esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Results and Discussion
### Modeling of Chemical Radiation-Induced Corrosion Processes Using the COMSOL Software Package
In turn, the specific concrete conductivity function of the moisture content in pores ((\(\rho\)), see Table 3) was entered into the electrolyte conductivity field of COMSOL, taking into account a change in conductivity throughout the calculation interval by multiplying it by the interpolation function with proportionality coefficients shown in Figure 3.  
In addition, the density of current, accompanying the reaction of iron oxidation before \"significant carbonization\" (i.e., before the expiry of 9 years (2.838e+8 s)), is determined by the expression \(c/C_{02\_ref}\ast l_{0\_O2}\), and after 9 years by the expression \(c/C_{02\_ref}\ast l_{0\_O2}\). Towards this end, Expression (11) is entered into the COMSOL \"exchange current density\" field:  
\[if(\mathrm{t}<2.83\mathrm{e}+8,\,\frac{c}{C_{02\_ref}\ast l_{0\_O2}},\,\frac{c }{C_{02\_ref}\ast l_{0\_O2}}); \tag{11}\]  
where c is the name of the atmospheric oxygen concentration variable. In the COMSOL boundary condition settings, we select two outer boundaries of the model that are in contact with the atmosphere. In the field for the numerical value of the concentration, we specify a logical Expression (12):  
\[if\Big{(}\mathrm{t}<2.838\mathrm{e}+8,C_{O2_{-ref}},C_{O2_{-ref,g,100}}\Big{)}; \tag{12}\]  
The computation performed in the COMSOL software package shows that the diameter of the reinforcement changes within tenths of its original diameter in the course of the oxygen diffusion in concrete. In 100 years, the approximate diameter change will reach 2.8-3.1 mm, which is 11.2-12.4% of the initial diameter of the reinforcement (see Figure 3). The values obtained by the authors are consistent with the corrosion rate provided in the Oak Ridge National Laboratory (NRC U.S.) Report [48], which says that the rate of the reinforcement corrosion is 0.001 to 0.03 mm/year (0.1 to 3.0 mm in 100 years, respectively), and exceed those provided in the IAEA Report [19], or 0.001-0.005 mm/year (0.1-0.5 mm in 100 years). In addition, values obtained by the authors do not comply with the experimental results obtained in accelerated tests by [PERSON] et al. [89], or \(6.4\times 10^{-6}\) %/day (0.234% for 100 years, which corresponds to the residual diameter of reinforcement of approximately 24.97 mm). At the same time, the corrosion acceleration process is observed, which is in agreement with the conclusions drawn in the study of [PERSON] et al. (2008) [58] at the gamma radiation dose rate of 11 Gy/h. Furthermore, [PERSON] et al. [60; 69] draw conclusions that in all cases gamma radiation shifts the corrosion potential to the negative domain, which shows an increase in the probability of reducing corrosion resistance. The above method can adequately predict the process of reinforcement corrosion in concrete during operation, provided that reinforced concrete structures are exposed to gamma radiation.
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 3 Ocean Applications
### Swell and Swell Height  
Ocean winds produce an increase in ocean roughness; this attenuates the signal, thus decreasing the NBRCS. When wind blows steadily in a certain direction, it produces long-wavelength swell waves. In [93] it was demonstrated that the surface scattering coefficient was affected by the presence of swell waves. This study, performed with empirical satellite-collected data, showed that the \"tails\" of the DDM were sensitive to swell presence. Airborne GNSS-R data also showed sensitivity to swell [94]. It was found that swell could be retrieved using higher bandwidth GNSS codes (e.g., L5/E5). For such cases where the first Fresnel zone was smaller than the crest-to-crest distance produced by the swell, two specular peaks could be identified at two or three consecutive waves crests, being able to retrieve swell period information from its distance. Several studies have also analyzed the impact of swell, among other parameters, on the measured GNSS-R signals. In [95], the authors performed a complete modeling of the bistatic radar cross-sectional magnitude variation due to swell. In [96], the authors studied the effects of swell, among other parameters, for the CYGNSS mission and its effects on wind speed retrievals. The study in [96] performed a thorough analysis and modeling of the specific effects of swell on the GNSS-R signature. Finally, ref. [97] presented estimations of swell from space, using CYGNSS data, showing that DDM parameters as the normalized integrated delay waveform (NIDW) correlated well with swell height, with correlation coefficients larger than 0.87 and RMSE between 0.51 m and 0.39 m, these depending on the variable used for the retrieval.
