esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Border Gateway Protocol Security Using Public Blockchain
## 4 Results  
We developed a proof-of-concept implementation of the designed architecture using the Ropsten Ethereum test network [17]. We have chosen the Ropsten network mainly because BGP information is publicly available. A set of smart contracts outlined in the previous section was implemented in Solidity language. As an access point to the blockchain network, we use an Infura node [18], which provides scalable API access to the Ethereum. Actors interact with smart contracts using wrappers we created with the use of Web3.py library [19].  
We first researched the process of allocating and assigning BGP resources with RPKI. On base this process, we found out how to correctly interpret relationships between our smart contracts. Three virtual machines with OS Ubuntu Server 20.04 LTS [20] created our test environment. Each of these virtual machines represented one of the participants, such as RIR, LIR and ISP. These virtual machines were deployed in various geographical locations. We created scripts for each participant. One for the RIRs is used for the assignment of the IP prefixes and for creating the LIR smart contract. LIR script was made for creating and removing ROA entries. Finally, there is an ISP script that manages subconfigurations of the routers.  
The first test scenario consisted of 100 measurements of time used to set the new ROA entry in the Ropsten network. These show that the average duration of setting ROA is 35 s. The median of the same operation is 31.11 s. The graph in Figure 5 shows results and the time distribution from these time measurements. The measured values range from 21.6 to 63.2 s.  
The second scenario was performed by making 100 measurements of time used to apply a new subconfiguration onto the ISP router. It is the duration of uploading a new subconfiguration to downloading load the new subconfiguration to the router. The duration average of this operation is 37.5 s. The minimal time of setting subconfiguration is 23 s. The maximum observed time is 64 s, and the median value is 34.7 s. Figure 6 depicts the results, with the distribution graph of measured values on the left side. The graph of duration time of downloading and loading the subconfiguration is shown on the right.  
Figure 5: Time duration distribution of setting Route Origin Authorisation (ROA).  
The third scenario examines the gas consumption. The gas unit measures the amount of work required to perform a specific operation on the network. The unit of gas is paid in Gwei. The term Gwei refers to a small denomination of Ethereum which can be an analogy with cents in the Euro [21]. We assume that if we increase the number of prefixes in one transaction, the transaction cost will rise. Figure 7 displays the gas consumption related to the number of prefixes grouped into one transaction. The graphs represent the gas unit's consumption per one transaction and one prefix, respectively.
## 3 Results and Discussion
### Physico-Chemical Properties of the Raw Materials
#### 3.1.2 Chemical and Mineral Composition of Raw Materials  
The chemical composition of the polyaluminum chloride slag and glass powder is presented in Table 1. The polyaluminum chloride slag has a high content of SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\), which combine to form aluminosilicate at high temperature to modify the mechanical properties of the sintered bricks. However, the chlorine salt contained in the polyaluminum chloride slag will affect the performance of the sintered brick, and the content of chloride ion should be considered when preparing sintered brick. The glass powder is mainly composed of SiO\({}_{2}\), Na\({}_{2}\)O, CaO and other chemical compositions. Na\({}_{2}\)O and CaO are the melting aids in the material, which can enhance the glass phase in the sintered brick.  
Figure 4 presents the results of the XRD analysis of the raw materials. The main phase of polyaluminum chloride slag is silicate minerals, including quartz, spinel, gibbsite, perovskite and kaolin. The main phase of glass powder is amorphous phase, which forms more liquid phase under high temperature [25].  
Therefore, the mixture of polyaluminum chloride slag and glass powder can be used as the raw material of sintered bricks.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Samples** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **K\({}_{2}\)O** & **TiO\({}_{2}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **Na\({}_{2}\)O** & **SO\({}_{3}\)** & **Cl** & **LOI** \\ \hline polyaluminum chloride slag & 51.2 & 27.4 & 3.85 & 3.48 & 0.62 & 5.9 & 3.8 & 0.1 & 0.3 & 2.6 & 11.31 \\ glass powder & 70.2 & 1.4 & 7.9 & 4.2 & 0.1 & 0.1 & 0.3 & 15.2 & 0.3 & 0.1 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: The chemical composition of raw materials (wt/%).  
