esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of an L-Band SAR Microsatellite Antenna for Earth Observation
## 4 Validation of Parabolic Mesh Reflector
### Verification of Surface Error
#### 4.1.2 3D Scanning of Mesh Reflector Surface  
To scan the reflector surface, the photogrammetric device was placed 2 m away from the center of parabolic reflector. A commercial 3D scanning device was used for reflector contour measurement [18]. Eleven overlapping images composed of triangular mesh cells were generated, covering the reflector surface area with spatial resolution of 1 mm. To avoid background scattering from the surroundings, an anti-reflective sheet was placed behind the reflector. The measurements showed that, although detectable, the thin mesh material scatters the projected light more than it reflects back towards the camera, resulting in very noisy surface estimations. Since the ribs presented clear shape, they were then used as measured reference for reflector shape. Each scan then was converted into a point cloud of 2,500,000 points. Noise and artifacts were removed and filtered. Each point cloud scan was then directly compared with the theoretical parabolic surface using a best fit function, and these distances computed as errors. Cloud-to-cloud (C2C) distances computations were based on a quadratic function. A surface map of calculated C2C distances using the \(k\)-nearest neighbor classifier can be seen in the color map displayed in Figure 9. Standard deviation \(std\) and mean \(\upmu\) of the normal distribution of C2C distances were computed, with overall RMS error of 3.86 mm.  
Figure 8: (**a**) Research model of deployable parabolic mesh reflector antenna; and (**b**) near-field measurement setup.
# Blockchain-Embedded Strategic Options for Satellite Operators: Sell, SataS, or Dual?
## 3 Models excluding Blockchain Technology
### NR Model: Sell Channel without Blockchain  
By setting Equation (1) to zero, we can derive the marginal customers who are indifferent as to purchasing a satellite and not purchasing a satellite. Since the production cost  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Definition** \\ \hline NR model & Sell channel without BCT \\ NS model & SataaS channel without BCT \\ ND model & Dual channel without BCT \\ BR model & Sell channel with BCT \\ BS model & SataaS channel with BCT \\ BD model & Dual channel with BCT \\ \(v\) & Customers’ valuation \\ \(p_{r}\) & The price of the satellite under the sell channel \\ \(e\) & The probability of the successful launch of the satellite \(e\in[0,1]\) \\ \(b\) & The coefficient of launch success improved by BCT \\ \(s\) & The utility loss due to data security \\ \(\beta\) & The coefficient of data security risks reduced by BCT \\ \(c_{r}\) & Per-satellite cost for satellite operator \\ \(c_{b}\) & Blockchain-related satellite unit customization cost \\ \(C\) & Fixed cost of SataaS channel for satellite operator \\ \(B\) & Fixed cost of BCT for satellite operator \\ \(D_{i}^{j}\) & Market demand \\ \(\pi_{i}^{j}\) & The profit of the satellite operator \\ \(CS\) & Consumer surplus \\ \(SW\) & Social welfare \\ BCT & Blockchain \\ \hline \hline \end{tabular} Subscripts \(i=r,s,d\) represent sell, SataaS, and dual channel, respectively. Superscripts \(j=N,B\) represent the cases without blockchain and with blockchain, respectively.  
\end{table}
Table 2: Notations used in this paper.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Assumption** \\ \hline Satellite Operator & SOs selling satellites need to cover the cost of manufacturing individual satellites. \\  & SOs choosing SataaS need to cover the cost of maintaining satellite operation. \\  & BCT will improve the satellite launch success rate. \\  & BCT will reduce the data security risks in SataaS. \\  & Satellite data users are heterogeneous in different channels \\ Customers & Customers face the risk of launching satellites if they choose to buy and \\  & launch their own satellites. \\  & The risk of data security in SataaS will lead to customer utility losses. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of assumptions.  
per satellite unit cannot be neglected, we denote it by \(c_{r}\). Then, the sell channel demand and the SO's payoff function can be measured as follows:  
\[\begin{split} D_{r}^{N}&=1-\frac{p_{r}}{e}\\ \pi_{r}^{N}&=(p_{r}-c_{r})\times(1-\frac{p_{r}}{e}) \end{split} \tag{3}\]  
After the derivation, we obtain the optimal selling price and profit for the SO in the sell channel, that is, \({p_{r}}^{*}=\frac{e+c_{r}}{2}\) and \({\pi_{r}^{N}}^{*}=\frac{(e-c_{r})^{2}}{4e}\), respectively.  
