esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Solid and Liquid Residues  
Table 4 presents the detailed results of the yield of collected solid and liquid residues and provides comprehensive mass balance data. The yield of solid residues (comprising unreacted char and ash) was significantly influenced by the experimental variables inves  
Figure 4: Effect of temperature on gas production for the three ER levels studied.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**T\({}_{\text{c}}\)\({}^{\circ}\)C**} & \multirow{2}{*}{**ER**} & \multicolumn{4}{c}{**Yield, g/kg**} \\ \cline{3-5}  & & \(\mathbf{Y_{H2}}\) & \(\mathbf{Y_{CO}}\) & \(\mathbf{Y_{CH4}}\) \\ \hline \multirow{4}{*}{750} & 0.1 & 7.17 & 136.17 & 32.55 \\ \cline{2-5}  & 0.3 & 13.37 & 318.00 & 96.48 \\ \cline{2-5}  & 0.5 & 9.02 & 309.08 & 73.25 \\ \hline \multirow{4}{*}{800} & 0.1 & 6.01 & 174.63 & 34.50 \\ \cline{2-5}  & 0.3 & 9.81 & 579.48 & 56.69 \\ \cline{2-5}  & 0.5 & 6.99 & 537.63 & 40.89 \\ \hline \multirow{4}{*}{850} & 0.1 & 7.34 & 183.85 & 36.93 \\ \cline{2-5}  & 0.3 & 11.63 & 544.98 & 70.66 \\ \cline{2-5}  & 0.5 & 9.74 & 560.32 & 42.18 \\ \hline \multirow{4}{*}{900} & 0.1 & 7.15 & 190.84 & 37.30 \\ \cline{2-5}  & 0.3 & 12.70 & 602.48 & 50.95 \\ \cline{1-1} \cline{2-5}  & 0.5 & 7.90 & 579.23 & 46.18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of temperature on production of H\({}_{2}\), CO, and CH\({}_{4}\) for the three ER levels studied.  
tigated. The results indicate a small correlation between temperature and the quantity of solid residues collected. Similarly, the equivalence ratio (ER) played a crucial role in determining the quantity of solid residues. Higher ER values corresponded to reduced solid residue yields. The yield of liquid residues, primarily consisting of tar and water vapor, showed an inverse relationship with temperature. As the temperature increased, the yield of liquid residues decreased, indicating more efficient cracking of heavier hydrocarbons into lighter gaseous products at higher temperatures. Additionally, the ER influenced the liquid residue yield, with higher ER values leading to lower liquid yields.  
Finally, to ensure the accuracy and completeness of the experimental analysis, a thorough mass balance assessment was performed. This involved accounting for all input and output streams, including solid, liquid, and gaseous components. The mass balance data, presented in Table 4, demonstrate an acceptable level of accuracy.
# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
## 5 Results and Discussion
### Model Uncertainty Reduction  
As mentioned in Section 4.5, in order to improve prediction accuracy, we have to reduce the uncertainties and confusion that plague the prediction model by using two independent detection models (computer vision and micro-seismic wave). Table 6 confirms that the detection-based micro-seismic wave has the ability to predict the fall a few moments before as well as confirm the rock-fall occurrence.  
The results in Table 6 show the micro-seismic wave response to two events with different frequency spectrum. In the first case, when detecting vibrations in the frequency spectrum 500 Hz-1000 Hz, this frequency appears several hours prior to the rock's fall. In this case, the probability of detecting a rock fall ranges between 0.31 and 0.39. Second, when detecting vibrations in the frequency spectrum 100 Hz-500 Hz, this frequency precedes the rock fall by a few moments or at the moment of rock-fall occurrence; in this case, the probability of detecting rock fall ranges between 0.83 and 1.00. These results confirm the ability of the seismic detector to detect rock-fall events before and after their occurrence.  
