esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Cloud Approach for Connected Vehicles
## 4 Analytic Model
### Model Description
#### 4.1.1 Network Modeling  
The parameters needed to model the network are presented and described in Table 2.  
**Definition of \(R_{c}\)**  
\(R_{c}\) is the distance between two bounded junctions divided by two. We could also define it as the radius of a junction.  
\[R_{c}=L/2 \tag{1}\]  
**Definition of \(\epsilon\)**  
In a square Manhattan model, the number of junctions \(\epsilon\) is given by the squared dimension \(D\), then we have the following expression:  
\[\epsilon=D^{2} \tag{2}\]  
**Definition of \(L_{R}\)**  
First, we note that we have four corners which have two segments (\(L_{1}\)). Furthermore, each edge of the city (there are four) contains as many junctions as the dimension of the grid to which we have to remove the corners, and each one of these junctions has three segments (\(L_{2}\)). Finally, we remove two from the dimension of the grid (which corresponds to the edges) and once it is squared, we obtain the number of junctions with four segments (\(L_{3}\)). The length of road \(L_{R}\) is obtained by multiplying the sum \(L_{1}+L_{2}+L_{3}\) with the length of a junction \(R_{C}\) (1). We have to distinguish the junctions with two roads (corners), the ones with three roads (edges), and the others with four roads.  
\[L_{1}=4*2 \tag{3a}\] \[L_{2}=4(D-2)*3\] (3b) \[L_{3}=(D-2)^{2}*4\] (3c) \[L_{R}=(L_{1}+L_{2}+L_{3})*R_{C} \tag{3d}\]  
**Definition of \(\varphi\)**  
We compute the percentage of each kind of junction using the following expressions:  
* \(\varphi_{4}=(D-2)^{2}/D^{2}*100\), the percentage of junctions linked to four segments.
* \(\varphi_{3}=4(D-2)/D^{2}*100\), the percentage of junctions linked to three roads.
* \(\varphi_{2}=4/D^{2}*100\), the percentage of junctions linked to two roads.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Designation** & **Description** \\ \hline \(R_{C}\) & Junction’s length (m) \\ \(L\) & Length of a segment (m) \\ \(D\) & Size of the grid \\ \(L_{R}\) & Overall road’s length of the network (m) \\ \(N_{S}\) & Number of road segments \\ \(\epsilon\) & Number of junctions \\ \(\beta\) & Number of RSUs \\ \(L_{RSU}\) & Length of road covered by RSUs (m) \\ \(\varphi\) & Average number of segments per junction \\ \(\delta_{RSU}\) & RSU’s coverage area (\%) \\ \(P_{RSU}\) & RSU’s range of transmission (m) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network parameters.  
The average number \(\varphi\) of segments linked to a junction is expressed as follows:  
\[\varphi=\left((4*2)+(D-2)*4*3+(D-2)^{2}*4\right)/e \tag{4a}\] \[\varphi=\frac{4D^{2}-4D}{D^{2}}\] (4b) \[\varphi=4*\frac{D-1}{D} \tag{4c}\]  
**Definition of \(L_{RSU}\)**  
The length of the road covered by the RSUs is expressed based on \(\varphi\) (4) and the range of an RSU (\(P_{RSU}\)) which we multiply by the number of RSUs.  
\[L_{RSU}=\varphi*P_{RSU}*\beta \tag{5}\]  
**Definition of \(\delta_{RSU}\)**  
The coverage area is the percentage of the total road covered by an RSU.  
\[\delta_{RSU}=L_{RSU}/L_{R}*100 \tag{6}\]
## 4 The Proposed Two-Step Access and Handover Strategy
### Importance-TOPSIS
#### 4.2.2 Weight Setting  
As we state in the literature review, in order to overcome the subjectivity and the problem of the existing weight setting methods, this paper intends to propose a new method to define the weight. We use the continuous replacement method to calculate the impact of individual changes of each attribute value on the target value, and give higher weight to attributes that have more impact on the target.  
