esa-sceva/satcom-chunk-collection huggingface dataset:
# Multifunction RF Systems for Naval Platforms
## 4 MFRF System Prototype Development Programs
### Amfrc
#### 4.1.2 Technical Description
These combined signals were then provided on four ports on each of the nine subarrays for the following processing. * The RF signals from three of the four subarray ports on each of the nine subarrays were sent to three nine-channel analog beamformers (i.e., one beamformer per port) for SATCOM links. Since beam pointing angles for SATCOM links change slowly, the capability afforded by digital beamforming to rapidly change beam direction was not needed, and the load on digital processing resources could consequently be reduced by the use of analog beamformers. * The RF signals from three subarray ports on each subarray were provided to narrowband digital preprocessors, where they were downconverted to IF, sampled with 14 bits at 60 MHz and passed on to three nine-channel digital beamformers (i.e., one beamformer per port), where beams were computationally formed by
phase shifting. Up to four simultaneous Rx beams per narrowband beamformer could be formed, resulting in a total of up to 12 beams. These beams were used for radar, CDL communication links, and ES. The RF signals from two subarray ports were provided to wideband digital preprocessors, where they were downconverted to IF with a bandwidth of 230 MHz, sampled with 8 bits at 960 MHz, digitally downconverted to a complex baseband signal, and passed on to two nine-channel digital beamformers implemented with vector processors. Up to two simultaneous beams per beamformer could be formed, where TTD processing was employed. These beams were mainly used for ES surveillance of weak emitter signals, where the detection of such signals benefits from the Rx array beamforming gain. As mentioned in Section 3.1, the wideband digitization and TTD beamforming accommodates detection of emitter signals with large instantaneous bandwidth. As a point of interest, the design decision in the AMRFC program to implement only subarray-level Rx beamforming without element-level beamforming capability has the following implications: (1) With only nine inputs to each beamformer, the maximum coherent Rx array gain was only \(\approx\)19 dB; (2) The presence of grating lobes in the beamformed Rx array pattern was effectively determined by the centre-to-centre subarray spacing, rather than the element spacing. The subarray separation was about 11 cm, which was larger than 0.54\(\lambda\) for all frequencies within the operating band of 6-18 GHz. Consequently, grating lobes at scan-off angles were likely an issue. The lack of any element-level beamforming in the design is an unusual decision that may have been motivated by cost, and perhaps by a conclusion that this feature was unnecessary for the purposes of demonstrating the benefits of MFRF systems.
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
## 3 Results and Discussion
### Solution of Optimization Problem: Model 2  
In this case, the optimization problem described by Equation (55) is solved by setting the lower \(R_{L}\) and upper \(R_{U}\) bounds for the resistance values to be the same as in model 1 (see Table 3). The capacitance values \(C_{i}\) are chosen to be 10% of the inherent capacitance of the piezoelectric patch, and the inductance values \(L_{i}\) are given by Equation (35). The optimal values of the resistance variables found by the PSO algorithm are \(R_{2}=12.29\) k\(\Omega\) and \(R_{3}=4.02\) k\(\Omega\). Figure 8 shows the magnitude of the tip FRF, before and after shunt damping, using the method proposed by [PERSON] et al. [16] (model 2). For comparison reasons, the corresponding FRF of the damped system using the proposed method (model 1) is also illustrated simultaneously in this figure. It is obvious from Figure 8 that the proposed method provides greater amplitude reduction in both target modes. Indeed, the FRF amplitude reduction of the second mode is 25.47 dB, i.e., 35.69% lower compared with that of model 1 (39.61 dB). Additionally, the FRF amplitude reduction of the third mode is 47.08 dB, i.e., 15.81% lower compared with that of model 1 (55.92 dB). The overall parameters of the shunt circuit for model 2 are shown in Table 6. It can be observed that the required inductance values for the convectional current-flowing method (model 2) are considerably large, especially those corresponding to low-frequency modes. In comparison with the optimal inductance values of model 1, it follows that the suitable choice of the capacitance values \(C_{i}\) via the proposed optimization method significantly reduces the inductive requirements.  
Figure 7: Iteration process of the fitness function using PSO—model 1.  
Figure 9 shows the iteration process of optimization. It can be seen from the figure, that the process converges after generation 15, when the fitness function remains quasi constant. It takes about 17 min to carry out the 39 iterations. In addition, the algorithm stops before the maximum iteration is reached, since the relative changes in objective function are less than the tolerance (\(10^{-6}\)).  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline \(R_{2}\) (k\(\Omega\)) & 12.29 \\ \(R_{3}\) (k\(\Omega\)) & 4.02 \\ \(\mathrm{C_{2}}\) (nF) & 1.83 \\ \(\mathrm{C_{3}}\) (nF) & 1.83 \\ \(L_{2}\left(H\right)\) & 134.07 \\ \(L_{3}\left(H\right)\) & 16.85 \\ \hline \end{tabular}
\end{table}
Table 6: Shunt parameters for model 2.  
