esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Quality Assessment of the BIC/B2a Signals from BDS-3 Operational Satellites
### Observation Models for Combined BDS-3, GPS, and Galileo RTK  
In this section, we will briefly introduce the observation models corresponding to loosely and tightly combined RTK with BDS-3 B1C/B2a, GPS L1/L5, and Galileo E1/E5a observations. Since our research is restricted to zero and short baselines, the atmospheric delays are ignored.  
Assuming receivers \(b\) and \(r\) simultaneously track satellites \(s_{G}=1_{G}\), \(2_{G}...m_{G}\) and \(s_{*}=1\), \(2...m\)-on overlapping frequency \(i=1,2...f\), where \(s_{G}\) and \(s_{*}\) are the number of satellites from GPS and constellation * (The symbol * is \(B\) for BDS-3 and \(E\) for Galileo) respectively, the undifferenced phase and code observation equation for receiver \(b\) and GPS satellite \(s_{G}\) on frequency \(i\) is given by [17; 18]:  
\[\begin{split}\phi_{b,i}^{s_{G}}&=p_{b}^{s_{G}}+dt_{ b}-dt^{s_{G}}+\lambda_{i}\Big{(}\delta_{b,i}^{G}+\varphi_{b,i}-\delta_{i}^{s_{G}}- \varphi_{i}^{s_{G}}+N_{b,i}^{s_{G}}\Big{)}+\varepsilon_{b,i}^{s_{G}}\\ P_{b,i}^{s_{G}}&=p_{b}^{s_{G}}+dt_{b}-dt^{s_{G}}+d_ {b,i}^{G}-d_{i}^{G}+e_{b,i}^{s_{G}}\end{split} \tag{1}\]  
where \(\phi\) and \(P\) are the phase and code measurements in meters, respectively; \(\rho\) is the geometric range from satellite to receiver; \(dt_{b}\) is the receiver clock bias; \(dt^{s_{G}}\) is the satellite clock bias; \(\lambda_{i}\) is the wavelength; \(\delta_{b,i}^{G}\) and \(\delta_{i}^{s_{G}}\) are the phase hardware delay in the receiver and satellite, respectively; \(\varphi_{b,i}\) and \(\varphi_{i}^{s_{G}}\) are the  
Figure 6: Zero-baseline double-differenced carrier phase and code residuals for representative satellite pairs of C19-C22 (**top panels**), G08-G01 (**middle panels**), and E09-E24 (**bottom panels**).  
initial phase bias in the receiver and satellite, respectively; \(d_{b_{i}}^{G}\) and \(d_{i}^{G_{G}}\) are the code hardware delays in the receiver and satellite, respectively; \(N\) is the ambiguity; \(\varepsilon\) and \(e\) are all other unmodelled errors including the measurement noise. Note that the subscript \(G\) means that \(\delta_{b,i}^{G}\) and \(d_{b,i}^{G}\) are related to the GPS system rather than a certain GPS satellite.  
Similarly, the undifferenced phase and code observation equation for receiver \(b\) and satellite \(s_{*}\) on frequency \(i\) is given by:  
\[\begin{split}\phi_{b,i}^{s_{*}}&=\rho_{b}^{s_{*}}+ dt_{b}-dt^{s_{*}}+\lambda_{i}\Big{(}\delta_{b,i}^{s_{*}}+\rho_{b,i}-\delta_{i}^{s_{*}}- \phi_{i}^{s_{*}}+N_{b,i}^{s_{*}}\Big{)}+d_{TO}+\varepsilon_{b,i}^{s_{*}}\\ P_{b,i}^{s_{*}}&=\rho_{b}^{s_{*}}+dt_{b}-dt^{s_{*}}+ d_{b,i}^{*}-d_{i}^{s_{*}}+d_{TO}+\varepsilon_{b,i}^{s_{*}}\end{split} \tag{2}\]  
where \(d_{TO}\) is the time offset between GPS and constellation *.
