esa-sceva/satcom-chunk-collection huggingface dataset:
# Architectural Design for Digital Twin Networks
## 5 Design and Implementation Strategies
### Modeling
#### 5.2.3 Mathematical Models  
For large-scale networks where simulators struggle due to resource constraints or complexity, mathematical abstraction methods are often employed to create efficient network models. These techniques include knowledge graphs, network calculus, and formal verification, each offering unique advantages in modeling complex network behaviors and predicting future states.  
Mathematical models are currently favored for larger networks due to their scalability and lower computational overhead compared to detailed simulations. However, there is an increasing trend towards combining mathematical methods with AI/ML techniques. This hybrid approach leverages the precision of mathematical models with the adaptability of ML, providing a more robust solution for managing and optimizing large, complex networks. Despite this, it is also feasible to integrate mathematical models with simulators, creating a versatile framework that can address both high-level network dynamics and detailed packet-level interactions.  
For instance, ref. [21] utilizes forecasting models to predict network behaviors and performance. Similarly, ref. [25] explores various approaches, including rule-based and policy-based models, as well as Markov models, and suggest that reinforcement learning could be used to extend these models further. This variety of methods highlights the flexibility and broad applicability of mathematical modeling in DTNs.  
In their work, ref. [36] implement knowledge graphs to represent network relationships and interactions, demonstrating how this method can capture the complexity of network structures. Additionally, ref. [28] explores multiple mathematical models to address different aspects of network performance and reliability, showcasing the versatility of mathematical approaches in DTN modeling.  
In summary, while mathematical models are indispensable for large-scale networks, the choice of the modeling technique depends on the needs and the network, as well as the experience and background of the developers of the DTN. The trend towards integrating these methods with ML and simulation techniques reflects the evolving needs.  
This convergence promises more comprehensive and adaptable solutions for network management and optimization.
# E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators
## 3 Methods
### Terrain Information Processing
#### 3.1.2 Environment Reconstruction  
In our study, the region of interest (ROI) is delineated by an axis-aligned bounding box to focus on terrain changes in front of the excavator bucket. The ROI is defined by coordinates \(\mathbf{min}_{ROI}\) and \(\mathbf{max}_{ROI}\), significantly reducing data processing volume and increasing efficiency.  
The Poisson Surface Reconstruction algorithm [14] seeks a smooth surface that approximates the normals of the point cloud. The mathematical formulation involves solving the Poisson equation:  
\[\Delta f=\text{div}(\mathbf{N}) \tag{5}\]  
where \(f\) is the scalar field whose gradient approximates the point cloud normals \(\mathbf{N}\), \(\Delta\) is the Laplacian, and div is the divergence operator. The normals \(\mathbf{N}\) are estimated from the input point cloud, and the divergence of \(\mathbf{N}\) forms the right-hand side of the equation, representing the flux of the normal vectors.  
The boundary conditions for the domain \(\Omega\) are typically set to Neumann conditions:  
\[\left.\frac{\partial f}{\partial n}\right|_{\partial\Omega}=\mathbf{N}\cdot \mathbf{n} \tag{6}\]  
where \(\frac{\partial f}{\partial n}\) is the derivative of \(f\) in the direction of the outward normal \(\mathbf{n}\) on the boundary \(\partial\Omega\).  
After solving the Poisson equation, the surface \(S\) is extracted as the isosurface where the scalar field \(f\) equals a chosen iso-value \(\tau\), typically zero:  
\[S=\left\{\mathbf{x}\in\Omega\mid f(\mathbf{x})=\tau\right\} \tag{7}\]  
resulting in a triangular mesh that represents the continuous surface of the point cloud data. This reconstruction fills gaps and creates a model with uniform density, facilitating further processing and analysis.
## 1 Introduction
### Statement of Significance  
* **Problem:** Clinical decision-making systems in the highly rushed intensive care scenario could benefit from an idea generator that is based on the practices of a leading hospital.
* **What is Already Known:** There are models that predict a small set of treatments. In intensive care settings, these models predict prescriptions within a long horizon (24 h or more), limiting immediate ICU applicability.