The closed form of the price and profit indicate that they both increase with the launch success rate and decrease with the per satellite cost, which is intuitive.
## 3 Results and Discussion
### Hardness and Residual Stress Measurements on the FPP-Treated Ti-22V-4 Al Alloy  
Figure 7 shows the distribution of Vickers hardness at various cross-sectional depths for the FPP-treated specimen. The hardness on the surface of the FPP-treated specimen was 453 HV. Compared with the substrate (261 \(\pm\) 2.9 HV), the surface became 73% harder. The interior hardness of the FPP-treated specimen decreased with increasing depth and corresponded to that of the untreated specimen at a depth of 30 \(\upmu\)m. In other words, the thickness of the work-hardened layer formed by FPP was approximately 30 \(\upmu\)m.  
Figure 6: EBSD analysis for cross section of FPP-treated specimen: (**a**) image quality (IQ) map; (**b**) IPF map; (**c**) phase map.  
Figure 8 shows the distribution of the residual stress and the XRD FWHM at various cross-sectional depths for the FPP-treated specimen. As shown in Figure 8a, the compressive residual stress on the surface of the untreated specimen was 306 MPa. This residual stress was caused by buff finishing. The thickness of the surface layer, at which compressive residual stress was generated, was approximately 50 \(\upmu\)m. As a result of the comparison of the FWHM profile (Figure 8b) and the Vickers hardness profile (Figure 7), it is clear that both profiles show a very similar behavior. It is well known that FWHM values are useful means to characterize the degree of work hardening, since they usually correlate well with the hardness in soft and medium-hard mechanically surface-treated materials [26]. Therefore, it is confirmed that the depth of work hardening was approximately 50 \(\upmu\)m.
# Extending the Performance of Hybrid NoCs beyond the Limitations of Network Heterogeneity
## 4 Evaluation
### Bypassing Rate  
We examine how frequently these bypass paths can be utilized by packets. For each router, the bypassing rate is defined by the number of flits that benefit from the bypass datapath over the number of total received flits in the router. The network bypassing rate comes from averaging the bypassing rate of all routers. We collect the bypassing rate statistics under uniform random traffic of an \(8\times 8\) mesh and \(12\times 12\) mesh network. Figure 9 shows the bypassing rate of the \(8\times 8\) and \(12\times 12\) network over different packet injection rates. We also plot the average packet latency in the figures. Bypassing the rate decreases as the PIR increases, and network latency increases gradually before saturation. In the \(8\times 8\) mesh, the zero-load bypassing rate (note: zero-load conditions could be used to model the bypass rate by employing the size of the mesh as parameters, which could be quantified (if a packet can be expected to \"bypass\" \(n\) routers and does not bypass \(m\) ones, its latency can be expressed as \(xn+ym\))) is 50.0%; in the \(12\times 12\) network, the zero-load bypassing rate is 62.3%. As the network size increases, non-bypassing actions like dimension-switching and injection account for a smaller portion in the total path causing the bypassing rate to increase.
# Internet of Food (IoF), Tailor-Made Metal Oxide Gas Sensors to Support Tea Supply Chain
## 5 Conclusions  
The work was based on the analysis of 11 different samples of green, black, and white tea. By applying the two techniques GC-MS and S3, the complete determination of the aromatic profile of the teas was achieved, evaluating and highlighting the similarities and differences between them so as to arrive at a discrimination based on the VOCs profile. Nearly, 100 different volatiles were identified by the mean in each tea sample. The GC-MS requires a longer and, in some ways, more elaborate analysis, while S3 allows one to obtain the result in a shorter time and more easily and user-friendly way. It can be concluded that an innovative technology such as S3 has the potential to be used from farm to fork, in food companies and in the production chain, so as to report anomalous products and prevent them from reaching the market or in any case arriving at a final product that must be excluded because it is not safe. In fact, once the anomaly is reported, it would be possible to implement a correction or exclusion of the batch from the distribution chain. It should also be emphasized that the use of this sensor technology once trained would have a positive economic impact for the production business, is also easy to use, and, thanks to the connection to the network, allows for remote data processing. In a short-term future, this technology could be applied in industrial reality, so that food safety and the producer can benefit from it. This kind of technology could be also implemented in household environments, to control not only the dried product but also the final stage of the beverage. In fact, its use is not limited only to tea but can concern any foodstuff, as already demonstrated in other studies.  
