esa-sceva/satcom-chunk-collection huggingface dataset:
# InP-Based Foundry PICs for Optical Interconnects
## 3 Building Blocks targeting Hybrid-Integration Applications
### Vertical Mirrors  
An alternative surface-coupling approach is based on vertical mirrors, which are etched through the waveguides to divert the light upward or downward from the PIC. For the generic platform, we transfer the process from our commercial device that employs such vertical mirrors, which is the so-called horizontal-cavity surface-emitting laser (HCSEL) that consists of a conventional InP DFB laser with a vertical mirror etched at the end [32]. For this commercial device, we are able to couple 10 mW of output power at 100 mA of applied current at the top of the vertical mirror, where around 20 mW are expected in conventional DFB lasers with a cleaved facet. This 3 dB loss for the vertical-mirror etch is roughly the same as the loss for the etched facets described earlier, which is not surprising considering the fact that the etching process and tool (CAIBE) is the same except for the etching angle. Again, here it is expected that these losses caused by the roughness of the etching will not be so high for vertical mirrors etched into the diluted waveguide with the larger mode size. Figure 10a,b show our implementation of these vertical mirrors for the generic-foundry platform. Similar to the etched facets, the vertical mirrors are etched in a CAIBE tool by sloping the InP PIC with respect to the ion beam in the CAIBE. Simultaneously, another oppositely sloped 45\({}^{\circ}\) mirror is created, which reflects the laser light downwards into the substrate (Figure 10b). An additional advantage of these mirrors is that the optical coatings can be applied simultaneously over the whole wafer before cleaving. Vertical mirrors provide high efficiency and virtually wavelength and polarization-independent coupling, but it requires an additional processing step on our generic-foundry process. The fact that the generic-foundry process offers SSC BB with a large mode size (\(10\times 7~{}\upmu\)m\({}^{2}\)) and small beam divergence, is advantageous for this coupling approach, as the light typically needs to travel some distance before reaching the SMF. A similar laser device employing vertical mirrors is the so-called lens-integrated surface emitting laser (LISEL), combining a laser, a vertical mirror, and an integrated collimating lens [33,34].  
Figure 9: Schematic showing an implementation of grating couplers on the generic-foundry-service process.
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 3 Results
### Large Area Mapping by Satellite  
The methods developed above are now applied to much larger areas, using satellite images from the WorldView-3 constellation. Two images were purchased from different dates but covering the same 38.5 km\({}^{2}\) area as described in Section 2.3.2.  
Separate geometries were drawn to mark out areas of land, water and weed in the 2017 and 2018 images. The image bands were sampled randomly within each of these polygons at a resolution of 0.3 m. The median, 25 and 75 percentiles for the 8-band reflectances of each of the classes for both images are shown in Figure 9. The 2017 image was on a day with wind coming from the Nor-Nor-East creating ripples aligning with the sun direction and thus creating reflections on the water. This explains the wider spread and different characteristics in the water reflectance in 2017 when compared with 2018. The 2017 image was taken in early autumn, whereas the 2018 image was from mid-summer with corresponding differences in vegetation on the land and weed in the channels. The variation in the reflectances of the classes over time is evident, again showing that a classifier generated from one image cannot be applied to accurately classify another image.  
Next, the automated unsupervised-supervised classification method (Section 2.4.6) was applied. The same land and channel training geometries were used for both image dates. Separate validation geometries were used (as weed areas were different from 2017 to 2018). The resulting error matrices are shown in Table 9. Kappa remains above 0.85 for both images, indicating excellent accuracy.  
The classified areas for each of the classes from the two images are shown in Table 10. The total area classified is 0.614 km\({}^{2}\) of the total satellite image size of 38.5 km\({}^{2}\), which consists of 20 m wide strips around the supply channels. The total length of channels mapped is then 30.7 km. The last column shows the proportion of the total channel area (weed + water) that is taken up by weed. The similarity in the land area between the two images verifies that this area is being classified correctly, as it does not change from image to image. The percentage of weed information will be very useful for irrigation network companies to track the weed status of their channels through the season, and from season to season. The classification and result calculations of these large high-resolution images took seconds in GEE.  
Figure 9: Reflectances of samples from each of the defined class polygons in the WorldView-3 images. The lines shows the median reflectances and the shaded areas the 25–75 th percentiles. (**a**) 2017 image; (**b**) 2018 image.