* **What This Paper adds:** This study extends treatment prediction to a broad set of interventions (including rare ones) in the more useful (for ICUs) time frame of two hours. The technique is novel: it uses a data-driven abnormality-based approach to identify similar cases and predicts by building a machine learning model from those similar cases.
## 5 Parameter Analysis
### Parameter Analysis for Initial Ary Number \(N(0)\)  
Initial any number \(N(0)\) is used to set the encoded any number of the initial population. The value of the initial any number determines the rate of change of the any number throughout the evolution process. If \(N(0)\) is too low, the algorithm will quickly degrade to a binary algorithm. If \(N(0)\) is too high, the algorithm is easy to fall into a locally optimal solution. Therefore, an appropriate initial any number will help improve the optimization effect of the algorithm.  
In this section, experiments are carried out on AD-ADENSGA with different values of \(N(0)\).  
In this experiment, set \(k_{min}=15\), \(k_{max}=200\), set 5 different values for \(N(0)\in[2\) 30 50 70 90]. When \(N(0)=2\), the algorithm is equal to the binary encoded ADENSGA. According to Section 4.3, set \(Score_{limit}=0.035\), \(k_{limit}=40\). Other parameters used in the experiment are shown in Table 1.  
The influences of initial any number \(N(0)\) for a random node placement are shown in Figure 5. Each figure is composed of (a), (b), (c) and (d), respectively corresponding to four evaluation indexes, average Coverage, average Rest Energy, average \(Score\) and max \(Score\).  
In order to observe the influence of \(N(0)\) on the optimization results more intuitively, the average any number of five runs changing with the generation is shown in Figure 6.  
Figure 5: The optimal results for different values of \(N(0)\) of node placement 3.  
Figure 5a shows the influence of \(N(0)\) on the objective of coverage. When \(N(0)\) is too low, the optimization speed of coverage is poor. When \(N(0)\) is too high, the optimization speed is fast but the final results of coverage are poor. Figure 5b shows \(N(0)\) have little influence on the objective of rest energy. Figure 5c,d show that considering the two optimization objectives comprehensively, \(N(0)\) should be chosen in the range of 30-70.  
Considering that \(N(0)\) is related to the encoding length, the value of \(N(0)\) may affect the running time. The average running time for different values of \(N(0)\) for 10 random initial placements are shown in Table 3.  
Table 3 shows that the running time has little relation with \(N(0)\). This is because that the running time of the algorithms mainly influenced by the predicting running of WSNs in \(f_{2}\). Therefore, the algorithm with shorter convergence generation takes less time.  
In this paper, considering the optimization objectives of coverage and rest energy, \(N(0)=50\) is chosen.
# Sea Surface Temperature in Global Analyses: Gains from the Copernicus Imaging Microwave Radiometer
## 3 Results
### Summary  
Adding CIMR data to the IR constellation reduced both the mean error and error standard deviation globally. There was also a clear improvement in areas of particular challenge, such as around strong ocean temperature gradients and dynamics and regions of persistent cloud. The \"perfect\" data results showed that this was a result of the improved coverage available from CIMR despite the lower weight given to each of the individual observations compared to the IR. The fact that this improvement remained when data with realistic errors was used showed that this improvement will hold true when CIMR is added to an IR-only constellation in the future.  
In the Southern Ocean, these improvements offer the prospect of greater insights into the Antarctic Circumpolar Current and other currents. Aside from regions with climatologically high amounts of cloud, CIMR observations may also improve the coverage of SST measurements for high-impact events with more transient clouds. For example, the inability of IR instruments to make measurements through clouds resulted in operational OSTIA and other SST products missing the rapid SST drop in the Hurricane Irma event ([PERSON] et al., 2016). Although observations from CIMR would still be restricted to rain-free regions, the ability to penetrate clouds means that CIMR has the potential to significantly increase the available observations for such events and thus improve the analysis reconstruction.
## 3 Results and Discussion
### Effects Graphs
#### 3.3.1 Effect of pH  
The coagulation-flocculation process is strongly influenced by the initial pH of the raw wastewater, as it affects the efficiency of particle aggregation and removal [33]. The initial pH of the water sample was 8.25. However, for this study, it was adjusted to 4, 7, and 10 to evaluate the coagulant performance in acidic, neutral, and alkaline conditions.  
