esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Vehicular VLC Methodologies
## 5 Vehicular VLC Applications
### Platooning  
The widespread availability of LEDs in vehicles has attracted the interest of many researchers, as these LEDs are used in current studies of headlights, brake lights, and turn signals for Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) communications [23; 68]. The faction support app is among the many attractive applications for VLC-based ITSs. The platoon implementation supports mutual data type requirements and a relatively short connection link [118]. Moreover, the platooning approach provides increased throughput on highways and urban areas and improves traffic safety. In the most common methods using the platoon approach, the existing conventional radio frequency technology is often the solution for exchanging messages between platoon members. Although the traditional frequency of radio communication technologies provides relatively long-range communication and high data rates and shares the same spectrum for vehicular applications, these technologies face some challenges and limitations in high-density traffic scenarios due to RF channel congestion [23].  
VLC has recently been utilized in ITSs for platoon applications. It can complement other networks such as C-V2X and IEEE 802.11p to facilitate communication and information exchange between the platoon leader and the other vehicles within the platoon, as depicted in Figure 8[119]. An analytical modeling of VLC is presented in [118] to define the key parameters of a platoon application and demonstrate its performance. The outdoor VLC system also incorporates a lateral and longitudinal platoon controller development approach. The lateral control unit aims to track the vertical vehicle path between V2V VLC connections, while the longitudinal control unit maintains a constant distance between  
Figure 7: Intersection assistance structure for V2I, I2V, and V2V.  
vehicles. Maintaining LoS continuity is one of the most challenging scenarios in vehicular applications, particularly for ITS. In order to address this challenge, information exchange is crucial among platoon members regarding relative directional location, including the forward and rear-facing directions of each vehicle. In [120], a compensation approach using four cars equipped with VLC and positioning systems is proposed. Different scenarios are simulated to examine the limits of compensation angles and varying trajectories. Furthermore, ref. [121] presents testing of a VLC system in several configurations using headlights and commercial taillights, focusing on their compatibility within a platoon. The system achieves a bit error rate (BER) of less than \(10^{-6}\) at a distance of 30 m and a data rate of approximately 100 kbps, with a latency of 4.2. The system's performance is also assessed in the presence of a jamming vehicle, demonstrating that it does not cause deterioration while crossing the platoon.
## 3 Materials and Methods
### UAS Set-Ups
#### 3.1.2 Advanced Set-Up  
Higher quality images benefit the SfM procedure and lead to a better end product. However, carrying a better and more tunable camera comes with a payload weight that likely requires an upgrade of the UAS. This means more overhead with equipment costs, flight endurance, regulations, and pilot certification (Appendix A). To investigate the relative value of using a more advanced UAS set-up, the snow-covered areas Green2, Green3, and Green4 were surveyed with a Nikon D3200 DSLR camera with a NIKKOR 18-55 mm lens (payload weight of 790 g in total), mounted on a DJI s900 hexacopter. The payload was mounted in a custom-built gimbal that dampens the rotors' vibration and allows for tilting of the gimbal angle (Appendix B). The Nikon D3200 sensor has a resolution of 24.2 effective mega-pixels (6016 \(\times\) 4000 pixels) and was triggered with a Polaroid intervolometer for setting the timing of image acquisition. The total weight of this UAS system was approximately 4.2 kg and cost around 1700 USD. In the current experiments, the Nikon D3200 lens focus was always set to infinity and the focal length was generally set around 18-20 mm, which provided the widest footprint possible at a given altitude with the available lens. This represents a more complex high-end solution in terms of flying difficulty and camera set-up and flying times are noticeably reduced to 7-10 min compared to the minimal set-up.  