The steps of the continuous replacement method are as follows:  
Step 1. Let \((\alpha,\beta)\to Q\), \((\alpha^{\prime},\beta^{\prime})\to Q^{\prime}\), so the difference is \(\Delta=Q^{\prime}-Q\). Suppose the order of substitution is to change \(\alpha\) first, then change \(\beta\).  
Step 2. Replace \(\alpha\) alone and we find \((\alpha^{\prime},\beta)\to Q_{1}\), \(Q_{1}\) appears due to the change of \(\alpha\) on the basis of \(Q\). Calculate the influence of \(\alpha\):  
\[Q_{\alpha}=Q_{1}-Q \tag{21}\]  
Step 3. Replace \(\beta\) alone and we find \((\alpha^{\prime},\beta^{\prime})\to Q_{2}\), \(Q_{2}\) appears due to the change of \(\beta\) on the basis of \(Q_{1}\). Calculate the influence of \(\beta\):  
\[Q_{\beta}=Q_{2}-Q_{1} \tag{22}\]  
We can see that \(\Delta=Q^{\prime}-Q=Q_{\alpha}+Q_{\beta}\)Step4. Calculate the weight  
\[\omega_{i}=\frac{Q_{i}}{Q^{\prime}-Q};i=\alpha,\beta \tag{23}\]  
This method can objectively reflect the importance of each attribute on the target.
## 2 Signal Modeling and CFR Method
### CFR Model Identification with Joint PAPR Clipping
The magnitude of the pulse is set equal to the peak in excess above the clipping threshold, the phase of the pulse is the same as the phase of the linear signal at the peak, a filter determines the response of the pulse, the pulse sequence filtered is subtracted from the linear signal to produce a band-limited signal with the desired PAPR calculates as,  
\[\mathrm{PAPR}(\mathrm{dB})=10\log_{10}\frac{P(t)_{peak}}{P(t)_{rms}}. \tag{6}\]
## 2 Motivation and Background Analysis
### Information-Centric Networking
#### 2.2.2 Communication Procedure in ICN  
In this paper, we focus on CCN, a representative technology to explain the ICN concept. CCN and NDN are getting much of the research interest and the concepts of CCN and NDN are almost similar except for the mechanism of forwarding the content request packet. There are two types of packets proposed by CCN and NDN for content request and reply named as interest packet and data packet. The CCN forwarding engine decides the next destination for sending the interest packet. Figure 2 shows the CCN communication mechanism in details between an end-user and a content provider.  
The CCN interest packet contains the content name instead of the content location address. The core network of the CCN is responsible for locating the data packet. The data packet contains some security-related information including required data that ensure the packet-level security in the CCN. CCN uses some key management systems and hash algorithms to secure their packet as well as the user. The CCN forwarding engine has three types of data structures: (1) Content store (CS), which is used for caching the incoming or outgoing content that is not available in the CS; (2) pending interest table (PIT), which is used to track the incoming interest packet including the face ID that helps to identify the individual user's path; (3) forwarding information base (FIB), which is used for selecting the appropriate outgoing face for the incoming interest packet. Following the above data structure in the CCN forwarding engine at each CCN node, the interest and data packets are handled in the CCN network architecture to send and receive the expected interest and data packets.
## 2 Overview European XFEL
### Layout of the European XFEL Facility  
The European XFEL facility consists principally of three sections. The first section includes the superconducting low emittance 17.5 GeV electron accelerator and the distribution of electron bunches to two beam lines comprising the FEL undulator sources. The electron beam transport is designed to accommodate up to five FEL sources. Each FEL source has a dedicated photon beam transport section to transport, steer, focus, and diagnose the X-ray FEL beams prior to their entry to the experiment hall. Mirrors in the photon beam transports will direct the X-ray FEL beam to one of the scientific instruments located at the respective FEL source. The third section is the experiment hall in which the scientific instruments are located and where the experiment program is run. In its first installment, only three FEL sources are constructed each leading to two scientific instruments. Figure 1 provides an overview of the European XFEL facility. Completion of the facility with five FEL sources and up to fifteen scientific instruments is expected to take place in the years following start of user operation.  