To obtain the uncertainty-decreasing factor (\(\delta\)), we first conducted experiments on the micro-seismic and computer vision detection models. The results indicate that the average probability of rock fall when occurrence is confirmed is 0.96 and 0.97, respectively, as in Table 7. Then, the average rock-fall probability of overall detection models was calculated as the joint probability of two independent events; the result indicates that the average probability of overall detection models is 0.93. Finally, by substituting the overall detection model probability into Equation (19), we obtain a minimum uncertainty-decreasing factor of about 0.07.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency Spectrum** & **The Signal Generation Moments** & **The Average Spectral Amplitude Ratio R** & **P(S) Rock-Fall Detection** \\ \hline
500 Hz–1000 Hz & Several hours prior to the rock’s fall & 2.7 \(\pm\) 0.32 & 0.31–0.39 \\
100 Hz–500 Hz & Precede the rock fall by a few moments OR & 7.1 \(\pm\) 0.68 & 0.83–1.00 \\  & moment of rock fall occurrence is confirmed & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Micro-seismic wave response.  
Figure 9: ALARP threshold triangle.
## 3 Results
### Streak Images Processing Accuracy  
In our previous streak camera calibration works, we obtained the calibration matrix that matches the temporal and spatial resolution of the streak images [24]. From the calibration results, the average temporal resolution of the streak image can reach 1.19 ns/pixel, with a temporal dynamic range exceeding 1.3 us for a 512 \(\times\) 1024 streak image.  
To validate the extracted temporal accuracy from the streak image echoes, 6000 streak images were captured during a 3 s continuous acquisition at a rate of 2000 Hz. The raw image is depicted in Figure 5a. A fixed 100 \(\times\) 100 region of interest (ROI), containing the echo laser spot, was extracted, as shown in Figure 5b. We selected a ROI test row, indicated by the dashed line in Figure 5b, with a high laser spot intensity to serve as the detected echo channel for the ranging accuracy verification. We conducted a quantitative comparison of various algorithms for extracting temporal feature points along the image row (time dimension).  
Figure 4: Laser spot images of a target surface at a 6 km distance captured by a CCD camera (false color): (**a**) raw image with projection distortion; (**b**) image after projection correction; (**c**) image region with a pixel spatial resolution of 4.3 mm/pixel and a calculated spot width of 0.66 m.  
Table 1 presents the time complexity of the echo feature point extraction algorithms and the standard deviation of the temporal feature coordinates extracted by these algorithms. The results from streak image processing demonstrate that the centroid algorithm stands out as a more effective method. It achieves more accuracy with a standard deviation of just 0.78 pixels, while also maintaining a low time complexity of O(n). This optimal balance between sub-pixel precision and computational efficiency makes the centroid algorithm particularly well suited for processing streak images that require accurate temporal feature points and can handle large data volumes of 1000 MB per second, with potential for implementation in real-time extraction processes in the future work.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 2 Current Status of Lunar Relay Navigation Satellite Networks
### ESA Lunar Navigation and Communication Program  
The European Space Agency (ESA)'s Project Moonlight is committed to building a shared lunar communication and navigation constellation. This concept proposes to deploy a satellite network around the Moon to expand the coverage of reliable satellite navigation to provide manned and unmanned lunar explorations. The constellation will eventually be used by official and commercial lunar exploration missions. Moonlight will be operated as a commercial service to reduce the development difficulty and cost of lunar exploration missions.  
Over the next decade, ESA aims to establish a common communication and navigation infrastructure for all lunar missions based on dedicated lunar satellites. Project Moonlight will support missions that cannot use Earth satellite navigation signals, such as landers on the far side of the Moon, and plans to achieve target positioning accuracy below 50 m.  
Project Moonlight will build a Lunar Satellite Communication and Navigation System (LCNS) via global cooperation, based on lunar landers from various countries. It consists of near-lunar orbit satellites and lunar surface static ranging beacon stations. It deploys a constellation around the Moon to provide support to manned and unmanned lunar exploration activities.  
Currently, ESA has a signed contract with Surrey Satellite Technology Limited (Guildford, UK), which will be responsible for developing the Lunar Pathfinder, the first commercial satellite for Project Moonlight. It is planned to be launched in 2026 and weighs approximately 208 kg. The satellite will carry out an eight-year mission to provide commercial relay services for lunar missions. It will also expand the operational limits of satellite navigation signals, aiming to verify that a commercial communication constellation can be built in the future.  
