esa-sceva/satcom-chunk-collection huggingface dataset:
# Transient Numerical Modelling of the Pin-in-Paste Technology
## 2 Materials and Methods
### Model Validation  
The model was validated by comparing calculated hole-fillings (for various hole diameters and printing speeds) to measured hole-fillings. For the hole-filling measurement, a 1.55 mm thick printed circuit board was designed, which included through-holes with different diameters (0.8, 1.0, 1.1 and 1.4 mm), and solder paste was deposited by stencil printing using different printing speeds (from 20 to 70 mm/s). The solder paste used was a Type-4 (particle size range is 20-38 \(\upmu\)m) lead-free paste, manufactured by KOKI. The composition of the alloy in the paste was SAC305 (Sn/3 Ag/0.5 Cu in wt%). The solidus and liquidus points of this alloy are 217 and 220 \({}^{\circ}\)C, respectively. The density is 7380 kg/m\({}^{3}\), and the (as cast) yield strength and ultimate tensile strength are 26-31 and 30-39 MPa, respectively [23]. The hole-fillings were measured by determining the ratio between the projected area of the solder paste and the projected area of the through-holes in side-view X-ray images. In Figure 4, for example, the projected area of the solder paste is 28,256 px, and the projected area of the through-hole is 38,000 px (152 \(\cdot\) 250), resulting in a relative hole-filling of 74.4%.  
The average error between the calculated (Figure 5) and measured hole-fillings was 6%, whereas the maximum calculation error was 12.8% (Figure 6). The model was considered appropriate for analyzing the hole-filling for various hole shapes and arrangements based on the comparison.  
Figure 4: Measuring the hole-filling by the solder paste on a side-view X-ray image; based on comparing the projected area of the solder paste to the projected area of the hole (\(d_{hole}\)-\(l_{hole}\)), the hole-filling is 74.4% in this specific case.  
Figure 5: Calculating the hole-filling by the numerical model.
# Noise-Assessment-Based Screening Method for Remote Photoplethysmography Estimation
## 2 Related Work
### Multi Channel rPPG Method  
CHROM [6] is a chrominance-based method that primarily focuses on extracting diffuse reflection associated with pulsation while eliminating specular reflection, which does not contain pulsatile signals. By utilizing a linear combination within the RGB domain, a chrominance space is defined where specular reflections are effectively suppressed. This method is commonly classified as a model-based approach, as it leverages the optical properties of remote photoplethysmography (rPPG). Among the algorithm-based approaches, CHROM demonstrates state-of-the-art (SOTA) performance and has been employed in the experiments conducted for this study. The effectiveness of the proposed method in enhancing performance has been verified [6].  
POS [7] introduced a motion-noise robust method based on the chrominance-based approach. The proposed method by POS effectively eliminates noise components and extracts the pulse signal by employing a projection plane orthogonal to the skin tone. Similar to CHROM, POS is considered a model-based method that demonstrates state-of-the-art (SOTA) performance among the proposed techniques. Through experimental validation using our proposed approach, significant performance enhancement has been confirmed [7].  
The 3 DCNN [13] method employs deep learning techniques. This approach utilizes a three-dimensional input structure, where image data from each frame are treated as two-dimensional input, and the 3D data are constructed along the temporal axis of the video. To facilitate training and testing, the OBF dataset [14] and the MAHNOB-HCI dataset [15], both comprising RGB images, are employed. However, this study also identified several challenges inherent in deep learning models. These challenges encompass the requirement for a substantial amount of training data, difficulties in utilizing 3 DCNN within embedded environments, and the model's dependency on the specific training dataset, which hampers its generalization to different datasets. Addressing these limitations in future investigations is crucial to enhancing the applicability and effectiveness of the 3 DCNN method [13].