Figure 3: Schematization of the methodology performed for each surveyed area. The output box summarizes the overall total output of the study for all the surveyed areas. The tools/equipment used for each step are shown within the white boxes associated with each step. Although this outlines the equipment/software used in this study, several alternatives are available. SDEM and TDEM refer to both snow and terrain Digital Elevation Models (DEMs) respectively. GCP stands for the number of ground control points and GNSS for Global Navigation Satellite System.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Area Name**} & \multirow{2}{*}{**UAS Set-Up or Land-Based Survey**} & \multicolumn{3}{c}{**Average Flying Altitude or**} & \multirow{2}{*}{**Camera Settings**} & \multirow{2}{*}{**Acquired Images**} \\ \cline{2-2} \cline{5-8}  & **SDEM** & **TDEM** & **SDEM** & **TDEM** & **SDEM** & **TDEM** & **SDEM** & **TDEM** \\ \hline \hline \multirow{4}{*}{Sva11} & \multirow{4}{*}{Advanced set-up} & \multirow{4}{*}{7} & \multirow{4}{*}{23} & \multirow{4}{*}{40} & 18 mm & \multirow{4}{*}{909} & \multirow{4}{*}{194} \\  & & & & & & 1/4.5 & \\ \cline{4-8}  
The summer conditions mapping of Svall area used a custom-built octocopter together with the Nikon D3200 attached with the same gimbal used with the DJI s900. This UAS set-up cost around 1000 USD, camera included. This set-up is considered analogous to the advanced set-up as camera and settings were unchanged.
# Grid Nodes Selection Strategies for Power Quality Monitoring
## 4 Problems and Future Prospects
### Sophisticated Attention to Every PQ Event
Some examples of possible situations:  
* Situations based on meteorological data in a specific part of a country: an expected increase in solar and wind parks generation, and levels of harmonics and flickers, respectively; annually expected thunderstorms and lightning (in May-June) or just wind storms, ice formation risk, high humidity (e.g., conditions which can intensify corona discharges);
* Situations regarding grid operation and maintenance: planned maintenance works in substations (power transformer disconnection from grid), a high number of operational commutations expected, changed grid topology, frequent protective relays triggering, hardly accessible locations;
* Grid research and condition monitoring: research of PQ mitigation strategy or specific equipment, research of impact on grid equipment, predictive maintenance, excellent PQ situation in monitor coverage zone;
* Situations regarding technological processes and customers' loads: expected intensive manufacturing mode (risk of outages, risk of production spoilage and quality drop), expected frequent electric motors starting (the cause of voltage sags), newly installed sensitive equipment, critical technological process (e.g., fire-fighting pumps, nitrogen loop for chemical inerting, cooling loops in nuclear power plants, nuclear waste repository, oil refinery), customers' complaints and surveys;
* Other technical issues: weak communication signal, signals interference, unavailable optical fiber, redundancy of analyzers in a region (due to unknown reasons), allocation algorithm limitations, errors or coefficient changing, etc. ;
* Political initiatives, economic evaluation, compliance verification and other legislative reasons, insurance policy, marketing, education, and science popularization. As mentioned previously, IEEE Std 1159 treats more phenomena as PQ events including the focus on electric current and EMC issues. Currently, many authors try to focus only on EN 50160 since it introduces some regulation and is simpler for large-scale application. In contrast, only IEEE Std 519 sets limit values for the harmonics, and IEEE Std 1159 gives only general information and recommendations (including some threshold values); however, the documents can be applied only to the case of the North American grid (i.e., it is significantly different from European systems: for example, frequency is 60 Hz, low phase-to-ground voltage--120 V, low phase-to-phase voltage--240 V). To conclude, amendments and sophisticated focus on IEEE Std 1159 are possible after the elimination of uncertainties in EN 50160 and IEC 61000-4-30. These documents require serious amendments which have not been made since legislative year. Some aspects can be eliminated immediately, some require more research and technical progress.