## 3 Results and Discussion
### Experimental Studies in Laboratory bioreactors
Figure 5: Daily biogas (biohydrogen) production (Q\({}_{\text{H}_{2}}\)) and volumetric content of H\({}_{2}\) and CO\({}_{2}\) in the biogas (%) from glucose in semi-continuous mode of operation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Day**} & \multirow{2}{*}{**Q1 (dm3/L Working Volume)**} & \multirow{2}{*}{**H2 [\%]**} & \multirow{2}{*}{**CO2 [\%]**} & \multirow{2}{*}{**pH [\%]**} & \multirow{2}{*}{**Q2 (dm3/L Working Volume)**} \\  & & & & & \\
1 & 0.00 & N/A & N/A & 5.36 & 0.00 \\
2 & 0.40 & 27.5 & 23.8 & 5.05 & 0.26 \\
3 & 0.29 & N/A & N/A & N/A & 0.14 \\
4 & 0.29 & N/A & N/A & N/A & 0.14 \\
5 & 0.29 & 38.63 & 42 & 4.72 & 0.14 \\
6 & 0.12 & N/A & N/A & 4.53 & 0.14 \\
7 & 0.09 & 21.65 & 37.61 & 4.52 & 0.18 \\
8 & 0.04 & N/A & N/A & 4.49 & 0.00 \\
9 & 0.04 & N/A & N/A & 4.37 & 0.45 \\
10 & 0.02 & N/A & 36.05 & N/A & 0.00 \\
11 & 0.02 & N/A & N/A & 4.36 & 0.00 \\
12 & 0.02 & N/A & N/A & N/A & 0.00 \\
13 & 0.00 & N/A & N/A & 4.32 & 0.01 \\
14 & 0.00 & N/A & N/A & 4.62 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Daily biogas production and hydrogen content from two-phase anaerobic digestion with hydrogen (BR\({}_{1}\)) and methane (BR\({}_{2}\)) production. Figure 6: Dynamics in biogas production from two-phase anaerobic digestion with hydrogen (BR\({}_{1}\)) and methane (BR\({}_{2}\)) production. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type of Process, Substrate and Organic Loading (g/L)**} & \multirow{2}{*}{**Experiment Duration (Day)**} & \multicolumn{2}{c}{**Cumulative**} & \multirow{2}{*}{**Maximal Value of 1 [\(\mathbf{H_{2}}\) Content in Volume (dm3)**} & \multirow{2}{*}{**Maximal Value of 1 [\(\mathbf{H_{2}}\) Content in the Biogas (\%)**]**} & \multirow{2}{*}{**Maximal Value of [\(\mathbf{H_{2}}\)S (ppm)**]**} \\  & & & **Volume (dm3)** & & \\ \hline Semi-continuous & & & & \\ process; 4 g glucose (D = 0.1 day\({}^{-1}\)) & & & & \\ Batch process; & 4 & 1.719 \(\pm\) 0.08 & 17.1 \(\pm\) 0.34 & N/A \\
15 g glucose & & & & \\ Batch process; & 15 & 2.751 \(\pm\) 0.05 & 5.9 \(\pm\) 0.12 & 705 \(\pm\) 35 \\
18 mL CSL & & & & \\ Batch process; & 10 & 4.428 \(\pm\) 0.03 & 14.5 \(\pm\) 0.29 & \textgreater{}1000 \\
300 mL CSL & & & & \\ Semi-continuous & & & & \\ process; 50 mL CSL & 10 & 1.319 \(\pm\) 0.11 & 4.2 \(\pm\) 0.08 & \textgreater{}1000 \\ (D = 0.1 day\({}^{-1}\)) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Batch and semi-continuous experiments with glucose and CSL.