Figure 4a-d presents the experimental results, demonstrating the influence of pH on the effectiveness of coagulation in removing turbidity, COD, UV 254, and phosphate.  
Figure 4: Graphs of the effects of different parameters used in the application of its coagulating agents on raw wastewater (**a**): turbidity; (**b**): COD; (**c**): UV 254; (**d**): phosphate.  
For turbidity and phosphate removal, the highest efficiency is achieved at pH 7 (Figure 4a,d). Under acidic conditions, a low pH leads to an excess of H+ ions, which compete with active compounds in the polymer chain, disrupting the flocculation process by hindering the bridging mechanism. Conversely, in alkaline conditions, an excess of OH- ions induces a sweeping effect, potentially enhancing turbidity and phosphate removal [34]. Nevertheless, as reported by [PERSON] et al. [35], pollutant removal efficiency improved as pH increased to 9, but further increases led to a decline in removal performance. At pH 3, the acidic environment rendered the process largely ineffective. Moreover, results at pH 7 indicated that the flocs formed by the selected natural coagulant were large and developed quickly, facilitating efficient settling [34; 36].  
For COD removal, the highest efficiency was observed at pH 4. As shown in Figure 4b, the COD concentration decreased to below 250 mg/L at this pH. However, when the pH increased to 7, COD removal declined significantly, stabilizing at 300 mg/L. A subsequent rise in COD removal was observed at pH 10. The optimal coagulation at pH 4 was attributed to favorable interactions between FeCl3, PSE, and wastewater contaminants. As the pH increased to 7, coagulation efficiency decreased, reducing the removal of dissolved organic matter, which primarily contributed to COD. At this pH, the balance of charged particles led to a stabilization in removal efficiency. Additionally, turbidity removal improved, reaching its peak at pH 7. The increase in pH neutralized the positive charge present at pH 4, enhancing particle agglomeration and flocculation [19; 37].  
For UV 254, the highest removal efficiency is observed at pH 10, as shown in Figure 4c. The data indicate a negative correlation between UV 254 removal and low pH levels. As the pH increases from 4 to 10, UV 254 removal improves, suggesting a high concentration of aromatic organic matter in the wastewater samples. The reduced UV 254 removal at low pH can be attributed to the protonation of functional groups, which weakens their interaction with organic compounds, preventing effective adsorption onto the coagulant surface.
# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 3 Methods
### WIST Algorithm
#### 3.1.3 Estimating Termination Date and Uncertainty  
Once a strong downward trend from senescence (\"s\") to dormancy (\"d\") was determined, the termination date (\"t\") was calculated using the originally unsmoothed observations in the following steps:  
1. Find all original clear satellite observations between dates \"s\" and \"d\". If there is no satellite observation on the dates \"s\" and \"d,\" the first clear observations before date \"s\" and the first clear observations after date \"d\" will be used as the bracketing observations.
2. Compute the rate of NDVI decrease between all adjacent observations in the set identified in step 1.
3. Select the period with the most-rapid rate of decrease from step 2 (i.e., date \"t1\" and \"t2\" in Figure 4).
4. Estimate termination date \"t\" using the mid-date between the two observations from step 3 (i.e., t = (t1 + t2)/2 in Figure 4). The uncertainty of the estimation is computed using half of the date range between the two observations, i.e., uncertainty = (t2 - t1)/2.  
The WIST algorithm uses the most up-to-date observations. Once the downward trend is confirmed, the termination date will be computed using the original (unfilled and unsmoothed) observations on the satellite overpass dates. Therefore, the WIST method can run in near-real-time on a daily basis. In near-real-time simulations, the dormancy date may not occur before the latest observation. In such a case, we used the most recent observation date as the ending date for the senescent period if the downward MACD momentum and the NDVI amplitude are significant. In this case, the cover crop termination dates are estimated based on the incomplete senescent period. After new observations become available, the termination dates are updated in the near-real-time simulation.