## 3 Methods
### Step 1: Analysis-Ready Data (ARD)  
#### 3.1.1 Preprocessing of the Full Dataset  
All Landsat images were converted to ARD using the FORCE Level 2 Processing System (v. 3.5). Clouds and cloud shadows were identified using a modified version of the Fmask algorithm [57; 66; 67; 68]. The cloud masking is currently assessed in the Cloud Masking Inter-comparison Exercise (CMIX, [[https://earth.esa.int/web/sppa/meetings-workshops/hosted-and-co-sponsored-meetings/acix-ii-cmix](https://earth.esa.int/web/sppa/meetings-workshops/hosted-and-co-sponsored-meetings/acix-ii-cmix)]([https://earth.esa.int/web/sppa/meetings-workshops/hosted-and-co-sponsored-meetings/acix-ii-cmix](https://earth.esa.int/web/sppa/meetings-workshops/hosted-and-co-sponsored-meetings/acix-ii-cmix)), accessed on 18 September 2021) as well as in [69]. The FORCE atmospheric correction [68] branched from the AtCPro algorithm [50] used in the reference studies, and was re-implemented to enable bulk-processing of large image volumes [70]; thus a high level of comparability is ensured. The images were radiometrically standardized using radiative transfer modeling [71], including object-based Aerosol Optical Depth (AOD) estimation over dense dark vegetation [72] and water features [73]. Water vapor absorption was corrected using nearly concurrent MODIS water vapor estimates [74] or a long-term climatology as an approximation [62]; the effect of using the climatology as a surrogate was recently assessed in [63]. AOD estimation, as well as surface reflectance retrieval, were assessed in the Atmospheric Correction Inter-comparison Exercise (ACIX) [75], as well as in the ongoing 2 nd edition (ACIX-II, [[https://earth.esa.int/web/sppa/meetings-workshops/hosted-and-co-sponsored-meetings/acix-ii-cmix](https://earth.esa.int/web/sppa/meetings-workshops/hosted-and-co-sponsored-meetings/acix-ii-cmix)]([https://earth.esa.int/web/sppa/meetings-workshops/hosted-and-co-sponsored-meetings/acix-ii-cmix](https://earth.esa.int/web/sppa/meetings-workshops/hosted-and-co-sponsored-meetings/acix-ii-cmix)), accessed on 18 September 2021). Topographic correction was performed with an enhanced C-correction, wherein the C-factor was estimated for each pixel in the image, then propagated through the spectrum using radiative transfer [76]. Three kernels of increasing size were used to approximate the background reflectance for adjacency effect correction [77]. Nadir BRDF-adjusted reflectance was retrieved using a global set of MODIS-derived BRDF kernel parameters [78]. Eventually, all images were re-projected to a single coordinate system (EPSG:3035) and then split into 30 \(\times\) 30 km\({}^{2}\) image chips using a custom grid, thus representing data cubes [79]. This data cube concept allows for non-redundant data storage, efficient data access, as well as simplified extraction of data and information, which is required for the higher level processing outlined in the following sections.
## 4 Discussion
### Changes in the Evaluation Results of Three Stages in Urban Infrastructure
#### 4.2.3 Responses  
Compared to the previous two stages, the response resilience of infrastructure in four cities varied within relatively noticeable differences, shown in Figure 5 and Table A4. On average, Chongqing had the highest response resilience score at 0.506, the only city in the moderate response stage. High-level civil air defence engineering construction and considerable investment in urban infrastructure maintenance have played a positive or vital role in the recovery and adaptability of urban infrastructure in Chongqing. Chongqing has continuously strengthened pollution control and construction of water environment in recent years, establishing the environmental monitoring and governance system [58]. Therefore, the sewage treatment rate doubled during this period, and the innocuous treatment rate of living garbage increased at an annual growth rate of 0.18%, both reaching leading national levels in 2018. Furthermore, medical service capabilities were also steadily improving, which manifested that the hospital beds per 10,000 population doubled in 17 years. As a result, response resilience in Chongqing shifted into the moderate response from the slight response.  
The development level of infrastructure response in Beijing was in the range of [0.418, 0.459], ranking the latest among the four cities. Beijing's water environment pollution was relatively severe [59]. In recent years, the continuous upgrading of sewage treatment plants and strengthening comprehensive treatment of the water environment has dramatically improved the sewage treatment capacity while still insufficient to absorb pollution. Moreover, the newly added civil air defence engineering area was also far lower than that of other cities, constraining the resilience and adaptability of its infrastructure to some extent.  
