esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Approaches to Combat Medical Device-Associated BioFilms
## 3 Novel Approaches to Combat Medical Device-Associated Biofilms
### Surface Modification
Another approach is to inhibit the colonization of microorganisms and the formation of biofilms by affecting the structural integrity of the biofilm. The immobilization of biofilm-dispersing enzymes on material surfaces is regarded as one of the most promising strategies for preventing biofilm formation (Figure 1). For example, a deoxyribonuclease I (DNase I) coating has a good anti-infection ability and biocompatibility, which is beneficial for improving the application success rate of implants [160]. Polypyrrole is an organic conductive polymer formed from a pyrrole ring structure that affects the integrity of biofilms by promoting a structure with repeated and positive charge-regulated ionic cross-bridging and subsequent aggregation so that biofilms can be easily washed away by water [161]. The combined application of the above measures may lead to stronger antibiofilm activity. Regardless of the measures taken, the safety of the host must be considered, and the functionality of the raw materials must be ensured. As mentioned above, in order to better protect implanted medical devices from bacterial invasion, preclinical studies on the surface modification of medical devices have made very successful progress, but there is still a significant discrepancy between the proposed strategies and their clinical application. Most of the coatings being studied may not be suitable for medical implants due to cytotoxicity, immunoreactivity, or genotoxicity problems. However, some technologies have shown sufficient evidence to prove the effectiveness, safety, and durability of antibacterial drug delivery coatings [146; 162], such as AgNP coatings, which could be proposed in clinical practice. Some have been applied in clinical practice. For example, the antimicrobial hydrogel-coated prosthesis can safely protect the prosthesis and reduced early surgical site infections without adverse effects at a mean follow-up of 2 years [163]. Another study showed that silver-coated megaprosthesis is effective in terms of a reduction in prosthetic joint infections, with 2.2% local argyria and without systemic side effects [164]. In order to be better applied in clinical practice, the development of device surfaces with excellent antibacterial properties, high biocompatibility, and a long-lasting antibacterial efficacy still needs a breakthrough.
## 3 Satellite Network Model
In addition, the path of the physical link has no directional it as follows:  
\[dir(n_{j}^{s},n_{i}^{s})=dir(n_{i}^{s},n_{j}^{s}). \tag{7}\]  
Before defining the node mapping cost, we need to define the CPU resources which can be used; the total CPU consumption of all \(n_{i}^{vr}\) deployed on \(n_{j}^{s}\) is shown in the following formula:  
\[Rest_{CPU}(n_{j}^{s})=CPU(n_{j}^{s})-\sum_{n_{i}^{vr}\to n_{j}^{s}}CPU(n_{i}^{vr }). \tag{8}\]  
Moreover, the cost of mapping virtual node \(n_{i}^{vr}\) to physical node \(n_{j}^{s}\) is shown in the following formula:  
\[Cost_{N}(n_{j}^{s})=\sum_{n_{i}^{vr}\to n_{j}^{s}}CPU(n_{i}^{vr}). \tag{9}\]  
Before defining the link mapping cost, we need to define the available bandwidth resources; the bandwidth consumption of \(l_{i}^{vr}\) deployed on \(l_{j}^{s}\) is shown in the following formula:  
\[Rest_{BW}(l_{j}^{s})=BW(l_{j}^{s})-\sum_{l_{i}^{vr}\to l_{j}^{s}}BW(l_{i}^{vr }). \tag{10}\]  
The cost of mapping \(l_{i}^{vr}\) to \(l_{j}^{s}\) is shown in the following formula:  
\[Cost_{L}(l_{j}^{s})=\sum_{l_{i}^{vr}\to l_{j}^{s}}BW(l_{i}^{vr}). \tag{11}\]  
In addition, the delay of the physical link should not be less than the delay required by VNR, as shown in the following formula:  
\[\mathbb{C}_{l_{i}^{vr}\to l_{j}^{s}}^{Delay}:Delay(l_{j}^{s})\leq Delay(l_{i}^{vr }). \tag{12}\]  
The revenue of successful deployment of VNRs is as follows:  
\[REV(G^{VR})=\sum_{n_{i}^{vr}\in LV^{\prime}}CPU(n_{i}^{vr})\times\phi_{ij}+ \sum_{l_{i}^{vr}\in LV^{\prime}}BW(l_{i}^{vr})\times\phi_{ij}. \tag{13}\]The cost of a successful deployment of VNRs is as follows:  
\[COST(G^{VR})=\sum_{n_{i}^{pr}\in N^{VR}}CPU(n_{i}^{pr})\times\varphi_{ij}+\sum_{l _{i}^{pr}\in L^{VR}}BW(l_{i}^{pr})\times\phi_{ij}\times hops(l_{i}^{pr}) \tag{14}\]  
where _hops_ indicates mapping hops of \(l_{i}^{pr}\). If \(hops!=1\), it means that \(l_{i}^{pr}\) is deployed to different satellite links. An increase in the number of paths means an increase in mapping costs. Therefore, minimizing the hops is an effective way to reduce mapping costs.
