esa-sceva/satcom-chunk-collection huggingface dataset:
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 4 Methods
### Output and Audit Module
#### 4.4.4 Record Keeping and Provenance
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## 4 Effect of Design Parameters on Strength
### Effect of W/S on Compressive Strength and Split Tensile Strength  
Most of the researchers used the W/S ratio in the range from 5.0 to 10.5. For the family I mix, the W/S ratio lies in between 5.0 and 8.5 and, for the family II mixes, the ratio reduced to the range between 3.5 and 8.0. For family III mixes, the W/S ratio was in the range between 2.13 and 10.61 and, for the family IV mixes, the W/S ratio was in the range between 3.43 and 11.63. For the family V mixes, the W/S ratio was in the range between 4.66 and 11.63 and, for family VI mixes, the W/S ratio is in the range between 8.9 and 11.0. With an increase in the W/S ratio, there is a decrease in compressive strength for the family I and II and there is no constant relationship between the W/S ratio and compressive strength for other families is observed from Figure 12.  
The lower the W/S ratio results in higher split tensile strength (greater than 4.75 MPa) for family II and IV. Initially, split tensile strength increases with an increase in W/S ratio for family III to 8.0 and after that there is a decrease in split tensile strength. For other families, there is no constant relationship exhibited between the W/S ratio and the split tensile strength of SCC is observed from Figure 13.  
Figure 11: Effect of Superplasticizer weight on various split tensile strengths of SCC concrete with recycled aggregate according to the literature.  
Figure 12: Effect of W/S ratio on various compressive strength of SCC concrete with recycled aggregate according to the literature.
# Corrosion and Scaling in Geothermal Heat Exchangers
## 4 Corrosion in Geothermal Environments  
Corrosion presents a critical challenge in geothermal plants due to the aggressive nature of geothermal fluids. These fluids typically contain highly corrosive ions like chloride (CI\({}^{-}\)) and may contain dissolved gases such as H\({}_{2}\)S and CO\({}_{2}\). Moreover, elevated temperatures and dynamic flow conditions can accelerate corrosion by facilitating the mass transport of corrosive species and corrosion products. Corrosion is a multifactorial phenomenon that is inherently challenging to predict. In corrosion scenarios, the decision often revolves around accepting generalized and predictable corrosion or minimizing corrosion rates by opting for corrosion-resistant alloys (CRAs). The latter, however, exposes the system to unpredictable localized attacks. The former typically involves the use of cost-effective carbon steel (CS), a preference when replacement costs are low and outweigh the use of more expensive CRAs. Replacement costs are closely tied to the corrosion rate or unexpected corrosion-related failure, making it essential to determine the type of corrosion susceptible to occurring in the geothermal system.
# Quantitative Retrieval of Soil Salinity Using Landsat 8 OLI Imagery
## 1 Introduction
An understanding of the spatial distribution of soil moisture can improve the inversion accuracy of soil salinity. In addition, detailed information on soil salinization obtained from remote sensing data can prevent further degradation of salinized soil and ensure the sustainable development of regional ecosystems. This approach also provides decision support for various departments to prevent and control salinization.
# Characterization of Dolomite Stone Broken Under Axial Impact
## 2 Numerical Simulation of Single-Cutter Rock Breaking Under Axial Impacts
### Results and Discussion of Numerical Simulation  
#### 2.4.1 Characteristics of Dynamic Distribution of Rock Stress Field  
The alteration in the stress concentration region on the cutter surface under an impact amplitude of 1.2 was minimal when compared with the conventional scenario with no impact at amplitudes of 1.0; however, the peak stress was heightened due to the relatively low impact amplitudes. In contrast, as the impact amplitudes escalated to 1.5, there was a notable expansion of the stress concentration region, accompanied by a significant increase in peak stress, which exhibited a 100% rise in the Mises peak stress relative to the impact amplitude of 1.0. Additionally, an intrusion pit was observed beneath the cutter, indicative of the rock-breaking effect characteristic of axial impact, as illustrated in Figure 5a. Although the enhancement at an impact amplitude of 1.2 was minor in terms of the Mises stress angle, analysis of the principal stress angle revealed that the impact altered the direction of tensile stress propagation, shifting from the orientation of the extended cutter surface toward the cutter front. This shift resulted in the deep propagation of tensile stress cracks beneath the cutter, ultimately leading to the formation of a tensile damage zone. Concurrently, with the substantial increase in axial impact amplitude, the stress region on the cutter surface at an impact amplitude of 1.5 was markedly enlarged, with the peak value of the principal compressive stress increasing by 220%, as depicted in Figure 5b. To assess the influence of impact frequency, the moment at 0.66 s, near the conclusion of the cutting process, was selected. Figure 6 presents the stress state at frequencies of 20 Hz and 80 Hz, indicating that variations in axial impact frequency did not result in significant changes to the overall stress. Specifically, the peak stress recorded at 80 Hz was 15.4% lower than that observed at 20 Hz.  
