esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Convective Heat Transfer Modelling in CFD Simulations of Fire-Driven Flows
## 3 Main Modelling Approaches
### Experimental Correlations
#### 3.1.1 Non-Dimensional Numbers  
Before presenting the main experimental correlations, an overview of the non-dimensional numbers involved in this approach is presented below:  
* Reynolds number: \[Re=\frac{uL_{c}}{\
u}=\frac{\rho uL_{c}}{\mu},\] (10) expressing the ratio of inertial forces to viscous forces, is typically used to characterize whether a flow is laminar or turbulent in forced convection problems. The critical Reynolds number (i.e., number above which the flow is turbulent) varies depending on the flow configuration (e.g., \(Re_{crit}=5\times 10^{5}\) for flow over a plate) and can be used to calculate the critical distance, \(x_{cr}\), from the leading edge of a surface, where the flow becomes turbulent.
* Grashof number: \[Gr=\frac{g\beta(T_{w}-T_{\infty})L_{c}^{3}}{\
u^{2}}\] (11)expressing the ratio of buoyancy forces to viscous forces, is typically used in natural convection problems.
* Prandtl number: \[Pr=\frac{v}{\alpha}=\frac{\mu c_{p}}{k}\] (12) expressing the ratio of molecular diffusivity of momentum to molecular diffusivity of heat across a fluid layer. The Prandtl number is used to characterize the relative thickness of the momentum and thermal boundary layers (e.g., thicker thermal boundary layer than momentum boundary layer for \(Pr<1\)).
* Nusselt number: \[Nu=\frac{hL_{c}}{k}\] (13) expressing the ratio of convective to conductive heat transfer across a fluid layer. For forced convection \(Nu=f(Re,Pr)\) while for natural convection \(Nu=f(Gr,Pr)\).
* Rayleigh number: \[Ra=Gr\cdot Pr\] (14) expressing the ratio of buoyancy and viscosity forces multiplied by the ratio of momentum and thermal diffusivities, is typically used to characterise whether a flow is laminar or turbulent in natural convection scenarios. The critical Rayleigh number varies depending on the flow configuration and can be used to calculate the critical distance, \(x_{cr}\), from the leading edge of a surface, where the flow becomes turbulent.  
In the equations above, \(\rho\) is the density, \(u\) is the velocity, \(L_{c}\) is the characteristic length (to be discussed in more detail below), \(v\) is the kinematic viscosity, \(\mu\) is the dynamic viscosity, \(k\) is the thermal conductivity, \(c_{p}\) is the specific heat and \(\beta\) is the volume expansion coefficient (\(\beta=1/T\) for ideal gases) of the fluid.
## 6 Recommendations
### Improve Calibration Methodologies
While this effort will place all the instruments on a consistent radiometric scale, it does not account for differences in sensor design (for example, spectral bandpass differences). Thus, because of this, when two sensors view the same target at the same time, there will be differences in sensor output. This observation advocates for sensor designs in the future that can mitigate these effects. In contrast, the Sentinel 2 program has been operational for only two years at the time of writing, but does have two nearly identical sensors in orbit collecting data. This recommendation, coupled with the recommendation for collection-based processing, provides clear guidance to the program for consistent cross-calibration of Sentinel 2 A and B as well as follow-on instruments. Currently, these two sensors show an approximate one percent difference in calibration (See Section 3). Resolving this issue is the clear recommendation. The second recommendation for cross-calibration was to generate consistent cross-calibration coefficients for Landsat and Sentinel 2. Differences between the two sensor systems (Landsat 8 OLI and Sentinel 2 A/B) were shown at the workshop to be 1-2%. At a minimum, the recommendation would be to publish values that would place Landsat on the Sentinel 2 radiometric scale, and vice-versa, resulting in two sets of coefficients. A slightly more difficult approach, but yet perhaps of more benefit to users, would be to use one system as the reference and cross-calibrating the other to it. On a broader scale, this is a need that permeates the remote-sensing community in that all optical sensors could be cross-calibrated to a reference sensor. This would be of significant benefit to all who use remote-sensing data. Third in the list of cross-calibration recommendations is the coordination of top-of-atmosphere (TOA) cross-calibration comparisons between Landsat and Sentinel 2. This could be effected by taking advantage of simultaneous nadir overpasses (SNO) whenever possible, or near-SNO opportunities over stable targets. The recommendation suggests developing a standard procedure for performing these types of comparisons, and implementing them on a regular basis. Inherent in this recommendation is the need for making observations in a manner that accounts for differences in sensors, viewing/illumination angles, coupled with target surface properties and atmospheric effects. All sensors could benefit from this type of activity, so in principle the recommendation is easily extended to the broader community; however, implementation details will have to be thoroughly addressed for successful comparisons. As a final note on this topic, it was recommended that cross-calibration activities mentioned here should be consistently used throughout the lifetime of each mission. This should be considered part of the normal operations for both the Sentinel 2 and Landsat programs.