# Autonomous Navigation Based on the Earth-Shadow Observation near the Sun-Earth L2 Point
## 2 Design of Navigation System  
### Circular Restricted Three-Body Problem  
The Circular Restricted Three-body Problem (CRTBP) is discussed in this paper to describe the motion of spacecraft in the Sun-Earth system. As shown in Figure 1, the CRTBP model reflects the motion of the spacecraft. The spacecraft is subjected to the gravitational force of the two primary bodies, which move in circular orbits around their barycenter point. It is assumed that the masses of the primary celestial bodies \(P_{1}\) and \(P_{2}\) are \(M_{1}\) and \(M_{2}\), respectively. The mass of the spacecraft \(m\) is much smaller than \(M_{1}\) and \(M_{2}\). Thus, it cannot practically influence the motion of the primaries or change the position of their barycenter.  
The motion of the spacecraft is described in the rotating frame. The origin center is at the barycenter, with the x-axis pointed in the direction from \(P_{1}\) towards \(P_{2}\). The z-axis is aligned with the direction of the angular momentum vector. The y-axis satisfies the right-handed rule.  
Typically, in order to simplify the calculation, the CRTBP dynamics are studied in the nondimensional form. The distance between the two primary bodies is denoted by \(L\). Meanwhile, the constant angular velocity of the two primary bodies around the barycenter is denoted by \(\omega\). The characteristic mass \([M]\), the characteristic length \([L]\), and the characteristic time \([T]\) are nondimensional variables, which are defined as  
Figure 1: Circular Restricted Three-body Problem.  
\[\left\{\begin{array}{l}[M]=M_{1}+M_{2}\\ [L]=\left\|P_{1}P_{2}\right\|\\ [T]=\left[\frac{\left\|P_{1}P_{2}\right\|^{3}}{G(M_{1}+M_{2})}\right]^{\frac{1} {2}}.\end{array}\right. \tag{1}\]  
The characteristic mass parameter \(\mu\) is defined as  
\[\mu=\frac{M_{2}}{M_{1}+M_{2}}. \tag{2}\]  
In the CRTBP, the nondimensional dynamic model in the rotating frame is expressed as follows:  
\[\ddot{\mu}+2\left[\begin{array}{c}-\dot{y}\\ \dot{x}\\ 0\end{array}\right]=\left[\begin{array}{c}\partial\Omega\\ \partial\tau\end{array}\right]^{\mathrm{T}}, \tag{3}\]  
where \(\Omega\) is the equivalent potential energy function  
\[\left\{\begin{array}{l}\Omega=\frac{1}{2}\left(x^{2}+y^{2}\right)+\frac{1- \mu}{r_{1}}+\frac{\mu}{r_{2}}\\ r_{1}=\sqrt{(x+\mu)^{2}+y^{2}+z^{2}}\\ r_{2}=\sqrt{(x-1+\mu)^{2}+y^{2}+z^{2}}.\end{array}\right. \tag{4}\]
# A Review of Passive Constant Flow Regulators for Microfluidic Applications
## 3 Mechanical Passive Constant Flow Regulators
### Elastomeric Tubing with a Diamond-Shaped Aperture in the Sidewall  
Another flow-controlled shunt design was disclosed by [PERSON] in 1996 [8]. This CRx Diamond valve was manufactured by Phoenix Biomedical Corporation. The shunt mechanism, as illustrated in Figure 11, comprises a high-pressure relief valve (cruciform), an internal tube of an elastic wall forming the central channel with a diamond-shaped aperture in the sidewall, and a low-pressure outlet check valve (cruciform). As pressure increases, the tube contracts and narrows the side aperture. By design, the hydraulic resistance is expected to increase linearly with the pressure to regulate the flow. Beyond a predetermined threshold pressure, the high-pressure relief valve opens and the CSF flows through the wide central channel to restore normal ICP.  
Figure 11: Schematic drawing of the CRx Diamond Valve, a flow control hydrocephalus shunt. The arrows indicate the flow direction. (**a**) Flow regulation stage, i.e., for pressure in the range \([18.6;30.6]\) mbar. The CSF flows through the diamond-shaped aperture that narrows as the pressure increases to induce nonlinearity and through the low-pressure relief valve. (**b**) High-pressure or safety stage. The high-pressure relief valve opens to drain the excess of CSF through the large channel and the low-pressure relief valve.  