# A Parallel Two-Stage Iteration Method for Solving Continuous Sylvester Equations
## 6 Conclusions  
In this paper we have proposed a two-stage iteration parallel method for solving the continuous Sylvester equations. The outer iteration scheme is based on the coefficient matrices' splitting. Furthermore, an inner iteration scheme is obtained using the SYMMLQ algorithm. Its parallel implementation and its improved strategy have been explained in detail. Moreover, we have proved the convergence of the proposed iteration method under certain conditions. Numerical results show that the new proposed algorithm is better than both MCG and HSS methods with regard to computational storage, computational time, and iteration steps. Crucially, these advantages become more significant for large-scale systems of continuous Sylvester equations.  
**Acknowledgments: This work has been supported by the National Natural Science Foundation of China (Grants No. 11302173)and the Natural Science Foundation of Shaanxi Province (Grants No. 2017Q1037). The computation of examples was executed in the Center for High Performance Computing of Northwestern Polytechnical University.**  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**TS**} & \multicolumn{4}{c}{**MCG**} \\ \hline \(p\) & 20 & 25 & 30 & 35 & 20 & 25 & 30 & 35 \\ T & 641.12 & 569.88 & 491.28 & 463.74 & 1563.70 & 1374.68 & 1184.62 & 1116.93 \\ \(\Pi\)s & 7194 & 7194 & 7194 & 7194 & 91,332 & 91,332 & 91,332 & 91,336 \\ S & & 22.50 & 26.10 & 27.65 & & 22.75 & 26.40 & 28.00 \\ \(E_{1}\) & 1.00 & 0.90 & 0.87 & 0.79 & 0.16 & 0.37 & 0.36 & 0.33 \\ \(\Delta\) & \(9.72\times 10^{-7}\) & \(9.72\times 10^{-7}\) & \(9.72\times 10^{-7}\) & \(9.72\times 10^{-7}\) & \(9.83\times 10^{-7}\) & \(9.83\times 10^{-7}\) & \(9.93\times 10^{-7}\) & \(9.54\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Numerical results of Example 2 with \(h=1/1001\).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**TS**} \\ \hline \(p\) & 16 & 20 & 24 & 28 \\ T & 2480.19 & 2351.59 & 2296.47 & 2241.46 \\ ITs & 509 & 509 & 509 & 509 \\ S & & 16.87 & 17.28 & 17.92 \\ \(E_{1}\) & 1.00 & 0.84 & 0.72 & 0.64 \\ \(\Delta\) & \(8.83\times 10^{-7}\) & \(8.83\times 10^{-7}\) & \(8.83\times 10^{-7}\) & \(8.83\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Numerical results of Example 3.  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived the key ideas, designed the algorithm, performed the examples and drafted the initial manuscript; [PERSON], [PERSON] and [PERSON] revised the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Priority-Based Layered QC-LDPC Decoder with Double Update Queues
### Detailed Illustration of the Proposed Decoder with High Performance
In the second layer, the third, seventh, second, fourth, and sixth VNGs are decoded in turn. In the third layer, the first, fifth, eighth, seventh, and fourth VNGs are decoded in turn. In the fourth layer, the second, ninth, first, eighth, and fourth VNGs are decoded in turn. After the LLRs are read from memory, they are used to calculate the variable-to-check messages. In the first layer, the variable-to-check messages of the fifth, first, and second VNGs are buffered into the non-overlapping FIFO since LLRs of the fifth and first VNGswill not participate in the decoding in the second layer and the updated LLRs of the second VNG will not be used in the second layer. In the first layer, the variable-to-check messages of the sixth and fourth VNGs are buffered into overlapping FIFO since these variable nodes are needed in the second layer after their LLRs are updated. The VNGs of other layers also buffer in this way. After the update of LLRs, LLRs of the overlapping submatrices are bypassed to the data path. They continue to be decoded in the next layer. LLRs of the non-overlapping variable nodes are written back to the memory. In the first layer, the fifth, first, and second VNGs are non-overlapping. Their LLRs are written back to memory. On the contrary, LLRs of the non-overlapping sixth and fourth VNGs are bypassed to the data path and participate in the decoding in the second layer. In this way, LLRs are updated in double queues and the occurrence of pipeline conflicts is obviously decreased. When a pipeline conflict happens, the solution in the priority-based decoder works as follows. According to the priority-based schedule, LLRs of the second VNG in the first layer have priority to update at the 11 th clock. At the eighth cycle, a pipeline conflict happens to the second VNG in the second layer. Therefore, it has to read the old LLR values because the LLR values of the second VNG in the first layer have not been updated yet at the eighth cycle. The variable-to-check messages of the second VNG in the second are calculated and passed to CNU but not buffered into overlapping FIFO or non-overlapping FIFO. The second VNG in the second layer calculates the gain on the basis of variable-to-check messages and stores the gain. At the 24 th cycle, LLRs of the second VNG in the fourth layer  
Figure 4: Illustration of the priority-based layered decoder with double update queues compared with the conventional layered decoder, the residue-based decoder and the hybrid decoder. The number of pipeline stages is set to 3.  