The Lunar Pathfinder will carry out the first satellite navigation and positioning mission in the lunar orbit by equipping an advanced satellite navigation receiver. It plans to use a highly stable frozen large elliptical orbit above the Moon's south pole to focus on the south pole as the main exploration target. Several unmanned lunar landing missions and NASA's Artemis program all plan to go to the lunar south pole in the future. The Lunar Pathfinder will communicate with probes on the lunar surface and in orbit around the Moon via UHF and S-band, and relay communication signals to the Earth via X-band. The signal is detected by a high-sensitivity receiver, and positioning accuracy of approximately 100 m will be achieved by using advanced space-borne orbit filters.
## 5 Discussion
### Searching the Source of the Error in the GNSS Data  
The survey of the Juarez Bridge and the analysis of data were made with much care, though with a sampling frequency of 1 Hz, too small for a stiff bridge; hence, the possibility of blunders should be excluded. Furthermore, measurements were made in consecutive days, with the same hourly plan, so that each group of observations was made under a rather uniform, slowly changing constellation of GPS satellites. In addition, the GPS antennas were at an elevation which does not justify offsets such as those of Figure 4 due to conventional multi-path (reflections of satellite signals to nearby buildings before they arrive the antennas), in spite of the fact that a low (10\({}^{\circ}\)) cut-off angle was adopted.  
Under these conditions, the only possible explanation is a combination of shadowing of some satellites in combination with dynamic multipath, i.e., corruption of the GPS signal by selective reflections to passing vehicles and the low frequency of sampling. Such measuring conditions do not cause systematic effects, i.e., a quasi-steady bias for each group of observations during observation sessions with the same satellite constellation. This is because the geometry and reflectivity of the surfaces of passing vehicles, their distance from the antennas and the velocity of the vehicles are different. For example, a highly reflective surface of a large lorry passing closer to a monitoring antenna with lower velocity is expected to produce maximum bias in all three coordinates. In addition, as Figure 4 indicates, multipath effects may vary considerably only after a few minutes.  
It can hence be concluded that transient shadowing of some satellites by vehicles and the associated dynamic multipath are effects critical for monitoring bridges, and they may highly influence the quality of results in low SNR conditions. Distortion of the GNSS signal under these conditions is not easily recognized because it correlates with passing vehicles and it may be assumed that it reflects structural response to dynamic loading. However, such effects are critical in short bridges but minimized at the midspan of major bridges with the deflections as those of Figure 1, for several reasons. (i) In long, flexible bridges deflections are large (cf. Figure 1) and the SNR is usually high, so that the contribution of dynamic multipath is usually small and ignored. (ii) Filtering can remove effects such as the spurious pulses in Figure 2, and even the multipath can be removed using an algorithm such as that proposed by [PERSON] et al. [27]. (ii) Major bridges allow enough space between an antenna and the top of passing vehicles, so that dynamic multipath and shadowing of satellites are minimized.
# Multi-Level Control and Utilization of Stormwater Runoff
###### Abstract  
This study proposes the technology of \"runoff storage and seepage utilization\" for achieving purification of road rainfall-runoff and presents a multi-level series purification system (PBT-GR) comprising porous asphalt pavement (PAP), a bioretention system (BS), a storage tank (T) and a hydroponic green roof (GR). The operation parameters of each component unit were optimized and the contribution of each unit to pollution was analyzed. The results showed that under typical simulated rainfall, the suspended solids (SS), total nitrogen (TN), total phosphorus (TP), Pb, Zn and Cu removal rates by filtration and interception of porous pavement were 62.26 \(\pm\) 3.19%, 16.29 \(\pm\) 1.74%, 29.27 \(\pm\) 1.37%, 37.61 \(\pm\) 2.58%, 35.57 \(\pm\) 4.64% and 31.17 \(\pm\) 3.27%, respectively. The average concentrations of SS, TN, TP, Pb, Zn and Cu in the effluent of the PBT-GR system were 14.70 \(\pm\) 2.21 mg/L, 1.52 \(\pm\) 0.24 mg/L, 0.14 \(\pm\) 0.04 mg/L, 0.09 \(\pm\) 0.04 mg/L, 0.11 \(\pm\) 0.03 mg/L and 0.04 \(\pm\) 0.01 mg/L, respectively, which met the water quality standards recommended in the Chinese guidelines and showed a high adaptability to pollution load. The contents of pesticide residues and heavy metals in cultivated vegetables met the national standards. The period required to recoup the investment in the system was approximately 3 years, indicating its good economic feasibility. The present study can provide a valuable reference of the construction of an efficient, low consumption and sustainable urban stormwater treatment system and can contribute to the improvement in the quality of the urban water environment.  