Laboratory evaluation of the CRx Diamond Valve demonstrated a stabilized flow within the range 0.36 (for descending pressure) to 0.63 mL/min (for ascending pressure) when pressure varied in the range \([18.6;30.6]\) mbar. However, the stabilized flow decreased significantly over time, from 0.63 to 0.2 mL/min after 18 days. [PERSON] et al. also reported that the valve was insensitive to rapid fluctuations of differential pressure, small particles in the fluid, and reflux [57].
## 5 Experiment
### Experiment Results
#### 5.2.2 Low-Resource Evaluation  
To further assess the model's generalization ability, we conducted an evaluation in a challenging low-resource scenario. Given the limited availability of training data for QALD-9 plus and QALD-10, we opted to utilize LC-QuAD 2.0 as a suitable dataset for exploring this setting. Subsequently, we randomly selected subsets comprising 1%, 5%,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & **F1** & **QM** \\ \hline AQG-net [35] & 44.9 & 37.4 \\ Multi-hop QGG [51] & 52.6 & 43.2 \\ CLC+Tencent Word [46] & 52.9 & 48.4 \\ CLC+BERT [46] & 59.3 & 55.4 \\ \hline BART [11] & 64.0 & - \\ PGN-BERT [11] & 77.0 & - \\ PGN-BERT-BERT [11] & 86.0 & - \\ SGPT\({}_{Q,K}\)[52] \({}^{\ddagger}\) & 89.0 & - \\ T5-small [11] & 92.0 & - \\ T5-base [11] & 91.0 & - \\ \hline T5-small \({}^{\star}\) & 92.4 & 90.3 \\ T5-base \({}^{\star}\) & 93.4 & 91.3 \\ \hline TSET-small & 94.0 (+1.6) & 92.0 (+1.7) \\ TSET-base & 95.0 (+1.6) & 93.1 (+1.8) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental result for LC-QuAD 2.0. The QM scores are not provided by [11; 52]. \({}^{\ddagger}\) denotes that only gold entities are provided. \({}^{\star}\) denotes re-implemented results. For the TSET model, the part inside the parentheses represents the comparative results with the T5 model of the same size.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Approach**} & \multicolumn{2}{c}{**QALD-9 Plus**} & \multicolumn{2}{c}{**QALD-10**} \\ \cline{2-5}  & **QM** & **F1** & **QM** & **F1** \\ \hline T5-small & 55.15 & 64.46 & 33.93 & 38.78 \\ T5-base & 58.09 & 69.50 & 36.48 & 39.86 \\ \hline TSET-small & 61.03 (+5.88) & 72.86 (+8.40) & 40.05 (+6.12) & 47.15(+8.37) \\ TSET-base & 61.76 (+3.67) & 75.85 (+6.35) & 39.03 (+2.55) & 51.37 (+11.51) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental result for QALD-9 plus and QALD-10. For the TSET model, the part inside the parentheses represents the comparative results with the T5 model of the same size.  
10%, and 20% of the original training data to train our model. The obtained results for the Query Match (QM) and answer \(F1\) scores are presented in Table 3.  
Remarkably, our model consistently outperformed the baselines across all four settings. Notably, even when the small size model was trained with just 1% of the data, our method achieved a substantial improvement in performance over the baseline model, with an increase of 21.19 points in the QM score and 20.56 points in the answer \(F1\) score. These findings provide strong evidence of our model's superior generalization capabilities, demonstrating its effectiveness in adapting to new tasks, even when confronted with limited training data.
## 3 Use Cases  
The STEP methodology introduced in [7], and further extended in this paper, has been applied in the context of evolving knowledge in the biomedical domain. More specifically, we have implemented STEP and performed evaluations using contributions extracted from the _Molecular and Cellular Biology (MCB)_[13] and the _Genetics_[14] Wiki projects (both sub-projects of Wikipedia). These use cases target completely different knowledge domains and hence provide a different perspective of the fine-grained provenance and enable us to evaluate the abstraction layer that renders our final model into a domain-agnostic form.  
The content of the MCB and Genetics projects constantly evolves through incremental changes made by authors to articles in these projects. Below we present a series of examples of micro-contributions, emerged from such incremental changes to existing articles in the MCB project (in this case by author _[PERSON]_) on different dates:  
* 4 February 2008--_Lipase_ article: _Lipoprotein lipase functions in the blood to act on triacylglycerlycerides carried on VLDL (very low density lipoprotein) so that cells can take up the freed fatty acids. lipoprotein lipase deficiency is caused by mutations in the gene encoding lipoprotein lipase_.