# A Heterogeneous Multi-Satellite Dynamic Mission Planning Method Based on Metaheuristic Algorithms
###### Abstract  
The increasing demand for satellite communication necessitates efficient resource management, especially as next-generation high-throughput satellites (HTSs) face challenges in optimizing user connections. This paper presents a novel method that integrates a discretely improved Particle Swarm Optimization (PSO) algorithm with a dynamic task management framework to enhance satellite resource allocation efficiency. The PSO algorithm is adapted for discrete selection problems to maximize a fitness function based on user priority, user preference, and capacity satisfaction, thus ensuring accurate user-satellite matching. A main loop function iteratively updates user data and connection statuses, thus achieving continuous optimization of satellite connections. Through simulation studies, we validated the effectiveness of this method under dynamically changing user demands, demonstrating that the Discrete PSO algorithm significantly outperforms Simulated Annealing (SA) and the Genetic Algorithm (GA) in user satisfaction, maintaining levels above 0.996 even under high demand. Additionally, it effectively prioritizes high-demand users, ensuring their satisfaction remains above 0.95. Overall, our method enhances the management of daily communication tasks, significantly improving service quality and user satisfaction.  
satellite communication; Particle Swarm Optimization; dynamic task management; resource allocation +
Footnote †: journal: Electronics Electronics
## 4 Results and Discussion
### Whole Finger Model  
After performing the ratio calibrations considering the whole finger model, the results are shown in Figure 6; from the EGA, Zone A contained 16 samples (80%), Zone B contained 4 samples (20%), and Zone C contained 0 samples (0%). The Bland-Altman analysis indicated that the bias was \(-\)0.0718 \(\pm\) 0.9260, and the limits of agreement ranged from \(-\)1.00 to 0.85. The limits of agreement of the whole finger model were smaller than those of the blood vessel model. For the estimated \(\%HbAlc\) values, statistical analysis using MSE, ME, MAD, RMSE, and Pearson's r yielded 0.224, \(-\)0.0559, 0.3914, 0.4736, and 0.9052, respectively.  
We considered value (_HbAlc_) calibrations in addition to ratio calibrations, and the EGA and Bland-Altman analysis results are shown in Figure 7. From the EGA, Zone A contained 17 samples (85%), Zone B contained 3 samples (15%), and Zone C contained 0 samples (0%). The Bland-Altman analysis indicated that the bias was 0.0066 \(\pm\) 0.8623, and the limits of agreement ranged from \(-\)0.86 to 0.87. For the estimated \(\%HbAlc\) values, statistical analysis using MSE, ME, MAD, RMSE, and Pearson's r yielded 0.194, 0.0066, 0.3662, 0.4400, and 0.9296, respectively.  
Figure 5: EGA and Bland–Altman analysis for the _HbAlc_ estimation with the blood vessel model (considering both ratio and value calibrations).  
Figure 6: EGA and Bland–Altman analysis for _HbAlc_ with the whole finger model (considering ratio calibrations only).
## 5 Conclusions  
The performance analysis of the CMOS plasmon detector in the subthreshold region was examined by using the equivalent circuit model, which consists of superposition and small-signal methods. The superposition method explains the effect of the transmission lines at the input stage of the detector  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **Freq.** & **CMOS** & **Responsivity** & **NEP** \\  & **[GHz]** & **Technology** & **1 [KV/W]** & **[pW/Hz]** \\ \hline \multirow{5}{*}{This work} & [20] & 650 & 0.25 \(\upmu\)m & 80 & 300 \\  & [28] & 856 & 65 nm & 140 & 100 \\  & [29] & 270 & 0.13 \(\upmu\)m & 300 & 18.7 \\ \cline{1-1}  & [30] & 365 & 90 nm & 1200 & 200 \\ \cline{1-1}  & without preamplifier & 200 & 0.25 \(\upmu\)m & 202 & 130 \\ \cline{1-1}  & including preamplifier & 200 & 0.25 \(\upmu\)m & 482 & 39.3 \\ \hline \hline \end{tabular}  
* Gate bias voltages and voltage gains of the main amplifier are different in each detector.  