# Analysis of Liquefaction in Tailings Deposits by Fem Modeling of Underained Cyclic Triaxial
## 5 Strategy and Characteristics of Numerical Modeling
### Modeling of the Cycling Stage  
This stage was modeled using the UBC3D-PLM constitutive model. The cycling stage was based on experimental results, and therefore, the same parameters were used for the cycling and confinement loads. The modeling parameters used in this stage are summarized in Table 4. The \"_Max Step_\" and \"Dynamic Time\" parameters are numerical control parameters of the Plaxis2D software; for more details of their operation, see [46].  
Since the experimental tests were performed under a controlled load, sinusoidal loads were used using the dynamic calculation method to simulate this condition. Figure 7 shows the results of the applied deviatoric stress in each test for both soils. These were correctly simulated for all deviatoric stresses in the silt and sand. Additionally, it was observed that at a frequency of 0.1 Hz--that is, in the tests on silt (CTX 1, 2, and 3)--when the CSR increases, the number of cycles required for liquefaction decreases. On the contrary, in the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & \multicolumn{2}{c}{**Load**} & \multirow{2}{*}{\(\sigma_{d}\) kPa} & \multirow{2}{*}{**CSR**} & \multirow{2}{*}{**Cycles**} & \multicolumn{2}{c}{_“Max_} & \multicolumn{2}{c}{_“Dynamic_} \\  & \multicolumn{1}{c}{**Frequency Hz**} & & & & & _Steps\({}^{\star}\)_ & _Time\({}^{\star}\)_ \\ \hline Silt (CTX 1) & 0.1 & 20 & 0.10 & 70 & 700 & 700 \\ Silt (CTX 2) \({}^{\star}\) & 0.1 & 23 & 0.12 & 28 & 280 & 280 \\ Silt (CTX 3) & 0.1 & 27 & 0.14 & 15 & 150 & 150 \\ Sand (CTX 4) & 0.5 & 20 & 0.10 & 370 & 370 & 800 \\ Sand (CTX 5) \({}^{\star}\) & 0.5 & 32 & 0.16 & 170 & 1700 & 340 \\ Sand (CTX 6) & 0.5 & 42 & 0.21 & 75 & 750 & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Load frequencies and CSR for \(\sigma^{\prime}_{c}=100\) kPa (* Tests used to perform the calibration).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & **Silt (ML)** & **Sand (SM)** \\ \hline \(E_{50}^{ref}\) (kN/m\({}^{2}\)) & 7500 & 26,000 \\ \(E_{d7}^{ref}\) (kN/m\({}^{2}\)) & 3950 & 18,000 \\ \(E_{u7}^{ref}\) (kN/m\({}^{2}\)) & 32,000 & 60,000 \\ \(\rho_{init}\) (\(\cdot\)) & 0.7  
modeling tests on sand (CTX 4, 5, and 6), which used a higher frequency of 0.5 Hz, the material can withstand more cycles before reaching the liquefied state, as judged using the criterion of \(R_{u}>0.8\). Thus, the material response is shown to depend on the loading frequency, which is in line with recent studies showing that the liquefaction resistance of sands increases with loading frequency [47] and that the cumulative strain and excess pore pressure are also influenced by the loading frequency, affecting the number of cycles needed to reach liquefaction [48].
###### Abstract  
Biodiesel is regarded to be a renewable, CO\({}_{2}\) neutral and thus sustainable biological alternative diesel fuel. With attention to the reduction of petroleum import, PM 2.5 aerosol particles and the greenhouse effect gas CO\({}_{2}\), biodiesel has drawn great research interests and efforts in the past decade in China. Generally, biodiesel refers to fatty acid methyl ether (FAME) which has a proved effect in reducing diesel emission, particularly PM. However, FAME has a limited cetane number and oxygen content, to study the effects of elevated cetane number and oxygen content on fuel properties, engine combustion and emissions, ethylene glycol monomethyl ether is used to produce a series of new models of biodiesel by transesterification method. The feedstocks are rapeseed oil, soybean oil, peanut oil, palm oil and cottonseed oil. Ether group alcohols used in this study include ethylene glycol monomethyl ether, ethylene glycol monoethyl ether, ethylene glycol monopropyl ether, propylene glycol monomethyl ether, diethylene glycol monomethyl ether. The molecular structure was proved by FT-IR and NMR analyses. Fuel properties were measured based on the corresponding standards. The developed new model biodiesel have cetane number (CN) over 70 and oxygen content over 17% by mass, which are higher than FAME (50 CN and 11% oxygen). They have the same level of lower heating value as FAME, but have a higher density, which helps to compensate the decrease of engine power. Meanwhile, the engine tests were carried out to investigate the effects of ether ester group on engine combustion and emissions. The test results show that FAME reduced smoke 30% to 50%, while the new model biodiesel fuels reduced engine smoke as high as 80% and have the potential to decrease engine HC, CO and NOx emissions 50% or more.  