* 15 February 2008--_Lipase_ article: _Pancreatic lipase related protein 1 is very similar to PLRP2 and HPL by amino acid sequence (all three genes probably arose via gene duplication of a single ancestral pancreatic lipase gene). However, PLRP1 is devoid of detectable lipase activity and its function remains unknown, even though it is conserved in other mammals._  
The fine-grained structure of the knowledge captured within such micro-contributions enables a completely novel way of modeling and using expertise. Short-term profiles, _i.e._, expertise profiles created over a restricted time period (e.g., January-June 2008)--allow one to determine bursts of activities related to particular topics, e.g., the level of participation of an individual in a project. In the example provided above, one could infer that _[PERSON]_ has been active within this period in the area of _Lipase genes_. Similarly, long term profiles, _i.e._, expertise profiles created over the entire history of an individual--can be used to determine how long were individuals involved in a specific topic, or how recent is their knowledge in this topic.  
From an analysis perspective, expertise profiles built from micro-contributions could provide insights into the alignment between the level of involvement of particular individuals in a specific topic and the focus on an underlying project. Moreover, a level of collaboration among experts can be determined (e.g., determining the most active group of experts in a topic over a period of time), which can span within and across multiple projects. Finally, such profiles enable a fine-grained comparison of the expertise of the individuals across topics and time. Consequently, the application of this technology ranges from task and project management to HR and job application processes.
## 6 Using MEC as Enabler for Vehicular Applications
### Tele-Operated Driving  
Under specific circumstances, the utilization of automated cars is already feasible today. However, complex environments, such as those seen at worksites, are still challenging for autonomous cars to master. A human controller is required to help the driving scenario from an exterior workspace if the car is unable to manage it autonomously. Hence, fully autonomous driving can be complemented by Tele-operated Driving (ToD), allowing a single person to oversee numerous cars simultaneously. Both the telecoms and automotive industries collaborate to enable this relatively new application. Standardization bodies 3 GPP [163], ISO [164], ETSI Intelligent Transport Systems (ITSs) [165], 5 GAA Association [166], and research initiatives such as 5G-PPP [167] are working on it. Some scientific works in this area are illustrated in the rest of this section.  
In ref. [68], researchers present a system that combines DL algorithms with 5G networks and supporting technologies (network slicing, MEC, SDN) to enable safety management in IoV. Their suggested platform's elements communicate via 5G slices. The protection servers are tied directly to the RSUs to realize the MEC infrastructure. The IoV layer, edge computing device layer, control and data layer (SDN network core), and QoS implementation make up the four key components of our suggested methodology. The IoV layer includes automated cars that produce different types of traffic data: news and entertainment traffic, and traffic generated by the safety service. In their study, the focus is on remotely monitoring and identifying a drowsy driver using AI techniques deployed on the MEC server. Once a drowsy driver is detected, a signal is sent to the safety server to transition the vehicle into the tele-operation state.  
In ref. [168], authors suggest a tele-operated transport approach for logistics. In order to provide continuous transboundary tele-operated transportation based on 5G networks, the 5G-Blueprint project [169] is designing and verifying a technological infrastructure as well as commercial and governance structures. Their project analyzes risks and advantages of tele-operated driving, by collecting all necessary technical components to complete a thorough verification and covering multiple use cases in an end-to-end perspective. Additionally, since this is thought to be essential for enabling broad-scale worldwide implementation, the project attempts to test both commercial and governance designs and provide unambiguous advice to the relevant standardizing authorities [168].
###### Abstract  
[Objective] The irrigation area in northern Henan is an important grain producing area in China. Native high arsenic groundwater exists in the area and has long been used for agricultural irrigation. Increased soil arsenic (As) content under long-term irrigation threatens the quality and safety of crop products. Soil passivation is the use of adding passivators to the soil to fix pollutants to achieve the purpose of limiting their migration. Therefore, the preparation of an efficient and clean passivator and its arsenic fixation effect in soil are important research areas to reduce the risk of high arsenic groundwater. [Method] Firstly, nano-manganese dioxide (MnO\({}_{2}\))-modified biochar was prepared via the pyrolysis of sawdust biochar, potassium permanganate and manganese sulfate monohydrate at a mass ratio of 1:0.18:0.29. Secondly, the adsorption characteristics were explored using adsorption kinetics and adsorption isothermal experiments. Scanning electron microscopy (SEM), X-ray powder diffraction (XRD) feature mapping and other characterization methods were used to study its physical properties and adsorption mechanism. Finally, a potting experiment was designed to explore the changes in arsenic content in soil when the passivator dosages were 0%, 1% and 5%. [Results] (1) The nano-MnO\({}_{2}\) modified biochar could reach the adsorption dynamic equilibrium after 180 min, and its maximum adsorption capacity was 58.12 \(\upmu\)g/g. (2) When the dosing ratio was 1%, the fixed efficiency of soil effective As content in potted crops of unplanted crops and planted crops was 4.18-5.51% and 1.99-3.83%. When the dosing ratio was 5%, it was 7.48-8.75% and 5.58-9.58%. [Conclusions] The results show that when the addition ratio is 0-5%, the passivation effect of soil effective As is positively correlated with the passivator dosage.  