Figure 5: Response of Mises stress (**a**) and maximum principal stress response (**b**) for different axial impact amplitudes.
## 3 Cognitive Radio for Smart City
### Cognitive Communication Architecture for Smart City  
As a conclusion of this section, we want to suggest how CHetNets and CM2M communications could be integrated in the communication infrastructure of a smart environment, proposing a high level architecture based on a 4G/5G cellular network. Indeed, even if different technologies will be used to support smart city communications, it is reasonable to assume that cellular networks will have a predominant role for multiple reasons--such as the performance they can guarantee in terms of data rate, delay and security, coverage, and the fact that they already exist and are widespread so can be used anywhere and at any time without extra costs to deploy a smart environment. The drawback is that the users of the cellular networks are continuously increasing in number and the demand of capacity is expected to grow exponentially in the near future. This could determine congestion and a consequent loss of performance of the smart system. To resolve these challenges, it is essential to adopt a network infrastructure that can efficiently integrate multiple disruptive wireless technologies and enable interworking of existing and deployed technologies. For this reason, we consider CR, HetNets, and M2M technologies.  
The architecture we propose is represented in Figure 1. This is able to provide both local and backbone connectivity to the smart city in an efficient way. We exploit the multi-layer nature of the communication infrastructure of a smart city, proposing a model where the cellular network, with its main macro-cellular coverage provides wide area connections, while in areas where smart services and applications are concentrated, the communications are supported by small cells deployed _ad-hoc_. Hence, where peaks of traffic are expected, the radio access is guaranteed by dedicated cognitive small cells (DCSC) that provide service only to the smart city users. Moreover, devices in close proximity can directly communicate (device-to-device, D2D) among them, thus offloading traffic from the BS. This enables the creation of another layer in the smart system communication architecture which is represented by the CM2M layer, thus supporting the massive diffusion of connected devices usually characterizing a smart city. In the CM2M layer, machines can communicate among themselves by opportunistically using the spectrum both in underlay or overlay mode. Then, the data collected and processed by the CM2M network are forwarded toward the control centre using gateway nodes that have two air interfaces and are able to connect with the DCSC. This follows the capillary network concept [45].  
Figure 1: Cognitive communication architecture for smart city. M2M: Machine-to-Machine; DCSC: dedicated cognitive small cell.  
To efficiently work CHetNets and CM2M, networks have to resort to suitable cognitive solutions, for which a critical review of possible approaches is provided in the next section.
# Multifilters-Based Unsupervised Method for Retinal Blood Vessel Segmentation
## 3 Materials and Methods
### Preprocessing  
In the preprocessing stage of the proposed method, two techniques are applied in parallel for improvement of the image contrast. The first is \"fuzzy logic-based histogram equalization (FBHE)\" [15] and second is \"contrast limited adaptive histogram equalization  
Figure 1: Block diagram of the suggested algorithm.  
(CLAHE)\". To apply FBHE, the RGB image is converted to the color space of HSV. In which V component is extended and stretched through conserving the chromatic information like Hue (H) as well as Saturation (S). Such approach is implied exclusively for improving the low contrast along with low bright regions in the color images. Here in, stretching of the component '\(V\)' is implemented considering the effect of the enhancement parameters average intensity value '\(M\)', where '\(K\)' is the degree to which '\(V\)' is intensified. This stretching will exactly transform the value '\(x\)' for current intensity into the improved intensity value '\(X\)'. The parameter '\(M\)' splits the intensities into two classes. For the first class, the \(X\)\(c_{c1}\) can be computed as given in Equation (1).  
\[X\)\(c_{c1}=X+\mu_{D1}(x)K \tag{1}\]  
For the second class, the \(X\)\(c_{c2}\) can be computed as given in Equation (2).  
\[X\)\(c_{c2}=(X\mu_{D2}(x))+(E-\mu_{D2}(x)K)) \tag{2}\]  
here,  
\[\mu_{D1}=\frac{1-(M-X)}{M} \tag{3}\]  
\[\mu_{D2}=\frac{E-X}{E-M} \tag{4}\]  
where '\(E\)' is the maximum possible value of intensity, and \(\mu_{D1}\) and \(\mu_{D2}\) are fuzzy membership values of class one and class two respectively. Details can be found at [15]. The image after enhancing using FBHE is shown in the middle of the second row, Figure 1.  