# Organic Crystals for THz Photonics
## 5 Applications of Organic THz Sources  
During the last decade, THz systems based on organic single crystalline THz sources reached many areas of applications from fundamental science to real-world applications. The research and practical applications such as THz spectroscopy and THz imaging in laboratories worldwide performed by organic NLO crystals are still more exceptional in comparison with competitive THz systems based on photoconductive antennas or inorganic NLO crystals that are more common, also because photoconductive antennas and inorganic NLO crystals are much more widely available. Nevertheless, the use of organic crystalline THz sources in fundamental science and development is moving to other interdisciplinary fields [174; 175; 176; 177; 178; 179], which indicates a great potential for the future transfer of organic-crystal based THz spectroscopic and imaging systems into industrial applications. According to the THz science and technology roadmap prepared by [PERSON] et al. [1], one can expect that organic THz sources will enter other fields of applications similarly as THz systems based on photoconductive antennas or inorganic NLO crystals. Furthermore, the last experiments showed that organic NLO crystals in a combination with photoconductive antennas provide good performance suitable for various applications [79]. Due to the possibility to reach very high THz electric fields with organic NLO crystals, several demonstrations of fundamental THz nonlinear photonics have been possible (see Section 5.1) using these sources, especially interesting due to the possibility to control matter with THz beams. The following sections summarize the past and current directions of use as well as provide an opinion on the future challenges and opportunities of organic crystalline THz sources.
## 2 Algorithm Principle
### INS+LEO 3-Satellite Alternate Switching Ranging Integrated Navigation Algorithm
#### 2.2.2 Algorithm Flow  
We maintain PRN1 at the true ranging value and do not participate in the alternation. PRN2 and PRN3 are alternate satellites that switch alternately and the alternate interval is \(\Delta T\).  
Figure 4: Schematic diagram of the integrated navigation algorithm of INS+2-satellite alternate switching ranging under LEO 3-satellite algorithm.  
Figure 3: Alternate switching ranging scenarios of 2 satellites under 3 satellites under the LEO 3-satellite algorithm. (**a**) Same orbit. (**b**) Alternately switch stars in the same orbit. (**c**) Alternately switch satellites in different orbits. (**d**) Different orbits.  
Unlike Section 2.1, at moment \(t_{1}\), we use the real ranging values \(\rho_{1}\) and \(\rho_{2}\) of PRN1 and PRN2 and employ the virtual ranging value \(\tilde{\rho}_{3}\) for PRN3. Then, we use the variation \(\dot{\rho}_{1}=\rho_{1}-\rho_{INS1}\) between \(\rho_{1}\) and \(\rho_{INS1}\), the difference \(\dot{\rho}_{2}=\rho_{2}-\rho_{INS2}\) between \(\rho_{2}\) and \(\rho_{INS2}\) and the disparity \(\dot{\rho}_{3}=\dot{\rho}_{3}-\rho_{INS3}\) between \(\dot{\rho}_{3}\) and \(\rho_{INS3}\) as the UKF-filtered observations for filtering.  
At moment \(t_{2}=t_{1}+\Delta T\), we use the real ranging values \(\rho_{1}\) and \(\rho_{3}\) of PRN1 and PRN3 and use the virtual ranging value \(\dot{\rho}_{2}\) for PRN2. The next processing flow is similar to the previous step. Finally, loop this process and alternate switch the LEO satellite's real ranging value and virtual ranging value every time \(\Delta T\) elapses until the aircraft's flight time ends.
# Optimal Estimation Retrieval of Aerosol Fine-Mode Fraction from Ground-Based Sky Light Measurements
## 4 Results
### Retrieval from Experimental Data
#### 4.2.2 Validation of AOD and FMF\({}_{\rm o}\)  
The spectral AOD and FMF\({}_{\rm o}\) retrieved synchronously by the OE algorithm were compared to AERONET AOD (direct solar products) and FMF (spectral deconvolution algorithm (SDA) products), separately. For the validation, the SMAC observation time was limited to the range of \(\pm 1\) min, based on the AERONET measurement time.  