Figure 4: XRD patterns of raw materials: (**a**) polyaluminum chloride slag; (**b**) glass powder.  
Figure 3: SEM image of raw materials: (**a**) polyaluminum chloride slag; (**b**) glass powder.
## 7 Bacterial Cellulose
### BC-Based Hydrogels  
BC-based hydrogels provide an alternative strategy for wound-healing applications since they offer a straightforward path to embed stem cells or healthy skin cells, to load various antibacterial drugs, and they are moldable, hydrophilic materials with high-water content, desirable for wound-dressings with no adverse inflammatory response [134; 135; 136]. [PERSON] et al. characterized cell-containing dressings for wound-healing applications [137]. They loaded human epidermal keratinocytes and dermal fibroblasts in BC-based hydrogels and evaluated their performance in full-thickness wounds. Their results showed maintained cell viability, cell transfer, in vivo wound closure, and overall accelerated wound-healing due to the hydrogel's presence, both with and without embedded cells [137]. In another study [138], semi-interpenetrating network (semi-IPN) hydrogels based on BC and chitosan were fabricated through blending the slurry of BC with CS solution and crosslinking the polymers with glutaraldehyde (Figure 3). They showed that the prepared hydrogel exhibited proper thermal stability, significant mechanical properties, and strong anti-bacterial activities. They proposed that the fabricated semi-IPN hydrogels could be useful wound-healing biomaterials.  
Almost all of the relevant studies refrain from using pure BC hydrogels, which can be attributed to the limited and poor rehydration of pure BC hydrogels following drying either during the intended wound-healing or during storage [120; 134]. Most of these studies combine BC with other polymers, such as chitosan, dextran, and hyaluron [134; 139; 134]. [PERSON] et al. demonstrated the advantage of adding a physiologically relevant polymer such as hyaluron. They prepared BC-hyaluronan nanocomposites with a 3D hydrogel via a modified solution impregnation method [139]. This combination exhibited improved water uptake capability, more pronounced growth, and viability of fibroblast cells compared to pure BC hydrogel, paving the way for subsequent studies with similar better wound-dressing outcomes [134; 139; 140]. Some studies conducted on BC-based wound-healing biomaterials have been provided in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
Figure 3: Fabrication process of semi-IPN hydrogel synthesis. Reprinted with permission from Reference [138].
# Analyzing the Performance of WBAN Links during Physical Activity Using Real Multi-Band Sensor Nodes
## 3 Results
### Standing Scenario
Given that the experiment took place indoors and in an environment with other equipment operating on the 2.4 GHz band, these intermittent losses could be caused by interference or by multipath fading due to slight subject movements or foreign changes [20].
## 3 Results and Discussion
### Turbidity (NTU)  
The Nephelometric Turbidity Unit (NTU) values suggest that the application of bentonite-based agents resulted in superior outcomes (Figure 2). The findings indicate that turbidity reduction was most effective when both the mineral (negatively charged) and protein (positively charged) agents were used at medium concentrations (MM). When higher doses of protein (MH, HH) were applied alongside medium or high doses of minerals, turbidity reduction efficiency was less pronounced, likely due to oversaturation effects. The samples that demonstrated the most efficacious outcomes were KL-MM and GF-MM, which exhibited the lowest turbidity values.  
