esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 3 Methodology
### Performance-Requirement-Driven Fast Satellite Selection Algorithm
#### 3.2.2 Satellite Number Constraint in Each Constellation  
In the multi-constellation system, extra measurements are needed to estimate the clock offset for each constellation. A special situation that only one satellite from a secondary constellation is included in the subset is helpless for the performance of the subset. As the single satellite is essential for the estimation of the clock offset between the receiver and its constellation, it provides little or even negative contribution to the accuracy of the subset but has a crucial impact on the state estimation result thus is hazardous for the integrity of the system. This situation should be avoided during the satellite selection process. A suggested configuration is that more than three satellites from each constellation are retained, or the insignificant constellation is abandoned in the final subset [31].  
In this algorithm, the abandonment operation is checked and executed in the initialization step. The satellite number constraint process is executed accompany by the valuable satellite introduction process. If the satellite number from any constellation in the current subset is less than three and the visible satellite number of that constellation is more than three, then the alternative satellites from that constellation will gain more weights when comparing their integrated performance impacts.
## 3 Results and Discussion
### Ignition of Waste Wood Dust
#### 3.3.3. Monitoring of Waste Wood Dust Ignition by a Smouldering Cigarette
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Ignition Source** & **Spruce Dust** & **Beech Dust** & **Oak Dust** \\ \hline \multirow{2}{*}{Hot surface} & Minimum ignition temperature (\({}^{\circ}\)C) & 301.6 \(\pm\) 2.870 & 290.8 \(\pm\) 2.227 & 280 \(\pm\) 0.2.280 \\  & Ignition time (s) \({}^{*}\) & 2060 & 2262 & 1500 \\ \hline \multirow{2}{*}{Electric coil} & Charred area (\%) & 18.02 \(\pm\) 0.487 & 40.01 \(\pm\) 0.303 & 70.02 \(\pm\) 0.337 \\  & Ignition time (s) \({}^{*}\) & 180 & 180 & 180 \\ \hline \multirow{2}{*}{Smouldering cigarette} & Charred area (\%) & 5.04 \(\pm\) 0.102 & 40.01 \(\pm\) 0.303 & 40.06 \(\pm\) 0.241 \\  & Ignition time (s) \({}^{*}\) & 1200 & 1200 & 1200 \\ \hline \hline \end{tabular}  
* * ignition time refers to 100% of dust layer area being charred.  
\end{table} 
Table 4: Comparison of the influence of ignition sources on selected dust samples. Figure 8: The charred surface of dust ignited by a smouldering cigarette, documented by a camera (Olympus PEN E-PL8 + ED 14–42 II R lens, Olympus Czech Group, s.r.o., Czech Republic). Legend: (a) spruce dust, (b) beech dust, (c) oak dust at the 3 rd min. degradation in all ignition sources. The temperature curves obtained via hot surface apparatus show slower thermal degradation of waste wood dust and the spruce and beech samples. Spruce waste wood dust, which also contains sawdust (95%) of particles with a size of more than 200 \(\upmu\)m), underwent thermal degradation in 2060 s (Table 4). The slowest degradation occurred in beech dust, with the dominating particle segment (98%) above 090 \(\upmu\)m. Fine spruce dust reached the hot surface temperature in half the time (1500 s). The effect of the particle composition can also be seen in thermal degradation caused by an electric coil (Figure 6b,e,h and Figure 7a-c). The most noticeable difference is seen in the ignition by a smouldering cigarette (Figure 8). Based on the given area, it is possible to identify a linear dependence in the increase of the area of wood dust under the same thermal conditions.
## 2 Materials and Methods
### Testing and Characterization  
#### 2.3.1 Density and Porosity Measurements  
Using the rule of mixtures, the theoretical densities of the synthesized Mg materials were calculated [16]. Experimental densities of the materials were calculated using the gas pycnometer. The samples were placed in the gas pycnometer and sealed. Helium gas was then released into the chamber to measure the samples' experimental density. The porosity of the samples was calculated using the theoretical and experimental densities, under the presumption that the differential value arising between the theoretical and experimental densities is due to the porosity entrapped in the materials. The formula used to calculate the porosity is given below:  
\[P=\ \ \frac{(\rho_{th}-\rho_{e})}{(\rho_{th}-\rho_{air})}\times 100, \tag{1}\]  
where \(P\) represents the porosity (in%), \(\rho\) represents density (g/cc), 'th' represents theoretical, and 'e' represents experimental.
