esa-sceva/satcom-chunk-collection huggingface dataset:
# The Potential of _Phaeodactylum_ as a Natural Source of Antioxidants for Fish Oil Stabilization
## 3 Results and Discussion  
### Phaeodactylum Biomass Addition (Part I)  
#### 3.1.1 Primary Oxidation  
Figure 2 shows the primary oxidation results of the fish oil with and without _Phaeodactylum_ biomass addition. Both systems show a significant increase towards high \(\Delta\)PV values, with the highest ones being observed after 14 days of storage, as follows: 250 (\(\pm\)37) and 397 (\(\pm\)8) med hydroperoxides/kg lipids with and without _Phaeodactylum_ biomass addition, respectively. The order of magnitude of these values was comparable to former research investigating primary oxidation during the storage of bulk fish oil [16; 40]. After 21 days, a decrease could be observed. This characteristic pattern of an increase followed by a decrease in primary oxidation products indicates strong lipid oxidation [5], and this is for fish oil as well as for fish oil with a 2.5% _Phaeodactylum_ biomass addition. Although _Phaeodactylum_ biomass addition gave rise to lower \(\Delta\)PV values for most of the time points (between 8 and 161 mega hydroperoxides/kg lipids lower), it did not inhibit the lipid oxidation given the increase and decrease in primary oxidation products at the same time points. A possible reason for the lower \(\Delta\)PV values (despite the increase and decrease at similar time points) could be found in the polarity increase in hydroperoxidized lipids. As described by [PERSON] et al. [41], who investigated molecular organization in hydroperoxidized lipid bilayers, the \(-\)OOH moiety is preferentially located towards the more polar regions. Given the sampling method of the 'Fish oil + _Phaeodactylum_ biomass' system in which the oil phase was separated from the solid _Phaeodactylum_ parts by centrifugation prior to PV determination, this could have caused an underestimation of hydroperoxides by an accumulation away from the apolar bulk oil phase (towards the _Phaeodactylum_ solid parts).  
Figure 2: Primary oxidation of fish oil with (white symbols) and without (black symbols) 2.5% _Phaeodactylum_ biomass addition during 21 days of storage at 37 \({}^{\circ}\)C. The peroxide values (mean \(\pm\) sd, \(n=2\)) are expressed as delta values, representing the relative increase or decrease compared to day zero. Information on statistical significance can be found in Supplementary Materials (Table S1).
# Parameter Optimization Method for Metal Surface pBRDF Model Based on Improved Strawberry Algorithm
## 3 Improved Strawberry Optimization Algorithm
### Improved Algorithm
#### 3.2.2 Update with Levy Flight  
By analyzing the root and runner position update process of the original strawberry algorithm, it can be seen that the root and runner are usually updated by random perturbation, and the distances from the mother plant are fixed, which makes the optimal solution easily fall into local optima. In this paper, the Levy flight strategy was used to update the position, which can make some individuals spread to a wider search space.  
The Levy flight strategy is a random search method proposed by mathematician [PERSON], according to the phenomenon that the trajectory of many biological activities in nature has long periods of random walks with small steps and occasionally jumps with larger steps [23]. The characteristic of the [PERSON] flight, which alternates between long and short distances, plays an important role in balancing the local exploration and global search capabilities of optimization algorithms, where smaller random walks are beneficial to conduct local exploration, and the occasional larger jump is beneficial to break out of local optima and improve global search capabilities. Therefore, the introduction of the Levy flight strategy is expected to compensate for the shortcomings of the strawberry algorithm in falling into local optima and reduce the number of iterations.  
Figure 5 shows the simulation comparison between random walking and the [PERSON] flight path trajectory on a two-dimensional plane with both paths set to 200 steps.  
Figure 4: Initial population generated by chaotic mapping.  
Figure 5: Comparison of [PERSON] flight and random walk.  