It was observed that samples made with gelatine appeared to contain fewer unwanted components and were more translucent. When vegetable protein was used, many samples performed at the same low efficiency as the control (KL-MM, KF-MM, KF-HH, SL-HM, SF-HH; \(p=1.000\) in all cases) and, in some cases, the samples significantly exceeded the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Klar Sol 30**} & \multicolumn{2}{c}{**GranuBent Pore-Tec**} & \multicolumn{2}{c}{**Seporit Pore-Tec**} \\ \cline{2-7}  & **L** & **F** & **L** & **F** & **L** & **F** \\ \hline MM & 2.95 & 3.11 & 9.63 & 9.11 & 9.28 & 8.57 \\ HH & 6.77 & 5.85 & 11.05 & 9.74 & 6.05 & 3.51 \\ MH & 11.38 & 10.11 & 10.95 & 7.46 & 10.08 & 7.41 \\ HM & 4.52 & 3.73 & 8.71 & 6.78 & 5.96 & 3.91 \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(\Delta\)E* values calculated between samples with gelatin and plant-based proteins.  
turbidity value of the control (KL-HM, KF-MH, GL-HH, GF-MM, GF-HH, SL-MM, SL-HH, SF-MH). This suggests that these combinations were not successful in terms of turbidity.
## 3 Results and Discussion
### UT Challenges for the Field Evaluation of HDPE ASTs  
While UT techniques are particularly useful for steel infrastructure inspection, using PAUT in the field for evaluating HDPE ASTs has some challenges as listed below:  
* Due to the AST wall curvature, all sides of the probe cannot be in contact with the AST wall, and therefore, the water leaks from the water probe. This can be solved by using a more powerful water pump. Note that the pump used in this study had an uninhibited flow rate of 23 L/min (6 gal/min); however, when tubing was used to attach the pump to the water wedge probe, the flow rate fell to 0.9 L/min (0.25 gal/min). In order to overcome this issue, a pump with a minimum uninhibited flow rate of 38 L/min (10 gal/min) is recommended to fill the water probe reservoir faster than the leakage discharge.
* Completing the PAUT on the upper section of the AST is difficult and requires a more powerful water pump to pump up the water to that elevation.
* The water pump requires electricity, so a portable generator may be required in the field.
* The water pump requires a consistent source of water, which can be accomplished by using a 5 gal (19 L) bucket, although larger volumes of water may be needed for longer inspections.
* The measurement should be done by moving the probe up and down or by an angle of 15\({}^{\circ}\) compared to the vertical line. This reduces the water leaks from the water probe and helps obtain better results. Moving the probe in angles greater than 15\({}^{\circ}\) from the vertical line will result in more leakage due to the AST wall curvature.
* Completing PAUT on the cone-shaped outlets is impossible. In such cases, using traditional pulse-echo UT is recommended.
* During the manufacturing of some HDPE ASTs, the interior of the AST can have a wavy texture, as shown in Figure 19. This texture will increase the background noise and can interfere with the results. High amounts of waviness inside the AST may reduce the effectiveness of UT techniques since the wavy texture will scatter the ultrasonic waves. If the investigator does not have enough experience with evaluating HDPE ASTs, then the waviness could mistakenly be reported as a defect. In such cases, the UT is still useful since it can detect areas where the wall thickness is less than the thickness mentioned in the AST specifications.
* UT methods, both pulse-echo UT and PAUT, are dependent on the temperature of the element being inspected. If UT is performed after IRT, then the UT equipment must be recalibrated to the new material temperature to generate accurate results. If it is desired to avoid recalibration, then UT can be performed either before IRT or after sufficient time has elapsed to allow the material to return to the ambient temperature. Likewise, UT could be performed on a different day than IRT.  
* The liquid inside the AST causes background noise in the returned signals, which may block some defect signal peaks. However, a skilled UT operator can differentiate the background noise to detect defects as small as 4.4 mm (0.17 in).
## 6 Results
### Optimal \(p\) as a Funtion of Window Length  
In this first study, the search for values of \(p\) which optimize \(e_{\mathcal{K}}^{\text{w.c.}},e_{\mathcal{X}}^{\text{w.c.}}\), \(\text{E}[|e_{\mathcal{K}}\left(\mathcal{K}\right)|]\), or \(\text{E}[|e_{\mathcal{X}}\left(\mathcal{K}\right)|]\) is repeated for Hann windows of various lengths, powers of two from 16 to 8192. The result is a family of curves, given in Figure 8, showing the optimal value of \(p\) for each window length and optimized metric.  