The layout of European XFEL is governed by a few basic conditions. First, the goal to reach FEL radiation exceeding 20 keV with high pulse energies and outstanding coherence properties has been driving the definition of the maximum electron energy to be 17.5 GeV. Using an acceleration gradient of 23.6 MeV/m this alone results in a length of nearly 1000 m for electron acceleration. A second condition was to expand the highly collimated FEL beam to a size of order 1 mm, requiring with divergences of order of 1 \(\upmu\)rad for hard X-rays another approximately 1000 m free-space transport. This is accompanied by another requirement of achieving lateral separations on the order of 17 m for the beam lines of the different FEL sources when arriving at the experiment hall.  
The facility is located in the western part of the Metropolitan area of Hamburg, reaching from the DESY campus in Hamburg Bahrenfeld to the town of Schenefeld, Schleswig-Holstein. Located in a partly inhabited area, the facility had to be built in underground tunnels, fully immersed in the ground water in this location. Access to tunnels is enabled by shaft buildings at the start and end of each tunnel, and access to the experiment hall is provided from the office and laboratory building build on top of this hall.
## 3 Case Study Application
### Data and Characteristics Used within the Vulnerability Conceptual Framework
#### 3.2.2 Spatial Vulnerability  
The map generated presents the accessibility classification and Fire Risk Index (FRI) for each building within the study area. To create this map, the road layer was initially clipped using the \"Cut\" tool in QGIS, based on the boundaries of the case study area. The resulting layer was then renamed as \"road_classification\" and symbolized using color coding to represent different road types.  
Moreover, the buildings were clustered based on their FRI values, employing a yellow-to-red color scheme to visualize the varying levels of fire risk. The map also includes visual representations of the case study zone, pedestrian zones, and buildings that were not evaluated for their Fire Risk Index.  
By presenting the accessibility classification and Fire Risk Index in this map, valuable insights into the potential impact of fire events in different areas of the study zone are provided. The color-coded representation of road types and the FRI clustering enhance the understanding of fire risk distribution and accessibility patterns within the historic city center of Guimaraes. Additionally, the inclusion of pedestrian zones and buildings not evaluated helps to delineate areas of interest and highlights potential areas of vulnerability.
## 4 Simulation and Results  
This section describes the results obtained from the proposed ROA. Simulation of all the following scenarios has been accomplished with known UAV start positions and destination positions. What is more, UAVs are assumed to be heading to the destination along a straight line from their start points in the grid model. According to [32], the minimum distance is 5 nmi (about 9.26 km). Considering the deviation and fine tuning of UAVs in reality, we set the radius of the protected sphere to be greater (10 km). Table 1 shows some variable values applied in the following scenarios. Therein, \(R\) denotes the safety distance of UAVs, which is a fixed value for a certain UAV; \(K_{1},K_{2},K_{3}\) are three variables affecting the weight for candidate points for the UAV in the next move, explained, respectively, in Equations (7), (8), and (12); \(\lambda\) is the decreasing coefficient of eigenvalue, listed in Equation (10), and the aim is to decrease the eigenvalue in the current best path so that the algorithm can get rid of local optimal value; \(\rho\) is the update coefficient of eigenvalue, listed in Equation (11), which is expected to adjust the affection of the current best path. After numerical experiments, we find that the choices of the variables depend on the specific task, and only affect the flying distance and computation duration, but do not lead to collisions. To clearly describe the performance of the ROA, the choice of variable values is just a random case.
# Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value
## 2 Neural Networks Training for SAR Imagery
### Supervised Training  
For feature extraction, there are a few state-of-the-art DNN models. Among them are Siamese Networks (Triplet Loss), or Metric Learning (similarity search). They facilitate the  
Figure 1: Example SAR image from 115.5\({}^{\circ}\) W 32.9\({}^{\circ}\) N WGS84 (Brawley US) from Capella Open Data, with zoom on the city in the middle.  