Conceptualization, E.N.-C., M.A. and V.S.; methodology, E.N.-C., M.A. and V.S.; software, E.N.-C., M.A. and V.S.; validation, M.A. and V.S.; formal analysis, E.N.-C., M.A. and V.S.; investigation, E.N.-C., M.A. and V.S.; resources, V.S.; data curation, M.A., E.N.-C. and V.S.; writing--original draft preparation, E.N.-C., M.A. and V.S.; writing--review and editing, E.N.-C., M.A.; visualization, E.N.-C., M.A. and V.S.; supervision, V.S. All authors have read and agreed to the published version of the manuscript.  
Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data available on request due to restrictions e.g., privacy or ethical. Conflicts of Interest: The authors declare no conflict of interest.
# A Method of Ontology Integration for Designing Intelligent Problem Solvers
## 3 The Knowledge Kernel
### Problems and Reasoning Methods on the Kernel Model
#### 3.2.1 Problem on an Object  
An object of the Kernel Model has basic behaviors for solving problems on it. It is equipped abilities to solve problems such as: (i) Determine the closure of a set of attributes; (ii) Determine the closure of a set of facts; and (iii) Execute deduction and give solutions for problems. In this section, the algorithm for solving the problem (ii) is presented. The other problems can be solved by using this algorithm.  
```
Input: Object Obj = (Attrs, Facts, RaIObj), F is a set of facts related to Obj; Output: Obj.Closure(F). Step 0: Initialize variables flag: = true; KnownFacts: = F \(\sqcup\) Obj.Facts; Step 1. Classify the kinds of facts in KnownFacts; Step 2. Determine new facts from facts in KnownFacts by using the reasoning rule; Stop 3. Search the closure of facts of kind 2 in KnownFacts; forinct2 in KnownFactsdo; ifKind(fact2) = 2 and fact2 \(\in\) C then # fact2 is a determined object; KnownFacts: = KnownFacts \(\sqcup\) fact2.Attrs; endif; enddo; Step 4. Search the closure of facts of kind 3 in KnownFacts; forfact3 in KnownFactsdo; ifKind(fact3) = 3; then for\(i\) from 1 to NumberElements(fact3) do  KnownFacts: = KnownFacts \(\sqcup\) fact3[]; endif; enddo;
```  
**Algorithm 1** Determine the closure of a set of facts.  
**Example 1**.: _In the knowledge domain about geometry, the triangle \(T\) is requested to give a solution for a problem [\(b\), \(A\), \(C\)]\(\rightarrow\)hb (hb is the length of altitude from the point B). The triangle \(T\) determines its closure of the set \(F\) = [\(b\), \(A\), \(C\)]. By Algorithm 1, the set T.Closure(F) is:_  
T.Closure(F) = [b, A, C, B, a, S, hb] by the following rules:  
r1: [\(A\), \(C\)] \(\rightarrow\) B by \(A+B+C=180\); r2: [\(A\), \(B\), \(b\)] \(\rightarrow\) a, by Determine \(a/\sin(A)=b/\sin(B)\); r3: [\(a\), \(b\), \(C\)] \(\rightarrow\) S, by \(S=a.b.\sin(C)/2\); r4: [\(S\), \(b\)] \(\rightarrow\)hb, by S = \(b.hb/2\);
## 3 Proposed Methodology
### Proposed GATransformer  
The GATransformer model is a combination of a GAT model [29] and a Transformer [30]. It is designed to reduce the intricacy of computation and architecture in neural architectures without sacrificing performance. GATransformer is employed to learn how to enhance and retain critical channels, assigning importance scores during training to facilitate channel pruning by building upon the attention mechanism. The proposed model calculates correlations and relationships among channel weights. The GAT captures dependencies, while the Transformer computes inter-channel correlations across layers. A feed-forward network processes the output of the multi-head attention network to extract deeper features, while a residual module prevents degradation from the network depth. This combination enhances feature representation, enabling effective channel pruning with minimal performance loss.