Figure 8: Simulated frequency response: undamped response (blue —), damped response—model 2 (red).  
Figure 9: Iteration process of the fitness function using PSO—model 2.
## 2 Material and Method
### Grey-TOPSIS Study  
The combination of multiple multi-criteria optimization methods help to simplify data processing, time-saving, providing decision-makers with a more efficient way to choose the right criteria. The decision-making model is developed to determine the laser beam micro-marking parameter and the performance criteria, and the fuzzy TOPSIS is studied for selecting the best parametric combination [40]. To evaluate the performance of three different drill bit types (High-speed steel (HSS), Titanium Aluminum Nitride (TiAlN), and Titanium Nitride (TiN)), the machining parameters, such as cutting speed and feed rate, are optimized with multiple performance characteristics as surface roughness, circularity, and cylindricity using the multi-objective Taguchi technique and TOPSIS [41]. The TOPSIS and Analytic Hierarchy Process (TOPSIS-AHP) hybrid MCDM approach has simpler calculations than the other traditional optimization methods, and reduces computational efforts. Therefore, this optimization method can be applied to different conflicting responses in machining situations [42]. In this study, the hybrid method was used to substitute the GRC into the TOPSIS process to evaluate the distances between the data chain. The computational process is presented in Figure 6.  
The TOPSIS method is used to solve problems that require the concurrent optimization of a particular part or feature. It separates the output responses into beneficial attribute and cost attribute. The maximized beneficial attribute and minimized cost attribute effectuate the best solution. TOPSIS solves out the positive best alternative and negative best alternative, which are used as references for the optimal solution. The positive best alternative has the efficiency of maximizing the beneficial attribute and minimizing the cost attribute. In the same way, the negative best alternative derogates the beneficial attribute and increases the cost attribute. All of the potential alternatives are assimilated with the positive best alternative and negative best alternative to figure out the alternative that is nearest to  
Figure 6: Flow diagram of the optimization and evaluation.  
the positive best and furthest from negative best. The greatest alternative has the closest range from positive best alternative and the furthest range from negative best alternative. In this case, the surface roughness, uncut fiber, and delamination attributes were all considered beneficial attributes, in which the smallest values were desired.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 3 The Architectural Model
### The Control Plane--Tiered Tunnel Automation
#### Encryption Automation  
The control plane is also responsible for the setup of encrypted channels between the VNFs. In this architecture, we suggest automating the exchange of encryption keys. We define that it is the origin Service Provider in an SFC that is responsible for key distribution and tunnel automation. A Key Management Server on the control plane is defined to distribute the setup of the secure channels. The key distribution depends on the SFC and is therefore dependent on the order of the VNFs and how they are routed. Hence, a key distribution protocol that supports dynamic endpoint configuration and the negotiations of encryption keys from a trusted third party authentication server is needed. Section 5 suggests a simplified prototype of such a protocol where two random endpoints contact a third party server to receive instructions on how to establish a Security Association for IPsec.
# Empowering Tourism Accessibility: A Digital Revolution in Pieria, Greece
## 2 Materials and Methods
### Data Integration and User Interface  
The platform was designed to offer two key functionalities to its users, specifically providing information on accessibility infrastructure and information on POIs' accessibility. As already mentioned, the platform aggregates all collected data related to pedestrian accessibility, including ramps, crossings, sidewalk widths, and obstacles. These data points are then mapped out using cartographic data to provide users with a clear visual representation of the accessibility infrastructure in the study area. This allows users to plan their routes based on their specific needs, ensuring that they can navigate the region safely and comfortably. In addition to route accessibility, the platform also provides detailed information about the accessibility features of individual points of interest. To respect user privacy, the platform ensures that any personal data collected are kept confidential and used solely for the purpose of providing a personalized route and POI recommendations. The platform's ability to offer personalized recommendations is considered its key strength, as a \"one fit for all\" approach is not functional when different types of disabilities are discussed. In this context, users can input their specific accessibility needs, and the platform will generate tailored suggestions based on the available infrastructure. For example, a user requiring wheelchair access will only be shown routes and POIs that meet the necessary accessibility standards. The platform also considers additional user-specific needs, such as proximity to accessible restrooms or the presence of eating areas along routes.  