Figure 3: Example of rotation variants (a) 0\({}^{\circ}\), (b) 90\({}^{\circ}\), (c) 180\({}^{\circ}\), (d) 270\({}^{\circ}\)and (e) zooming out \(\times\)5.  
Figure 2: Operations of mirroring and trimming of a SAR scan. Image (a) presents the original image for mirroring, (b–d) are mirrored, (e) presents simple image trimming for data augmentation.  
generation of high-quality features for transfer learning. The drawback of these networks is the need for proper image labeling and preprocessing by human operators.  
Due to the particular characteristics of the SAR scans mentioned above, it is hard to design a pretext task (goal of the AI to learn that does not require manual labeling).
# Engineering of a FGM Interlayer to Reduce the Thermal Stresses Inside the PFCs
## 3 Conceptual Design of a Steel-Based PFC with a Engineered W/Steel FGM Interlayer
### Mechanical Results
The interlayer designed with the proposed methodology therefore effectively fulfills its purpose of substantially decreasing the thermal stresses inside the heat sink, almost matching the limit case without both the interlayer and the PFM (Table 2). Moreover, if the verification of the design criteria is carried out, shown in Table 8, it is possible to assess that for this component the criteria on the primary plus secondary membrane stresses is not anymore satisfied along the segment \(\overline{CD}\), proving the effectiveness of this design study. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**1 FGM layer**} \\ \hline Path & \(\overline{P_{m}+Q_{m}}\) [MPa] & \(\overline{P_{m}+P_{b}}\) [MPa] & \(\overline{Q}\) [MPa] \\ \hline \(\overline{AB}\) & 15.542 & 10.153 & 85.571 \\ \hline \(\overline{CD}\) & 119.71 & 14.533 & 169.06 \\ \hline \(\overline{EF}\) & 58.240 & 0.86552 & 137.61 \\ \hline \(\overline{GH}\) & 47.237 & 9.6263 & 52.204 \\ \hline \multicolumn{4}{c}{**2 FGM layers**} \\ \hline Path & \(\overline{P_{m}+Q_{m}}\) [MPa] & \(\overline{P_{m}+P_{b}}\) [MPa] & \(\overline{Q}\) [MPa] \\ \hline \(\overline{AB}\) & 15.503 & 10.136 & 85.378 \\ \hline \(\overline{CD}\) & 120.11 & 14.515 & 169.07 \\ \hline \(\overline{EF}\) & 58.96 & 0.81780 & 138.23 \\ \hline \(\overline{GH}\) & 47.204 & 9.6330 & 52.189 \\ \hline \multicolumn{4}{c}{**3 FGM layers**} \\ \hline Path & \(\overline{P_{m}+Q_{m}}\) [MPa] & \(\overline{P_{m}+P_{b}}\) [MPa] & \(\overline{Q}\) [MPa] \\ \hline \(\overline{AB}\) & 15.702 & 10.131 & 85.557 \\ \hline \(\overline{CD}\) & 119.95 & 14.516 & 168.78 \\ \hline \(\overline{EF}\) & 58.742 & 0.80452 & 138.00 \\ \hline \(\overline{GH}\) & 46.984 & 9.6333 & 51.997 \\ \hline \multicolumn{4}{c}{**4 FGM layers**} \\ \hline Path & \(\overline{P_{m}+Q_{m}}\) [MPa] & \(\overline{P_{m}+P_{b}}\) [MPa] & \(\overline{Q}\) [MPa] \\ \hline \(\overline{AB}\) & 15.359 & 10.133 & 85.183 \\ \hline \(\overline{CD}\) & 120.43 & 14.516 & 169.28 \\ \hline \(\overline{EF}\) & 59.374 & 0.81060 & 138.61 \\ \hline \(\overline{GH}\) & 47.345 & 9.6332 & 52.312 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Linearized stress in the steel heat sink with a discretized FGM. Paths are indicated in Figure 5.
