esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive LCA of Biobased Sustainable Aviation Fuels and JET A-1 Multiblend
## 1 Introduction
### Background  
Biobased SAF can be a potential way to reduce GHG emissions in the aviation sector and to fulfill climate protection targets. In this case, there will be a huge demand for SAF with a high GHG mitigation potential compared to fossil jet fuel. It is expected that airports in Europe will be supplied with JET A-1 containing various types of renewable SAF in the medium term (so called multiblend JET A-1) [6]. In order to count SAF towards national targets for the use of renewable energy (e.g., under the RED II framework), compliance with sustainability criteria has to be monitored along the entire supply chain [4]. Furthermore, it must be ensured that fuel mixtures containing different alternative fuel components are in line with current fuel specifications for JET A-1. As the composition of sustainable aviation fuels may differ from that of conventional fossil JET A-1 kerosene dependingon the process used, market introduction is subject to certain restrictions. For example, synthetic kerosene cannot yet be placed on the market in its pure form and instead has to be mixed (\"blended\") with fossil-based JET A-1 [7]. The SAF, the blending process, and the final product all have to fulfil the requirements of ASTM D7566 (\"Standard Specification for Aviation Turbine Fuel Containing Synthesized Hydrocarbons\") [7]. Once certified, the blend is considered a turbine fuel in accordance with the requirements of ASTM D1655 [8] and can be used accordingly.  
The DEMO-SPK research and demonstration project (R&D), a model project under the German Mobility and Fuel Strategy (MFS) [9] that ran from 2016 to 2020, investigated the use of multiblends consisting of several types of SAF and fossil JET A-1 under realistic conditions within the general fuel supply infrastructure of a large airport [10]. It also included a comprehensive environmental and economic assessment of the used multiblends. The selection of SAF used in DEMO-SPK was based on market availability in 2017/2018 and included alcohol-to-jet (ATI), hydroprocessed esters and fatty acids (HEFA), and synthetic iso-paraffinic fuels (SIP).  
In addition, the properties of SAF and multiblends were analyzed, and a logistic chain to the airport as well as combustion emissions from SAF use in aircraft under real conditions were studied [11]. Moreover, issues relating to sustainability analyses and documentation as well as options to verify and account for the use of SAF in the European emissions trading system were investigated.  
The DEMO-SPK project offered a unique opportunity to address open questions directly relating to the large-scale introduction of alternative aviation fuels and multiblends [6].
# Concrete Silos: Failures, Design Issues and Repair/Strengthening Methods
## 4 Repair and Strengthening Methods for Reinforced Concrete Silos
### Strengthening of RC Silos  
The strengthening of RC silos is considered from the perspective of external pre-stressing, distributed tuned vibration absorbers vertically (d-MTVAs), distributed tuned mass dampers (d-TMDs) and the replacement of steel with FRP materials, which have better tensile strength.  
2.1 Distributed Tuned Mass Dampers (D-TMDs) and Distributed Tuned Vibration Absorbers Vertically (D-MTVAs)  
According to [PERSON] (2019) [77] and [PERSON], [PERSON] and [PERSON] (2016) [78], showed that the controllers were so effective to reduce the response of chimneys, that would prevent the structure from possible earthquake-induced damages. In particular, the D-TMSs, were proven effective in mitigating the effects of earthquakes on RC structures. However, the effectiveness of the D-TMSs is largely dependent on the mode and distribution, distance from the base, the inner and outer wall diameters of the structure, mass, and stiffness. In brief, the risk of earthquake induced displacement is mitigated following the installation of both systematically and arbitrary distributed tuned mass dampers (TMDs). However, the performance of the systematically distributed TMDs is superior when a building is exposed to white noise and Kobe earthquake excitations [77]. The observations were later confirmed by [PERSON] (2019) [77] who reported that distributed tuned vibration absorbers vertically (d-MTVAs) were highly efficient compared to other distributors due to a higher mass ratio (\(\upmu\)). However, the effectiveness of the controllers is also influenced by the soil characteristics.
# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 3 Materials and Methods
### Traversing the Terrain: A Machine Learning Pipeline for Soil Organic Carbon Estimation  
Our machine learning model for SOC estimation consists of two stages, each of which performs a specific function in order to produce the final estimates. The first stage involves training a standard segmentation deep neural network (DNN) with the U-Net architecture. This DNN is trained to estimate the land cover (LC) of an observed region based on multi-spectral images from the Sentinel-2 satellite and a ground truth segmentation map. The trained ANN generates an LC estimation for a region of interest (ROI). This estimation is then used to extract the latent feature vector for each output pixel by rolling back from the output layer of the U-Net and accessing the last available convolutional layer of the same dimension as the output.  
The second stage uses the feature vectors extracted at the coordinates for which ground truth SOC measurements exist. Since we are primarily interested in agricultural land and in order for the LUCAS and CHLSOC data to have roughly the same distribution in terms of the type of plots they were sampled at, we retained from the two LUCAS datasets the values for which the observed LC was _cropland_ and _grassland_.  
The pairs of feature vectors and SOC measurements were used to train an independent ML model, which performs the actual SOC estimation. At inference time, the two stages generate a spatially continuous dataset of SOC estimations for patches processed by U-Net. These estimations for different patches are then stitched together to create a map of SOC estimations for an arbitrary geographical region.  
In this study, we generated and shared a sample map for the region of Tuscany, Central Italy, with a resolution of 10 m, which is consistent with the highest available resolution of the Sentinel-2 satellite. Tuscany covers about 22,990 km\({}^{2}\), encompassing a diverse range of terrains including mountains, hills, and coastal plains. Tuscany is world-renowned for its agricultural production. Tuscany's agricultural sector is celebrated globally for its quality and diversity, from its wine production in Chianti to its olive groves spanning the rolling hills. The region's fertile soils support a broad range of other crops as well, including grains, legumes, and vegetables, making it a cornerstone of Italy's food and wine industry. Given the vital role of soil organic carbon (SOC) in maintaining soil fertility and promoting sustainable agricultural practices, Tuscany seemed a good choice to test our model on and provide added value from our research.
# An SSI-Based Solution to Support Lawful Interception
## 4 Proposed Approach
### Credential Revocation  
The duration of an interception procedure is influenced by several factors, mainly the complexity of the case, and predicting how long the procedure should take is not easy. When an expiration time for the verifiable credentials cannot be established, credential revocation should be used. The revocation process occurs after the LI procedure is complete or when the access needs to be restricted under any other circumstances to prevent unauthorized access to entities previously authorized.  
Revocation can be triggered by both the LEA and the EA. In the first case, the LEA may revoke both the credential \(VC_{LEMF}\) and the credential \(VC_{LM}\) to remove the participation of the LEMF and [PERSON] in the LI procedure, respectively. In the second case, the EA can revoke (i) the credential \(VC_{L}\) or (ii) the credential \(VC_{E}\). In case (i), the EA removes the participation of [PERSON] in the LI procedure. Case (ii) happens when [PERSON] is fired, resigns, or changes role in the agency EA.  
The LEA and EA use the same procedure for revoking credentials, which is based on cryptographic accumulators [26]. A cryptographic accumulator is a data structure that allows the compact representation of a set of elements, enabling efficient membership verification. Given a set of elements, the accumulator produces a concise value that encapsulates all the elements. This structure provides two properties:  
1. Membership Proofs: It can be demonstrated that a specific element belongs to the set without revealing other elements of the set.
2. Dynamic Updates: Elements can be efficiently added to or removed from the set.  
Each revoked credential becomes part of the revoked credentials set, which updates with each new revocation. Verifying the state of the \(VC\) corresponds to verifying the membership of that \(VC\) to the set. If the result of the membership of a \(VC\) is true, then the verification fails. Using a cryptographic accumulator, our solution provides a secure and decentralized method to manage credential revocations while maintaining the privacy and efficiency required for LI procedures.  
Figure 2: Sequence diagram of the EA integration in the LI procedure.