nutrients removal; stormwater management; PBT-GR system; hydroponic green roof; water quality +
Footnote †: journal: Applied Sciences
## 1 Introduction  
To date, the trend of using GNSS (Global Navigation Satellite System) satellite technology in aviation has been exclusively based on single-frequency receivers, recording the publicly available L1-C/A code measurement. Consequently, ICAO (International Civil Aviation Organization) technical standards in the aspect of quality of GNSS positioning in aviation are referred to the L1 frequency [1]. Thus, for many years, only GNSS L1-C/A code observations were used in flight tests to determine the accuracy, reliability, continuity and availability of the aircraft navigation solution. However, the development of GNSS satellite receiver technology and the growth of the GNSS space segment made it possible to use observations on the other carrier frequencies as well. In practice, the use of GNSS dual-frequency observations in navigation calculations adds value to the aircraft position determination process.  
For GNSS satellite observations, we distinguish both absolute and differential (relative) positioning methods in navigation calculations [2]. Among the absolute positioning methods, one of the precise measurement techniques is the dual-frequency PPP (Precise Point Positioning) method [3]. The PPP measurement technique uses code observations (P1 and P2) and phase observations (L1 and L2) from a dual-frequency GNSS satellite receiver on-board the aircraft [3]. Thus, during the flight operation, the aircraft crew should be equipped only with a dual-frequency GNSS receiver for precise computation of the aircraft flight path using the PPP method. A completely different situation occurs in the case of differential positioning, where the differential RTK (Real-Time Kinematic) technique with an OTF (On The Fly) solution for GNSS phase measurements is used as a precise measurement technique [4]. The differential RTK technique uses a GNSS satellite receiver on-board the aircraft and a GNSS satellite receiver installed at the reference station (base station) [5]. The differential RTK technique requires the use of a minimum of two GNSS satellite receivers, which, in turn, translates into increased costs and the need to purchase technical infrastructure. In order to reduce the financial costs and the number of personnel to operate the GNSS reference station infrastructure, the PPP measurement technique is increasingly used in flight tests.  
The common feature of the absolute PPP method and the differential RTK technique is their use to determine an aircraft's reference position in flight tests. Consequently, both measurement methods are significant for the problem of determining the true trajectory of an aircraft determined from dual-frequency GNSS observations. Knowing the aircraft's true position is essential for both pilots and air traffic ground personnel, including controllers and navigators.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 6 Simulation Results
### Codebook Comparison  
For an IID Gaussian target matrix with \(K=256\) columns, we compare various codebook matrices in Figure 5. In contrast to Figure 1 in [43], we use a single wiring matrix to avoid the effects of codebook evolution. The wiring matrix is designed as described in Section 4.2.2.  
The Gaussian codebook, of course, is not useful for practice due to its high computational cost. It is just shown as a benchmark. For all numbers of rows \(N\) that are shown in the figure, Gaussian codebooks perform significantly superior while the self-designing and two-sparse codebooks perform the worst. The mailman codebook, which only exists for \(N=8\), bridges half the gap to the Gaussian codebook. The choice of the codebook affects the slope of the curve.  
Codebook evolution can be quite helpful to obtain a good codebook. Note that in Figure 4 there is hardly any difference between the Gaussian and the mailman codebook. This is due to codebook evolution. Whenever the target matrix is a good codebook, codebook evolution can be utilized to convert an initial poorly performing codebook into a good one. In that case, we need multiple wiring matrices.  
If the target matrix is a poor codebook, we cannot utilize codebook evolution directly. However, we can invest few wiring matrices to build up the codebook Gaussian-like, before we fix the approximately Gaussian codebook and wire all the remaining computation via a single wiring matrix. The creation of a good codebook initially incurs additional computational costs, but these can be more than offset by a faster decay of the distortion.  