In Figure 7a, the \(R\)-value for AOD at 500 nm was 0.99, while the \(RMSE\) was 0.1, which represented good AOD retrieval results. However, the slope of the fit line was 1.16, which indicated that the retrieved AOD is larger than AERONET AOD in highly polluted conditions. For the validation of FMF\({}_{\rm o}\) in Figure 7b, the \(R\)-value and \(RMSE\) were 0.948 and 0.099, respectively. However, it is worth noting that the slope of the fit line had a relatively low value of 0.59. The results showed a strong correlation while suffering from a positive bias. In other words, there was an overestimate of fine-mode aerosol and an underestimate of coarse-mode aerosol. In this regard, our results were consistent with the work of [PERSON] et al. (2018). Such results were caused by the difference in definition and acquisition methods. Moreover, the validation of AE was also performed, as shown in Figure 7c. As seen in Figure 7c, there was a positive bias in retrieved AE value, and the bias decreased gradually as the AE value increased. The AE value was inversely related to the average size of the particles in the aerosol; the smaller the particles, the larger the exponent. Thus, the validation results were consistent with Figure 7b, which indicated either more fine-mode particles related to the coarse-mode particles, or smaller radius fine-mode particles. In general, there was a slight system offset between the retrieved results from SMAC and the direct solar products. There may have been two reasons for the offset. One was due to the applicability of aerosol model parameters at high AOD conditions, and the other comes from the SMAC calibration errors.
# The Availability of Space Service for Inter-Satellite Links in Navigation Constellations
###### Abstract  
Global navigation satellite systems (GNSS) are widely used in low Earth orbit (LEO) satellite navigation; however, their availability is poor for users in medium Earth orbits (MEO), and high Earth orbits (HEO). With the increasing demand for navigation from MEO and HEO users, the inadequate coverage of GNSS has emerged. Inter-satellite links (ISLs) are used for ranging and communication between navigation satellites and can also serve space users that are outside the navigation constellation. This paper aims to summarize their application method and analyze their service performance. The mathematical model of visibility is proposed and then the availability of time division ISLs is analyzed based on global grid points. The BeiDou navigation constellation is used as an example for numerical simulation. Simulation results show that the availability can be enhanced by scheduling more satellites and larger beams, while the presence of more users lowers the availability. The availability of navigation signals will be strengthened when combined with the signals from the ISLs. ISLs can improve the space service volume (SSV) of navigation constellations, and are therefore a promising method for navigation in MEO/HEO spacecraft.  
GNSS; inter-satellite link; space service volume; availability +
Footnote †: journal: Sensors
## 2 Current Capabilities of Modern FPGAs
### Features and Platforms of FPGA Vendors
#### 2.2.1 Xilinx  
As Actel did in the past, Xilinx recently started to base their platforms on hard processor blocks. For instance, the Zynq-7000 platform [16] is based on a dual-core Cortex A9 processor that configures the FPGA itself, providing a SoC oriented platform focused on video processing and wireless communication. Likewise, Xilinx includes in its road-map the development of reconfigurable platforms for analog signal processing, similar to the mixed-signal FPGAs of Actel (Actel Smart Fusion).  
Xilinx classifies their FPGAs in three series: Virtex (high-end), Artix (low-power), and Kintex (low-cost). The FPGAs are based on reduced energy consumption, 28 nm and of High-K Metal Gate (HKMG) fabrication process [17].  
These heterogeneous platforms consist of fine and coarse grained components such as 6 input LUTs and powerful embedded blocks like 28 Gbps serial transceiver, 25 \(\times\) 18 bits multipliers, 48-bit accumulators and pre-adders working at the maximum frequency allowed by the core. Moreover, Xilinx supports the 32-bit MicroBlaze soft-processors, based on the high-performance interconnection bus AXI.  
On the other hand, several authors continue proposing designs using old-fashioned FPGAs from Xilinx such as the Spartan-3, Spartan-3E and Spartan-6 platforms due to its low-cost (particularly crucial in sensor nodes construction) and still powerful embedded blocks. Table 1 depicts the static power consumption of mid-range platforms of the different series of Xilinx. These values have been obtained via the Xilinx Power Estimator (XPE) tool.