esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes
## 2 Trust Metric Model of Unmanned Aerial Vehicle Cluster Nodes Based on Hierarchical Blockchain
### A Dynamic Evaluation Method for Drone Node Trust Based on Task Perception
#### 2.2.4 Trust Fusion Algorithm  
After the UAV cluster head node receives the trust degree evaluation list sent by the common node, it needs to fuse the trust degree evaluation of multiple UAV nodes to the same UAV node into a comprehensive trust degree. This fusion process can be calculated using the following message summary formula based on Bayesian inference, which can more accurately calculate the result of fusion trust. The specific calculation formula can be expressed as  
\[P\big{(}e_{j}\mid C_{j}\big{)}=\frac{P\big{(}e_{j}\big{)}\times\prod_{k=1}^{q}p \Big{(}c_{k}^{j}\mid e_{j}\Big{)}}{P\big{(}e_{j}\big{)}\times\prod_{k=1}^{q}p \Big{(}c_{k}^{j}\mid e_{j}\Big{)}+P\big{(}\xi_{j}\big{)}\times\prod_{k=1}^{q}p \Big{(}c_{k}^{j}\mid e_{j}\Big{)}}. \tag{6}\]  
The cluster head node stores the data in \(C\) after receiving all of the credibility lists, such as \(C=\big{\{}c_{1},c_{2},c_{3},\ldots,c_{j}\big{\}}\). According to the formula, \(P\big{(}e_{j}\big{)}\) is the prior probability of event \(e_{j}\) occurring. \(P\big{(}\
eg e_{j}\big{)}\) is the prior probability that event \(e_{j}\) does not occur (that is, the prior probability of the complement of event \(e_{j}\)). \(P\Big{(}c_{j}^{k}\mid e_{j}\Big{)}\) is the conditional probability that category \(c_{j}^{k}\) occurs under the condition that event \(e_{j}\) occurs. \(P\Big{(}c_{j}^{k}\mid\
eg e_{j}\Big{)}\) is the conditional probability of category \(c_{j}^{k}\) occurring under the condition that event \(e_{j}\) does not occur. \(q\) is the number of nodes. We can define the event \(e_{j}\) as the event that the UAV node is evaluated as credible, and the category \(c_{j}^{k}\) represents the corresponding comprehensive trust evaluation. Given a list of trust evaluations, we can calculate \(P\big{(}e_{j}\big{)}\) and \(P\big{(}\
eg e_{j}\big{)}\) for each drone node. At the same time, for each evaluation index \(c_{j}^{k}\), we can calculate \(P\Big{(}c_{j}^{k}\mid e_{j}\Big{)}\) and \(P\Big{(}c_{j}^{k}\mid\
eg e_{j}\Big{)}\), and substituting these values into the formula, we can calculate that each UAV node is evaluated is the credible posterior probability \(P\Big{(}c_{j}^{k}\mid e_{j}\Big{)}\). Then, according to the calculated posterior probability, we can adopt a weighting strategy to fuse the trust evaluations of multiple nodes to obtain a comprehensive trust evaluation for the same UAV node.
## 4 System Design and Analysis
### Synergetic Software-defined Sampling and Detection Approach
#### 4.1.1 Synergetic Sampling  
The sampling probability of the switch that a flow passes through can be expressed as:  
\[P(S_{i})=e^{\frac{-\Gamma}{S_{i}}},S_{i}\geq 0 \tag{1}\]  
where \(\Gamma\) is the average of the statistics of each switch, \(S_{i}\) is the sampling statistics of each switch for this flow. \(S_{i}\) is related to sampling rate \(v\) and all flow number \(m\). For any switch, if preset flow statistics threshold is \(\gamma\). the probability that the statistical result is greater than the threshold is The sampling probability of the switch that a flow passes through can be expressed as:  
\[P[S_{i}\leq\gamma]=1-e^{\frac{-\Gamma}{S_{i}}} \tag{2}\]  
The joint probability that all switch statistics are less than the threshold is  
\[P[S_{1,2,\ldots,n}<\gamma]=(1-e^{\frac{-\Gamma}{S_{i}}})^{n} \tag{3}\]  
where \(n\) is the number of switches that deploy synergetic software-defined sampling approach. We are concerned about whether the long flow can be detected, so the probability that at least one switch detected the long flow is  
\[P[S\geq\gamma]=1-(1-e^{\frac{-\Gamma}{S_{i}}})^{n} \tag{4}\]  
If the network has already been running ECMP, we can use the path as the basic unit instead of the switch to consider the relevant probability.