# Design of a Double-Matched Cross-Polar Single Antenna Harmonic Tag
## 7 Conclusions  
This study presents a comprehensive design, prototyping, and measurement analysis of a harmonic tag. By incorporating a dual-band harmonic circuit and a perturbed annular ring patch antenna, able to radiate orthogonal polarization at harmonic frequencies, the tag achieves enhanced performance, adaptability, and flexibility for various wireless sensing applications. The tag has been designed to handle low-power impinging signals and it can be detected at several meters of distance. From the analysis of the obtained results, it is clear that the new concept of single-port transponder is valid and works adequately. The measurements of conversion losses are aligned with those in the literature but there is ample room for improvement; in particular, a more accurate implementation with better technology can improve conversion efficiency. Since the proposed tag is a good candidate to be worn, as a possible future improvement, it can be enriched with sensor functionality.  
Conceptualization, E.D.; Methodology, A.D. ([PERSON]) and A.D. ([PERSON]); Software, A.D. ([PERSON]) and A.D. ([PERSON]); Validation, A.D. ([PERSON]) and A.D. ([PERSON]); Formal analysis, E.D.; Writing--original draft, A.D. ([PERSON]); Writing--review & editing, A.D. ([PERSON]), E.D. and P.T.; Supervision, E.D. and P.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author [PERSON] contributed to this research before he was employed by the company Thales Alenia Space Italia.
## 6 Conclusions  
To validate the modeling of pipe whipping phenomenon based on fluid-structure interaction, the Ansys computational tool was used, utilizing both Fluent and Transient Structural modules coupled together. Two different simplified pipe geometries were modelled using a coupled 1D/3D modeling approach differentiating in their longitudinal length (550 mm and 150 mm) at the elbow region of the free end of the Aquitaine pipe model. An instantaneous break of high-pressure fluid at the inlet of the pipe was considered to model the pipe whipping effect using CFD computation. The relative pressure differences inside the walls of the pipe were coupled to the Transient Structural module of Ansys to predict the structural response of the model until impact on the neighboring wall. A fully 3D model was also solved under similar conditions and then compared to the simplified models for model verification.  
The numerical results as predicted by the Ansys computational tool provided fair validation with the experimental results of the CEA pipe whipping tests on the structural behaviour under dynamic conditions. Both the extended and shortened models (550 mm and 150 mm) along with the fully 3D model provided good correlation with the experimental results on the velocity at impact with deviations in the impact time against the ABAQUS-EPGEN (V4.5) and TEDEL (CEASEMT-V1) codes which used specific beam formulations, which diminished certain structural parameters, like strain rate and crushing stiffness. The pipe models were then compared for the accuracy on structural response from geometrical variance. The displacement and stresses came into good agreement with all three models while the 150 mm model containing much fewer elements than the 550 mm model decreased the computational time by 55%.  
Thus, the simplified 1D/3D coupled modeling approach by minimizing the area of the solid region by constricting it to the impact area with appropriate contact modeling at the junctures can be considered in the future for decreasing the computational time and the formulation of finite elements. This computational research can be applied to internal FSI problems involving dynamic response and structural non-linearity. Structural impact simulations can further be developed using beam-shell element formulation by prioritizingthe contact models for localized coupling approaches and to estimate the various other mechanical characteristics, like strain rate and localized stiffness behavior.  
Conceptualization, G.D.; methodology, G.D. and I.S.; software, I.S.; validation, G.D. and I.S.; formal analysis, I.S.; investigation, G.D.; resources, I.S and G.D.; writing--original draft preparation, I.S.; writing--review and editing, I.S.; visualization, G.D.; supervision G.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research did not report any data.  
The authors declare no conflict of interest.
