant-based raw material. The most prominent ACC applications are currently found in the food packing, medical, technical and vehicle industries. All-cellulose nanocomposites (ACNCs) expand the current research field and can offer a variety of more specific and functional applications. This review provides an overview of the manufacture of sustainable ACCs from lignocellulosic, purified cellulose, and cellulosic textiles. There is an introduction of the cellulose dissolution practices of creating ACCs that are currently researched, the structure of cellulose during complete or partial dissolution is discussed, and a brief overview of factors which influence composite properties is presented.  
all-cellulose composite; biocomposite; cellulose; dissolution; natural fibres; single-polymer composite +
Footnote †: journal: Applied Sciences
# Personal Protective Equipment as a Potential Source of
## 2 Materials and Methods
### Health Risk Assessment  
The health risk of exposure to the phthalate group of interest in the PPE samples in adults was assessed. The chronic daily intake (CDI) through inhalation and dermal pathways for face masks, inhalation pathways for face shields, and dermal pathways for gloves were considered. The CDI through inhalation and dermal pathways was calculated using Equations (1) and (2), respectively [29]:  
\[\mathrm{CDI_{Inhalation}}=\frac{\mathrm{C_{i}\times InhR\times ExFr\times ED}}{ \mathrm{PEF\times BW\times AT}} \tag{1}\]  
\[\mathrm{CDI_{Dermal}}=\left(\frac{\mathrm{C_{i}\times SA\times AF\times DAF \times ExFr\times ED}}{\mathrm{BW\times AT}}\right)\times\mathrm{CF} \tag{2}\]  
where \(\mathrm{C_{i}}\) is the mean concentration (\(\mathrm{\SIUnitSymbolMicro g}\)/g) of a phthalate compound in a PPE sample, InhR is the inhalation rate (20 \(\mathrm{\SIUnitSymbolMicro m}\)/\(\mathrm{cm^{2}}\)), ExFr is the exposure frequency (350 days), ED is the exposure duration (24 years), PEF is the particle emission factor (1.36 \(\times\) 10\(\mathrm{\SIUnitSymbolMicro m^{3}/kg}\)), BW is the body weight (70 kg), AT is the averaging time (365 \(\times\) 24 days), SA is the surface area of the skin that comes in contact with a PPE sample (445.5 and 840 \(\mathrm{cm^{2}/event}\) for face masks and gloves, respectively), AF is the skin adherence factor (0.07 \(\mathrm{mg/cm}\)), DAF is the dermal absorption factor (0.001, unitless), and CF is the conversion factor (1 \(\times\) 10\(\mathrm{\SIUnitSymbolMicro}\) 6 kg/mg). The levels of these parameters were recommended by the USEPA [29] and the United States Department of Energy [30]. Then, the hazard quotient (HQ) for noncarcinogenic risk was determined using Equation (3):  
\[\mathrm{HQ}=\frac{\mathrm{CDI}}{\mathrm{RfD}} \tag{3}\]  
where RfD is the reference dose that characterizes the health risk of noncarcinogenic adverse effects due to exposure to a phthalate compound. The RfD values of DBP, BBP, DEHP, DnOP, and DINP are 100, 200, 20, 40, and 37, respectively [31, 32, 33, 34, 35]. Notably, no RfD value for DIDP was found in the literature.  
For the noncarcinogenic risk assessment, the hazard index (HI) was determined using Equation (4):  
\[\text{HI}=\sum\text{HQ}= \text{HQ}_{\text{inhalation}}+\text{HQ}_{\text{Dermal}} \tag{4}\]  
The carcinogenic risk (CR) factor was calculated using Equation (5):  
\[\text{CR}=(\text{CDI}_{\text{Inhalation}}+\text{CDI}_{\text{Dermal}})\times \text{CSF} \tag{5}\]  
where CSF is the cancer slope factor of an individual phthalate compound. The CSF values of DBP, BBP, DEHP, DnOP, and DINP are 0.142, 0.0019, 0.014, 0.036, and 0.06, respectively [31; 32; 33; 34; 35; 36]. Notably, no CSF value for DIDP was found in the literature.
## 7 Time Domain Optimization
### Combined Feedback and Feedforward Control
#### 7.1.3 Collective Pitch Feedforward Controller  
The basic idea of the \"two-degrees-of-freedom-control\" [59] is to complete the two main tasks of a controller (reference signal tracking and disturbance compensation) independently by a feedback and a feedforward controller, while the controllers should not obstruct each other. For example, it would be possible to calculate an update to the generator torque to cancel out wind speed changes and to maintain constant generator speed. However, this would yield changing electrical power and thus interfere with the control goal of the ISC.  
Here, the feedforward controller is designed such that changes from the wind speed \(u_{\textit{eff}}\) to the rotational speed \(\omega\) are compensated by an additional blade pitch angle \(\beta_{FF}\). In this case, the feedforward controller is not counteracting the control action of the CPC and ISC.  