From Figure 5, it can be seen that the Levy flight has a wider search range and stronger search ability compared to the normal distribution. In this paper, the random step length \(Levy(\beta)\) was generated by the Mantegna algorithm and represented as [24]:  
\[Levy(\beta)=\frac{u}{|v|^{\frac{1}{p}}} \tag{14}\]  
where the value \(\beta\) is in the range of [0, 2] and usually taken as 1.5; \(u\) and \(v\) are random variables obeying a normal distribution and can be represented as follows:  
\[\left\{\begin{array}{ll}u\,\sim N\big{(}0,\sigma_{u}^{2}\big{)},&\sigma_{u} =[\frac{\Gamma(1+\beta)\sin\left(\frac{\pi\beta}{2}\right)}{\beta\Gamma(1+ \frac{\beta}{2})2^{\frac{p-1}{2}}}]^{\frac{1}{p}}\\ v\,\sim N\big{(}0,\sigma_{v}^{2}\big{)},&\sigma_{v}=1\end{array}\right. \tag{15}\]  
where \(\Gamma(\cdot)\) is a Gamma function.  
In order to expand the location search range of the strawberry algorithm, the runner position update formula of the long-distance search is modified in this paper as follows:  
\[X_{runner}(i)=X(i)+d_{runner}r_{2}+\alpha\cdot Levy(\beta) \tag{16}\]  
where \(\alpha\) usually takes an empirical value of 0.01.
## 3 Main Simplifying Hypotheses Assumed in the Optimization Tool
### Calculation of the Thicknesses of the Dielectric Layers  
Once the optimized variables of the system are derived, the whole cable geometry can be determined. This corresponds to the inner and outer radii of each layer composing the coaxial design of the cable. This process is performed sequentially from the innermost layer's radius, which corresponds to the inner radius of the former \(R_{f}\), and requires knowing the thicknesses of the various layers, as detailed in Section 2.1. These thicknesses  
Figure 4: Trend of the normalized active power transferred by the line over a year adopted in this work. The yellow area highlights the overestimation of energy compared with the assumption that the cable operates at its full capacity over the whole year.  
are set by the user for all layers (numerical examples will be given in Section 7.2), except for those referring to the insulating layers.  
After determining the outer radii of the superconducting layers directly beneath each of the three insulating layers in the cable (hereinafter generalized as the \(m\)th insulating layer), these values also correspond to the inner radius of the \(m\)th layer \(R_{int_{ins\ m}}\). As for their thicknesses, these have to be sized to withstand two distinct electrical stress conditions which might occur during cable operations: the AC nominal voltage and the lightning impulse voltage [51; 52; 53]. For each scenario, a corresponding insulation thickness is required:  
\[t_{ins_{AC\ m}}=R_{int_{ins\ m}}\left[exp\left(\frac{k_{1_{AC}}\;k_{2_{AC}}\;k_ {3_{AC}}\;V_{AC}}{R_{int_{ins\ m}}\;E_{AC}}\right)-1\right] \tag{11}\]  
\[t_{ins_{im\ m}}=R_{int_{ins\ m}}\left[exp\left(\frac{k_{1_{m}}\;k_{2_{im}}\;k_ {3_{im}}\;V_{im}}{R_{int_{ins\ m}}\;E_{im}}\right)-1\right] \tag{12}\]  
where \(k_{1_{AC}}\) and \(k_{1_{im}}\) are the corrective aging coefficients which compensate for the degradation of the dielectric properties under electric stress. \(k_{2_{AC}}\) and \(k_{2_{im}}\) are the corrective temperature coefficients taken into account when considering potential insulation failures due to heat aging. \(k_{3_{AC}}\) and \(k_{3_{im}}\) are the correction safety coefficients, which can be used as design safety margins. \(V_{im}\) is the lightning impulse target's withstanding voltage, and \(V_{AC}\) is the AC rated voltage, usually calculated as \(2.5\big{(}\sqrt{2}V_{rms}\big{)}/\sqrt{3}\) according to IEC 62067 [51; 54]. \(E_{AC}\) and \(E_{im}\) are the breakdown voltages for both scenarios. These parameters depend on the insulating material selected and can be found in the literature.  