\end{table}
Table 1: Comparison of the CMOS detector performances  
Figure 8: Signal-to-noise ratio at the outputs of the two detector integrated circuits using the modulation frequency of 200 Hz in the harmonic mixer.  
Figure 7: Measured voltage responsivity and noise equivalent power (NEP): (**a**) A detector without the preamplifier; (**b**) A detector including the preamplifier.  
using the characteristic impedances. The plasmon detection at the subthreshold region was described by using the small-signal method. The analysis results expressed the voltage responsivity and the NEP of the detector as a function for the gate-to-source voltage. As a result of the analysis, the relationship between the input and output impedances of the detector and detector performance shows that the buffer amplifier at the output of the detector core increases the performance more than the characteristic enhancement by the gain of the amplifier. The detection characteristics and performance improvement were demonstrated in the measurement results of two fabricated detectors operating at 200 GHz. The proposed analysis shows that the method to improve the performances are presented for the CMOS plasmon detector by using the simple equivalent model.  
J.-H.S. and J.-R.Y. conceived and designed the experiments; J.-H.S. performed the experiments; J.-H.S. and J.-R.Y. analyzed the data; J.-R.Y. contributed reagents/materials/analysis tools; J.-H.S. and J.-R.Y. wrote the paper.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (grant number 2017R1C1B2002285). The chip fabrication and EDA tool were partially supported by the IC Design Education Center (IDEC), Korea.  
The authors declare no conflict of interest.
## 2 Regulatory Aspects
### Usa  
The Food and Drug Administration (FDA) approved the use of BPA under the food additive petition (FAP) process in 1960. In 2008, FDA released a draft describing the safety assessment of BPA using available data on the toxicity of BPA. In this draft, the FDA recommended that the No Observed Adverse Effect Level (NOAEL) for BPA for systemic toxicity be set at 5 mg/kg bW/day derived from two multigenerational rodent studies [30]. The 2009 CFSAN (Center for Food Safety and Applied Nutrition) low-dose updates along with 2011, 2012, 2014 Working Group assessments constituted a progressive series of evaluations with each subsequent memorandum building on the conclusions from the previous memoranda. While the FDA ruled that BPA-based epoxy resins would no longer be used as coatings in packaging of infant formula with effect from July 2013, this decision was taken largely due to abandonment by manufacturers, rather than due to concerns about BPA safety [31]. After finishing its ongoing safety review in 2014, the FDA announced that BPA was safe for the currently approved uses in food containers and packaging [32].  
However, similar to Europe, several States within the USA have drafted legislation designed to offset the potential negative health effects of BPA used in many consumer products. For example, the Californian assembly bill 1319 (2011) prohibits the manufacture, sale or distribution of BPA-containing bottles or cups with a detection level above 0.1\(\upmu\)g/kg if these products are meant to be filled with food items consumed by children, effective from July 2013. Similarly, the Connecticut House Bill 6572 (2009), Maryland House Bill 33(2010) and Senate Bill 213 (2010), Wisconsin Senate Bill 271 (2010), Delaware Senate Bill 70 (2011), and Illinois Senate Bill 2950 (2011) prohibit the manufacture, sale or distribution of children's food and beverage containers that contain BPA [33].