As for Shanghai, it decreased from 0.503 (Moderate response) in 2002 to 0.478 (Slight response) in 2018, primarily attributed to a gradual decrease in its investment in the operation and maintenance of urban infrastructure, with ended up only three-fifths of the average of all research cities. However, Tianjin had relatively stable development trends in response resilience, with an average response resilience of 0.452. It was found **that** in the case of low infrastructure resilience and adaptability, most of the indicators that characterize learning ability are far inferior to other cities, such as hospital beds per 10,000 population, mobile phone coverage rate, leading to Tianjin with only a slight response at the end of the research period.  
Figure 4: Trends in state resilience from 2002 to 2018.
## 4 Simulation Study
### Case Study 4: Implementation on the Tibet Power Grid  
In this case study, the IEEE 300 bus system is further considered to show the effectiveness of the proposed critical nodes identification method for a larger power system. The simulation method and process are the same as in case studies 1 to 3, and the simulation results are shown in Figure 8. Figure 8 shows the initial identification results, where some nodes also carriers of higher controllability indexes, which are consider of criticality. Figure 8 shows the minimum number of driver nodes of the remaining network after every targeted and random attack. It can be seen that there needs to be an increase of approximately 12 driver nodes to maintain full control of the network after five targeted attacks. The increased numbers are more than the corresponding results in the IEEE 118 bus system, which means the larger the size of the system, the more difficult it is to maintain full control of the system when the small numbers of critical nodes are absent. Thus, it is more important to identify the critical nodes as the system increases. In addition, there needs to be an increase of approximately 23 driver nodes to maintain full control of the system after 10 targeted attacks. On the contrary, the minimum number of driver nodes is not changed clearly after 10 random attacks. Figure 8 shows the controllability index distribution after 10 targeted attacks, and the identification results are changed clearly compared with the results in Figure 8. However, the identification results are very similar before and after 10 random attacks, as shown in Figure 8,d. Therefore, the simulation results mentioned above have once again proven that the system is very robust to random attacks but not targeted attacks, which further verifies the effectiveness of the proposed method.  
Figure 7: Effect of random attacks on controllability index distribution. (**a**) Original distribution; (**b**) Distribution after five random attacks.
# A New Index Assessing the Viability of PAR Application Projects Used to Validate PAR Models
## 3 Results
### Regional PAR Models
#### 3.1.2 Site-Adaptation  
To verify if the results obtained by Model 1 with MR--chosen as the best model for the first ranking--could be improved through a correction using local data, the site-adaptation technique for both the Mediterranean and Oceanic regions was applied. Likewise, this same technique was also applied to MR Model 8, as it was chosen as the best model for the second ranking, to check if an improvement could be achieved.  