With the reduced nonlinear model introduced above, it can be shown that, if \(M_{gen}\) is used to control electrical power, then the aerodynamic torque needs to be held at its rated value to maintain  
Figure 6: Degrees of freedom for the reduced nonlinear model (**left**). Control and disturbance inputs to the modules of the reduced model (**center**). Look-up tables of power coefficients (**right**).  
constant rotor speed. This can be achieved for changing \(u_{\mathit{eff}}\) by adjusting the pitch angle using the static pitch curve \(\beta_{ss}(u_{\mathit{eff}})\):  
\[\beta_{FF}=\beta_{ss}(u_{\mathit{eff}}). \tag{14}\]  
This curve is obtained by steady-state simulations (see Figure 7 (right)).
# Study on Nitrogen-Doped Graphene Ink and Its Effects on the Heat Dissipation for the LED Lamps
###### Abstract  
This study explored the application of nitrogen-doped graphene (NDG) ink to the coating of light-emitting diode (LED) lamps for the thermal management. Unlike the general solutions of graphene, the NDG ink used in this study was tuned with high electrical resistance and quietly suitable for the application of the LED lamp coating, which prevented the short circuit problem; besides, the NDG ink also provided a high heat dissipation effect to improve the performance of the LED lamp. The investigation of adhesion and resistance for the NDG ink were conducted by the cross-cut test and the four-point probe resistance measurement, respectively. Three types of LED lamps including the original lamp, fin-removed lamp, and NDG-ink coated (NGC) lamp were tested for the actual operating temperature distribution by K-type thermal couples and for the lumens by the integrating sphere. The results showed that the heat dissipation of the NGC lamp was better than that of the original lamp. In addition, the inspections for the properties of NDG powder were also performed by x-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), and Raman spectroscopy. According to the inspectional results, the NDG powder had a percentage of nitrogen of about 3.8% by XPS, a surface roughness R\({}_{\text{q}}\) of 0.9 nm by AFM, and over ten layers by Raman analysis. It showed that the NDG powder in this study belonged to the type of multi-layer graphene.  
LED; nitrogen-doped graphene; heat dissipation; integrating sphere +
Footnote †: journal: Applied Physics Letters
## 3 System Model
### Computing Model  
Let user \(u\) have computing task \(T_{u}=\{o_{u},n_{u}\}\), where \(o_{u}\) represents the size of the task content, and \(n_{u}\) represents the number of cycles that the central processing unit (CPU) needs to run to complete the task. Because there are multiple users and MEC servers, it is impossible to know how much computing power is allocated to user \(u\). Therefore, a random variable \(\Xi_{u}^{m}\) is established to represent the computing power of assigning MEC server \(m\) to user \(u\). \(\Xi_{u}^{m}\) is divided into \(M^{\prime}\) discrete intervals, \(\Pi=\left\{\Pi_{0},\Pi_{1},\dots,\Pi_{M^{\prime}-1}\right\}\). The computing state of time \(t\) can be expressed as \(\Xi_{u}^{m}(t)\), \(t\in\left\{0,1,2,\dots,T-1\right\}.\) According to a certain transition probability, the computing state \(\Xi_{u}^{m}(t)\) is transferred from one state to another state. Let \(\varepsilon_{\overline{S51552}}(t)\) denote the probability of transition from state \(\overline{S51}\) to state \(\overline{S52}\). The state transition probability matrix \(E_{u}^{m}(t)\) of \(M^{\prime}\times M^{\prime}\) dimension can be expressed as  
\[E_{u}^{m}(t)=\big{[}\varepsilon_{\overline{S51552}}(t)\big{]}_{M^{\prime} \times M^{\prime}} \tag{21}\]  
where \(\varepsilon_{\overline{S51552}}(t)=\big{(}P_{r}\Xi_{u}^{m}(t+1)=\overline{S52 }|\Xi_{u}^{m}(t+1)=\overline{S51}\big{)}\), \(\overline{S51},\overline{S52}\in\Pi\).  
The execution time of task \(T_{u}\) on MEC server \(m\) is  
\[t_{u}^{m}=\frac{n_{u}}{\Xi_{u}^{m}(t)}. \tag{22}\]  
Thus, the computing rate is  
\[CompR_{u}^{m}(t)=a_{u}^{m}(t)\frac{o_{u}}{t_{u}^{m}}=a_{u}^{m}(t)\frac{\Xi_{u }^{m}(t)o_{u}}{n_{u}}, \tag{23}\]  
and \(\sum\limits_{u\in u}a_{u}^{m}(t)o_{u}\leq O_{m}\), where \(a_{u}^{m}(t)\) indicates whether the user uses the MEC server \(m\). \(a_{u}^{m}(t)=1\) means that the user uses MEC server \(m\); otherwise, \(a_{u}^{m}(t)=0\). \(O_{m}\) represents the maximum value that can be calculated on server \(m\)[36].