## 4 Antenna Measurement Results
Furthermore, the measured gain is about 11.5 dBi along the radiation direction at 8.483 GHz, which is only 0.2 dB lower than the simulation. The measured spatial axial ratios at 8.483 GHz in the YZ and XZ planes are presented in Figures 17 and 18. As can be seen in these figures, the AR remains below the 3 dB threshold over an angular regions of 60\({}^{\circ}\) in the YZ plane and 30\({}^{\circ}\) in the XZ plane around the boresight, and the YZ plane value is slightly better than the XZ plane value, for example, 1.4 dB in compared with 1.6 dB at 8.483 GHz. This performance can be attributed to the high rotational symmetry of the EBG-based structure, which ensures polarization stability across orthogonal planes and mitigates the depolarization effects. The results highlighted the effectiveness of our proposed design in maintaining the CP quality, especially in dynamic orientation scenarios of the satellite platform when operating in orbit. The measured 3 dB ARBW over frequencies is shown in Figure 19. In this figure, the measured 3 dB ARBW is around 250 MHz (from 8.37 GHz to 8.62 GHz), which is better than the simulated result. These measured results confirmed the CP characteristics of the antenna. Figure 15: The measured S\({}_{\textbf{II}}\) of the CP EBG MPA. Figure 16: The measured radiation patterns of the CP EBG MPA in (**a**) the YZ plane and in the (**b**) XZ plane. Figure 12:0 The measured spatial AR at 8.483 GHz in the YZ plane. Figure 12:1 The measured spatial AR at 8.483 GHz in the XZ plane. A summary of the CP EBG antennas is tabulated in Table 3, and a comparison of our work and other X-band and near-X-band microstrip patch antennas, including other authors' research and commercial antennas, is listed in Figure 12:2 \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref. No.** & \begin{tabular}{c} **Center Freq.** \\ **(GHz)** \\ \end{tabular} & \
\begin{tabular}{c} **Antenna Size** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **Max. Gain** \\ **(dBi)** \\ \end{tabular} & **Polarization** & **Remarks** \\ \hline
[4] & 8.2 & 72.6 \(\times\) 72.6 \(\times\) 11 & 11.5 & CP & - Complicated design. \\
[4] & 8.2 & 72.6 \(\times\) 72.6 \(\times\) 11 & 11.5 & CP & - Feed network needed. \\  & & & & - The height exceeded the 6.5 mm threshold specified in Cal Poly’s CDS. \\ \hline
[5] & 8.25 & 100 \(\times\) 100 & 17 (dBic) & CP & - Need 4 feed points to create differential pairs of excited signals for CP. \\
[5] & 8.25 & 100 \(\times\) 100 & 17 (dBic) & CP & - The height is not mentioned. However, it exceeded 6.5 mm and cannot be applied to CubeSats that follow Cal Poly’s CDS. \\ \hline
[7] & 7.19 (RX) & 120 \(\times\) 80 & 9 & CP & - Too big for 1U CubeSat surface. \\
[7] & 8.45 (TX) & 120 \(\times\) 80 & 9 & CP & - Feed network needed. \\  & & & & - The height is not mentioned. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of our work with other X-band and near-X-band MPAs. Figure 19: The measured 3 dB ARBW over frequencies.
###### Abstract  
Natural disasters are phenomena that can occur in any part of the world. They can cause massive amounts of destruction and leave entire cities in great need of assistance. The ability to quickly and accurately deliver aid to impacted areas is crucial toward not only saving time and money, but, most importantly, lives. We present a deep learning-based computer vision model to semantically infer the magnitude of damage to individual buildings after natural disasters using pre- and post-disaster satellite images. This model helps alleviate a major bottleneck in disaster management decision support by automating the analysis of the magnitude of damage to buildings post-disaster. In this paper, we will show our methods and results for how we were able to obtain a better performance than existing models, especially in moderate to significant magnitudes of damage, along with ablation studies to show our methods and results for the importance and impact of different training parameters in deep learning for satellite imagery. We were able to obtain an overall F1 score of 0.868 with our methods.  
computer vision; artificial intelligence; disaster management; remote sensing; damage magnitude; satellite imaging; buildings +
Footnote †: journal:
## 5 Results and Discussion  
Two models called MDDM and MTDM are considered in this section for the solar cell parameters estimation problem using two CLPSO and EHO algorithms under different irradiance and temperature levels. The computation proceeds on a stated data in [12] for the 7.7 cm\({}^{2}\)--Q6-1380 multi-crystalline solar cell at low irradiation level and also a CS6P-240P solar module under different irradiance, temperature levels, and a 57 mm diameter commercial silicon solar cell (R.T.C. France) at 1000 W/m\({}^{2}\)[43] are implemented. The cell (I-V) curves are obtained from [12]. The studied models are introduced to emulate this cell. CLPSO and EHO algorithms are applied to the two models to extract their unknown parameters.  