To better illustrate the operational process of the Olympus Riviera Digital Platform, the flow chart depicted in Figure 5 outlines the key steps involved, from defining the study area and collecting accessibility data to processing and presenting personalized recommendations. This visual representation highlights the systematic approach taken to ensure a seamless user experience.  
Supplementary Material for the Olympus Riviera is available at the Platform's original website at [[https://www.olympus-riviera.gr](https://www.olympus-riviera.gr)]([https://www.olympus-riviera.gr](https://www.olympus-riviera.gr)) (accessed on 28 November 2024).
# Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review
## 3 Reviewing Positioning Systems and Their Capabilities
### Improving the Categorization

Table 2: Comparison of underwater mapping and exploration technologies. In underwater research, the horizontal range of a vehicle is a critical factor, as it defines the extent of the vehicle's movement freedom during its mission. Cost is another significant consideration, especially for those working within a budget. Table 2, column 5, addresses both the construction and pre-build costs of positioning systems. Depth is also crucial, particularly for deep-water operations, as pressure at great depths can be damaging if not properly managed. Maintenance costs, including those related to saltwater corrosion, are another important factor, though many papers do not provide this information. Maintaining the electronics that keep the positioning system functional is vital. Overall, Table 2 is essential for understanding the characteristics and implications of different positioning systems. Each category in the table is interdependent. For example, in row 5, a system with limited range and medium cost is revealed, while row 11 shows that an ROV with a range of capabilities could significantly increase the cost.
## 3 Results
### Fasting Blood Glucose, IPGTT, and AUC  
During the experimental period, fasting blood glucose concentrations were measured at four and eight weeks (Figure 2A). At four weeks, significantly higher blood glucose concentrations were measured in the HFD group compared with the ND group (\(p<0.005\)). In addition, blood glucose concentrations were also significantly lower in the Catechin and FGK 250 groups. At eight weeks, blood glucose concentrations were significantly elevated in all experimental groups except the FGK 250 group. At eight weeks, we also conducted an intraperitoneal glucose tolerance test (IPGTT) in HFD-induced mice (Figure 2B). The glucose sensitivity in mice fed the HFD was comparable to that in the ND group. At 30 min after the administration of insulin (1 U/kg body weight), an increase in blood glucose concentrations was measured in all groups. Furthermore, compared with the ND group, the HFD groups exhibited significantly higher insulin resistance sensitivity (\(p<0.01\)). The increased blood glucose concentrations following insulin administration decreased over time with FGK treatment. In the FGK 250 group, high insulin resistance sensitivity was not evident even 30 min after insulin administration; at 120 min post-insulin administration, blood glucose concentrations were comparable to those of the ND group (\(p>0.05\)). The analysis of the area under the curve (AUC) showed an increase in the HFD group and FGK 50 group compared with the ND group (Figure 2C). Additionally, the FGK 250 groupexhibited significantly reduced AUC values compared with the HFD group (\(p<0.01\)). These results suggest that the administration of FGK is effective in regulating blood glucose and insulin resistance sensitivity in the context of high-fat diet-induced hyperglycemia, given that blood glucose concentrations in the FGK 250 group were comparable to those of the ND group.
## 4 RGB Scanning System  
By virtue of the high-speed image generation of the MEMS SLM, the time-multiplexing technique can be used not only for viewing zone enlargement but also for color image generation. The RGB lasers are combined with a viewing zone scanning holographic display to generate color 3D images [21].  
The viewing zone scanning system shown in Figure 2 is modified to be able to generate 3D color images. Holograms for R, G, and B colors are displayed sequentially by the MEMS SLM, and the MEMS SLM is illuminated by the R, G, and B lasers with the synchronization of the hologram pattern generation by the MEMS SLM. Then, as shown in Figure 8, the reduced viewing zones for the three colors are scanned simultaneously on the viewing zone. For the original viewing zone scanning system described in Section 2, the width of the reduced viewing zone depends on the wavelength of light. For the RGB scanning system, a rectangular aperture is used for the SSB filter, and its width is determined for blue light having the shortest wavelength among the three colors. Since the images of the SSB filter become the reduced viewing zones, the widths of the reduced viewing zones and the amount of overlap among the reduced viewing zones become identical for the three colors. The directions of the three laser lights illuminating the MEMS SLM should be appropriately adjusted considering the grating equation. In this case, due to the color of 3D images changing in the vertical direction, the vertical diffuser must be placed close to the galvano mirror, although the vertical diffuser was optional for the monochromatic display systems. The details of this system are described in Ref. [18].  