**Keywords:** biodiesel; ethylene glycol monomethyl ether; engine; combustion; emission
# Metric Embedding Learning on Multi-Directional Projections
## 3 Methodology
### Embedding Learning  
As previously stated, metric based similarity learning can be done using a Siamese architecture, or a Triplet Network. The contrastive loss of the SNN reduces the distance between the representations to become minimal, while the triplet loss based on three samples creates clusters of similar observations.  
To optimize performance, the mining of negative samples is key [35] and three strategies can be distinguished:  
* random mining;
* semi-hard mining;
* hard mining.  
The ideal course of action would be to train the model with triplets where each anchor is accompanied by the ideal positive and negative pairs. It is evident, that the selection of the farthest positive pair is feasible, however, selecting the closest negative pair is computationally intense, as all possible negatives have to be checked.  
According to this, random mining is the most efficient, as randomly selecting the negative pair is simple; these negatives are often referred to as _easy_ negatives. However, as the distance between the negative pair and the anchor could be higher than the distance between the positive pair and the anchor, calculated loss could yield zero (as given in (2)).  
To solve this issue, semi-hard negatives should be mined. A negative pair is considered _semi-hard_ when the difference between the negative distance and the positive distance is less than the margin.  
Figure 4: Visualization of the MDIPFL transformation. Intensity values of covered elements are summed, in the rate of coverage.  
In this case, the positive pair is actually closer to the anchor, but the marginal distance results is a positive loss. While the mining is more intense, the positive loss results in convergence.  
The most intense method to mine negatives, referred to as _hard_ mining, is done by selecting those negatives that are closer to the anchor than the positives. These different categories of triplets are visualized on Figure 5.  
The triplet mining method introduced in OpenFace [34] is efficient when compared to so-called offline mining, as a batch of samples are pre-selected, all triplets are generated, and invalid triplets are masked.
# A Comparative Study of Two Rule-Based Explanation Methods for Diabetic Retinopathy Risk Assessment
## 5 Experiments and Results
### Evaluating the Locality of the Methods  
The proposed explanation system is local, because it focuses on the behaviour of RETIPROGRAM around the specific instance \(x\). The Fidelity metrics defined above validate the models' performance in terms of locality with respect to the generated neighbours and the instance to be explained. Assuming that we have access to the test set used to evaluate the black-box model, we can validate the locality of the model with respect to the test set by defining a new metric, the **xt-Fidelity**. It is the fidelity measure computed on the set of instances from the test set with a distance to the instance \(x\) less than or equal a threshold \(t\). The overall xt-Fidelity on a set \(X\) given a threshold \(t\) is computed by taking the average of xt-Fidelity for all \(x\in X\). We use it to measure the locality vs the globality of the explanation method. It is expected that a local method shows a degradation in its performance with large thresholds, as a significant number of the selected instances will belong to subspaces different than the one used to build the explanation model.  
We compared the xt-Fidelity results of both C-LORE-F and DRSA under different thresholds as illustrated in Figure 4. In general, we can find that, as the threshold increases, the overall score decreases, which means we lose the models' locality. In other words, the input space turns out to be more global, and the model fails to cover that space. The degradation in the performance is obvious in the case of DRSA. On the other hand, the C-LORE-F method mostly preserves the performance (the degradation is minor than in DRSA). We can attribute that to the fact that the multiple and small decision trees of C-LORE-F were trained on subspaces of the global space and formed a random forest model. Such a model can show comparable performance on the test set to the performance of RETIPROGRAM (a fuzzy random forest model built from multiple fuzzy decision trees).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **Hit** & **Fidelity** & **I-Fidelity** & **cHit** & **cl-Fidelity** \\ \hline C-LORE-F & 1.00 \(\pm\) 0.00 & 0.99 \(\pm\) 0.002 & 0.99 \(\pm\) 0.002 & 0.89 \(\pm\) 0.290 & 0.88 \(\pm\) 0.282 \\ DRSA & 0.97 \(\pm\) 0.152 & 0.831 \(\pm\) 0.32 & 0.93 \(\pm\) 0.176 & 0.830 \(\pm\) 0.315 & 0.83 \(\pm\) 0.298 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results of the C-LORE-F and DRSA explanation methods.  