## 1 Introduction
### Single Modal Feature Construction Method
#### 2.1.3 Image Signal Feature Extraction Based on Faster R-CNN  
For the image modality, we focus primarily on the thermal radiation intensity of the device. The preprocessed image is input into the Faster R-CNN [36] model. The Region Proposal Network (RPN) generates multiple potential Region of Interest (RoI) candidate boxes at each image location using a sliding window approach. We then combine the bounding box B of the target region with the feature representation x extracted by Faster R-CNN to obtain the image feature embedding \(\mathrm{z^{i}}\in\mathbb{R}^{\mathrm{l_{i}<h_{i}}}\).  
\[\mathrm{z^{i}}=\mathrm{AvgPool(RoIAlign(x,B))} \tag{6}\]  
where RoIAlign extracts a fixed-size feature map based on the bounding box B. AvgPool is used to unify the length and width of the feature map. \(\mathrm{l_{i}}\) represents the sequence length of key frames, and \(\mathrm{h_{i}}\) represents the feature dimension of each frame.
## 3 Effect of Fixed-Altitude Heterogeneous LEO Constellations on CIT
### Results of Geometric Distribution of Rays
Consistent with the addition of LEO satellites at 300 km altitude, the percentage gain in the total number of ray-crossing voxels does not show a multiplicative increase after adding 192 LEO satellites at 500 km, 800 km, and 1000 km. The greatest gain after adding different numbers of LEO satellites at an altitude of 300 km is in the altitude interval from 300 to 500 km, with a maximum gain of 31.69% after adding 96 LEO satellites. After adding different numbers of LEO satellites at  
Figure 2: Spatial distribution of rays from individual GNSS observations (**left**) and GNSS + 192 LEO observations at 300 km altitude (**right**) at 00:00:00 UT on 19 March 2024. 500 km altitude, the altitude interval with the largest gain is 500-800 km, and the maximum percentage gain is 22.37% after adding 96 LEO satellites. With the addition of different numbers of LEO satellites at altitudes of 800 km and 1000 km, the altitude intervals with the largest gain are both 100-300 km, and the maximum percentage gains are 22.69% and 23.05%, respectively, while the plan with the largest gain is to add 192 LEO satellites. In the altitude interval of 100-300 km, the percentage gain of the four plans increases with the number of LEO satellites, indicating that the number of LEO satellites should be increased as much as possible in the low altitude interval.
# Designing Audio Equalization Filters by Deep Neural Networks
## 6 Results
### Over-Determined Case  
In the selected use cases, the number of filters is larger than the number of microphones. To assess the validity of the method in single-channel configurations and in the over-determined case (\(\mathcal{M}>\mathcal{S}\)) we have conducted further experiments selecting a subset of the available impulse responses, thus simulating the presence of a lower number of speakers. The results are reported in Table 7. As can be seen, the CNN scores better than the FD, meaning that the optimal solution in the least-squares sense can be further improved by non-convex optimization techniques. The performance degradation from the \(1\times 1\) case to the \(2\times 1\) case is extremely low. This suggests that the two impulse responses are quite similar. On the other hand, the performance improvement achieved by the CNN with the \(2\times 7\) or the \(4\times 7\) cases (Tables 2 and 4) with respect to the \(1\times 1\) cases suggests that the proposed method is able to efficiently exploit a large number of filters to greatly reduce the error at all microphones.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Input**} & \(\overline{MSE}\) & \(\overline{\sigma}\) & **Conf.** \\ \hline Impulse Responses & \(6.31\cdot 10^{-5}\) & 0.034 & Conv \#1 \\ Random Iteration & 0.14 & 2.152 & Conv \#1 \\ Random Fixed & \(1.35\cdot 10^{-4}\) & 0.052 & Conv \#1 \\ All 1s & \(1.17\cdot 10^{-4}\) & 0.049 & Conv \#1 \\ All 0s & \multicolumn{3}{c}{ill-conditioned} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effect of the input type on the results of the CNN (filter order 1024). For each case, the best result and the related configuration is reported.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Car**} & \multirow{2}{*}{**Setup**} & \multicolumn{2}{c}{**CNN**} & \multicolumn{2}{c}{**FD**} \\ \cline{3-6}  & & \(\overline{MSE}\) & \(\overline{\sigma}\) & \(\overline{MSE}\) & \(\overline{\sigma}\) \\ \hline \multirow{2}{*}{Giulia} & \(1\times 1\) & 0.52 & 8.57 & 0.62 & 9.84 \\ \cline{2-6}  & \(2\times 1\) & 0.57 & 7.81 & 0.64 & 9.19 \\ \hline \multirow{2}{*}{Renegade} & \(1\times 1\) & 0.03 & 1.34 & 0.12 & 2.01 \\ \cline{2-6}  & \(4\times 1\) & 0.22 & 2.76 & 0.44 & 3.62 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Audio equalization in the single-channel and over-determined cases. Setup is \(\mathcal{M}\times\mathcal{S}\).