###### Abstract
In [26], the authors exposed glass blended mortar to temperatures of 500 \({}^{\circ}\)C and 800 \({}^{\circ}\)C. Better performance was experienced in glass blended mortar below 500 \({}^{\circ}\)C, but worse performance was recorded within 500-800 \({}^{\circ}\)C with reference to the control while the strength loss was adduced to the dehydration of portlandite [26]. The strength loss of partial Portland cement replacement in mortar by the fine glass powder cementitious materials was studied by [26]. This study showed that 20% glass powder mortar mix had the highest residual strength (15% loss) due to the reduction in portlandite in the mixture. The incorporation of glass waste into concrete containing silica fume and flyash had low density and absorption, even though compressive and tensile strength were lower than the value obtained from conventional concrete [27]. In [28], the authors conducted a review on silica fume and glass powder, while in recent times, the authors of [29] investigated ternary blended concrete containing silica fume, waste glass powder and ordinary Portland cement. Most of these studies reported the strength performance of glass within 28 days and durability characteristics such as shrinkage, absorption and thermal properties of paste and mortars. This study focuses on both mortar and concrete in terms of fresh properties, strength characteristics with predicting models and thermal performance of concrete prepared at a lower cement ratio. Furthermore, the study also presents an understanding of the microstructural characteristics, mineral phases, and bond characteristics of Si-O-Si, -OH and C-O using scanning electron microscope/energy dispersive spectroscopy, X-ray diffraction and Fourier Transform Infrared spectroscopy, respectively. This study gives a better understanding of the behavior of concrete in terms of strength and thermal performance for infrastructural applications.
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 6 Results and Analysis
### Battery Parameter Estimation  
The parametrization is performed using the HPPC dataset at 25 \({}^{\circ}\)C. Initial values are assigned for the parameters, \(V_{oc},R_{0},R_{1},\tau_{1},\ \ R_{2},\tau_{2}\) for each \(SOC\) = [0:0.1:1], with \"1\" equivalent to a battery 100% charged.  
The HPPC dataset is then loaded into the parameter estimator tool [46]. The used software formulates the parameter estimation as an optimization problem, aiming at minimizing a sum-squared error (SSE) cost function.  
Table 1 shows the results for the parameters for the \(SOC\) = [0:0.1:1]. The results for the battery parameter optimization is depicted in Figure 4. The first plot shows the current (A) as detailed in the previous section, that is, pulses of 10 s equivalent to 0.5C, 1C, 2C, 4C, and 6C, with each pulse set apart by 20 min rest. A total of five pulse discharge HPPC tests were performed at each \(SOC\) = [100%:5%:0%].  
Both the terminal voltage and the SOC without the parameter optimization are shown in the two subsequent plots: it can be seen that the model running with non-optimized  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{_SOC_} & \(\begin{array}{c}\mathit{VOC}\\ \mathbf{(V)}\end{array}\) & \(\begin{array}{c}\mathbf{R_{0}}\\ \mathbf{(\Omega)}\end{array}\) & \(\begin{array}{c}\mathbf{R_{1}}\\ \mathbf{(\Omega)}\end{array}\) & \(\begin{array}{c}\mathbf{\tau_{1}}\\ \mathbf{(s)}\end{array}\) & \(\begin{array}{c}\mathbf{R_{2}}\\ \mathbf{(\Omega)}\end{array}\) & \(\begin{array}{c}\mathbf{\tau_{2}}\\ \mathbf{(s)}\end{array}\) \\ \hline
0.10 & 2.6216 & 0.0999 & 0.0098 & 1.1541 & 0.0097 & 12.7400 \\ \hline
0.20 & 3.9563 & 0.1000 & 0.0099 & 66.8010 & 0.0097 & 48.8030 \\ \hline
0.30 & 3.7915 & 0.0353 & 0.0100 & 49.5000 & 0.0097 & 52.7700 \\ \hline
0.40 & 3.8678 & 0.0696 & 0.0100 & 73.2770 & 0.0097 & 58.1370 \\ \hline
0.50 & 3.8172 & 0.04304 & 0.0099 & 47.4710 & 0.0097 & 51.9410 \\ \hline
0.60 & 3.8620 & 0.0666 & 0.0099 & 72.8030 & 0.0097 & 57.7570 \\ \hline
0.70 & 3.4726 & 0.0272 & 0.0006 & 3.5125 & 0.0042 & 173.9300 \\ \hline
0.80 & 3.7527 & 0.0291 & 0.0076 & 12.7400 & 0.0094 & 123.7300 \\ \hline
0.90 & 4.0085 & 0.02918 & 0.0070 & 8.6985 & 0.0100 & 37.9880 \\ \hline
1.00 & 4.1717 & 0.0357 & 0.0100 & 11.9480 & 0.0075 & 79.9610 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Battery ECM parameter optimization for different SOC levels.  
Figure 4: HPPC with and without battery parameter optimization.  
parameters does not reach the target values. The voltage (ECM), modeled with non-optimized parameters \(R_{0},R_{1},\tau_{1},R_{2},\tau_{2}\) does not fit the Voltage (Actual) profile. Similarly, the SOC (EKF), computed according to Section 3.1, does not follow the SOC (Coulomb) reported by the battery intrinsic modeling.  