Figure 9 shows the produced color 3D images. The wavelength of the B, G, and R lasers were 488, 515, and 635 nm, respectively. The color profile conversion considering the three laser wavelengths was performed before the hologram calculation for the three colors in order to improve the color representation. The color 3D images were successfully produced. As the DMD can generate only binary images, the error diffusion technique modified for the hologram pattern generation was developed [25]. Although the color change in the vertical direction was mitigated by the vertical diff  
Figure 8: RGB viewing-zone scanning scheme to produce color 3D images.  
redder when they were viewed from the left side and more cyan when they were viewed from the right side.  
The generation of 3D color image using MEMS SLMs has been proposed by numerous research groups [15; 21; 26]. There are two techniques that can be used to generate 3D color images: the time-multiplexing technique using a single MEMS SLM and the space-multiplexing technique using three MEMS SLMs. The majority of works in the literature make use of the time-multiplexing technique because of the high frame rate of the DMDs.
## 3 Theoretical Foundations
### FPN and FPR  
Fuzzy Petri net (FPN) has been extensively applied in the domains of social safety and risk assessment [31], where researchers have utilized fuzzy Petri net for safety evaluations of long-distance oil pipelines [32], PPSs for petroleum industrial facilities in high-risk areas [33], chemical production processes [34], railway infrastructure [35], and air traffic management systems [36].  
Following the complex network-based modeling of PPS topology, it is essential to model both vulnerability generation and propagation during intrusion processes. In risk assessment, a conventional approach integrates fuzzy production rule (FPR) and fuzzy Petri net, where fuzzy production rule establishes causality for vulnerability triggeringand diffusion, while fuzzy Petri net quantifies propagation pathways and overall system risk--collectively termed fuzzy production rule-based fuzzy Petri net [31; 37].  
Within the risk assessment literature, definitions and representations of this fuzzy Petri net variant exhibit minor variations while maintaining consistent functional principles. The standard definition characterizes fuzzy production rule-based fuzzy Petri net as an eleven-tuple network, structured as follows [34]:  
1. \(\mathrm{P}=\{\mathrm{p}_{1},\mathrm{p}_{2},\ldots,\mathrm{p}_{n}\}\) is a finite nonempty set of places represented by circles;
2. \(\mathrm{T}=\{\mathrm{t}_{1},\mathrm{t}_{2},\ldots,\mathrm{t}_{n}\}\) is a finite nonempty set of transitions that are represented as rectangles;
3. \(\mathrm{D}=\{\mathrm{d}_{1},\mathrm{d}_{2},\ldots,\mathrm{d}_{n}\}\) is a finite nonempty set of propositions, where there is a one-to-one mapping between \(\mathrm{P}\) and \(\mathrm{D}\);
4. \(\mathrm{\beta}\): \(\mathrm{P}\rightarrow\mathrm{D}\) is an association function, a bijective mapping from places to propositions;
5. \(\mathrm{\alpha}\): \(\mathrm{P}\rightarrow[0,1]\) is a truth degree function that maps each place to a real value in \([0,1]\), i.e., \(\mathrm{\alpha}(\mathrm{p}_{\mathrm{i}})\) indicates the truth degree of proposition \(\mathrm{\beta}(\mathrm{pi})\), where \(\mathrm{\beta}(\mathrm{p}_{\mathrm{  
The fuzzy Petri net transformed from the OR rule is shown in Figure 3, where  
\[\alpha_{g}=\max\Bigl{(}\alpha_{1}\text{cf}_{g1}+\alpha_{2}\text{cf}_{g2}+\dots+ \alpha_{k}\text{cf}_{gk}\Bigr{)}. \tag{6}\]  
In the OR fuzzy Petri net model, a transition t can be fired when \(\alpha_{\text{m}}>\mu_{\text{m}}\), indicating that this fuzzy inference can occur.