## 4 Discussion
Patient selection bias was mitigated by consecutive enrollment. Information bias is expected to be minimal, considering the low missing data rate (<1%) and short recall period (\(\leq\)7 days) of PROs. Nonetheless, past HCRU estimates may be affected by recall bias, while those pertaining to the last year before the visit may have been underestimated as a result of the COVID-19 pandemic-related public health/social measures taken, including transport restrictions and healthcare access constraints. Though the extent and persistence of the COVID-19 pandemic's negative effects on the subjects' well-being are unclear [66; 67; 68; 69], they should be taken into consideration when interpreting this study's HRQoL-related findings. Furthermore, in the absence of statistical comparisons, any observed similarities/differences between the m-AD and s-AD subpopulations are purely descriptive. It should be further noted that, although no specific diagnosis criteria were enforced for study participation, this is not expected to have introduced any bias since clinical diagnosis was confirmed at the expert centers. On the other hand, the fact that the APOLO study included patients seeking consultation for the first time in expert centers may partly account for the high severity scores and PROs; hence, the results might not be entirely generalizable to the M2S-AD population encountered in routine care settings. Moreover, since this study was conducted solely within Greece, the findings may not be fully generalizable to other settings. Factors such as the specific ethnic composition, healthcare access, socioeconomic background, and cultural attitudes prevalent in Greece could affect the captured burden of atopic dermatitis. Consequently, the results should be interpreted with caution when extrapolating to populations with different demographics and healthcare system profiles.
_Article_  
Numerical Predictions of Early Stage Turbulence in Oscillatory Flow across Parallel-Plate Heat  
Exchangers of a Thermoacoustic System  
[PERSON] 1 and [PERSON] 2,*  
1 Centre for Advanced Research on Energy, Faculty of Mechanical Engineering,  
Universitat Teknikal Malaysia Melaka, Hang Tuan Jaya, Durian Tunggal, Melaka 76100, Malaysia;  
[EMAIL_ADDRESS]
2 Faculty of Engineering, University of Leeds, Woodhouse Lane, Leeds LS2 9 JT, UK
* Correspondence: [EMAIL_ADDRESS]; Tel.: +44-113-343-4871  
Academic Editor: [PERSON] Received: 19 April 2017; Accepted: 31 May 2017; Published: 30 June 2017  
This work focuses on the predictions of turbulent transition in oscillatory flow subjected to temperature gradients, which often occurs within heat exchangers of thermoacoustic devices. A two-dimensional computational fluid dynamics (CFD) model was developed in ANSYS FLUENT and validated using the earlier experimental data. Four drive ratios (defined as maximum pressure amplitude to mean pressure) were investigated: 0.30%, 0.45%, 0.65% and 0.83%. It has been found that the introduction of the turbulence model at a drive ratio as low as 0.45% improves the predictions of flow structure compared to experiments, which indicates that turbulent transition may occur at much smaller flow amplitudes than previously thought. In the current investigation, the critical Reynolds number based on the thickness of Stokes' layer falls in the range between 70 and 100. The models tested included four variants of the RANS (Reynolds-Averaged Navier-Stokes) equations: k-\(\epsilon\), k-\(\omega\), shear-stress-transport (SST)-k-\(\omega\) and transition-SST, the laminar model being used as a reference. Discussions are based on velocity profiles, vorticity plots, viscous dissipation and the resulting heat transfer and their comparison with experimental results. The SST-k-\(\omega\) turbulence model and, in some cases, transition-SST provide the best fit of the velocity profile between numerical and experimental data (the value of the introduced metric measuring the deviation of the CFD velocity profiles from experiment is up to 43% lower than for the laminar model) and also give the best match in terms of calculated heat flux. The viscous dissipation also increases with an increase of the drive ratio. The results suggest that turbulence should be considered when designing thermoacoustic devices even in low-amplitude regimes in order to improve the performance predictions of thermoacoustic systems.  
parallel-plate heat exchanger; oscillatory flow; standing wave; thermoacoustic system; turbulence; transition
## 2 Literature Review
### The Attributes of Technological Factors
#### 2.3.2 Perceived Convenience  
Research has emphasized the critical role of convenience in perceived service quality and in driving the evaluation of SST [39]. [PERSON] et al. [40] asserted that the key attribute to retail success is retail location. A convenient location enhances consumers' ease of access to services. In this study, the perceived convenience of SST reflects its flexibility attribute for consumers. Compared with traditional staff-assisted services in physical stores, SST offers advantages such as time flexibility, self-paced operation, and accessibility [14; 18].  