# Physarum-Inspired Bicycle Lane Network Design in a Congested Megacity
## 4 Computational Experiments
### Mobility Problems and BLN Prospects in a Congested Mega City: Dhaka as a Case Study  
Dhaka is the ninth-largest and the sixth-most densely populated city in the world, with a population of over 21 million. Moreover, the population of Dhaka is increasing daily and putting pressure on public transportation systems that are not well planned for expansion. The city has neither efficient public transport nor mass transit [41]. The city's road transport systems are not capable of handling huge traffic; thus, the city is very congested at present, especially in the city centre. On the other hand, every year about twenty thousand private cars are introduced [42]. According to a World Bank report, the average traffic speed in Dhaka has dropped from 21 km/h to 7 km/h in the last several years, only slightly above the average walking speed [43]. By 2035 the speed could drop to 4 km/h, which is slower than the standard walking speed [42]. Traffic congestion in Dhaka consumes about 5 million working hours a day and costs the country $11.4 billion a year [42]. Additionally, traffic jams and huge vehicles worsen other problems like air pollution, noise pollution, extra fuel consumption, etc.  
To improve mobility and environmental impacts, construction of new roads or expansion of the existing road network(s) in the city is not feasible due to space scarcity and cost. Although vertical expansion with an elevated expressway and a metro service is under construction, such huge, costly facilities will cover only limited areas and might not solve mobility problems entirely. Therefore, how green mobility may be promoted within existing road networks is a challenging issue. Although bicycling is an alternative means of travel, in the present scenario, riding a bicycle is difficult (i.e., neither safe nor comfortable) in congested city areas due to space scarcity; footways are not suitable because street vendors and hawkers occupy these spaces, and in some cases, footways do not exist [14]. In such a scenario, the introduction of a dedicated BLN, using local link roads (avoiding main roads) to connect potential locations, can yield several advantages. Firstly, it may encourage many people to use green mobility for short-distance travel. Secondly, it may alleviate overloaded transport and congestion, which directly reduces environmental impacts and wastage of time. Moreover, the bicycle is less expensive than other forms of transportation; and the use of existing local roads for the BLN might not incur construction costs, as it is a matter of planning and declaring selected roads for use in the BLN. Finally, the BLN would provide a valuable link to the future mass transit system, e.g., the metro rail network, and contribute to shifting the city's mobility paradigm from a congested megacity to a smart megacity.
## 4 Results and Discussion
### Computation Data Visualisation
However, the Q-factor is related to the OSNR by the following equation:  
\[\text{Q}_{\text{Factor}}=\text{OSNR}\;+\;10\log\frac{\text{B}_{\text{O}}}{ \text{B}_{\text{e}}}. \tag{27}\]  
In the above equation, \(\text{B}_{\text{o}}\) is the optical bandwidth of the photodetector (end device) and \(\text{B}_{\text{e}}\) is the electronic bandwidth of the receiver electronic filter. Hence, we can say that Q is somewhat proportional to the OSNR. There is OSNR and Q-factor Interdependency, depending on whether the system is single-wavelength or multiwavelength. In a single wavelength system (which is the assumption in our paper), the OSNR and Q-factor have a direct relationship. Increasing the OSNR improves the Q-factor, resulting in better signal quality. Conversely, decreasing the OSNR degrades the Q-factor, leading to a higher BER and poor signal quality. But this is not completely true for a multiwavelength system. The interdependence of OSNR and Q-factor is more complex.