## 3 Results and Discussion  
### CQD Properties  
The light absorption spectrum of the synthesized quantum dots was measured using a DH-2000 BAL Mikropak deuterium and halogen lamp source combined with an Ocean Optics detector and is shown in Figure 1a. The concentration of CQDs was 0.5 mg/mL. The CQDs show a broad light absorption from UV, through the entire visible region and further extending in the near IR region. As can be seen in the spectrum, the Ag\({}_{2}\)S CQDs do not display an excitonic peak. Instead, there is a shoulder at 820 nm, corresponding to a peak in the second derivative spectrum (Figure S1). Also in Figure 1a, the photoluminescence spectrum, measured using a Horiba Jobin Yvon FluoroLog modular fluorospectrometer is shown. The spectrum shows a peak at 965 nm with a faint shoulder at 906 nm (Figure S2) and a 'blue' tail. The concentration of this sample was 2 mg/mL, as seen in the right vial in Figure 1b. In Figure S3, a similar photoluminescence spectrum can be seen. It shows the emission of a thin spin-coated film of the CQDs.  
Figure 1: _Cont._The CQDs were studied using a field emission transmission electron microscope (FE-TEM) JEOL (Model JEM-2100F) with electron diffraction function, operated at 200 kV and with probe size under 0.5 nm. Figure 1c,d were analyzed to assess the size and morphology of the Ag\({}_{2}\)S CQDs. The average size calculated from Figure S4 was 3.59 nm and this sample's histogram is shown in the Figure S5. The CQDs are all below the size of 8 nm and the average size is lower than 4 nm, which has been proposed as the size below which strong quantum confinement effects change the absorption properties of the nanocrystals [18]. This is further confirmed by absorbance and photoluminescence studies (see Figure 1a) in which absorption ends around 1000 nm and an emission peak at 975 nm is seen, showing that the electrons are confined as compared to bulk Ag\({}_{2}\)S. It is notable that the Ag\({}_{2}\)S CQD dispersion is rather stable and almost no aggregation occurred over periods of months under ambient conditions after synthesizing them.  
Figure S6 shows a FFT-pattern of a single monoclinic crystal and was measured using the FE-TEM. The FFT-pattern shows how the distances between planes in the reciprocal lattice are related to one another. The distances were measured and the ratios were calculated and compared to theoretical lattice parameters found in literature [29]. Matching these parameters supports our suggestion that the synthesized nanocrystals are monoclinic Ag\({}_{2}\)S. X-ray diffraction conducted on thiol-capped Ag\({}_{2}\)S nanoparticles shows similarity in shape to a theoretical standard diffractogram, even though the peaks are broad due to particle size broadening and Ag\({}_{2}\)S being monoclinic (Figure S7). In Figure 1d, the distance d between two planes in a crystal is shown. This distance corresponds to 20 = 37.4\({}^{\circ}\) in the diffractogram in Figure S7 using Bragg's law.
# Multi-Hazard WebGIS Platform for Coastal Regions
## 4 Discussion  
The risk management in complex territories subject to multiple sources of hazards, such as North Atlantic coastal zones, requires integrated and user-targeted approaches that can scientifically support emergency interventions and long-term planning initiatives alike. IT platforms, integrating multiple sources of information, from either in-situ or remote sensing devices, or high-resolution process-based modelling systems, are the stepping-stones to address these challenges and constitute a single point of access to all required knowledge for timely and comprehensive coastal risk management. The MOSAIC platform introduced herein takes advantage of agile computer science technologies and multidisciplinary research knowledge and tools to provide real-time and long-term responses to coastal risks of inundation and erosion applied to the North Atlantic coasts. This platform materializes an innovative multi-hazard framework for coastal risks, assimilating in-situ and satellite remote sensing data with real-time forecast results under the concept of observatories. The platform is demonstrated herein in the western Portuguese coast, focusing on the Cova-Gala observatory. This demonstration included the implementation of a forecast using models SCHISM and XBeach through the WIFF framework [12]. This framework allows models to build and operate forecasts based on these two models. While the implementation of these forecasts requires both expert knowledge and computational resources, these requirements can be alleviated using OPENCoastS [20]. OPENCoastS automates the generation and automation of forecasts powered by SCHISM and simultaneously provides the required computational resources.