# Kirchhoff Migration for Identifying Unknown Targets Surrounded by Random Scatterers
## 3 Structure and Properties of Kirchhoff Migration
### Introduction to Kirchhoff Migration and Its Mathematical Structure
\begin{split}\mathfrak{F}_{\text{KIR}}(\mathbf{x})& \approx N\left|\sum_{m=1}^{M}\alpha_{m}^{2}|\mathbf{B}_{m}|\left( \frac{\varepsilon_{\mathbf{A}}^{(m)}-\varepsilon_{0}}{\sqrt{\varepsilon_{0} \mu_{0}}}\right)J_{0}(k|\mathbf{x}_{\text{ANO}}^{(m)}-\mathbf{x}|)^{2}+\sum_{s =1}^{S}\sigma_{s}^{2}|\mathbf{B}_{s}|\left(\frac{\varepsilon_{\mathbf{R}}^{(s )}-\varepsilon_{0}}{\sqrt{\varepsilon_{0}\mu_{0}}}\right)J_{0}(k|\mathbf{x}_{ \text{RND}}^{(s)}-\mathbf{x}|)^{2}\\ &-\sum_{m=1}^{M}\alpha_{m}^{2}|\mathbf{B}_{m}|\left(\frac{\mu_{0} }{\mu_{\mathbf{A}}^{(m)}+\mu_{0}}\right)\left\{\left(\frac{\mathbf{x}_{\text{ ANO}}^{(m)}-\mathbf{x}}{|\mathbf{x}_{\text{ANO}}^{(m)}-\mathbf{x}|}\cdot \mathbf{e}_{1}\right)^{2}+\left(\frac{\mathbf{x}_{\text{ANO}}^{(m)}-\mathbf{x} }{|\mathbf{x}_{\text{ANO}}^{(m)}-\mathbf{x}|}\cdot\mathbf{e}_{2}\right)^{2} \right\}J_{1}(k|\mathbf{x}_{\text{ANO}}^{(m)}-\mathbf{x}|)^{2}\\ &-\sum_{s=1}^{S}\sigma_{s}^{2}|\mathbf{B}_{s}|\left(\frac{\mu_{0} }{\mu_{\mathbf{R}}^{(s)}+\mu_{0}}\right)\left\{\left(\frac{\mathbf{x}_{\text{ RND}}^{(s)}-\mathbf{x}}{|\mathbf{x}_{\text{RND}}^{(s)}-\mathbf{x}|}\cdot \mathbf{e}_{1}\right)^{2}+\left(\frac{\mathbf{x}_{\text{RND}}^{(s)}-\mathbf{x} }{|\mathbf{x}_{\text{RND}}^{(s)}-\mathbf{x}|}\cdot\mathbf{e}_{2}\right)^{2} \right\}J_{1}(k|\mathbf{x}_{\text{RND}}^{(s)}-\mathbf{x}|)^{2}\right|,\end{split}  \tag{6}\]  
_where \(J_{n}\) denotes the Bessel function of order \(n\) of the first kind._  
Proof. : See Appendix B.
## 2 Experimental Methods
### Physical Model
#### 2.1.4 Free Decay Tests  
Free decay tests with different load resistances were conducted to obtain the natural frequency of the experimental system in air (\(f_{n,air}\)). The load resistances (\(R_{L}\)) were varied from 8 \(\Omega\) to 41 \(\Omega\). The damping ratios (\(\zeta_{total}\)) were obtained by free decay tests with different load resistance values [32]. For each \(R_{L}\), free decay tests were performed four times for the respective cases in air, as shown in Figure 6. Then,\(f_{n,air}\) was calculated by using a simple averaging method. The results are listed in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{\(R_{L}\)(\(\Omega\))} & \multicolumn{2}{c}{**Upstream Prism**} & \multicolumn{2}{c}{**Downstream Prism**} \\  & \(C_{total,LUTP}\) & \(\zeta_{total,LUTP}\) & \(C_{total,LUTP}\) & \(\zeta_{total,DTP}\) \\  & **(N.s.m\({}^{-1}\))** & & **(N.s.m\({}^{-1}\))** & \\ \hline
8 & 125.192 & 0.305 & 127.980 & 0.312 \\
11 & 100.951 & 0.246 & 102.958 & 0.251 \\
13 & 85.482 & 0.208 & 87.371 & 0.213 \\
16 & 72.783 & 0.177 & 73.424 & 0.179 \\
21 & 62.842 & 0.153 & 62.349 & 0.152 \\
31 & 52.115 & 0.127 & 53.325 & 0.130 \\
41 & 48.680 & 0.119 & 50.043 & 0.122 \\ \(\infty\) & 34.903 & 0.085 & 33.636 & 0.082 \\ \hline \end{tabular}
\end{table}
Table 4: The parameters of the two T-section prisms.  
Figure 5: T-section prisms: (**a**) T-section prism size diagram; (**b**) Spacing ratio of prisms.  
Figure 6: Free decay tests for \(K\) = 1400 N/m and \(R_{L}\) = 41 \(\Omega\): (**a**) Upstream prism; (**b**) Downstream prism.  