## 1 Introduction  
With the fast development of Micro Electro Mechanical Systems (MEMS) and micromachining technology, micro-parts at micro scale are developed and used rapidly, such as micro gears, micro motors and micro sensors, _etc._ These micro-parts have sizes ranging from sub-millimeter to tens of millimeters, and their local geometrical features need to be measured at sub-micrometer accuracy. In many cases, the complete inner and outer geometry of these micro-parts must be verified to ensure their quality and functionality, whereas small geometrical features are inaccessible by conventional Coordinating Measuring Machines (CMMs). In order to measure those microparts, some novel CMMs with accuracy of tens of nanometers (called micro CMM or nano CMM) have been developed [1], such as the Molecular Measuring Machine developed by the National Institute of Standards and Technology (NIST) [2], the High-Precision Micro-CMM developed by the University of Tokyo and the National Institute of Advanced Industrial Science and Technology (AIST) [3], the special CMM developed by the Physikalisch-Technische Bundesanstalt (PTB) [4], the small-sized CMM developed by the National Physical Laboratory (NPL) [5], the Nanopositioning and Nanomeasuring Machine (NPMM) developed by the Ilmenau University of Technology [6; 7; 8], _etc._ The measurement range of these micro CMMs is not larger than 50 mm in the \(X\), \(Y\) and \(Z\) directions. Some other CMMs with a large measurement range have also been used in practice, for example, Zeiss F25 [9], Isara 400 [10], _etc._In this paper, an innovative micro CMM with zero Abbe error has been developed which includes some new design ideas, such as the self-made probe system, the metrological system and the co-planar stage. It has achieved the measuring uncertainty with 40 mm of about 100 nm (\(2\sigma\)).
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
## 6 Simulation
### Simulation Results
#### 6.2.3 Impact of Message Generation Interval  
Figure 5 shows the impact of the message generation interval. From Figure 5a, we can see that the delivery rates of Epidemic and Prophet are very low when the increase of the message generation interval is small, which indicates that in the high-intensity transmission scenario, the high message discard rate caused by the uncapped replication method will seriously reduce the final delivery rate of messages. In the low-intensity transmission scenario, the delivery rate of AR-RT has an advantage due to its ability to dynamically adjust the number of copies of the initial message. From Figure 5b, it can be seen that the average delay of Epidemic and Prophet gradually decreases as the message generation interval increases, but always remains at a high level. The average delay of Bubble Rap remains stable and at a high level, and the average delay of the remaining  
Figure 4: Comparison of the results (95% CI) of various algorithms under different TTL. Subfigures (**a–d**) compare the performance of the five algorithms with the variation of the four parameters, delivery rate (**a**), average delay (**b**), overhead (**c**) and average hop count (**d**), with TTL, respectively.  
two algorithms remains stable and at a low level, with the average delay of AR-RT being smaller. Figure 5c shows that the network overhead of Epidemic increases significantly as the message generation interval increases, the network overhead of Prophet increases and then stabilizes, the network overhead of AR-RT increases slightly but is within an acceptable range, and the network overhead of Bubble Rap remains stable and is always at the lowest level. Figure 5d shows that as the message generation interval increases, the average hop count of Epidemic and Prophet increases and then decreases, but always remains at a high level, and the average hop count of the remaining three algorithms remains stable, with AR-RT performing better.