# Non-Mypopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks
## 2 Model Description and Problem Formulation
### Probabilities of Dynamic Models  
After action \(a_{n}\) is applied to target \(n\) at each time, its dynamic model is \(m\) with probability \(u_{n}^{a_{n},m}\), for \(m=1,2,\ldots,M\). Define \(\mathbf{U}_{n}\triangleq\left[\mathbf{u}_{n}^{0},\mathbf{u}_{n}^{1}\right]\), where \(\mathbf{u}_{n}^{a_{n}}=\left[u_{n}^{a_{n},1},u_{n}^{a_{n},2},\ldots,u_{n}^{a_{n}, M}\right]^{\prime}\). Assume that \(u_{n}^{0,1}>u_{n}^{0,m}\), \(m=2,3,\ldots,M\), so targets that are not being tracked are more likely to move into model \(m=1\) (CV). \(u_{n}^{1,1}<u_{n}^{1,m}\) for \(m=2,3,\ldots,M\), so targets that are being tracked are less likely to move into model \(m=1\).  
Consequently, the smart targets are characterized by the dynamic model probability matrix, and the states \(\mathbf{x}\) of the targets are affected not only by the CV model but also by the other dynamic models.
###### Abstract  
In optical fiber communication, recent advances in multiple-input and multiple-output (MIMO) systems using space-division multiplexing have helped achieve higher spectral efficiency and data rates. Propagating higher-order modulation formats over MIMO systems further strengthens the capacity of the transmission link. In the optical-MIMO system, the dispersion impairments originating from a 1.4 km long multi-mode fiber (MMF) are mitigated using the proposed joint-transceiver equalization technique. A numerical convex optimization algorithm is used to compute and optimize the pre- and post-equalization (PPE) coefficients jointly restricted by cost and power budgets. The potential of the proposed joint-PPE technique is tested on an MMF link, which is severely degraded by dispersion compared to a single-mode fiber channel. From the experimental results, the average optical received power gain necessary to reach \(10^{-4}\) bit-error rate is improved by nearly 2.5 dB using the joint-PPE compared to the post-equalization only based on the minimum mean-squared error principle. When the efficiency of the conventional zero-forcing (ZF) principle-based PPE and the joint-PPE is compared, the joint-PPE scheme outperforms the ZF-PPE by approximately 1.5 dB. The enhancement in the transmission quality is observed with experimentally measured eye diagrams using the joint-PPE scheme. Under the analyzed scenarios, computer simulation also confirms the hypothesis, which establishes the effectiveness of the proposed joint-transceiver equalization over the conventional ZF-PPE scheme. Moreover, the simulated performance benefits of the joint-PPE are evaluated using the singular value decomposition (SVD) technique. Improvement of \(\approx\)3.86 dB in the average optical received power gain required to reach \(10^{-4}\) bit-error rate is witnessed with the PAM-4 format. Overall, the joint-transceiver equalization technique is proven to be beneficial in optical MIMO systems.  