The convenience and accessibility of service providers play an essential role in shaping consumer perceptions and behavior. Research suggests that when customers can easily access services, they tend to view the outcomes more favorably [41]. This phenomenon extends to various service contexts, including SST. Studies have shown that geographic proximity is a critical factor in channel selection for traditional purchases, with distance negatively affecting usage [42]. In the context of SST, location becomes even more crucial. When SST is in congested areas, users must consider additional environmental factors, potentially diverting their attention and energy from using the technology. Convenience has emerged as a key driver of customer satisfaction in service encounters. When consumers perceive SST as providing convenience benefits, they are more likely to report higher satisfaction levels [39]. These benefits often include increased transaction efficiency and effectiveness, reduced waiting time, and reduced physical and mental effort. The positive impact of convenience on service evaluations goes beyond satisfaction. It can influence customers' overall perceptions of service quality, their likelihood to use the service again, and their willingness to recommend the service to others. Therefore, service providers should prioritize convenience when designing and implementing SST to improve customer experience and drive positive outcomes.
## 2 The Permanent Facility for Altimeter Calibration
### Infrastructure and Instrumentation
#### 2.1.6 The Operations Control Center  
The central control of the PFAC takes place at the Operations Control Center at the Technical University of Crete, where all observations from all instrumentation in the field are transferred for archiving and analysis. Two file servers (one acts as back-up unit) are used for data archiving. The main file server, called \"THALES,\" and the back-up units are installed inside the University Data Center, where optimal and controlled computer conditions are kept throughout the year. Scientific software for data processing is installed in dedicated computer servers at the University as well (Figure 18).  
At times, precise orbit determination for the satellite altimeters has been supported by local Satellite Laser Ranging. As altimeters fly over Cal/Val sites and eccentric to their ground track, their orbit has been tracked by the French Ultra-Mobile Transportable Laser Ranging System, operated by the Observatoire de la Cote d'Azur, France. The observation campaigns took place between March and October 2003 (Figure 19). During these 6 months, the Jason-1 satellite has been tracked in ascending and descending orbits over the Gavdos Cal/Val sites. About 1400 observation passes have been collected for Jason-1 and TOPEX/Poseidon, as well as the Lageos, Starlette, and Stella satellites.  
Between May 2013 and May 2014, and before the transponder was transferred and installed to its permanent CDN1 Cal/Val site on the mountains of Crete, the transponder had been deployed at  
Figure 19: The French Transportable Satellite Laser Ranging System in 2003 collocated with the TUC2 GNSS site on the University Campus, Chania, Crete, Greece. This laser facility conducts laser measurements between the facility and the satellite radar altimeter. It is only a 350 kg unit, which transmits laser pulses with an energy of 20 mJ at a frequency of 10 Hz through a telescope of 13 cm in diameter.  
Figure 18: The Data Center at the Technical University of Crete, where field observations, data archiving, and processing is carried out for the Cal/Val operations.  
another site on University Campus for testing and evaluation. At this site, called \"SLR2,\" several transponder calibrations took place for CryoSat-2 (Figures 20 and 21).