M 2023 1 2023 202
# An Outline of Fused Deposition Modeling: System Models and Control Strategies
## 2 Systems
### Deposition
#### 2.2.2 Surface Roughness  
The surface finish of the printed part is primarily affected by the slicing strategy, which is responsible for setting the height of each layer. Intuitively, this operation can be seen as a discretization of the otherwise continuous ideal object, leading to an unavoidable loss of geometric fidelity. Smaller resolutions lead to smoother surfaces but at the expense of the deposition rate since (i) a lower diameter of the nozzle \(d\) and (ii) a higher number of layers \(n_{l}\) are required to build the part. Specifically, the deposition rate decreases with the square of the increase in resolution [42].  
The surface quality of the part is also determined by the roughness of each individual layer. Several models of surface roughness in FDM have been derived in the literatures [43; 44]. In general, it has been shown that the surface roughness mostly depends on the layer height \(h\) and build orientation angle \(\alpha\), as defined by [45]  
\[R_{a}=\begin{cases}p_{0}h,&\alpha=0^{\circ}\\ (p_{1}h^{4}+p_{2}h^{3}+p_{3}h^{2}+p_{4}h+p_{5})\frac{h}{\sin\alpha},&0^{\circ }<\alpha\leq 30^{\circ}\\ p_{6}\frac{h}{\sin\alpha},&\alpha>30^{\circ},\end{cases} \tag{17}\]  
where \(p_{0},\ldots,p_{6}\) are constants that were identified from data. Other parameters such as feed rate, temperature, road width and air gap do not have much influence on the surface finish of the part since they are mostly responsible for filling the layers rather than defining the external contour.
# Design and Analysis of Novel Low-Cost Linear Vibration Motor for an Electronic Cigarette
## 3 Analysis Methods  
### Magnetostatic Analysis  
This study employed a magnetostatic finite element method model of the linear vibration motor to obtain the magnetic field and electromagnetic force. Based on [PERSON]'s equations [6,7], magnetostatic equations describe magnetic fields that do not vary with time, which are caused by a permanent magnet or by the steady flow of electricity (Direct current). The magnetostatic equations in differential forms are [PERSON]'s magnetic law and [PERSON] law:  
\[\
abla\cdot B=0, \tag{1}\]  
\[\
abla\times H=J, \tag{2}\]  
where \(\
abla\) with the dot denotes divergence, and \(B\) is the magnetic flux density. \(\
abla\) with the cross denotes curl, \(J\) is the current density, and \(H\) is the magnetic field intensity. The magnetic field can be derived from the vector potential as follows:  
\[\
abla\times A=B, \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parts** & **Material** & **Density (kg/m\({}^{3}\))** & **Young’s Modulus (GPa)** & **Poisson’s Ratio** \\ \hline Upper case & SUS 304 & 7850 & 193 & 0.25 \\ Spring & SUS 301 EH & 7800 & 110 & 0.28 \\ Yoke/Top plate & SvecC & 7830 & 207 & 0.29 \\ Permanent magnet & Neodymium & 7010 & 160 & 0.30 \\ Voice coil & Copper & 5321 & 126 & 0.34 \\ Dummy jig & Polyoxymethylene & 1417 & 3.15 & 0.30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of the linear vibration motor.  
Figure 4: The B-H curve of SvecC.  
Figure 3: (a,b) The measurement method for acceleration of the vibration motor.  
where the field \(A\) is called the magnetic vector potential. The flux density on voice coil is used to calculate the force factor, which is the transmission factor between the electromagnetic domain and mechanical domain. The Lorentz force resulting from the interaction between the electric current and magnetic field can be expressed as:  
\[F_{Lorentz}=\oint\!idl\times B, \tag{4}\]  
where \(i\) and \(l\) denote the coil current and total coil length, respectively.