To obtain parameters \(a\) and \(b\) of Equation (6), half of the measured and modeled \(PAR\) data were fitted, and the other half of the data were used to validate the new model. Thus,  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Oceanic Cluster** & **Mediterranean Cluster** \\ \hline Model 1 & \(PAR=13.81+2.02 GH1\) & \(PAR=1.37+2.04 GH1\) \\ \hline Model 2 & \(PAR=40.93+2.15 GH1-170.78k_{t}\) & \(PAR=41.46+2.14 GH1-158.21k_{t}\) \\ \hline Model 3 & \(PAR=-15.37+2.01 GH1-76.16k_{t}\) & \(PAR=-22.74+2.00 GH1-65.58k_{t}\) \\  & \(+112.38\cos\theta\) & \(+134.37\cos\theta\) \\ \hline Model 4 & \(PAR=-94.91+1.99 GH1+0.91 RH\) & \(PAR=-144.66+2.01 GH1+1.32 RH\) \\  & \(+4.09T\) & \(+4.96T\) \\ \hline Model 5 & \(PAR=-83.20+2.01 GH1+0.64 RH\) & \(PAR=-127.69+2.00 GH1+1.07 RH\) \\  & \(+3.31T-81.38k_{t}+82.15\cos\theta\) & \(+4.22T-68.19k_{t}+85.44\cos\theta\) \\ \hline Model 6 & \(PAR=29.07+2637.98k_{t}\cdot\cos\theta\) & \(PAR=31.07+2634.29k_{t}\cdot\cos\theta\) \\ \hline Model 7 & \(PAR=14.02+194.87k_{t}\cdot\cos\theta\) & \(PAR=1.86+178.16k_{t}\cdot\cos\theta\) \\  & \(+1.87 GH1\) & \(+1.90 GH1\) \\ \hline Model 8 & \(PAR=115.40+2501.47k_{t}\cdot\cos\theta\) & \(PAR=115.09+2478.65k_{t}\cdot\cos\theta\) \\  & \(-1.46 RH+3.92T\) & \(-1.65 RH+3.19T\) \\ \hline Model 9 & \(PAR=-90.38+63.03k_{t}\cdot\cos\theta\) & \(PAR=-146.44-20.03k_{t}\cdot\cos\theta\) \\  & \(+1.94 GH1+0.86 RH+4.06T\) & \(+2.03 GH1+1.34 RH+4.98T\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Equations of the nine models for each region.  
half of the data randomly selected from Lugo--USC; Villaviciosa--SERIDA; and Vitoria--NEIKER were utilized to calculate \(a\) and \(b\) for the Oceanic region, and the same with data from Albacete--ITAP; Cordoba--IFAPA; Salamanca--CIALE; and Zaragoza--AULA DEI to calculate \(a\) and \(b\) for the Mediterranean region. The equation for the site-adaptation correction to the Oceanic region was (13), whereas for the Mediterranean region was Equation (14).  
\[y_{new\ mod}=y_{\rm mod}-(0.01x_{local}-18.16) \tag{13}\]  
\[y_{new\ mod}=y_{\rm mod}-(0.01x_  
Figure 4: Comparison between the statistical results obtained by MR Model 1 and its site-adaptation correction.  
Figure 3: Statistical results of the nine MR models for each station.
# Joint Multi-Scenario-Based Earthquake and Tsunami Hazard Assessment for Alexandria, Egypt
###### Abstract
Conversely, in this current work, maps of inundation zones were estimated based on the last updated tsunami hazard maps computed along the near shoreline of Alexandria using a multi-scenario approach [3]. Furthermore, various Manning's Roughness Coefficients, i.e., 0.015 for sea floor and open land, 0.03 for thinly populated areas, 0.05 for moderately populated areas, and 0.08 for densely populated areas, were considered. To assess the maps of average and maximum inundation depth at each location, we developed an empirical model that utilizes the tsunami wave height maps in the work of [PERSON] et al. [3]. Through interpolation, this model provides average and  
Figure 1: Location map of Alexandria Governorate and main districts (source of census data is the Central Agency for Public Mobilization and Statistics [16]). maximum inundation depth maps on a 30 \(\times\) 30 m grid. The assignment of these coefficient values was informed by imagery and Google Earth satellite data. GIS data processing and analysis were performed with Quantum GIS (QGIS 3.34.11 [15]). We emphasize the need for a comprehensive, multi-hazard assessment that considers the interrelationship between seismic and tsunami risks. Our study addresses this gap by employing advanced modeling techniques and high-resolution data to produce detailed hazard maps for Alexandria. We acknowledge the limitations of our study, such as the availability of high-resolution topographic data, current mitigation strategies or defenses for water intrusion, and the focus on rock site conditions, and we discuss potential future research directions to address these limitations. In this current study, we developed, for the first time, high-resolution seismic and tsunami hazard maps jointly for Alexandria, providing a more detailed and accurate assessment of risk. We employed advanced modeling techniques and up-to-date data to produce these high-resolution maps. Our results can be directly applied to urban planning, multi-hazard risk analysis, disaster risk reduction, and coastal resilience strategies in Alexandria.