Table 1 reports the open-circuit voltage and short circuit current for the tested cells at different irradiance and the corresponding fill factor for each case. It is noticed that a low fill factor (0.5404) of the Q6-1380, which is low. The fill factor is computed by using Equation (16) as:  
\[\text{Fill factor }(\text{FF})=\frac{P_{MP}}{V_{\text{OC}}\cdot\ I_{SC}} \tag{16}\]
# A New Monocular Pose Estimation Method for the Coplanar P4P Problem
## 2 Methodology  
In the new method, the camera system will be taken as a reference system to simplify the expression of the projection relationships. Similarly, the number of unknown values will be reduced according to the relative coordinate constraints of the parallelogram's four vertices. Then, a set of linear equations will be established and solved. After that, the coordinates of the four vertices can be obtained by using a side length of the parallelogram. With the coordinates of the four vertices, an object system can be established on the parallelogram and the coordinates of each vertex in the object system can be calculated. With the four vertices' coordinates in the camera system and the object system respectively, the transformation parameters between the two systems can be obtained, including the position and attitude parameters of the object relative to the camera.  
In general, the new method to estimate a parallelogram object's pose parameters includes two steps: Step 1. To calculate the real coordinates of the parallelogram's four vertices by taking the camera system as a reference system, and Step 2. To calculate the parallelogram's pose parameters relative to the camera based on the four vertices' known coordinates in the camera system and the object system, respectively.
# On the Optimal Calculation of the Rice Coding Parameter
## 2 Related Work
### Derivation for Data Following Geometric Distributions  
Rice coding is a special case of Golomb coding as proposed by [PERSON] in [PERSON] (1992). It has been shown in [PERSON] and [PERSON] (2018) that if the set of numeric data that needs to be encoded is assumed to follow a geometric distribution, i.e., the probability of the number \(n_{i}\) occurring in the input sequence is:  
\[P(n_{i})=p^{n_{i}}\cdot(1-p), \tag{4}\]  
for some probability \(p\in(0,1)\), then the optimal Golomb parameter is given by the integer number \(m_{G}\) that minimises the following expression:  
\[p^{m_{G}}-1/2. \tag{5}\]  
Therefore, the value of \(m_{G}\) can be calculated as:  
\[m_{G}=\left\lceil-\frac{\log_{2}\left(1+p\right)}{\log_{2}p}\right\rceil. \tag{6}\]  
As Rice encoding is the special case of Golomb encoding when the parameter \(m_{G}\) of the Golomb coder is a power of two, i.e., \(m_{G}=2^{r}\), such an inequality is resolved for Rice encoding to:  
\[r_{[GEO]}=\log_{2}\left[-\frac{\log_{2}\left(1+p\right)}{\log_{2}p}\right]. \tag{7}\]Under the same assumptions, viz. of following a geometric probability distribution, [PERSON] in [35] proposed the selection of the code parameter considering only the mean value of the distribution as follows:  
\[k_{geo}^{*}=\max\left\{0,1+\left\lfloor\log_{2}\left(\frac{\ln(\phi-1)}{\ln( \frac{\mu}{\mu+1})}\right)\right\rfloor\right\},\,\text{where} \tag{8}\]  
\(\mu\) corresponds to the estimate of the mean value of the data and \(\phi\) is the golden ratio constant value \((\sqrt{5}+1)/2\).  
Previously, other authors considered the case when the input data follow a geometrical distribution and provided a means of calculating the optimal Golomb encoding parameter, hence providing a bound for the Rice encoding parameter. Clearly, these bounds do not apply for all distributions.  
In this work, we provide bounds on the value of the Rice encoding parameter regardless of the type of distribution of the data.