# Effective Multi-Frame Optical Detection Algorithm for GEO Space Objects
## 5 Conclusions  
In this paper, an effective multi-frame detection algorithm is proposed to solve the problem of GEO space object detection, in which the 2D deep learning object detector named PP-YOLOv2 is firstly introduced to extract candidate target from single-frame images; and a new post-processing technique called CFS is applied to further filtrate and supplement true space objects. Our work shows the adaptation of the popular PP-YOLOv2 framework for predicting the objects and their locations in satellite images with high accuracy and inference speed. We utilized the transfer learning for faster convergence of the model on the SpotGEO dataset. Accordingly, we analyze the performance metrics including F1 score, MSE and FPS. The comparable detection performance that the trained model resulted in F1 score of 84% with an inference speed reaching 72.1 FPS on the Tesla V100 GPU. With the help of the CFS technique, the proposed pipeline achieves 9.39% improvement in F1 score. Experimental results demonstrate that the proposed algorithm has achieved good results with an overall accuracy of 93.47%. In addition, the deployment results on NVIDIA Jetson Nano platform prove that the proposed pipeline has a competitive application prospect for the surveillance of resident space objects. In the future, our algorithms are applicable to both ground and space platforms, with the foresight to integrate into the vision processing units of the new generation smart satellites. This research work will further pave the way for future advancement in the field of space situation awareness.  
This research received no external funding.  
Not applicable.  
Figure 10: Visual examples of missed detection problem in the SpotGEO dataset: (**a**–**c**) show space targets that can’t all be detected correctly in different spatial distribution within one sequence. Left column: the prediction results in terms of the sequence images. Right column: the first frame with annotations in the corresponding sequence where the prediction results of the proposed algorithm are marked by red circles and the ground truth is marked by green circles.
# mDARAL: A Multi-Radio Version for the DARAL Routing Algorithm
## 6 Conclusions  
This paper presents an evolution of the _DARAL_ routing algorithm focused on multi-radio environments like Smart Cities. We analyzed three key parameters for WSNs during the set-up phase (i) the convergence time; (ii) the number of control messages (control overhead); and (iii) the energy consumption. We have carried out intense experiments intending to validate the multi-radio routing algorithm presented in this work. In order to make a fair analysis, a comparison with the single-radio protocol has been developed, considering different environmental factors within the process.  
After the analysis of the results, it has been found that _mDARAL_ presents a lower number of control messages during the set-up phase for small scenarios under different node densities and propagation models. For medium and large scenarios, the presented configuration of the algorithms results in a better performance of _DARAL_ under different node densities and propagation conditions. It is necessary to point out that the differences between _DARAL_ and _mDARAL_ in terms of number of control messages sent are small enough to consider the results similar. The consistency of the results measured is very similar, going from 0.12 to 0.31 and it does not present any relevant difference. Taking into account the convergence time, it is noticed that _mDARAL_ decreases the convergence time in all of the considered scenarios, even for different propagation conditions and densities. Moreover, _mDARAL_ shows better results and consistency in terms of stability.  
_mDARAL_ shows a better energy consumption compared to _DARAL_ under all the scenarios tested in this work. In our analysis, the propagation model seems to impact the consumption, resulting in the LogNormal Shadowing configuration having a larger difference between models. Finally, _mDARAL_ presents an overall improvement in comparison with _DARAL_.  
**Acknowledgments:** This work has been partly supported by the Spanish grants DPI2013-47347-C2-2-R, P11-TIC-7462 and TIN2015-67020-P (Spanish \"Ministerio de Economia y Competitividad\" and European Regional Development Funds (ERDF)). In addition, the authors thank [PERSON] for his help with the OMNeT++ simulations.  
**Author Contributions:** [PERSON] has contributed to the study design, simulation, data collection and writing of the manuscript. [PERSON] and [PERSON] contributed to the study design and writing of the present manuscript. [PERSON] and [PERSON] directed the research, and contributed to the study design and review of this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Experiment
### Simulation Experiment A  
In this experiment, six maps of 20 \(\times\) 20 m were selected for simulation, which differed in the number of obstacles and the shapes of obstacles. Figure 8 shows the optimal trajectories planned by GA, SSA, and MuL-ACO. To obtain more specific performance indicators, including length, number of iterations, smoothness, height difference, and running time, the experiment was performed 30 times to obtain the average value. Table 2 summarizes the qualitative comparison of the performance of MuL-ACO, GA, and SSA.  
Figure 8: (**a–f**) Optimal trajectories generated by GA, SSA, and MuL-ACO on different 20 \(\times\) 20 maps.  