Then, to comply with both scenarios, the thickness of the \(m\)th insulating layer is taken to be equal to the maximum value calculated in the two conditions, or  
\[t_{insm}=max\big{(}t_{ins_{AC\ m}},\;t_{ins_{im\ m}}\big{)} \tag{13}\]
# New Nonlinear Second-Order Phase-Locked Loop with Adaptive Bandwidth Regulation
## 3 Simulation and Results
The frequency difference, namely \(\Delta f\), is set to be 6 kHz, 6.2 kHz, 6.3 kHz, and 6.4 kHz, respectively for NPLL\({}_{1}\). The corresponding frequency of NPLL\({}_{2}\) is 18.7 kHz, 18.8 kHz, 18.9 kHz, and 19 kHz, respectively. Finally, the maximum acquisition bandwidths of NPLL\({}_{1}\) and NPLL\({}_{2}\) are compared with those of the traditional PLL. The simulation results are shown in Figure 11.  
Figure 11: Phase error of PLL under different frequency offsets. Figure 10: Phase error of PLL under a frequency offset of 4 kHz: (**a**) Phase error of PLL under a frequency offset of 4 kHz; (**b**) Phase error of PLL after 2.95 ms.  
As the input-output frequency offset increases from 4 kHz to 6 kHz and then to 6.2 kHz, NPLL\({}_{1}\) still can be locked successfully (arrow line of NPLL\({}_{1}\) in Figure 11). However, a cycle-skipping phenomenon is developed when the input-output frequency offset increases to 6.3 kHz and above. The acquisition bandwidth of NPLL\({}_{1}\) is increased to 6.2 kHz from 4 kHz of the traditional PLL. When the input-output frequency offset is increased to 18.8 kHz, NPLL\({}_{2}\) still can be locked successfully (arrow line of NPLL\({}_{2}\) in Figure 11). However, a cycle-skipping phenomenon is developed when the input-output frequency offset equals 18.9 kHz and above. The acquisition bandwidth of NPLL\({}_{2}\) is increased from 4 kHz to 18.8 kHz.
# A Proof-of-Multiple-State Consensus Mechanism for Mobile Nodes in Internet of Vehicles
## 6 Simulation Results
the PoET consensus time overhead has nothing to do with the vehicle speed itself, and the reason for its time increase lies more in the increased difficulty of establishing communication  
Figure 8: Single hop time with different occupancy of malicious nodes. Figure 7: Variation of throughput with different occupancy of malicious nodes: n = 15 in (**a**) and n = 25 in (**b**). connections at high speeds; \(SF_{i}\) Compared with [29], the consensus proposed in this paper increases the weight of the speed parameter in the state factor, and distinguishes between same-direction and opposite-direction vehicles. \(SF_{i}>SF_{j}\), whereupon the miner nodes are able to complete the construction of forward transmission links more easily, reducing the time spent on each message hop. Figure 10 shows the ability of other normal nodes to successfully participate in consensus when there are different percentages of malicious nodes in the network. A specific reputation mechanism in the literature [30] ensures that honest nodes can be effectively verified to participate in the consensus when malicious nodes exist in the system; The algorithm proposed in this article is able to receive fewer blocks of valid votes as the number of malicious nodes increases, and the number of nodes participating in the consensus gradually decreases. However, with the function of RSU to record the malicious behavior of nodes, the algorithm can still allow the remaining nodes to successfully complete consensus if half of the nodes are malicious nodes and the network is able to resist the attack of malicious nodes. In summary, the experimental tests conducted in this article demonstrate that the proposed consensus algorithm has less time overhead and better transmission rate compared to the selected consensus. This is due to the algorithm optimizing the computational difficulty  
Figure 10: Number of nodes involved in mining. Figure 9: Effect of vehicle speed on single hop time. required for nodes to obtain accounting rights, and dynamically adjusting the weights of different nodes based on multiple state factors (\(P(diff_{j})\), \(P(SINR_{i})\) and \(P(Com_{i})\)). Nodes with excellent communication conditions in the network are more likely to become relays, and the voting verification mechanism of nodes limits the harm that malicious nodes can cause to system security.