Hence, if the locality of the explanation model is more important than the globality, we can choose DRSA. Otherwise, C-LORE-F is ideal.
# Micro-Raman Spectroscopy Investigation of Crystalline Phases in EAF Slag
## 2 Materials and Methods
### Xrd  
The measures on sample B, C, D and E were performed using a Panalytical X'Pert Pro diffractometer equipped with an X'Celerator detector and Cu anode (CuK\(\alpha\) = 0.15406 nm), operating at 40 KV and 40 mA, and crystalline phases were identified. The patterns were collected in Bragg Brentano geometry  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Steel Type** & **Furnace Temperature [°C]** & **Slagging Procedure** \\ \hline A & Special Steel & n.d. & n.d. \\ B & Carbon steel 0.167\% \(<\) C \(<\) 0.185\% & 1550–1620 & on chute and cooled by water \\ C & Carbon steel & 1600 & on chute and cooled by water \\ D & Carbon steel C \(<\) 0.25\% & 1600 & on chute and cooled by water \\ E & Carbon steel: 0.05\% \(<\) C \(<\) 0.8\% & 1650 & on chute and cooled by water \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of samples, details of some steel making process parameters (2 nd and 3 rd column) and slagging procedure (4 th column).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{4}{c}{**Techniques**} \\ \cline{2-5}  & \multicolumn{2}{c}{**XRD**} & \multicolumn{2}{c}{**Raman Spectroscopy**} & \multicolumn{2}{c}{**SEM-EDXS**} \\ \cline{2-5}  & **2D XRD** & **Powder** & & \\ \hline A & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) \\ B & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) \\ C & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) \\ D & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) \\ E & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analytical techniques adopted on each sample.  
between \(5^{\circ}\) and \(80^{\circ}\) (in 2\(\Theta\)). Qualitative analysis of the crystalline phases was performed by X Pert high score plus database. Sample A was analysed in three different areas by two-dimensional X-Ray Diffraction (2D-XRD). The analysis was obtained using Bruker D8 Discover instrumentation (copper anode, VANTEC-500 2D detector, voltage 40 kW and current 40 mA, POLYCAP lenses, 1 mm beam size). The three investigated areas were similarly analysed using SEM and EDXS.
## 4 Simulations and Discussion
### Comparative Experiment  
To better illustrate the difference between the existing algorithm, PI-based ACC is simulated based on scenario 1. Actually, PI-based ACC can be seen as a special case of (10), where \(\kappa_{d}^{1}(k)\) and \(\kappa_{d}^{2}(k)\) are constants. In this experiment, \(\kappa_{d}^{1}(k)=500\) and \(\kappa_{d}^{1}(k)=20\). The simulation results are shown in Figure 6. From Figure 6a, the PI-based ACC vehicle can track the leader vehicle with the desired speed and distance, but the tracking error cannot be limited in the prescribed range. In addition, PI-based ACC takes speed tracking and distance tracking as the overall target and cannot control each single target separately at different stages. Compared to PI-based ACC, the proposed algorithm has a strong decoupling ability and can achieve the velocity- or distance-tracking control at different stages. Therefore, the proposed algorithm in this paper is more flexible.  
Figure 3: Simulation results of scenario 1: (**a**) distance curves, distance is limited in the prescribed safe range within 12–25 s; (**b**) speed curves, speed is limited in the prescribed safe range within 0–12 s; (**c**) control input, distance control within 12–25 s and speed control within 0–12 s.  
Figure 4: Simulation results of scenario 2: (**a**) distance curves, distance is limited in the prescribed safe range within 15–22 s even if the speed of the leader car suddenly decelerates from 30 m/s to 1 m/s; (**b**) speed curves, vehicle speed can track the velocity of leader vehicle within 15–22 s; (**c**) control input.  
Figure 5: Simulation results of scenario 3: (**a**) distance curves, distance is limited in the prescribed safe range within 1–25 s even if leader vehicle frequently starts or stops; (**b**) speed curves, vehicle speed can track the velocity of leader vehicle within 1–25 s and there exists no negative speed; (**c**) control input.  
Figure 6: Simulation results of scenario 1 based on PI ACC: (**a**) distance curves, distance cannot be limited in the prescribed safe range; (**b**) speed curves, vehicle speed cannot be limited in the prescribed safe range; (**c**) control input.