# Anti-Fouling Properties of Phosphonium Ionic Liquid Coatings in the Marine Environment
## 2 Materials and Methods
### General Procedure
#### 2.2.7. Trioctyl(vinylbenzyl)phosphonium Docusate ([P\({}_{888\mathrm{Vb}}\)][AOT])  
Sodium docusate (2.39 mmol, 1.06 g) was dissolved in 10 mL of DCM. The [Na][AOT] solution was added dropwise to a separate round-bottom flask containing [P\({}_{888\mathrm{Vb}}\)][Cl] (2.39 mmol, 1.25 g) dissolved in 10 mL of DCM. The reaction mixture was stirred for 12 h at room temperature. The resulting suspension was then filtered through Celite. The filtrate was evaporated under reduced pressure and dried under high vacuum for 2 h to produce [P\({}_{888\mathrm{Vb}}\)][AOT] as a viscous pale-yellow liquid (1.5 g, 73.62%). **1H NMR** (400 MHz, CDCl\({}_{3}\)) & 7.43-7.25 (m, 4H, H\({}_{14}\), H\({}_{15}\), H\({}_{16}\), H\({}_{17}\)), 6.67 (ddd, J = 17.6, 11.0, 2.1 Hz, 1H, H\({}_{18}\)), 5.84-5.70 (m, 1H, H\({}_{19}\)), 5.33-5.22 (m, 1H, H\({}_{20}\)), 4.18 (dd, J = 11.4, 3.6 Hz, 1H), 4.08-3.84 (m, 5H, H\({}_{21}\), H\({}_{22}\), H\({}_{31}\)), 3.27 (dd, J = 17.5, 11.4 Hz, 1H, H\({}_{30}\)), 3.14 (dd, J = 17.5, 3.7 Hz, 1H, H\({}_{30}\)), 2.29-2.13 (m, 4H, H\({}_{1}\), H\({}_{5}\)), 1.99 (s, 1H, H\({}_{23}\)), 1.60 (q, J = 5.7 Hz, 1H, H\({}_{32}\)), 1.56-1.19 (m, 18H, H\({}_{2}\), H\({}_{3}\)), H\({}_{6}\), H\({}_{7}\), H\({}_{10}\), H\({}_{11}\), H\({}_{26}\), H\({}_{27}\), H\({}_{28}\), H\({}_{24}\), H\({}_{35}\), H\({}_{36}\), H\({}_{37}\)), 1.25 (s, 7H, H\({}_{24}\), H\({}_{35}\), H\({}_{36}\), H\({}_{37}\)), 0.96-0.76 (m, 17H, H\({}_{4}\), H\({}_{8}\), H\({}_{12}\), H\({}_{25}\), H\({}_{29}\), H\({}_{34}\), H\({}_{38}\)). **13C NMR** (101 MHz, CDCl\({}_{3}\)) & 171.76, C\({}_{12}\) 169.16, C\({}_{8}\) 135.80 C\({}_{61}\), C\({}_{64}\), 130.36, 130.30, 129.63, 129.34, 129.29, 127.83, 127.17, 127.13, 126.09, 115.36, 114.88 C\({}_{65}\), 67.57, 67.49, 66.89, 61.97, 53.40, 38.66, 38.64, 38.52, 38.46, 34.36, 31.66, 30.84, 30.70, 30.29, 30.25, 30.07, 30.01, 28.91, 28.87, 28.81, 27.01, 26.85, 26.56, 23.62, 23.38, 22.96, 22.93, 22.55, 21.80, 21.75, 18.85, 18.39, 14.05, 14.01, 10.92, 10.87, 10.76, 10.73.  
**FT-IR: v/cm\({}^{-1}\)=** 2926 (s,sh), 1630 (w,sh), 1512 (w,sh), 1732 (s,sh), 1325 (w,sh) 1155 (s,sh), 856 (s,sh), 989 (s,sh), 906 (s,sh). **MS** (+ESI) (Acetonitrile): m/z 487.4426 [C\({}_{33}\)H\({}_{60}\)P] calc. 487.4433. (\(-\)ESI) (Acetonitrile): m/z 421.2263 [C\({}_{20}\)H\({}_{37}\)O\({}_{7}\)S] calc. 421.2260
## 2 OBS Networks
### Functional Mechanisms of OBS Nodes
#### 2.2.3 Routing  
This is the process of selecting the paths along which to send the network traffic. Thus, it is also considered a crucial functionality for network performance. In OBS networks, a routing process is used to decide the path of a burst through the core network and this can be accomplished in three different ways: on a hop-by-hop basis, based on the generalized multiprotocol label switching (GMPLS), or using explicit pre-calculated setup. When routing is done on a hop-by-hop basis, like in the IP networks, a fast table look-up algorithm must be used to determine the next hop. In the second approach, routing is done by deploying GMPLS based routing protocols to compute explicit or constraint-based routes at OBS edge nodes as described in [25]. The third approach is to use explicit pre-planned setup connections, which can be established via constraint-based route label distribution protocol (CR-LDP) or resource reservation protocol with traffic engineering (RSVP-TE). Explicit routing is very useful in a constraint based routed OBS network where the traffic routes have to meet certain QoS metrics such as delay, hop count, bit error rate, or bandwidth [26].