This plot exemplifies a few interesting aspects of the search for optimal \(p\). For any particular metric, the optimal \(p\) is a function of the shape of the main lobe within the region \(\pm 1.5\) bins of its center. The reason for this is that the true DTFT peak may not be further than \(\pm 0.5\) bins from a DFT bin. Since only the three closest bins are used to calculate any QIFFT-derived estimate, parts of the main lobe shape further than \(\pm 1.5\) DFT bins of the DTFT peak will never be used directly in a QIFFT-derived method.  
Main lobe shape is, of course, defined by the window type. A subtler but important point is that main lobe shape changes appreciably as a function of window length. This is due to the aliasing of sidelobe components of the window transform. For the Hann window, aliased sidelobe components have a decreasing effect on the main lobe shape as the window gets long due to the sidelobe rolloff of 18 dB/octave--in Figure 8 this is visible as a \"flattening out\" of the curves as window length increases.  
Another interesting facet of this family of curves is its structure. The optimal \(p\) depends in a structured way on the window length. The same way that, e.g., Candan fits a polynomial to tabulated bias correction factors to avoid tabulating and storing every single possibility [15], it would be possible to fit a function to this family of curves to predict the optimal \(p\) for a particular metric and window shape without performing the optimization each time.
# Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data
## 3 Methods
### Data Processing  
3.1.1 Satellite Data  
Thirty-two years (1988-2019) of temporal Landsat data (including Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI downloaded from the USGS website: ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), accessed on 31 August 2021) were used to develop an accurate approach to detect abandoned salinized farmland and to analyze the progress of salinization in Hetao. A total of 207 and 67 cloud-free (rate of cloud cover less than 10%) temporal imageries of Landsat 5 TM and Landsat 8 OLI from 1988 to 2011 and 2013-2019 were investigated, respectively. Due to the failure of the scan line corrector (SLC) in the ETM+ instrument  
Figure 2: Methodological workflow. after 2003, the imagery taken by Landsat 7 lost approximately 22% of the information [31]. Consequently, we deployed only six ETM+ imageries for 2012 when the TM instrument was retailed and the OLI was still not online. In addition, high spatial resolution imagery from Sentinel 2 was used to establish a regression model between the salinity index (SI) and measured soil electrical conductivity (EC). Detailed information on the satellite data used is shown in Table 1. Information about acquisition time and identifiers of used data is summarized in Table S1 (Supplementary Material). The thermal band of the downloaded Landsat level-1 product was radiometrically calibrated with the brightness temperature data using ENVI 5.3 software (Harris Geospatial, Boulder, CO, USA). For other bands, the surface reflectance data were retrieved from the level-2 product. The missing information in the ETM+ imageries was recovered using the interpolation toolbox of ArcGIS 10.3 software (Environmental Systems Research Institute, West Redlands, CA, USA). All thermal imageries were interpolated to 30 m to maintain consistency with VI data For the Sentinel 2 level-1C product (top-of-atmosphere reflectance), the atmospheric correction procedure was conducted by the Sen2 Cor toolbox developed by ESA ([[https://step.esa.int/main/snap-supported-plugins/sen2](https://step.esa.int/main/snap-supported-plugins/sen2) cor/]([https://step.esa.int/main/snap-supported-plugins/sen2](https://step.esa.int/main/snap-supported-plugins/sen2) cor/), accessed on 31 August 2021). The tiles of imageries were mosaicked and subsequently clipped to fit into the predrawn boundary of the study area. The cloud contamination problem is the main obstacle to deploying optical remote sensing products for monitoring Earth's surface. The simple cloud score algorithm was used to calculate the pixel-based cloud score as follows [32]:  
\[\text{cloud}_{\text{score}}=\text{clamp}[\text{min}(\text{term}_{1},\text{ term}_{2},\text{term}_{3},\text{term}_{4},\text{term}_{5}),0,1] \tag{1}\]  
\[\text{term}_{1}=\frac{\rho_{\text{high}}-0.1}{0.2} \tag{2}\]