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 2 Literature Review
Our study aligns with these insights by evaluating how different AV behaviors, including cautious and aggressive behaviors, influence intersection performance, but we also explore the effects of platooning and signal timing optimization, adding a unique dimension to the analysis. Despite the extensive research on AVs, there remains a gap in understanding the nuanced effects of different AV driving behaviors--cautious, normal, aggressive, and platoon--at signalized intersections. This study aims to fill this gap by evaluating the impact of these behaviors on traffic performance, including queue lengths, travel times, delays, emissions, and fuel consumption, at a four-leg signalized intersection with heavy traffic volume and different cycle time optimizations. By incorporating these diverse AV behaviors and varying penetration rates, this research provides a comprehensive analysis that enhances the existing body of knowledge and offers practical insights for urban traffic management and AV integration strategies. This study distinguishes itself from previous research by focusing not only on the individual effects of AV driving behaviors (cautious, normal, aggressive, and platooning) but also on their interaction with signal control optimization at a heavily trafficked urban intersection. This work specifically examines the impact of these behaviors across varying AV penetration rates and cycle times, with a particular emphasis on the role of signal control optimization. By evaluating performance metrics such as queue lengths, delays, emissions, and fuel consumption under these diverse conditions, this research offers a comprehensive perspective on how optimizing signal timing can influence the integration of AVs in urban traffic systems.
## 6 Discussion on the Results
### Comparative Assessment of Alternative Precast Beam-Column Connection Strategies  
Alternative precast beam-column connection methodologies, such as grouted sleeves or post-tensioned unbonded bars, are frequently designed to achieve monolithic behavior,facilitating moment transfer through mechanical couplers or cables to simulate the continuity typical of cast-in-place constructions. Although these systems can exhibit adequate ductility under moderate loading conditions, they introduce complexities in design, installation, and maintenance. Strict management of grout tolerances or the anchorage zones of cables is essential to mitigate issues related to slippage or loss of prestressing over time. In contrast, the ACPH approach incorporates a more straightforward, intentionally weaker hinge mechanism at the beam's end, focusing on localizing inelastic rotations within a replaceable steel component instead of relying on continuous rebar or high-tension cables throughout the joint. From a production perspective, fabricating a limited number of standardized hinge plate subassemblies is likely to be less labor-intensive compared to the meticulous control required for multiple coupler placements or the precise tensioning of extended unbonded cables. Furthermore, the ACPH design greatly streamlines post-earthquake repairs, instead of addressing cracking around sleeves or the challenges of detensioning cable systems, one can simply detach the damaged hinge plates and replace them with new ones, thus preserving the integrity of the main beam and column. This simplicity in manufacturing and reparability, combined with the flexibility to swiftly dissemble or relocate structural modules, may lead to significant cost reductions in both construction and lifecycle management.
## 3 Circuit Models
### CPWs Loaded with ELC Resonators
Thus, at the upper limit, \(\theta=90^{\circ}\), \(M^{\phi}\) is maximum while \(M_{0}\) vanishes completely; the magnetic coupling cancellation disappears and the resonator is expected to be tightly coupled to the line. Figure 9: Bisymmetric ELC (electric-LC) resonator. The electric and magnetic walls at the fundamental resonance, as well as a sketch of the distribution of charges and currents, are indicated. The circuit of Figure 10a is somewhat complex and has too many parameters for both parameter extraction and design purposes. An equivalent and simplified circuit model is the one depicted in Figure 10b, where an effective mutual inductance \(M\) is defined. Such model is equivalent to that of an SRR magnetically coupled to a CPW transmission line, which can be transformed to the circuit model of Figure 10c [42, 43]. From the latter model, it can be concluded that not only there is a transmission zero at \(f_{0}=\omega_{0}/2\pi\) as long as \(M\) is different from zero, but also the rejection bandwidth broadens with \(M\) (the reason is that the susceptance slope of the parallel resonator decreases as the ratio \(L_{e}\)'/\(C_{e}\)' increases). Furthermore, the lumped element values of that circuit can be easily extracted from the method reported in [43]. To deal with different orientations, we will consider a circular-shaped structure as a case study (see Figure 11) [41]. The capacitance of the ELC is enhanced by using the semi-circular metallic patches, thus resulting in a relatively small electrical size. The results of the electromagnetic simulation for different resonator orientations are plotted in Figure 12. The extracted parameters for the circular structure (under different orientations) are listed in Table 1. It is remarkable that with the exception of the mutual inductance, all the circuit parameters are roughly invariant with the rotation angle. The reflection and transmission coefficients inferred from the circuit simulation with the extracted parameters are also depicted in Figure 12. Good agreement between the circuit and the electromagnetic simulations in the vicinity of the transmission zero frequency (_i.e._, the region of interest) results, thus validating the proposed circuits with arbitrary ELC orientation. Figure 10: **(a)** Equivalent circuit model of a CPW loaded with an ELC resonator, including the different magnetic coupling mechanisms, accounted for through the mutual inductances \(M\)e and \(M^{0}\); **(b)** simplified circuit model; **(c)** transformed simplified circuit model. The details of the parameter extraction procedure, the analytical dependence of \(M\) with the angle, as well as the effects of losses can be found in [41].