# Tensegrity Applications to Architecture, Engineering and Robotics: A Review
## 3 Space Applications
### Antennas  
One of the first contributions to the deployable aerospace structures was made by [PERSON] in his thesis [66]. In this work, he proved that it is possible to build deployable structures using a kinematic platform geometry. The tensegrity prism used for this purpose was a 6-6 (hexagonal antiprism), and the approach to unfolding was to manipulate the legs connecting the hub to the antenna to create a tensegrity structure (Figure 20a). He proposed an improvement for the 6-6 design, resulting in the structure shown in Figure 20b. In 2001, [PERSON] et al. patented the concept previously studied [67]. It describes a tensegrity antenna support architecture driven by screw movements. The deployment of the antenna should be performed by controlling the adjustable energy focus range.  
The next idea of a tensegrity-based antenna was given by [PERSON] and [PERSON] [68]. In this paper, they developed a reflector for an antenna made of three parts: a deployable ring structure, two identical cable nets connected by tension ties, and the reflecting mesh, attached to the front net. They also demonstrated that the structure can be easily folded into a compact configuration. This design was used and improved by [PERSON] [62], resulting in a feasible antenna (Figure 20c) for a future small-satellite mission that approximately meets the requirements on the stored dimensions and the mass goal.  
In 2013, ESA promoted a project for large antenna reflectors, developed by Kayser Italia, University of Rome TorVergata and KTH (Stockholm, Sweden) [69]. They proposed a deployable tensegrity ring for a small satellite deployable structure (Figure 21). One of the main conclusions was the reliability of the prototype by means of a drastic reduction in the number of articulated joints in comparison with non-tensegrity architectures. It was finally the object of a patent granted in 2016 [70].  
Figure 20: (**a**) Creation of 6T-Prism Structure Rotation from a hexagonal prism [66]. (**b**) The original proposal for the deployable tensegrity antenna [66]. (**c**) Prototype for the deployment of the antenna [62] (Image courtesy of [PERSON]).
## 3 Results and Discussion
### Total Phenolic Content  
The health benefits of garlic are attributed to its bioactive components, among which phenolics are particularly notable. These compounds possess antioxidant activities and are believed to play a role in preventing various degenerative illnesses such as cardiovascular diseases and cancer [43; 44]. The numerous benefits of phenolic compounds highlight the importance of their preservation in natural food products to enhance consumer health and prevent chronic diseases. However, it has been demonstrated that processing techniques can influence the content of antioxidants in garlic. According to a literature review, raw garlic typically exhibits higher antioxidant activity compared to cooked garlic, while fermented varieties like black garlic demonstrate even greater antioxidant potential than its unprocessed form [37; 45].  
In this study, the impact of the drying process on TPC expressed as gallic acid equivalents (mg GAE per 100 g DW) was assessed using the Folin-Ciocalteu assay. The TPC in raw garlic was 148.7 \(\pm\) 16.4 mg GAE/100 g DW (Figure 5).  
Dehydration of garlic slices through hot air drying affected the level of phenolic compounds they contained (Figure 5). The significance of this effect was confirmed by the analysis of variance (MS = 1320.8, \(p\) = 0.0073). Similar findings on the heat sensitivity of phenolic compounds in various vegetables and fruits subjected to drying have been reported by other researchers [46; 47; 48]. Further analysis using the Tukey HSD test indicated that significant differences in TPC levels occurred only between garlic dried at 50 \({}^{\circ}\)C and the raw samples. Garlic dried at 70 and 90 \({}^{\circ}\)C exhibited slightly higher TPC than that preserved at 50 \({}^{\circ}\)C, though the levels remained lower than those in raw samples. Greater antioxidant retention in garlic dried at higher temperatures could be attributed to shorter drying times at higher temperatures, while longer exposure to thermal conditions at lower temperatures could lead to greater loss of phenolic compounds. These findings are in agreement with those observed in studies by [PERSON] (2016) and others [49; 50; 51; 52]. It is also possible that higher processing temperatures facilitate the disruption of cell walls and the release of phenolic compounds from cellular constituents [49; 53].