## 2 Problem Description and Methodology
### MEO Satellite Failure and Staged Deployment  
#### 2.2.1 Satellite Failure Model  
The availability of a constellation, also referred to as constellation value (CV), quantifies the navigation performance of various constellation configurations in the event of satellite failure and indicates the constellation's availability under specific thresholds. The formula for calculating the \(CV\) is as follows:  
\[CV=\frac{\sum\limits_{t=t_{0}}^{t_{0}+\Delta T}\sum\limits_{j=1}^{L}bool\big{(} PDOP_{t,j}\leq Th_{DOP}\big{)}\times aera_{j}}{\Delta T\times\sum\limits_{j=1}^{L} aera_{j}} \tag{9}\]  
The initial time of simulation, denoted as \(t_{0}\), and the total duration of simulation, denoted as \(\Delta t\), are defined. Additionally, \(PDOP_{t,j}\) represents the PDOP of the \(j\) grid at time \(t\); \(Th_{DOP}\) is the precision factor threshold; \(bool\) is a Boolean function; \(L\) stands for the total number of grid points; and \(area_{j}\) denotes the area of the \(j\) grid point. The area of \(5^{\circ}\times 5^{\circ}\) is the grid of points.  
Single-satellite failures, double-satellite failures, and three-satellite failures refer to the number of failures of satellites in the MEO navigation constellation. The failure modes of single or double satellites are limited and can be simulated using an enumeration method.  
Figure 2: Constellation optimization design model based on NSGA-II algorithm.  
Conversely, three satellites or more satellites present a wider range of failure modes, necessitating the use of the Monte Carlo method to effectively address random probability events. While the Monte Carlo method can approach the optimal solution with increased sampling times, it also results in significantly higher computational energy consumption. The likelihood of failure for three satellites or more MEO navigation satellites is low. This paper specifically examines the impact of three-satellite failure on constellation performance, and its model is applicable to analyzing failures involving four or more satellites.  
The three-satellite failure flow chart based on the Monte Carlo method is depicted in Figure 3. It is assumed that the failure probability of the MEO navigation constellation is uniform, with \(N\) satellites in the constellation, \(x\) of which fail, resulting in \(C_{N}^{x}\) combinations of failed satellites and \(N-x\) satellites providing navigation services. Firstly, we establish the input-output relationship and simplify its mathematical form to express the relationship between \(x\) and \(Y=f(x_{1},x_{2}\ldots x_{N})\), with \(Y\) representing the CV. Secondly, we determine the variable's value range based on \(x_{p}(x_{p}(1),x_{p}(2),\ldots)\in(1,N/P)\), which represents the total number of constellations without repetition. Finally, considering practical scenarios, we determine simulation times and analyze experimental data.
# Framework for Developing Bio-Inspired Morphologies for Walking Robots
## 4 Phase: Design
### Adjustment to the Design  
Robot morphology followed the biological model's morphology (Section 3.4), except in four respects: (I) a _difference in size_, (II) an _offset of front legs' origin point_ of 20.2 mm in the X-direction and 36.0 mm in the Z-direction (Table 1), (III) _merging the CT and TF joints_ into the CTF joints, and (IV) a _simplification of the foot design_ (end-effector).  