## 3 Results and Discussion  
### Blue-Violet Femtosecond Pulse Generation by a GaInN MLLD  
By varying the intracavity GVD, the ultrashort optical pulses from the GaInN MLLD changed drastically [10,11]. A typical intensity autocorrelation trace for output optical pulses from the MLLD with a negative intracavity GVD is shown as the blue trace in Figure 2a. The gain current was 140 mA and the reverse bias voltage applied to the SA section was 6.0 V. The average output power was about 12 mW and the pulse repetition rate was about 1 GHz. The intracavity GVD was estimated to be \(-0.058\) ps\({}^{2}\) using the geometrical parameters of the dispersion-compensation optics. The blue trace in Figure 2b shows the corresponding optical spectrum. As reported previously, femtosecond optical pulses were obtained by extracting the short wavelength components of the optical spectrum. Figure 2a also shows an intensity autocorrelation trace for the optical pulses after the bandpass (BPF) filter (red trace). The spectral bandwidth of the BPF was 1.3 nm and the output beam was passed through the BPF twice in order to suppress the intense spectral components at longer wavelength. The resulting spectral width was 0.8 nm and the pulse duration was 210 fs, assuming a sech\({}^{2}\) function pulse shape. The time-bandwidth product was estimated to be 0.35, close to that of a Fourier transform limited pulse. The shortest pulse duration of 200 fs was obtained using another BPF of different spectral width [10]. The spectral components at the short wavelength edge of the optical spectrum were almost free from chirp. The average power after the BPF was about 0.8 mW. The pulse peak power was estimated to be about 4 W. The femtosecond pulse components were less than 10% of the total output power of the GaInN MLLD.  
Figure 2: (**a**) Intensity autocorrelation traces for optical pulses from a GaInN MLLD with negative intracavity GVD measured without a bandpass filter (blue trace), and with a bandpass filter (red trace); (**b**) Corresponding optical spectra without a bandpass filter (blue trace), and with a bandpass filter (red trace).
# A Review on Design Considerations and Connection Techniques in Modular Composite Construction
## 2 Conceptual Background
### Advantages of Modular Construction  
Modular construction methodologies present numerous advantages when compared with conventional cast in situ techniques. Such advantages encompass expedited construction durations, financial savings, enhanced labour efficiency, and ecological benefits. Modular construction can reduce project timelines by approximately 50% [45]. This reduction is feasible due to the simultaneous nature of off-site manufacturing and on-site assembly, accelerating the overall construction timeline. The comparative analysis of construction timelines (Figure 6) illustrates that modular construction can significantly reduce the duration required compared to traditional methodologies.  
Beyond the aspect of time efficiency, modular construction also proves to be financially advantageous. It can potentially lower total construction expenditures by nearly 20% [45]. This financial efficiency arises from mass-producing modules within a controlled factory environment, reducing labour costs and minimising material waste.  
Labour efficiency represents another critical advantage. Incorporating automation and robotics in manufacturing processes lessens the reliance on manual labour. This advancement assists in alleviating labour shortages and addressing the challenges posed by an ageing workforce within the construction sector. Modular techniques are well suited to navigate these labour dynamics, rendering them essential for contemporary construction practices.  
The environmental advantages associated with modular construction are also noteworthy [46]. This approach can diminish on-site waste by as much as 90%. Such a reduction fosters sustainable building practices and adheres to prevailing environmental standards to mitigate ecological footprints. These advantages subsequently pave the way for innovative design methodologies. As modular construction continues to evolve, it increasingly aligns with design principles that prioritise efficiency and sustainability. Understanding these design considerations is vital for fully harnessing the capabilities of modular techniques in contemporary construction initiatives.