However, when the battery ECM is loaded with the values shown in Table 1, both the terminal voltage and the SOC meet the accuracy required and track the target values.
## 9 Routing in CRSNs
### CRSN Routing Modules
#### 9.1.2.2 Control Signaling  
Control signaling is the process whereby nodes with data to send to the sink negotiate for a path with their neighbors via a CCC. Although Section 3.2.1 laid the basis for control signaling, we only mention the most relevant studies in this respect under the following categories: dedicated common control channel, sequence-based control channel negotiation and group-based control channel.
## 5 Case Studies  
The Northeast Power Coordinating Council (NPCC) 140-bus system, representing the equivalent power grid in the Northeastern United States, is utilized as a test system [24; 25]. The simulations are carried out in MATLAB environment on a computer with an Intel Core i5 3.3 GHZ processor and 8 GB of RAM.  
For database generation, the active power output and the terminal voltage of generators vary within \(\pm\)20% and \(\pm\)2% of the base operating condition, respectively, and the active and reactive power of loads both vary within \(\pm\)20% of the base operating condition. A transmission line with permanent three-phase short-circuit is randomly selected as the fault condition and the fault duration is set to 0.12 s.  
A total of 16,000 samples with 270 features are generated to form the original database of the TSSP. After two-stage feature selection preprocessing, 87 features are retained and the classification database is formed. A total of 60% of the classification database are randomly selected as the training set. Another 20% are randomly selected as the validation set and the remaining 20% are formed as the testing set. The sample distribution is shown in Table 3.  
From Table 3, there is an obvious imbalanced data problem in the classification database and the number of stable samples is about 4.8 times the number of unstable samples.  
After applying the data segmentation strategy, the sample distribution in each training subset is tabulated in Table 4.  
Figure 4: Flowchart of the DSEC method for TSSP.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{4}{c}{**Classification Database**} \\ \cline{2-5}  & **Training Set** & **Validation Set** & **Testing Set** & **Total** \\ \hline Stable samples & 7911 & 2636 & 2692 & 13239 \\ Unstable samples & 1689 & 564 & 508 & 2761 \\ Total & 9600 & 3200 & 3200 & 16000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample distribution in the classification database.
## 4 Discussion
### Results and Analysysys
We assumed that the relay vehicle situated in the middle position between the request vehicle and content source vehicle in case 2 of our theory analysis, which makes the theory value higher than the simulation value. Figure 8: The relationship between the Request Success Ratio and content size. Figure 7: The relationship between the Average Delay and content size. Figures 11 and 12 show the variations of the Average Delay and the Request Success Ratio in the case of different Zipf distribution parameters. As depicted in the graphs, the request delay of vehicles decreased, and the probability of content success transmission increased as the parameter increased. This is because increasing the Zipf distribution parameter increases the probability that a vehicle can find desired content in neighboring vehicles. Compared with the strategy described by the authors of [19], our strategy possesses a lower request delay and higher probability of content success transmission. Because our strategy caches content chunks at multiple vehicles in a cluster, request vehicles can obtain content chunks from vehicles in the cluster one by one, which increases the transmission duration between vehicles, besides. Our strategy also adopts the multiuser multichannel transmission mode in the communication process between clusters to solve the problem of the communication duration being too short to complete content transmission. The probability that a vehicle can obtain content with the participation of RSU decreased as the  
Figure 10: The relationship between the Request Success Ratio and vehicle caching size. Figure 9: The relationship between the Average Delay and vehicle caching size. Zipf distribution parameter increased, which weakened the advantages of our strategy, so the gap in performance between those two strategies decreased as the parameter increased. Figure 12 also illustrates that the theory value and the simulation value of the Request Success Ratio were similar to each other. The former was slightly higher than the latter, and the gap increased as the Zipf distribution parameter increased. This is because the probability that a vehicle will obtain content from neighboring vehicles increases as the Zipf distribution parameter increases. We assumed that the relay vehicle situated in the middle position between the request vehicle and the content source vehicle in case 2 of our theory analysis, which makes theory value higher than the simulation value. Figure 11: The relationship between Average Delay and Zipf distribution parameter. Figure 12: The relationship between Request Success Ratio and Zipf distribution parameter.