# Reduction of Coupling Interface Degrees of Freedom in Mixed-Interface Component Mode Synthesis
## 3 Normal Mode Correction for the Master Substructure
### Static Condensation of the Slave Substructure  
One way of normal mode correction is static condensation [39], which imposes the effect of the slave substructure on the coupling interface of the master substructure. The coordinate transformation of static condensation can be written as \[\left\{\mathbf{u}_{c}\right\}=\left\{\begin{array}{c}\mathbf{u}_{c}^{I}\\ \mathbf{u}_{c}^{B}\end{array}\right\}=\left[\begin{array}{c}\mathbf{\Phi}_{c}\\ \mathbf{I}\end{array}\right]\left\{\mathbf{u}_{c}^{B}\right\}=\left[\mathbf{\Psi}_{c} \right]\left\{\mathbf{u}_{c}^{B}\right\} \tag{14}\]  
Then, the generalized stiffness and mass matrices of the slave substructure are  
\[\left[\mathbf{K}_{c}^{*}\right]=\left[\mathbf{\Psi}_{c}^{\mathrm{T}}\right]\left[\mathbf{K }_{c}\right]\left[\mathbf{\Psi}_{c}\right]\quad,\quad\left[\mathbf{M}_{c}^{*}\right]= \left[\mathbf{\Psi}_{c}^{\mathrm{T}}\right]\left[\mathbf{M}_{c}\right]\left[\mathbf{\Psi}_ {c}\right] \tag{15}\]  
Adding \(\mathbf{K}_{c}^{*}\) and \(\mathbf{M}_{c}^{*}\) on the original stiffness and mass matrices of the master substructure that corresponds to the coupling interface, i.e., \(\mathbf{K}_{f}^{BB}\) and \(\mathbf{M}_{f}^{BB}\). The revised stiffness and mass matrices of the master substructure and the eigenvalue problem are,  
\[\left[\mathbf{K}_{f}^{*}\right]=\left[\mathbf{K}_{f}\right]+\left[\mathbf{K}_{c}^{*} \right]\quad,\quad\left[\mathbf{M}_{f}^{*}\right]=\left[\mathbf{M}_{f}\right]+\left[ \mathbf{M}_{c}^{*}\right] \tag{16}\]  
\[\left(\left[\mathbf{K}_{f}^{*}\right]-\omega^{2}\left[\mathbf{M}_{f}^{*}\right]\right) \left\{\mathbf{\phi}\right\}=\mathbf{0} \tag{17}\]  
In this case, the order number of the eigenvalue problem is totally as same as the master substructure.
## 2 Materials and Methods
### Eligibility Criteria  
The reviewers, who worked in pairs, chose works that satisfied the following criteria for inclusion: (1) human subject-only research; (2) clinical studies (3); research concerning children treated with SDF on deciduous molars.  
Exclusion criteria were (1) in vitro studies (2), animal studies, (3) systematic reviews, narrative reviews, and meta-analyses (4) case reports. Duplicate studies were removed manually.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multirow{4}{*}{Article screening Strategy} & Database: Scopus, Web of Science and Pubmed \\ \cline{2-3}  & Keywords: “silver diamine fluoride” AND (“primary molar” OR \\  & “primary molars”) \\ \cline{2-3}  & Boolean variable: “AND” and “OR” \\ \cline{2-3}  & Timespan: 2013–2023 \\ \cline{2-3}  & Language: English \\ \hline \hline \end{tabular}
\end{table}
Table 1: Database search indicators.  
Figure 2: Before (**A**) and after (**B**) the application of SDF around the embrasures of carious lesions with the use of a microbrush.  
The review was conducted using PICO criteria:  
* Population: children, both male and female, who had a primary molar treated with SDF;
* Intervention: SDF on deciduous molar;
* Comparison: untreated deciduous molars with SDF;
* Outcome: effectiveness of SDF in arresting caries or preventing its development on deciduous molars.
## 3 Results
### Synthesized Data Experiments
#### 3.2.1 Quantitative Comparisons Using Synthesized S2 Data
These bands are again highlighted because they are hard to super-resolve. MuSA appears to have a constant error in the 60 m band, B9, as can be seen in Figure 7c but does much better for B12. Strange geometrical patterns are also visible in the 60 m\(\rightarrow\)10 m ATPRK image in subfigure a. The disadvantages of DSen2 and Sen2 res are visible in subfigures b and f, respectively. Their residuals are larger than for the remaining methods. SSSS appears to give the best results in this case, with the least apparent structural textures visible in subfigure e, despite Table 3 showing that it has higher 60 m RMSE than S2 Sharp and SupReME, whose localized errors are more eye-catching. The results for B12 are more similar. The fact that building shapes are discernible in most error maps suggests structural image definition has been lost compared to the ground truths. In Figure 8 the errors appear to be more evenly distributed over the whole image for DSen2 and Sen2 res. More localized errors reveal clear building shapes with Musa, S2 Sharp, SSSS, and SupReME. ATPRK and S2 SSC give the visibly best result, which broadly matches the SRE results in Table 3.  
Figure 7: Absolute error map of Escondido crop, band B9, on log scale. Lower values are better.