In the second step of preprocessing, we have applied CLAHE [16] to expand the contrast of green channel from color fundus images. In green channel, red objects such blood vessels are well-contrasted. In CLAHE, the images are subdivided into contextual regions along with histograms on each of which contextual region are computed locally. On elevated peaks of the histogram, clipping is done as it shows noise. After that, histogram specification is applied on contextual regions. The CLAHE enhanced image is obtained and then bilinear interpolation is utilized to combine the individually enhanced histograms. The level of clipping determines the noise level considered to be smoothened and the contrast level intended to be increased in the histogram. The resulting image after applying CLAHE is shown in the first row last column of Figure 1. Images obtained after applying FBHE and CLAHE are added and scaled by a factor of 0.5, the resulting image is shown in the last column second row of Figure 1. It can be noticed that vessels are well-contrasted now.
# Experimental Characterization of the Capacitively Coupled RF-Plasma Thruster
## 2 Materials and Methods  
### Thruster Description  
C-STAR is a capacitively coupled radiofrequency plasma thruster, employing a magnetic nozzle. The principal design, a view of the plasma plume and an estimation of the magnetic field profile is given in Figure 1. The construction is rather simple. It consists of an inner rod electrode (1), an outer ring electrode (2), an insulator, acting also as propellant dispenser (3), and a magnet system (4). Rod magnets are installed inside the central electrode, and ring magnets are mounted onto the outer side of the grounded ring electrode (s. Figure 1a). For preliminary tests permanent magnets are used, but those will most likely be exchanged for electric magnets in the future. The gas dispenser is designed to deliver a uniform gas distribution inside of discharge channel. It also guides the propellant along the electrode walls, delivering additional convectional cooling.  
The RF power is applied through a matching unit to the inner electrode. The outer electrode is connected to the grounded thruster housing. The plasma is generated inside the ring channel between two electrodes. Neutral propellant leaves the thruster due to thermal  
Figure 1: (a) C-STAR principal design, (b) plasma appearance, and (c) simulation of magnetic field.  
expansion and pressure gradients. The electrons are fully magnetized with the present configuration of the magnetic field. The Larmor radius for electrons with an energy of 5 eV ranges from 0.05 mm inside the discharge channel (160 mT) to 1 mm at the far edge of plasma plume (3 mT). The electrons follow the magnetic field lines and create an ambipolar electric field (sometimes called the electric double layer) which in turn contributes to the acceleration of ions.
## 2 Materials and Methods
### Data Collection and Processing
#### 2.2.2 Calculation of NDVI Value of Each Park  
The normalized difference vegetation index (NDVI) is the most commonly used spectral indicator in ecology and agriculture for assessing vegetation conditions [44; 45; 46]. NDVI values are categorized as follows: 0.60-1.00 indicates the highest density, 0.45-0.60 is high, 0.30-0.45 is medium, 0.15-0.30 is low, 0.00-0.15 is the lowest, and \(-1\) to 0 may represent water [47]. Utilizing ENVI 5.3 software, standardized NDVI values are computed through remote sensing data processing and NDVI value extraction [48; 49]. In this study, Landsat 8-OLI satellite images, obtained from the U.S. Geological Survey and covering the entire study area with less than 6% cloud cover [50], were processed using ENVI 5.3 software. First, atmospheric correction was applied to the images, converting the original Digital Number (DN) values to radiance and then to surface reflectance. Each image was subsequently cropped to the appropriate size and extent and extracted using the study area mask in ESRI ArcGIS ArcMap 10.5 software. Based on the Landsat 8-OLI band configuration, bands 2 to 5, corresponding to blue, green, red, and near-infrared (NIR), were selected for spatial subsetting. Vegetation indices for each image were then calculated using the band math tool in ENVI 5.3. The NDVI was computed according to the standard
# Mass Processing of Sentinel-1 Images for
###### Abstract  
The free, full and open data policy of the EU's Copernicus programme has vastly increased the amount of remotely sensed data available to both operational and research activities. However, this huge amount of data calls for new ways of accessing and processing such \"big data\". This paper focuses on the use of Copernicus's Sentinel-1 radar satellite for maritime surveillance. It presents a study in which ship positions have been automatically extracted from more than 11,500 Sentinel-1A images collected over the Mediterranean Sea, and compared with ship position reports from the Automatic Identification System (AIS). These images account for almost all the Sentinel-1A acquisitions taken over the area during the two-year period from the start of the operational phase in October 2014 until September 2016. A number of tools and platforms developed at the European Commission's Joint Research Centre (JRC) that have been used in the study are described in the paper. They are: (1) Search for Unidentified Maritime Objects (SUMO), a tool for ship detection in Synthetic Aperture Radar (SAR) images; (2) the JRC Earth Observation Data and Processing Platform (JEODPP), a platform for efficient storage and processing of large amounts of satellite images; and (3) Blue Hub, a maritime surveillance GIS and data fusion platform. The paper presents the methodology and results of the study, giving insights into the new maritime surveillance knowledge that can be gained by analysing such a large dataset, and the lessons learnt in terms of handling and processing the big dataset.  