Keywords:optical fiber communication; multi-mode fiber; optical MIMO technique; pre- and post-equalization; space-division multiplexing +
Footnote †: journal: _Photonics_
# The Oxidation of Isobutyraldehyde to Isobutyric Acid Using a Microenactor Technology System
## 3 Results and Discussion
### Laboratory-Scale Experiments with Hydrogen Peroxide as Oxidizing Agent
According to [PERSON]'s patented process [11], there are two main pathways for the oxidation of isobutyraldehyde to isobutyric acid:  
In the oxidation reaction, isobutyraldehyde first combines with oxygen to form a per-oxyacid. This peroxyacid then reacts with another molecule of isobutyraldehyde, leading to two different outcomes depending on the molecular arrangement. In Figure 4, Electrons move from a C-H bond on the peroxy acid-aldehyde intermediate, leading to the formation of two moles of isobutyric acid. In Figure 5, electrons move from a C-C bond on the peroxy acid-aldehyde intermediate, leading to the formation of one mole of isobutyric and one mole of isopropyl formate. The reaction pathway in Figure 5 is referred to as the Bayer-Villiger oxidation. According to the study by [PERSON] et al. [14], using hydrogen peroxide as the oxidant is unfavorable for the occurrence of the Bayer-Villiger reaction. Based on the results shown in Figures 2 and 3, isobutyric acid is not effectively formed under the experimental conditions described. It is therefore evident that pathway A in Figure 4 is not the main reaction pathway. Moreover, the Bayer-Villiger reaction in Figure 5,  
Figure 3: Percentage conversion/selectivity vs. feed ratio plot for oxidation of isobutyraldehyde by hydrogen peroxide. which could occur as a side reaction, is reported in the literature to be unlikely under such conditions. In summary, the results indicate that the strong oxidizing power of hydrogen peroxide is unfavorable for the formation of isobutyric acid. The complex GC signals indicate the formation of multiple byproducts, which also supports the discussion presented above. Compared with previous research findings [9; 15], it can be concluded that the use of a catalyst may be one of the necessary conditions for the hydrogen peroxide-based process. However, the use of solid catalysts also significantly increases the risk of microchannel clogging. At this point, this study concludes that hydrogen peroxide is not suitable as the oxidizing agent for this reaction. In addition to its inability to effectively produce isobutyric acid, the use of acetone as a co-solvent also increases the cost of product separation and purification. Therefore, the next step will be to use oxygen as the oxidizing agent for further testing.
## 3 Results and Discussion
### Effects on TV Inactivation  
The inactivation effect exerted by ADCP treatment on TV in CBCs at 24 kV for 3 min was evaluated, and the reduction of TV in ADCP-treated CBCs was 1.08 \(\pm\) 0.15 log PFU/cube (Table 2). Substantiating the above results, [PERSON] at al. showed that TV on lettuce was reduced by 1.3 \(\pm\) 0.2 log PFU/g following ADCP treatment at 34.8 kV for 5 min [9]. [PERSON] et al. exhibited that TV on blueberries was inactivated by 1.5 PFU/g after atmospheric plasma jet treatment for 45 s [18]. [PERSON] et al. reported that atmospheric pressure plasma jet treatment on fresh beef loin, fresh chicken breast, and fresh pork shoulder produced 2.05, 2.01, and 2.11 log PFU/mL reductions of Murine norovirus, respectively [28]. Reactive species and UV photons generated during ADCP treatment reportedly destroy the polypeptide chain of the viral capsid, while inhibiting protein biosynthesis by reacting with viral RNA encoding surface, capsid, maturation, and lysis proteins, as well as replicase protein [8; 18; 29; 30; 31]. Hence, the findings of this study, which indicate that TV is a surrogate of human norovirus, suggests that ADCP is effective in inactivating human norovirus that may infect in RTE-type chicken meat products.
# Constraints of Using Conductive Screen-Printing for Chipless RFID Tags with Enhanced RCS Response
## 3 Tags with Enhanced RCS Response
### Array of Detuned Planar Dipoles Closely Coupled to a Rectangular Metallic Plate  
The second type of RCS response enhancement is based on an array of detuned planar dipoles closely coupled to a rectangular metallic plate; see Figure 8. The monotonous RCS response is reached by the rectangular plate, whereas the resonance drops are caused by the detuned dipoles situated on the substrate whose thickness is 1.1 mm. The substrate comprises of the Folanorm polyester film and a foam layer. The relative permittivity of the film is \(\varepsilon_{\mathrm{r}}=4\), the loss tangent is \(\tan\delta=0.005\), and the thickness is 0.3 mm, while the relative permittivity is \(\varepsilon_{\mathrm{r}}\sim 1.3\), the loss tangent is \(\tan\delta\sim 0.02\), and the thickness is 1 mm. These properties enable the effective permittivity reduction and, consequently, they enable the deepening of the resonant drops [33]. The simulated RCS response is depicted in Figure 9.