The robot design was developed according to the features of interest (Section 3.2), and therefore did not aim to reproduce the biological model's size. The technical challenges in downsizing all mechanical components, actuators, controllers and batteries would be too daunting and infeasible for the investigation. Instead, the robot was developed with a scaling factor that enlarges the _biological model_ sufficiently to house all components and permit the use of industrial components: off-the-shelf motors, screws, bolts, cables, batteries and controllers. Among these, motors have the largest influence on the robot's size, shape and dynamics, and are typically selected first based on torque density, efficiency, mechanical robustness, integrated sensors and compact size [22]. The functional model showed that it was possible to fit all components compactly into a 22:1 scale model of _S. galenius_'s and recreate the full biological movement range.  
The only dimensions where the robot morphology deviates from the enlarged biological model are the x and z-value of the front legs' origin points (Figure 5 and Table 1), and merged CTF joints (Section 3.4). The origin points of the robot's front leg are positioned higher than the biological model, as the beetle's front legs have a different morphology to its middle and hind legs. The coxa of the _S. galenius_'s front legs are considerably larger than the ones of the middle and hind legs, which positions the front legs' CTF joint further from the TC joint (Figure 4). Therefore, the TC and CTF joint axes of _S. galenius_'s front legs do not intersect but are shifted instead. However, the selected components (Table 2) could not feasibly recreate the shifted joint axes in the functional model or prototype. The beetle's foot has, among other structures, the tarsus and tibial spine that provides traction. An engineering study of these mechanisms is postponed. Instead, a round rubber foot was chosen as a simplified design with good traction characteristics.  
Hence, the (I) scaling factor of 22:1, (II) moved x and z-value of the front legs' origin points, (III) merged CTF joints, and (IV) simplified foot design are considered acceptable adjustments for the prototype when considering the scope of the study, mobility and compact design.
## 3 Material and Methodology
### Methodology
#### 3.2.3 Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)  
TOPSIS approach is presented by [PERSON] and [PERSON] [38]. The main concept of TOPSIS is that optimal alternatives must have the shortest geometric distance from the PIS and NIS [39].  
_Step 1_: Determine the normalized decision matrix, and raw values (\(x_{ij}\)) are converted to normalized values (\(n_{ij}\)) by:  
\[h_{cd}=\frac{y_{cd}}{\sqrt{\sum\limits_{c}^{g}y_{ab}^{2}}},\ c=1,\ldots g;d=1, \ldots h. \tag{15}\]  
_Step 2_: Calculate the weight normalized value (\(v_{ij}\)), by:  
\[l_{cd}=P_{cd}h_{cd},\ c=1,\ldots,g;d=1,\ldots,h. \tag{16}\]  
where \(Pj\) is the weight of the \(c\)th criterion and \(\sum\limits_{c=1}^{h}p_{p}=1\).  
_Step 3_: Calculate the PIS (\(F^{+}\)) and PIS (\(F^{-}\)), where \(l_{c}^{+}\) indicate the maximum values of \(l_{cd}\) and \(l_{c}^{-}\) indicates the minimum value \(l_{cd}\).  
\[F^{+}=\{l_{1}^{+},\ldots,l_{h}^{+}\}=\{(\max\limits_{d}l_{cd}|c\in C),(\min \limits_{d}l_{cd}|d\in D)\}, \tag{17}\]  
\[F^{-}=\{l_{1}^{-},\ldots,l_{h}^{-}\}=\{(\min\limits_{d}l_{cd}|c\in C),(\max \limits_{d}l_{cd}|d\in D)\}, \tag{18}\]  
where \(A\) is related to profit criteria, and \(F\) is related to cost criteria.  