## 3 Data and Methodology
### Urban LULC Modeling  
In this section, the study utilized the LULC model to quantify the urban and future LULC changes as shown in Figure 3. On the whole, the analysis workflow is the following: (i) Cellular Automata-Markov Chain; (ii) Multi-Layer Perceptron-Markov Chain; (iii) Model validation; and (iv) Model application. The details of each part are described in the following subsections.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Data** & **Year** & **Scale/Resolution** & **Sources** \\ \hline Demographic data: density of & 1993, 1998, 2003, 2008, & 1:50,000 & Department of Provincial \\ population and housing, growth rate & 2011, 2014 & & Administration \\ \hline Economic data: Gross regional and & 1988, 1993, 1998, 2003, & & Office of the National \\ provincial product & 2008, 2011, 2014 & 1:50,000 & Economic and Social \\  & & & Development Board \\ \hline Geo-data base of physical and & & & Bangkok Metropolis \\ environment related to urban features & n.d. & 1:25,000 to 1:50,000 & Administration GIS center \\ \hline Digital Elevation Model (DEM) & n.d. & 30 m & Bangkok Metropolis \\  & & & Administration GIS center \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input data for the urban land use and land cover (LULC) modeling.  
Figure 3: Urban LULC Modeling.
## 3 Validation of Multi-Layer PFFT-BEM Solutions
We compare the averaged difference of the acoustic pressure at \(z\) = 36 m between with \(I\) layers (\(p_{I}\)) in the water column and \(I+1\) layers (\(p_{I+1}\)) in the water column. The difference used for comparison is defined in Equation (26). Figure 9b shows that the numerical results converge when the number of layers increases. This result demonstrates that the PFFT-BEM can be used for shallow water environments with inhomogeneous sound profiles:  
\[Difference=average\Bigg{(}\frac{\left|p_{I}-p_{I+1}\right|}{p_{I+1}}\Bigg{)}. \tag{26}\]  
We then demonstrate the use of the PFFT-BEM to realistic range dependent underwater acoustics problems. The same waveguide geometry is used but with the summer sound profile from [18]. The bottom is homogeneous with sound speed 1700 m/s, density 1.5 g/cm\({}^{3}\) and medium attenuation \(\alpha\) = 0.5 dB/\(\lambda\). Figure 10a shows the spatial variations of sound speed in the water column. The sound frequency is 60 Hz located at 70 m below the sea surface. The computational domain is 2 km \(\times\) 2 km \(\times\)  
Figure 3.0 Frequency dependence of PFFT-BEM computational performance: (**a**) memory and (**b**) operational counts. Figure 3.1 (**a**) Sound profiles used in the convergence tests; (**b**) normalized average difference of acoustic pressure at z = 36 m obtained using the PFFT-BEM as a function of number of layers used in the water column. 100 m. The boundary element model consists of uniform quadrilateral element with \(\Delta l\) equals to \(\lambda\)/8. The numerical method computes the transmission loss (TL) along the range at depth of at z = 70 m. Since no theoretical results are available for validation, we show the convergence of the numerical results with respect to the number of layers used in PFFT-BEM. We consider four cases with different number of layers: (i) in the two-layer case, the sound speed is 1500 m/s in \(z\) = 0-100 m region and 1700 m/s in \(z\) > 100 m region. (ii) In the three-layer case, the sound speed is 1530 m/s in \(z\) = 0-30 m region, 1500 m/s in \(z\) = 30-100 m region and 1700 m/s in \(z\) > 100 m region. (iii) In the four-layer case, the sound speed is 1530 m/s in \(z\) = 0-20 m region, 1515 m/s in \(z\) = 20-40 m region, 1500 m/s in 40-100 m region and 1700 m/s in \(z\) > 100 m region. (iv) In the five-layer case, the sound speed is 1530 m/s in \(z\) = 0-20 m region, 1520 m/s in \(z\) = 20-30 m region, 1510 m/s in 30-40 m region, 1500 m/s in 40-100 m region and 1700 m/s in \(z\) > 100 m region. After five layers are used, Figure 10b shows the numerical results converging with errors only in high TL region. This example demonstrates the use of PFFT-BEM for shallow water environment with inhomogeneous sound profiles.