## 4 Parametric Analysis and Parametric-Sensitivity Analysis
### Influence of Shear Modulus (\(G_{2}\))  
The shear modulus, \(G_{2}\), is increased by 20%, 40% and 60% and decreased by 20%, 40% and 60%, while other parameters are kept constant. Figure 5 shows the settlements at the centre of the rectangle with time for different \(G_{2}\). The analysis of viscoelastic solutions in the previous section shows that \(G_{2}\) does not affect the instantaneous settlement but affects the ultimate settlement, and the larger \(G_{2}\) is, the smaller the ultimate settlement value. Moreover, Figure 5 shows that in the creep deceleration stage of \(0<t<+\infty\), the settlement values and the amplitudes of the change in the settlement also decrease with the increase of \(G_{2}\). When \(G_{2}\) increases from 24 to 96 MP, the ultimate settlements are 0.0475, 0.0379, 0.0331, 0.0302, 0.0282, 0.0268 and 0.0257 m, with corresponding amplitudes of the change in the settlement of 31.79%, 15.89%, 9.60%, 6.62%, 4.64% and 3.64%.  
Figure 4: Creep curves under different \(G_{1}\).  
Figure 5: Creep curves under different \(G_{2}\).
## 3 Results and Discussion
### Comparative Analysis  
No study is found in the literature that calculates heating and cooling degree days taking into account solar radiation impact, considers all directions, including intermediate directions (south, north, west, east, southeast, northeast, northwest, and southwest), and examines three different building usage scenarios (cooling-only, heating-only, and both cooling and heating). However, a study by [PERSON] et al. (2016) is found to be useful for the comparative analysis. They have taken into account solar radiation, but they have only examined the heating season and conducted analyses for only the main directions (south, north, west, east). They carried out analyses for Istanbul, which is different from the province examined in this study but is located in the same degree-day region. The results related to the jointly reviewed parameters are presented in Table 4. It can be observed that the results are close to each other. When examined directionally, for example, the ordering of insulation thicknesses is determined as follows in both studies: \(\rm x_{north}>x_{east}\geq x_{west}>x_{south}\).
## 6 Simulation and Experiment  
### Simulation  
Simulations of several different algorithms are conducted to prove the theoretical results proposed in this paper, including: conventional LOS (\(LOS\)), 3 DOFALOS (\(ALOS\)), proportional LOS (\(PLOS\)) and 3 DOFAPLOS (\(APLOS\)). The results and analysis are discussed as follows. Note that the predetermined range of look-ahead distance of \(ALOS\) and \(APLOS\) employs 8-15 m in the simulation. The look-ahead distance of \(LOS\) and \(PLOS\) employs 15 m in the simulation. The optimal range used for simulation of different AUV models varies slightly, depending on the dynamics of different models, etc.
# Electrospinning-Based Carbon Nanofibers for Energy and Sensor Applications
## 4 Application of Carbon Nanofibers  
### Energy Storage Applications  
Carbon materials have been continuously developed as electrode materials for energy storage applications [84, 85, 86, 87]. There are many types of energy storage using carbon fiber electrodes including supercapacitors, batteries, fuel cells, dye-sensitized solar cells (DSSC), and catalysts [87, 89].
# Integrated Silicon Photonics for Enabling Next-Generation Space Systems
## 4 Space Radiation Effects
### Mach-Zehnder Modulators
#### 4.3.2 MZM: Displacement Damage Effects  
Two prevalent studies that bombarded an MZM with 20-MeV neutrons up to \(1.2\times 10^{15}\)[84] and 3 \(\times\) 10\({}^{16}\) neutrons/cm\({}^{2}\)[63] confirm that the modulator shows no change in modulation efficiency and bias control. Radiation tolerance to neutrons is attributed to the high doping levels used in the modulator. The modulators for these studies have doping levels on the order of \(1\times 10^{17}\) to \(1\times 10^{18}\) cm\({}^{-3}\)[86]. Assuming that the defect density caused by the neutrons is on the same order of magnitude as the neutron fluence, then 3 \(\times\) 10\({}^{16}\) neutrons/cm\({}^{2}\) fluence would result in roughly 3 \(\times\) 10\({}^{16}\) cm\({}^{-3}\) induced defect density, which is an order of magnitude lower than the doping level [56]. The difference between damage and doping levels suggests that the neutron fluence is not large enough to damage an MZM in any significant way. Therefore, it can be concluded that MZMs should be reasonably robust to DD effects.  
Figure 12: Simulation results showing (**a**) pre and (**b**) post X-ray exposure of a phase shifter used in an MZM. The pinch-off effect can be seen with the depletion of the carriers post radiation exposure (after [63]).