_Step 4_: Determine a distance of the PIS (\(Q_{c}^{+}\)) separately by:  
\[Q_{c}^{+}=\left\{\sum\limits_{d=1}^{h}\left(l_{cd}-l_{d}^{+}\right)^{2}\right\} ^{\frac{1}{2}},\ c=1,\ \ldots,\ g \tag{19}\]  
Similarly, the separation from the NIS (\(Q_{c}^{-}\)) is given as:  
\[Q_{c}^{-}=\left\{\sum\limits_{d=1}^{h}\left(l_{cd}-l_{d}^{-}\right)^{2}\right\} ^{\frac{1}{2}},\ c=1,\ \ldots,\ g \tag{20}\]  
_Step 5_: Determine the relationship proximal to the problem-solving approaches, proximal relationship from option \(F_{c}\) to option \(F^{+}\)  
\[C_{c}=\frac{Q_{c}^{-}}{Q_{c}^{+}+Q_{c}^{-}},\ c=1,\ldots,g. \tag{21}\]  
_Step 6_: Rank alternatives to determine the best option with the maximum value of \(C_{c}\)
# Global Power Dynamics in the Contemporary Space System
## 4 System Actors--Space Powers  
Nation-states have historically dominated the space sector since the inception of space-related activities. Traditionally, activities in outer space have been associated with strategic issues for a country, such as security, defense, communication, and research. The cost to other non-state actors to enter the space industry has been exceptionally high.  
Despite the gradual emergence of private actors in recent decades, nation-states continue to yield significant influence in the 21 st-century space system. While the private sector has assumed a more prominent role in the emerging space industry, these entities remain dependent on governments for financial support through public contracts and legal obligations under the Outer Space Treaty. A similar dynamic is observed among other significant stakeholders in the space industry, such as universities, research laboratories, and think tanks, which continue to rely heavily on governmental support. Consequently, given the persistent primacy of nation-states in the realms of international relations and space exploration, this research initiative will concentrate on the distribution of power among them.
###### Abstract
However, it is noted that in these studies, the properties of the particles used cannot cover the entire range of typical debris formed in CDAs of SFRs (e.g., a size range from submillimeter to several millimeters), and the particle-fluid interactions are not clearly observable due to the limitations of the experimental setup. Therefore, a series of experimental and modeling studies were performed by focusing on the DBF flow-regime characteristics [24; 66; 67; 68; 69; 70; 71; 72]. It was confirmed that due to the different inter-particle and particle-fluid interactions during different situations, four types of flow regime, including particle-suspension regime (Regime I), pool-convection dominant regime (Regime II), transitional regime (Regime III), and particle-inertia dominant regime (Regime IV), were found to form, leading to the formation of a flat bed, concave bed, trapezoid bed, and convex bed, respectively. Based on the different formation mechanisms of these flow regimes, an empirical model was developed to predict the DBF flow regime transitions, and its predictability was further extended owing to the knowledge accumulated from the experiments under more realistic conditions (e.g., mixed-sized particles and sodium boiling situations). Figure 2: Summary of experimental and modeling investigations on DBF behavior focusing on overall accumulated-bed characteristics [23; 28; 29; 30; 41; 61; 62; 63; 64; 65; 73] and flow-regime characteristics [24; 67; 68; 69; 70; 71; 72]. Considering the great importance of understanding DBF behavior to improve the safety evaluations of SFRs, a systematic review and critical discussion are performed in this paper based on experimental and modeling studies of DBF characteristics and mechanisms in the context of SFRs. The knowledge accumulated from valuable experimental evidence and reliable modeling predictions is significant to computer model improvement and verification and the optimization of the structural designs of the core catcher for SFRs, thereby promoting the development and application process of SFRs. The objective of this paper is to provide a comprehensive understanding of DBF characteristics and mechanisms in CDAs of SFRs through the systematic and critical review of previous experimental and modeling investigations. In Section 2, investigations focusing on overall accumulated-bed characteristics are introduced and summarized; while in Section 3, the investigation focuses on flow regime characteristics, which are described and discussed. Moreover, for the purpose of providing useful guidance and serving as a reference for further investigations regarding DBF behavior, a conclusion and a discussion of future prospects are described in Sections 4 and 5, respectively.