are first updated normally after the occurrence of the pipeline conflict. At this moment, the gain of the second VNG in the second layer is added to the updated LLRs of the second VNG in the fourth layer. In this way, the loss caused by the pipeline conflict is compensated.
# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
## 2 Methods and Data
### Factor System of Flood Susceptibility and Model Building
#### 2.8.2 Flood Inventory Map  
The key to accurately predicting flash-flood-prone areas is to survey the areas already affected by torrential events. It is necessary to investigate prior flooding incidents in the study area to determine the chance of future flooding. Rapid surface runoff frequently induces flash floods that spread down the valley's slope [6]. To identify and detect flood areas in the study area, we gathered a set of Sentinel-1 images (level-1 ground range detected (GRD), more interferometric wide swath (IW)) acquired between 12 April 2021 and 6 May 2021, and we manipulated them using the sentinel application platform (SNAP 7.0). The time-series interferogram construction technique was applied on pre-and post-flood Sentinel-1 data to map flooded areas [38; 66]. Two images from separate dates were utilized to represent the area before and after the flood occurred; this strategy is based on the unique nature of SAR interaction with the water surface and flooded vegetation compared to other features. The flood inventory map was created using data from flood occurrences between 1996, 2008, and 2021. The Sentinel-1 images and interpretation of satellite and Google Earth images and news reports were used to create the flood inventory map [27]. For flooding susceptibility mapping, flood and non-flood points are required [100]. The training flood areas (300 points) were chosen based on past disaster reports and sentinel data and the same number of non-flooded areas were randomly created [101]. Figure 6 shows the spatial distribution of the 600 points (flooded and non-flooded) used to prepare flooding susceptibility maps. A flood layer was prepared as a dependent component. In this layer, the points corresponding to flood and non-flood areas were indicated by the values 1 and 0, respectively. Of the total points, 75% were used to train the models and the remaining 25% were used for the validation of the trained models [102; 103].  
2.8.3. Applied ML Models for Flood Susceptibility Mapping  
In this study, ArcGIS 10.3 and R 3.6.1 software are used to analyze flood susceptibility. Figure 7 shows the methodology used in this research study. The first step involved data collection and preprocessing, second--multicollinearity testing of flood causative factors, third--data split into 75% for model training and 25% for validation, fourth--the preparation of flood susceptibility maps by RF, XGBoost, NB, and KNN, and the last step is the validation and comparison of flood susceptibility maps.  
Figure 6: Flood inventory map of the study area.
# A Novel Cloud Approach for Connected Vehicles
## 4 Analytic Model
### Approaches  
To compare the network overhead (CR) and the data freshness (FR) with each approach, we propose an analytic study, involving the different variables of Tables 2-4. For each approach, we calculate the expression of each variable and we then refer to the definition of CR and FR to give the final expression of the overhead and the data freshness of the associated approach.  
**Definition of CR**  
To measure the overhead generated by ICN exchanges, on the G5 channel, we define the network overhead as being the sum of the network overhead caused by the interests \(CR_{i}\) and the network overhead caused by the data \(CRd\).  
\[CR=CR_{i}+CR_{d} \tag{18}\]  
**Definition of FR**  
We define the data freshness \(FR\) as being the percentage of the total vehicles which receive the data before its expiration.  
\[FR=\frac{\alpha_{D}}{\alpha}*100 \tag{19}\]
## 3 Methodology and Case Description  
### TC Center Positioning Method  
We adopt two center-positioning algorithms: (1) scatterometer-based [23] and (2) AHI-based [24].  
Scatterometer-based (Figure 2 example):  
1. Find the max wind speed location (triangle).
2. Within a 3\({}^{\circ}\)\(\times\) 3\({}^{\circ}\) box, find all minimum wind speeds (circle) that are less than their immediate neighbors.