## 3 Numerical Analysis and Verification  
The geometric model is symmetrical along the longitudinal section of the line. Therefore, the symmetric longitudinal plane is selected as the research section (as shown in Figure 7), and a two-dimensional numerical model of unsteady heat conduction without an internal heat source is established considering periodic boundary conditions. The results of the numerical simulation and model testing are compared to verify the numerical model.
# Adjustable Stiffness-Based Supination-Pronation Forearm Physical Rehabilitation
## 3 Adjustable Stiffness Rehabilitation Device
In neutral positions, the four tensions in the cables are equal. When performing supination, the torque produced by \(F_{1}\) and \(F_{3}\) is greater than the one produced by \(F_{2}\) and \(F_{4}\), resulting in total torque \(\tau\) illustrated in Figure 5b. This situation is reversed when performing pronation (see Figure 5c). Since forces \(F_{1}\) and \(F_{3}\) are driven by actuator 1 while forces \(F_{2}\) and \(F_{4}\) are driven by actuator 2, it can be established that \(F_{1}=F_{3}=F_{13}\) and \(F_{2}=F_{4}=F_{24}\) (see Figures 3 and 5). Taking into account that pulley radius \(r_{p}\) is constant, the total torque generated by this device is provided in Equation (1). \[\tau=2\,r_{p}(F_{13}-F_{24})\hat{k} \tag{1}\]  
It is highlighted that at all times \(F_{i}\perp r_{p}\) with \(i=1,\ 2,\ 3,\ 4\). Then, the total torque is always parallel to the \(z\)-axis. The following details are aimed to provide a better understanding of the developed experimental platform:  
1. The springs are assumed to be linear elements working under tension at all times. 2. Vibration and elongation in the cables are neglected. 3. The linear actuators are mounted outside the system workspace. The standing or seated position of the patient requires that the actuators are not mounted under or above the forearm to prevent injuries. 4. Safety must always be guaranteed for both the patient and the therapist by considering the following: to avoid sharp edges in the mechanical structure (aluminum and polylactic acid (PLA) parts). Several components of the experimental platform are manufactured by 3D-printing using PLA material. The design respects the anatomical ROM of human upper limbs. The chances of a severe accident are low because the maximum speed is limited by the properties of the linear actuators and by electronic-computational restrictions. 5. The motion of the rehabilitation system is generated as follows (see Figure 4). Each linear actuator transmits a linear displacement to one end of the respective spring. Once the spring is deformed, the displacement is transmitted to the associated rigid cables. Each spring provides the possibility to modify the tensions in the cables according to a particular rehabilitation phase since smooth motions could be required depending on the patient's medical condition. Moreover, tensions adjustments are required when performing assisted-active and active rehabilitation phases. 6. The cables (copolymer monofilament) are used to connect the springs with the handle through a series of pulleys that modify the direction of each force in order to guarantee a constraint-free operation of the system. 7. The system requires to meet and exceed the torque and the ROM requirements while recreating ADL movements (see Table 2). Figure 5: Left wrist–forearm. (**a**) Neutral position. (**b**) Supination. (**c**) Pronation.
## Sample Application  
A key advantage of the framework is the ability for a number of researchers to access shared data capture from multiple data sources for subsequent analyses. In this sample application, the test data set was from six inertial sensors per participant (\(n=12\)) doing six sessions each with video data segments. Specific details of the study's purpose including researcher and participant obligations were given, and, after agreeing to participate, volunteers signed the associated institute's ethical committee approved informed consent form (ENG/05/10/HREC). Each session involved a two-lap swim of a 25 m swimming pool.  
This large amount of data would be difficult to distribute and search if it were stored as flat files. Using the developed framework, the data capture can be indexed and becomes accessible to any researcher it is shared with. The indexed metadata is also searchable, providing easy access to sessions and data relevant to a current area of interest. Analysis of data is typically performed using standalone tools either commercially available or written in-house. An example of one of these tools is the SABEL data analysis tool [23], which was applied to one of the swimmers in the data capture set (Figure 6). This tool enables the time series inertial sensor data to be synchronised to video data, thereby giving the data context. The data can then be analysed to find key patterns or events to be used in specific algorithm development.  