m 2017201727 June 2017201724 May 2017201727 June 2017201727 June 2017
# Trident: A Deep Learning Framework for High-Resolution Bridge Vibration Monitoring
## 4 Case Study Dataset
### Vehicle Passage Dataset  
For a more realistic simulation, it is essential to have reasonable and diverse values of the load imposed by a vehicle on the bridge. Furthermore, the distance between the axes (Wheelbase, WB) is also critical for properly modeling moving loads. The National Highway Traffic Safety Administration (NHTSA) is a valuable resource in this regard. We utilized the database of 528 passenger vehicles presented in the technical report from Bixel, Heydinger [40]. Wheelbase and gross axle weights of different models from 36 car manufacturers between the years 2000-2008 are considered as shown in Figure 5.  
In the simulations, we utilized the Front and rear Gross Axle Weight Rating (GAWR), which represents the highest value an axle can support. The axle loads are scaled with a factor of 0.25 to be consistent with the quarter scale model in this numerical experiment. Considering the linear structural simulation of damaged and undamaged structure, appropriate scale analysis can be conducted for full-scale deployment of Trident. The distributions of the axle weight and wheelbase from the NHTSA database are given in Figure 6. The dataset is split into training, validation, and test sets based on alphabetic letters of car makes to minimize bias. The information about these splits is given in Table 1.  
Figure 5: Vehicle dataset distributions with respect to make (**a**) and year (**b**).  
Figure 6: Distributions of axle weights and wheelbase obtained from the NHTSA dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Split** & **Makes** \\ \hline \multirow{2}{*}{Training} & Acura, Audi, BMW, Buick, Cadillac, Chevrolet, Chrysler, Daewoo, Dodge, Ford, Honda, Hummer, Hyundai \\ \hline Validation & Infniti, Isuzu, Jaguar, Jeep, Kia, Land Rover, Lexus, Lincoln, Mazda \\ \hline \multirow{2}{*}{Test} & Mercedes-Benz, Mini, Mitsubishi, Nissan, Pontiac, Saab, Saturn, Scion, Smart, Subaru, Suzuki, Toyota, Volkswagen, Volvo \\ \end{tabular}
\end{table}
Table 1: Passenger vehicle car makes in different data splits.  
The real loads can differ from the nominal values obtained from the NHTSA dataset. Table 2 provides information on a series of diversification factors that are randomly sampled from a range to consider potential variations in loads and damage. The load intensity noise parameters, \(\sigma_{n}\), and \(dt_{n}\), are defined in Section 3. Vehicle speed (v) also determines the duration of loading (base of the triangular load pattern) for the deck nodes on the bridge. The axes do not always carry loads equal to their full capacity and the distribution of loads between the front and rear axles can vary compared with GAWR. The axle load is adjusted by a modifier coefficient (\(\alpha\)) that is randomly sampled for each realization to consider this variation. Another parameter is the number of damaged elements (\(n_{d}\)) that is assumed to vary from 0 (no damage) to 10. The dataset is created by conducting 52,800 vehicle passing simulations, having 100 realizations for each vehicle model.