# Temporal Continuity Expression for Network Topology of Space Information Systems
## 2 Link Relationship Graph--The Time Continuity Model of Network Topology
### The Link Distance Intensity Factor  
According to Formula (1), the distance at any time between \(S_{i}\) and \(S_{j}\) in the space information system can be calculated. Let \(d_{\max S_{i}S_{j}}\) and \(d_{\min S_{i}S_{j}}\) denote the longest distance and the closest linkable distance between the nodes, respectively (considering that there is an oversaturation of the link load receiver in actual connection, there is a minimum linkable distance corresponding to the minimum distance threshold of the link load receiver), the link distance intensity factor is defined as \(\sigma_{2}(t)=\frac{d_{\max S_{i}S_{j}}-d_{S_{i}S_{j}}(t)}{d_{\max s_{i}S_{j}}- d_{\min S_{i}S_{j}}}\) when \(\sigma_{1}(t)=1\). Based on the definition, \(\sigma_{2}(t)\) is a time-varying function, and \(\sigma_{2}(t)\in[0,1]\). If \(d_{S_{i}S_{j}}(t)=d_{\max S_{i}S_{j}}\), then \(\sigma_{2}(t)=0\); if \(d_{S_{i}S_{j}}(t)=d_{\min S_{i}S_{j}}\), then \(\sigma_{2}(t)=1\). It is important to note that \(\sigma_{1}(t)\!=\!0\) when \(d_{S_{i}S_{j}}(t)<d_{\min S_{i}S_{j}}\). \(\sigma_{2}(t)\) indicates the link quality affected by the distance between nodes, i.e., the link quality deteriorates as the distance increases.
# A Throughput and Energy Efficiency Scheme for Unlicensed Massive Machine Type Communications +
## 4 Energy Efficiency
In Figure 11 we can see the behavior of the optimal energy efficiency while \(\lambda\) and outage threshold level is increasing. As it was shown earlier, due to the previously explained reasons, the energy efficiency is concave with respect to \(\lambda\) meaning that while increasing at first, it starts decreasing after a certain density of interferers is met in the network. Hence, it is understandable that the same thing is happening in the case of optimal energy efficiency as well. It is also shown in this figure that while the scenarios when the outage threshold is somewhat strict in the network (\(\epsilon=0.001\), \(\epsilon=0.001\)), \(EE^{*}\) is almost the same, and the difference is negligible. However, when the outage threshold gets loose (\(\epsilon=0.1\)) the optimal throughput that the system can attain is slightly higher than the other two cases. The crossing point between these cases can also be explained the same way as described in Figure 8.
# Improving the Sensory Quality of Black Tea by Blending Varieties During Processing
## 3 Results and Discussion
### Analysis of the Main Taste Compounds  
The taste quality of black tea is influenced by its chemical composition, which includes catechins, caffeine, theaflavins, and amino acids [21]. To reveal the effects of blending methods on the taste quality of black tea, the contents and components of eight catechin monomers, gallic acid, caffeine, four theaflavin monomers, and twenty-one free amino acids were quantitatively compared in black tea samples. As shown in Supplementary Materials Figure S1A, Fud, JX, and JGY were grouped based on their taste compounds, indicating significant differences in the taste compounds among the samples. Similarly, in the Fud, FJX, and FJG samples (Supplementary Materials Figure S1C), blending fresh leaves during processing resulted in changes to the components and contents of the main taste compounds. Blended finished tea samples (AFJX and AFJG) also showed significant differences in their taste compounds. (Supplementary Materials Figure S1E). Moreover,black teas produced through fresh leaf blending exhibited different profiles compared to those from finished tea blending (Supplementary Materials Figure S1G). The effects of blending on the composition and contents of taste compounds in black tea samples will be further discussed in the following sections.
# IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks
## 6 Future Challenges
Of course, the increase of UAV hardware/software complexity introduced by this possible functionality needs to be considered. * Security: Some IoT solutions (especially the commercial ones) have been designed, including data encryption mechanisms to guarantee secure data exchanges between IoT devices and users. However, further analysis should be carried out in order to evaluate the impact of UAVs and satellites (e.g., larger delays, possible outage and handover events, etc.) on the security and safety of the supported applications. Figure 8 shows the architecture integrating IoT devices, UAVs, and satellites in the 5G paradigm along with the possible challenges previously mentioned.