# Estimating Road Segments Using Natural Point Correspondences of GPS Trajectories
## 4 Experiments
### Competition Results and Relative Performance  
At the competition, this approach took the fifth place with only a little distance to the winner (See Table 1). The competition results are available at [[http://cs.uef.fi/sipu/segments/results.html](http://cs.uef.fi/sipu/segments/results.html)]([http://cs.uef.fi/sipu/segments/results.html](http://cs.uef.fi/sipu/segments/results.html)) [6]. For the competition \(\epsilon\) was set to 0.1 and a HC-SIM of 0.666 was reached on the training set which we used for experiments presented in this work. On the test set of the competition, NaPoCo reached a score of 0.615. Commonly used approaches, like Medoid and CellNet, reached HC-SIM values of 0.571 and 0.647 respectively. The winning algorithm Korasek-2 reached a HC-SIM of 0.671 on the training set and 0.620 on the test set. This was only slightly higher then the performance of our method. It is worth noting that the top five submissions to this challenge were based on some variant of sequence averaging. Our method was the only one not using outlier rejection as well as subsampling the trajectory line segments.  
Figure 4: Average HC-SIM over the data set as function of Douglas–Peucker (DP) epsilon.  
Figure 5: HC-SIM of single samples as function of DP \(\epsilon\).
## 5 Simulation Results  
To verify the performance of the recovery planning framework, representative simulations were conducted using MATLAB. The simulations in this section are divided into three major parts. First, the path coverage evaluation of the homotopic path planning approach in long-range missions was carried out. Then, the verification of the recovery scheduling process was conducted. In the end, the overall performance of the multi-UAV recovery planning framework was validated for stochastic scenarios. A Monte Carlo study that considered the quantity of the UAVs is also presented.
## 1 Introduction  
High-resolution in vivo retinal imaging facilitates the diagnosis and study of retinal diseases. Nevertheless, ocular aberrations limit the optical resolution of retinal imaging systems. To solve this problem, adaptive optics, which was originally developed to correct atmospheric imaging aberrations [1], has been applied to retinal imaging [2; 3; 4; 5; 6; 7; 8]. By integrating adaptive optics, a scanning laser ophthalmoscope, which is a widely used retinal imaging system, can achieve in vivo retinal imaging at the cellular level [4; 9; 10; 11]. Thus, the identification of cone cells (one type of photoreceptor found in the retina) can be implemented by using adaptive optics scanning laser ophthalmoscope (AO-SLO) images. Although cone cells can be identified manually, such a method is often time and work intensive. Thus, automated and semi-automated methods for cone cell identification have been devised [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25].  
Automated cone cell identification mainly includes image denoising and target identification. Reliable denoising of AO-SLO images often relies on averaging multiple AO-SLO images. However, the directly averaged AO-SLO image is blurred due to eye motion. To correct eye motion artifacts, the AO-SLO images need to be registered. Although hardware-based registration is effective [3; 4; 26; 27; 28; 29; 30], software-based registration is more commonly used due to its low costs [31; 32; 33; 34; 35; 36]. For software-based registration, optical flow estimation provides high performance [36]. However, this registration method requires preprocessing that increases its complexity. To avoid preprocessing, we adopt TV-L1 optical flow [37] to achieve direct software-based AO-SLO image registration. Then, for target identification, we use \(K\)-means clustering [38], which despite being a common unsupervised learning algorithm, has not been applied to cone cell identification.  
To verify the effectiveness of the proposed method, we compared it with manual labeling to obtain various evaluation measures, namely, recall, precision, and F1 score. We further evaluated the identification performance of the proposed method for cone cell identification of AO-SLO images from a healthy subject with low cone cell density and subjects with either diabetic retinopathy or acute zonal occult outer retinopathy, as far as we know for the first time.