3. Define eight two-component vectors \(\overrightarrow{q}_{i}\) (\(i=1\),...8) at the eight nearest points around each wind-speed minimum: if the raw wind direction is within 0\({}^{\circ}\)\(\pm\) 22.5\({}^{\circ}\), 45\({}^{\circ}\)\(\pm\) 22.5\({}^{\circ}\), 90\({}^{\circ}\)\(\pm\) 22.5\({}^{\circ}\), 135\({}^{\circ}\)\(\pm\) 22.5\({}^{\circ}\), 180\({}^{\circ}\)\(\pm\) 22.5\({}^{\circ}\), 225\({}^{\circ}\)\(\pm\) 22.5\({}^{\circ}\), 270\({}^{\circ}\)\(\pm\) 22.5\({}^{\circ}\), and 315\({}^{\circ}\)\(\pm\) 22.5\({}^{\circ}\), the discrete vectors \(\overrightarrow{q}_{i}\) take the values of (1, 1), (1, 0), (1, \(-\)1), (0, \(-\)1), (\(-\)1, \(-\)1), (\(-\)1, 0), (\(-\)1, 1), and (0, 1), respectively.
4. Compute the absolute sum of these eight direction vectors: \(Q=|\sum\limits_{i=1}^{8}\overrightarrow{q}_{i}|\).
5. The minimum wind point with the smallest sum \(Q\) is the TC center (cross). AHI-based:  
1. Use the system-clustering method to identify three points with the lowest brightness temperature in the eyewall. The original channel-13 TB observations are averaged onto a 0.3\({}^{\circ}\)\(\times\) 0.3\({}^{\circ}\) grid. The data points where TB observations are less than an empirical value of \(T_{b}^{th}\) = 202 K are determined. Each of the data points is considered as an initial cluster. If there are \(N\) data points with TB observations being less than \(T_{b}^{th}\), there are \(N\) clusters. The two clusters with the closest minimum distance among the \(N\) clusters are merged together to form a new cluster. The new cluster is further merged with one of the remaining clusters whose distance from the new cluster is the smallest. This procedure is repeated until only the newest cluster and one remaining cluster are left. The three points of the minimum TB values in the newest cluster are identified for the next step.
2. Fit a circle passing the three points identified by the cluster method; the circle center is the first guess.
3. Perform an azimuthal spectral analysis for each grid in a 4\({}^{\circ}\)\(\times\) 4\({}^{\circ}\) domain centered at the first guess point with 0.15\({}^{\circ}\)\(\times\) 0.15\({}^{\circ}\) resolution, and the grid location leading to the largest wavenumber-0 amplitude is taken as a refined center over the guess point.
4. Repeat the spectral analysis for each grid in a 2\({}^{\circ}\)\(\times\) 2\({}^{\circ}\) domain centered at a refined center with 0.025\({}^{\circ}\)\(\times\) 0.025\({}^{\circ}\) resolution, and the grid corresponding to the largest wavenumber-0 amplitude is taken as the final TC center.
## 3 Trust-Optimized 6G-IoT Framework
### Trust Management System
IDSoft (19.6%), CECB (17.5%), RAT6G (21.3%), HM-IoT (22.1%), SREITS (19.3%), DREE (23.1%), and RREN (22.0%). In the second scenario, focused on power consumption costs in IoT security, TEO-IoT reduced costs by 23.7%, surpassing IDSoft (18.3%), CECB (16.1%), RAT6G (20.2%), HM-IoT (21.4%), SREITS (18.7%), DREE (22.5%), and RREN (21.8%). Finally, the third scenario evaluated costs associated with data transmission under high-security workloads. TEO-IoT achieved 25.1% cost savings, outperforming IDSoft (20.5%), CECB (18.6%), RAT6G (22.4%), HM-IoT (23.3%), SREITS (20.2%), DREE (24.0%), and RREN (22.9%). ## 5 Conclusions

The proposed TEO-IoT framework is designed to focus on its efficiency in resource management, power consumption, network performance, and cost-effectiveness in IoT networks. The proposed TEO-IoT framework integrates dynamic pricing, adaptive routing, and incentive mechanisms to optimize resource allocation. The novelty of this research lies in the intelligent, trust-based economic optimization strategies applied to 6G-enabled IoT environments. The framework practical implications have potential for real-world applications in industrial IoT systems by improving cost-efficiency, network performance, and energy management. In the Edge-IIoTset dataset, the TEO-IoT achieved a 22.7% reduction in operational costs, surpassing models like IDSoft (18.5%) and CECB (16.4%). Similarly, in the N-BaloT dataset, the