## 3 Bayesian Coupled with Markov Chain Monte Carlo Approach
### Bayesian Approach for FSSN Mixture Model
For each \(\mathbf{z}_{i}\), there are \(K\) possible mixture components, so \(\mathbf{z}_{i}=(z_{i1},z_{i2},\ldots,z_{iK})\) follows the multinomial \((1,\omega_{i1},\ldots,\omega_{iK})\) distribution where: \[\omega_{ij}=\frac{f\big{(}y_{i\in j}\big{|}\mu_{j},\sigma_{j},\gamma_{j}\big{)} p_{j}}{f\big{(}y_{i\in j}\big{)}}\] (13)  
To estimate the parameter of the FSSN-MM, the Gibbs sampling framework as the process of Bayesian approach was applied. Algorithm 1 states the steps of the Gibbs sampling algorithm. ```
1:Initialize parameters \(\mu_{1}^{(0)},\ldots,\;\mu_{K}^{(0)},\sigma_{1}^{(0)},\ldots,\;\sigma_{K}^{(0) },\gamma_{1}^{(0)},\ldots,\;\gamma_{K}^{(0)},p_{1}^{(0)},\ldots,\;p_{K}^{(0)}, \mathbf{z}_{1}^{(0)},\ldots,\;\mathbf{z}_{N}^{(0)},\text{set}\;t=1\), and determine \(T\). 2:While\(t\leq T\)do
3: Update value \(\mu_{j}\), \(j=1,\;2,\;\ldots,\;K\) by generating \(\mu_{j}^{(t)}\) according to Equation (9). 4: Update value \(\sigma_{j}\), \(j=1,\;2,\;\ldots,\;K\) by generating \(\sigma_{j}^{(t)}\) according to Equation (10). 5: Update value \(\gamma_{j}\), \(j=1,\;2,\;\ldots,\;K\) by generating \(\gamma_{j}^{(t)}\) according to Equation (11). 6: Update value \(p_{j}\), \(j=1,\;2,\;\ldots,\;K\) by generating \(p_{j}^{(t)}\) that follow the Dirichlet distribution with parameters \(\Big{(}1+\sum_{i=1}^{N}z_{i1},\ldots,\;1+\sum_{i=1}^{N}z_{iK}\Big{)}\). 7: Update value \(\mathbf{z}_{i}\) by generating \(\mathbf{z}_{i}^{(t)}\) that follows the multinomial \((1,\;\omega_{i1},\;\ldots,\;\omega_{iK})\) distribution, where \(\omega_{ij}\), \(i=1,\;2,\;\ldots,\;N,\;j=1,\;2,\;\ldots,\;K\) as in Equation (13). 8: Set \(t=t+1\). 9:Return\(\mu_{1}^{(T)},\ldots,\;\mu_{K}^{(T)},\sigma_{1}^{(T)},\ldots,\;\sigma_{K}^{(T)}, \gamma_{1}^{(T)},\ldots,\;\gamma_{K}^{(T)},p_{1}^{(T)},\ldots,\;p_{K}^{(T)}, \mathbf{z}_{1}^{(T)},\ldots,\;\mathbf{z}_{N}^{(T)}\). ```  
**Algorithm 1** Gibbs sampling for the Fernandez-Steel skew normal-mixture model (FSSN-MM)
## Materials and Methods
### Data Measurement and Collection  
The system draws air from the outdoor environment through an intake duct, having an internal diameter of 400 mm, that leads directly to the inlet of the cyclonic bag filter. The flow rate of air is measured by a KIMO differential pressure device equipped with a Debimo air flow measuring blade. The operating principle of this device replicates that of the pitot tube, in which the velocity is calculated based on the difference between total pressure and static pressure; in this case, the measurement is not made at a single point, as in the case of a standard pitot tube, but is obtained by averaging the measurements taken at five different points uniformly distributed along the diameter of the pipe section. The conversion of the differential pressure value into velocity is made according to Equation (2).  
\[v=C_{M}\sqrt{\frac{2\Delta p}{\rho}} \tag{2}\]  
where \(C_{M}\) is a conversion coefficient, depending on the geometry of the device, provided by the manufacturer. The technical specifications of the device are provided in Table 4.  
The outlet section of the filter, at the head of the cylindrical body, is connected to the suction section of the fan. The fan, located downstream of the filter, generates a vacuum in the same, and throughout the upstream section. A butterfly valve is installed in the pipeline connecting the filter to the fan. This valve, together with the control of the fan frequency by means of an inverter, allows the regulation of the flow rate processed by the system. The duct at the outlet of the fan returns the flow of purified air to the outdoor environment. At the ends of the filter, the pressure drop generated by the filter itself is monitored with an Endress Hauser Deltabar S PMD75, whose technical specifications are given in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Pressure Range** & **Velocity Range** & **Operating Temperature** & **Sensitivity** & **Precision** \\ \hline \(\pm\)10,000 Pa & 3–100 m/s & 0 \(\div\) +70 \({}^{\circ}\)C & 0.01 mbar & \(\pm\)0.5\% \\ \hline \end{tabular}
\end{table}
Table 4: Technical specifications of KIMO differential pressure device equipped with a Debimo air flow measuring blade.  
Figure 4: Detail of the lines on which the results were evaluated in terms of tangential velocities.  
An EE650 hot-wire anemometer was selected to measure the velocities inside the filter, and it was installed in the location identified by the CFD simulations. To install the sensor, a hole was drilled in the cylindrical body wall. The anemometer was then inserted from the outside, and, using the graduated scale, it was placed at the desired acquisition point (taking into account the thickness of the wall). The technical specifications of the sensor are given in Table 6.