In Figure 8a-f, the trajectory planning based on Mul-ACO tended to bypass steep or concave areas and generated a comprehensive optimal target trajectory with good safety, fewer turns, and a shorter length. The main reason is that Mul-ACO introduces the height factor in environments into the heuristic function, and the trajectory optimization algorithm based on mutual learning further optimizes the trajectories. In contrast, the GA and SSA algorithms do not actively bypass steep or concave areas when generating trajectories, so mobile robots will incur certain losses when following trajectories and the quality of trajectories is average. In the case of a small difference in trajectory length, as shown in Figure 8a,b,e, Mul-ACO can find a flatter and safer trajectory through the trajectory optimization algorithm based on mutual learning. In Figure 8c,d,f, the trajectories generated by Mul-ACO were shorter, smoother, and safer than GA and SSA.  
As shown in Table 2, compared with GA and SSA, the hybrid scheme of Mul-ACO proposed in this paper had a large improvement in the convergence performance, which was about 70% to 85%. The reason is that the SA-based pheromone adaptive adjustment strategy accelerated the convergence of the algorithm. In terms of the height difference of the trajectory, the trajectory planned by Mul-ACO tended to avoid passing through steep uneven areas, so the total height difference of the trajectory was lower than GA and SSA. Compared with the other two algorithms, the final trajectory generated by the mutual learning-based trajectory optimization algorithm was better in terms of length and smoothness. In addition, it can be seen from Table 2 that Mul-ACO had a shorter running time when planning the trajectory.
## 3 Results and Discussion
### Dynamics of Compatible Solutes Accumulation after Hypoosmotic or Hyperosmotic Shock
The concentration of glycerol spiked at 8 h (263 umol/g dry mass) and was still detected 48 h after this hypoosmotic shock, coinciding with the transient upregulation of _gpd_ (Figure 5). The activation of _gpd_ gene and the subsequent production of glycerol is not generally regarded as a response to hypoosmotic shock, but in this case, it highlights the notion that this mechanism is associated with osmotic stress regardless of the direction of the stimulus. Figure 4: Accumulation of compatible solutes in _A. ydowii_ after different osmotic shocks. The labels on the right indicate the culture conditions before and after the osmotic shock. The data represent the average and standard deviation of at least three replicates (\(n=3\)). Statistical analyses of the data are summarized in Supplementary Table S5. When changed from the optimal salinity to the hypersaline condition, instead of the expected increase in the concentration of compatible solutes, there was a reduction in trehalose, mannitol, erythroid, and arabitol and only a slight increase in glycerol (Figure 4). The _mtld_ and _m2 dh_ genes were highly upregulated shortly after the shock, and remained so even after 24 h, which does not explain mannitol dynamics. The _ltkB_ gene was significantly  
Figure 5: Transcriptional regulation of enzyme genes involved in the synthesis of osmolytes after hyperosmotic shock, hypoosmotic shock, and stress-inducing shock. The values correspond to the average and standard deviations of three biological replicates (\(n=3\)) and two technical qPCR replicates. The statistical significance (*) was assessed by a randomization test performed with the software Rest ([PERSON] et al., 2018). The dashed horizontal lines correspond to a cutoff logFC = \(\pm 2\). downregulated shortly after the hypoosmotic shock, which might account for the reduction of erythroid and arabitol. The _gpd_ gene was transiently upregulated between 30 min and 8 h after the shock, preceding the small surge of glycerol in this condition (Figure 5). The physiological responses of _A. sydowii_ to hyperosmotic changes in the medium are similar in terms of accumulated osmolytes and enzyme gene regulation but are counterintuitively more pronounced in the change from 0.5 M NaCl than from the medium without salt. Altogether, these observations show that the response of this fungus is different from other halotolerant and halophile model fungi. Moreover, we observed that the accumulation of glycerol in _A. sydowii_ is a response to hypersaline stress more than a response to salinity, as we have previously proposed [29].