framework maintained 91.3% optimal resource usage compared to 85.5% by IDSoft. Power efficiency analysis further revealed a 15.2% reduction in transmission power for IoT-23, outperforming IDSoft's 11.1%. Moreover, packet delivery ratios consistently exceeded 98%, underscoring the framework's reliability. Future work could focus on extending TEO-IoT's capabilities to emerging IoT applications in real-world testing, including autonomous systems and real-time security in large-scale environments. Conceptualization, O.Z.A., K.A.A. and A.M.A. ; methodology, O.Z.A., K.A.A. and A.M.A. ; software, O.Z.A. ; validation, O.Z.A., K.A.A. and A.M.A. ; formal analysis, O.Z.A. ; investigation, O.Z.A. ; resources, O.Z.A. ; data curation, O.Z.A. ; writing--original draft preparation, O.Z.A., K.A.A. and A.M.A. ; writing--review and editing, O.Z.A., K.A.A. and A.M.A. ; visualization, O.Z.A. ; project administration, A.M.A. ; funding acquisition, A.M.A. All authors have read and agreed to the published version of the manuscript. The authors gratefully acknowledge the funding of the Deanship of Graduate Studies and Scientific Research, Jazan University, Saudi Arabia, through project number: GSSRD-24. Not applicable. The original data presented in the study are openly available in the following repositories: Edge-IIoTset at IEEE Dataport Repository (DOI: 10.1109/ACCESS.2022.3165809); N-BaloT at Kaggle Repository (DOI: 10.1109/MPRV.2018.03367731); and IoT-23 at NIAID Data Ecosystem (DOI: 10.5281/zenodo.4743745). The authors declare no conflicts of interest.
## 6 Future Projects of the Production of Green Hydrogen on an Industrial Scale
### GH2 Projects--Future Facilities (2025-2050)
#### 6.1.5 Punta Delgada Project--Magallanes Region  
The Punta Delgada on-shore wind farm is a facility that will be located in the northeast of Punta Arenas, Magallanes region. It will have 333 wind turbines, the owner of which will be EDF Renewables. Furthermore, the height of the towers from ground level to the hub will be in the range of 110 m. Each wind turbine will have an installed capacity of 6.0 MW, and considering all the wind turbines, the wind farm will have a total installed capacity equivalent to 2000 MW, with an estimated capacity factor of 0.57. Figure 14 shows a panoramic image of the landscape of the project site, and Table 7 provides some key technical data for this GH2 production facility.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Units** \\ \hline Location & North of Punta Arenas, Magallanes Region & - \\ \hline Concession Owner & EDF Renewables & - \\ \hline Phase of the Project & Industrial Plant & - \\ \hline Type of Wind Farm & On-shore & - \\ \hline Number of Turbines & 333 & - \\ \hline Diameter of Turbines & 145 & m \\ \hline Hub Height & 110 & m \\ \hline Installed Capacity per Turbine & 6.0 & MW \\ \hline Total Installed Capacity & 2000 & MW \\ \hline Capacity Factor & 0.57 & - \\ \hline Equivalent Operating Hours (EOHs) & 5000 & Hours \\ \hline Source of Water & Sea Water—Desalination Plant & - \\ \hline Water Supply & 1,950,000 & m\({}^{3}\)/year \\ \hline Electrolyzer Capacity & 1500 & MW \\ \hline GH2 Production & 172,500 & Tons/year \\ \hline NH\({}_{3}\) Production & 967,500 & Tons/year \\ \hline CO\({}_{2}\) Avoided & 1,725,000 & Tons/year \\ \hline \end{tabular}
\end{table}
Table 7: Punta Delgada GH2 plant main specifications.  
Figure 14: Panoramic view of the landscape in the Punta Delgada project.  
Considering Figure 14, it is evident that the Punta Delgada on-shore wind farm is situated near the desert part of Patagonia, where no contiguous human settlements are present.  
According to Table 7, this GH2 production plant will use seawater from a desalination plant, considering a water supply of 1,950,000 m\({}^{3}\)/year. Also, the plant will have an electrolyzer with 1500 MW of installed capacity. In this sense, the GH2 production plant will generate 172,500 tons/year of GH2, and 967,500 tons/year of NH\({}_{3}\). Finally, this facility will save 1,725,000 tons/year of CO\({}_{2}\) from being emitted into the atmosphere.