To illustrate the concept, one of the swimmer's from the data capture was used to visualise the body roll when swimming a single lap of freestyle. The body roll is defined as the clockwise and anti-clockwise roll of the body whilst the arms are performing the strokes. The aim for the athlete is to be as consistent as possible in the strokes to minimise extraneous effort such as profile drag. This, in turn, means that the body roll will also be consistent. The sensor unit was located on the sacrum of the swimmer and the gyroscope channel examined aligned to the direction of the spine. The data was initially examined using the tool displayed in Figure 6. The state-space diagram with a delay of three was generated using the method detailed in [PERSON] et al. [24] (Figure 7). The maximum and minimum roll velocities of the body during the left and right arm stroke actions are given at the extents of the diagram. It can be seen that, by examining the contours at the extents of the diagram, the action is asymmetric with variability that changes over time. The coach can then use this information to provide feedback to the swimmer.
## 3 Machine Learning Techniques for Data Preprocessing
### Data Completion and Denoising
#### 3.1.2 Robust Principal Component Analysis  
Observing Figures 1 and 2, we find that the solar radiation data is contaminated by some large sparse noise, which is detrimental for forecasting the future trend. For a low-rank data matrix corrupted by small dense noise, principal component analysis (PCA) can effectively perform dimension reduction, noise elimination, and feature extraction [39]. However, generally speaking, PCA does not work well when the studied dataset is superposed by large sparse noise or outliers. Hence, research on the robustness of PCA has always been the main focus of attention.  
The emerging robust principal component analysis (RPCA) decomposes a matrix into the sum of a low-rank matrix and a sparse noise matrix, and the principal component pursuit is proposed to obtain the optimal decomposition [40]. This robust version of PCA can accurately recover the low rank component and the sparse noise under some conditions [41; 42]. Formally, RPCA is modeled as follow:  
\[\underset{\mathbf{A},\mathbf{E}}{\text{min}}\|\mathbf{A}\|_{*}+\lambda\|\mathbf{E}\|_{1}, \text{ s.t.}\widetilde{\mathbf{Z}}=\mathbf{A}+\mathbf{E} \tag{2}\]  
where \(\mathbf{A}\) is the low-rank component, \(\mathbf{E}\) is the sparse noise matrix, \(\lambda>0\) balances the low rankness and the sparsity, \(\|\cdot\|_{1}\) is the \(l_{1}\)-norm of a matrix (i.e., the sum of absolute values of all elements). The alternating direction method of multipliers is frequently used to solve the nuclear norm minimization (1) and (2). The optimal solution of the above minimization is denoted as \(\widetilde{\mathbf{A}}\).
# Folded Heterogeneous Silicon and Lithium Niobate Mach-Zehnder Modulators with Low Drive Voltage
## 2 Materials and Methods
The microwave delay line is another ingredient needed to be carefully designed for large modulation bandwidth. Due to the large group index of the silicon waveguide (\(\approx\)4.0), the optical waves slow down considerably after coupling to the silicon optical U-turn. The velocity mismatch between optical and microwave waves in their respective U-turns can be  
Figure 1: (**a**) Schematic of the folded Si/LN MZM. (**b**) Cross-sectional view of the phase shifter. (**c**) Scanning electron microscopy (SEM) image of the cross-section of the phase modulation region. (**d**) SEM image of the Si U-turn waveguides and crossing. (**e**) Microwave time delay as a function of the length of delay line (L\({}_{\text{D}}\)). (**f**) Optical image of the 12 mm folded and unfolded MZMs, the actual device lengths including coupling regions (\(\approx\)200 \(\mu\)m per side) are 5.6 mm and 13.4 mm, respectively. compensated by choosing a proper length for the microwave delay line. We first calculated the time delay of the optical wave including silicon optical U-turn, VAC, and the mismatch in modulation region, with obtained result of \(\Delta\tau\) = 9.15 ps. Figure 1e shows the calculated time delay for microwave delay lines at different lengths (the details of calculation can be found in Supplementary Materials, Section S3). The simulation result indicate that the time delay of optical and microwave U-turns can be matched for a microwave delay line of L\({}_{\text{D}}\) = 313 \(\upmu\)m, corresponding to the actual length of 626 \(\upmu\)m.  
The fabrication process started with patterning silicon waveguides, including 3 dB MMI couplers, inverse tapers for VACs, waveguide crossing, Euler bent waveguides, and grating couplers, using electron beam lithography (EBL) and inductively coupled plasma reactive ion etching (ICP RIE). Next, an X-cut LNOI (from NANOLN) was bonded on top of the fabricated silicon circuits by using BCB die-to-wafer bonding technology. After removing the substrate and the buried oxide layer of the LNOI die, the patterns of LN waveguides were defined by EBL formed by the dry etching process in an Ar\({}^{+}\) plasma. Finally, the gold TWEs were fabricated by optical lithography, electron beam evaporation, and the lift-off process.