The damping ratio \(\zeta_{total}\) expression is defined as:  
\[\zeta_{total}\ =\ \frac{\ln\eta}{2\pi}\ =\ \frac{1}{2\pi}\ln(\frac{A_{i}}{A_{i+1}}), \tag{1}\]  
where \(\zeta_{total}\) is the damping ratio, \(A_{i}\) is the _ith_ peak of the free decay curve, \(A_{i+1}\) is the \(i+1 th\) peak of the free decay curve.  
The damping (\(C_{total}\)) of the experimental system:  
\[C_{total} = 2\zeta_{total}\cdot\sqrt{K\cdot m_{osc}}, \tag{2}\]  
where \(C_{total}\) is damping, \(\zeta_{total}\) is the damping ratio, \(K\) is the system stiffness, \(m_{osc}\) is the system oscillation mass.
## 5 Experiment and Results  
In this section, we describe the hardware and the associated software code involved in enabling the secured key generation and transmission. We begin with the key generation phase and continue with the transformation to optical signals and the rest of the implementation. Testing the correctness and functionality of the proposed solution was successfully executed, and it passed all the functional tests. For non-functional testing, we revealed a reduced pace of the transmission process. This is mainly due to the transmission via different medias, bit-wise and optical-wise, and the immense difference in the transmitted volume. However, since the key size is limited, we did not notice an impact that would cause us to classify this solution as impractical.
## 2 MIPs
### Advantages and Applications
The use of MIPs as receptor mimics has allowed the screening of combinatorial libraries and the synthesis of drug candidates in the recognition sites of imprinted materials [52]. * Biotechnology The use of MIPs as an aid to biocatalytic processes has been suggested. These practices include shifting an unfavorable equilibrium in an enzyme-catalyzed process away from an unwanted side-product or toward enhancing the yield of the anticipated product, or the isolation of the anticipated product from a crude extraction or from a fermentation broth [53]. * at Surfaces with specified shapes and roughness were proved to enhance cell culturing. Because the size, density, and even surface chemistry of such patterns can be tuned in molecular imprinting, MIPs are attractive materials for this purpose [54]. * Antibody mimics The idea is to produce particles with a specific selectivity that could be given to patients as a substitute for natural antibodies produced in immune responses [55; 56]. * Crystallization The structural determination of proteins is limited by their ability to form well-diffracting and large crystals. Because many proteins have abnormal shapes, they are difficult to crystallize. One very new application for MIPs is to initiate protein crystallization; it was shown that they can initiate protein crystal growth for various different proteins and they have also increased the speed of crystal formation significantly [57]. * Other applications: 1. Food industry MIPs imprinted with methyl pyrazines have been proposed as useful vehicles for flavors and flavor profile analysis in the food industry [58; 59]. 2. Microfluidic devices The use of MIPs in microfluidics machines has also been suggested and a computational fluid dynamics model of the performance of such devices has been created. This procedure involves a broad range of flexibility by which to imprint different sets of biomolecules with a variety of structures, sizes, and physical and chemical characteristics [60].
# Effect of Nanodisks at Different Positions on the Fano Resonance of Graphene Heptamers
## 3 Simulation Results and Discussions
### The Effect of the Chemical Potential of the Central Nanodisk on Fano Resonance
The radius of the central nanodisk and the satellite nanodisks are 90 nm and 50 nm, respectively. The chemical potential of the central nanodisk is set as 0.6 eV, and that of the six satellite nanodisks are 0.5 eV. The extinction spectrum of this structure is depicted in the bottom of Figure 3b, whereas the near-field distribution at the Fano resonance frequency is presented in Figure 4. It is clear that the dipole resonances of the top two nanodisks and the bottom two nanodisks in this structure are in phase. However, compared with these four nanodisks, the dipole resonance of the central nanodisk is an out-of-phase oscillation. The Fano resonance of this heptamer becomes possible due to the opposite dipole resonances. Furthermore, not all nanodisks have an effect on this collective behavior. The dipole resonances produced by the leftmost and rightmost nanodisks are weaker than those of the other nanodisks. Therefore, when the structure of the leftmost and the rightmost nanodisks changes, the impacts on the collective behavior are negligible. Therefore, to dwell on the influence of ring nanodisks on this collective behavior, we modify the chemical potential and radius of the target nanodisk and calculate the extinction spectra. Figure 3: (**a**) Distribution of different chemical potentials in graphene heptamers. (**b**) Extinction spectra with different chemical potentials of the central nanodisk. (**c**) The electric field (I\(\left|\text{E}\right|\)) distribution of plasmonic resonance peaks B to G.