Figure 5: Median total squared error \(D\) for target matrix of unit Frobenius norm vs. the parameter \(S\) (approximately the total number of additions per column required for all wiring matrices). Gaussian IID target matrices of size \(N\times 256\). Results are averaged over \(10^{6}\) random matrix entries.
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 2 Materials and Methods  
This section outlines the methodology, data sources, and processing steps used in our flood detection approach. Calibration and testing were performed in Madagascar, with Sentinel-1 satellite imagery as the primary data source. The processing chain includes the delineation of flood-prone areas based on the HAND index, choice of reference images acquired before flooding to compare to post-flood images, radar image filtering, and delineating flood by change detection.
## 4 Market Overview
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Start-Up Date** & **Localisation** & **Application Area** & **Connected** & **Connected** & **Supplier** & **Targeted Capacity** & **Number of** & **Types of Waste Collected** \\  & & & & **Dwellings** & **Inhabitants** & **Company** & **(Tonnes(day))** & **Fractions** & **Types of Waste Collected** \\ \hline
1975 & Roosevelt Island, New York, USA & Existing area & 4300 & 14,000 & Envac & 7 & 1 & RHW \\ \hline
2002 & Forum, Barcelona, Spain & New district & 6200 & 16,000 & Ros Roca & 20 & 2 & Food waste, RHW \\ \hline
2004 & Hammurby Sjostad, Stockholm, Sweden & n.a. & 2095 & n.a. & Sweco & n.a. & 3 & Paper, organics, RHW * \\ \hline
2005 & Buenavista, Portuguese, Spain & Existing area & 7600 & 19,000 & Ros Roca & 22 & 2 & Packaging, RHW \\ \hline
2007 & Sondra Station, Stockholm, Sweden & n.a. & 3240 & n.a. & n.a. & n.a. & n.a. & n.a. \\ \hline
2008 & Wembley Stadium, UK & New district & 2300 & 6400 & Envac & 23 & 3 & RHW, food waste, packaging \\ \hline
2008 & Espeti Sant Hospital, Santa Coloma Cramender, Spain & New development & n.a. & n.a. & Ros Roca & 2-1 & 3 & RHW, metal, plastic, line \\ \hline
2008 & Torrijs Market, Madrid, Spain & New development & 18 & 500 & Ros Roca & 3 & 2 & RHW, metal, plastic \\ \hline
2009 & T1 Barcelona Airport, Spain & New development & n.a. & n.a. & Ros Roca & 10 & 4 & RHW, paper, organics, metal, plastic \\ \hline
2011 & Rivas, Portuguese, Spain & Existing area & 2000 & 6500 & Ros Roca & 25 & 2 & Packaging, RHW \\ \hline
2011 & Galdakao, Yicxaya, Spain & Existing area & 6100 & 19,500 & Envac & 25 & 4 & RHW, paper, packaging, food waste \\ \hline
2011 & Est Ensemble Romainville, France & Existing area & 2300 & 5300 & Envac & 12 & 2 & HRV *, RHW \\ \hline
2011 & Pamplona, Spain & Existing area & 4000 & 12,500 & Ros Roca & 12 & 3 & RHW, paper, metal, plastic \\ \hline
2011 & Harim Grand Mosque in Mecca, Saudi Arabia & Existing area & n.a. &
\begin{tabular}{c} 2 million \\ ( visitors/day) \\ \end{tabular} & MariMattic & 600 & 1 & RHW \\ \hline
2011 & King Abdullah Financial District, & New area & n.a. & n.a. & Envac & 145 & 2 & RHW \\ \hline
2012 & Lussil City Marina District, Qatar & New area & 21,000 & 55,000 & Envac & 60 & 2 & RHW, metal, plastic \\ \hline
2012 & 220, Barcelona, Spain & New district & 14,000 & 14,000 & Ros Roca & 14 & 2 & Food waste, RHW \\ \hline
2012 & Helsinki, Finland & Existing area & n.a. & 5091 & n.a. & 5.5 & 4 & RHW, organics, paper, carbon \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Representative commercial vacuum waste collection systems. Based on [22] and completed by the authors.