esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Security Analysis of the Proposed Protocol
### Heuristic Analysis
#### 4.2.2 Session Key Agreement  
The session key agreement means that no one can solely pre-compute the session key without interacting with another entity. Factually, in the proposed protocol, \(SK=h(h(r_{u}\parallel T_{1})\parallel r_{s}\parallel h(V_{i}\parallel EID_{i}))\) contains an indispensable part from \(U_{i}\) (secret parameter \(r_{u}\)) and \(MS_{j}\) (secret parameter \(r_{s}\)); thus, our scheme meets this well-defined attribute.
## 2 Materials and Methods
### Recording Session  
Four violinists (3 females, \(M_{age}=22\)) took part in the recordings. They were paid according to the ethical protocol. They agreed with the use of the material recorded as stimuli for this study. Musicians performed inside an Immersive Virtual Environment (IVE) with the use of a system of three screens, seven TITAN QUAD 3D projectors (Digital Projection Limited, Manchester, UK), and stereo glasses presenting seamless and perceptively coherent 3D images. Two different virtual environments were created for this experiment: a room filled with an audience behaving naturally and attending the concert, and the same room without the audience (Appendix Figure A1). The virtual audience was composed of high quality agents with realistic facial expressions and behaviors. An agent is created using four different components: (1) realistic body from a 3D scan of real actors; (2) realistic body animations created from motion capture footage; (3) accurate and controllable facial expressions based on FACSGen [65; 66]; and (4) expressive behavior and social interaction modeling. The audience behavior could smoothly change from engaged to disengaged behaviors. Specifically, in the engaged behavior, the audience attention increases through the convergence of individual gazes focusing on the musician [67]. On the other hand, a disengaged audience is rendered by allowing the gaze of avatar to wander around as generally observed in distracted people. Furthermore, for realism purposes, each avatar had random idle animations of their body while looking at the musician in order to approximate usually seen fluent behaviors at concerts. To avoid unnatural uniformity, part of the audience (5-10%) is always modeled in the disengaged condition when the majority is engaged, and vice versa [68].  
Each musician was instructed to play 30-second-long interpretations of [PERSON]'s Partita No. 2 in D minor, BWV 1004: Sarabande. The part of the musical score to be played was carefully selected to correspond to complete musical phrases. The excerpts were interpreted according to three selected expressive intents: deadpan, projected, and exaggerated [43]. The different combinations of conditions were performed in a pseudo-randomized order by each musician. Six excerpts were recorded for each of the 4 musicians, adding up to a total of 24 excerpts. The sound was captured using an Olympus LS-10 (Olympus, Tokyo, Japan). Motion capture data was also recorded for every piece using a VICON optical motion tracking system (Vicon Motion Systems Ltd., Oxford, UK) composed of eight Bonita 3 cameras (Vicon Motion Systems Ltd., Oxford, UK). A total of 26 markers were used, covering selected body parts based on recent literature on the analysis of music performance, i.e., the head, arms, and torso [48; 51; 52; 53].
The welding deformation of FSW is small compared to conventional fusion welding, but that does not mean that there is no welding deformation. In general, welding deformation modes that occur during fusion welding include transverse shrinkage, longitudinal shrinkage, rotational distortion, angular distortion, bending, and buckling [10]. For the FSW of thin plates, there is hardly any temperature gradient at the upper surface in contact with the tool shoulder and at the bottom surface near the end of the tool pin, because of the thin thickness and the heat transfer to the bottom by the tool pin during welding [11]. Therefore, the welding deformation mode of transverse shrinkage occurs in FSW. In the FSW automation process, the transverse shrinkage must be considered to correct the welding deformation from the previous step in the next step. Figure 3 shows a section view of the transverse shrinkage problem, which occurs in actual FSW automation processes. In Step 1, the tool is initially set in the middle of Structures A and B, so there are no problems during the welding process. After the welding is finished in Step 1, we move on to Step 2. At this point, Structure C moves z mm to the position of Structure B, and the tool moves b mm, which is the width of Structure B. However, as shown in the Step 2, a gap occurs between Structures B and C, to be processed in Step 2, due to the welding deformation in Step 1. As a result, the position of the tool is also located on the left side of Structure C. The FSW gap becomes larger as the steps increase. In addition, the gap may not be joined after welding, and even if it is joined, the mechanical property values at the joint will be significantly reduced, thus having a great influence on the quality [12; 13; 14]. This study calculated the transverse shrinkage for each step of welding for a 2.5 mm thick aluminum battery housing structure with nine welding lines. The structures and tools of each step were placed in the correct positions by reflecting the calculated welding deformation. Eventually, we could solve the quality issues that may occur during welding. In this study, thermal and structural analyses of the local thermomechanical model and real size aluminum battery housing model were performed using ANSYS Workbench. Figure 3: An example of a problem that can occur during the FSW automation process due to welding deformation.
## 3 Methodology
### Reliability Importance Measure Of Components
#### 3.2.2 Component Reliability Importance  
In this paper, the reliability importance considering component swapping with the imprecise probabilities are combined to explore the uncertainties of failure times on the reliability importance of a component. For a single component \(i\) of type \(k\), the reliability importance is the difference between the probability that the system functions at time \(t\) given that component \(i\) functions at time \(t\), and the probability that the system functions at time \(t\) given that component \(i\) does not function at time \(t\)[15]. Therefore, the marginal reliability importance (MRI) of component \(i\) at time \(t\) considering component swapping can be defined as  
\[MRI(i;t)=P(T_{s}>t|T_{i}>t)-P(T_{s}>t|T_{i}<t) \tag{8}\]  
where \(P(T_{s}>t|T_{i}>t)\) and \(P(T_{s}>t|T_{i}<t)\) are conditional survival functions that can be easily calculated by the corresponding survival signature.  
Figure 6: Lower and upper bounds of system reliability considering swapping.  
The imprecise reliability importance with considerations of the uncertainties of failure times is studied. The imprecise failure probabilities of components are represented as distributional p-boxes indicated with \(M:P\in M{=}\big{\{}[\underline{\lambda_{1}},\overline{\lambda_{1}}],[ \underline{\lambda_{2}},\overline{\lambda_{2}}],\cdots,[\underline{\lambda_{n}}, \overline{\lambda}_{n}]\big{\}}\). Therefore, the lower and upper bounds of marginal reliability importance can be calculated by  
\[\begin{cases}\frac{MRI(i;t)}{MRI(i;t)}=\inf_{P\in M}MRI(i;t|\underline{P})\\ \frac{\sup_{P\in M}MRI(i;t|\overline{P})\end{cases} \tag{9}\]  
where  
\[MRI(i;t|\underline{P}) =\sum_{l_{1}=0}^{m_{1}}\cdots\sum_{l_{k}=0}^{m_{k}-1}\cdots\sum_{ l_{K}=0}^{m_{K}}\big{[}\Phi_{1}^{w}(l_{1},\cdots,l_{K})-\Phi_{0}^{w}(l_{1}, \cdots,l_{K})\big{]}\prod_{k=1}^{K}\underline{P}\{C_{k}(t)=l_{k}\} \tag{10}\] \[MRI(i;t|\overline{P}) =\sum_{l_{1}=0}^{m_{1}}\cdots\sum_{l_{k}=0}^{m_{k}-1}\cdots\sum_{ l_{K}=0}^{m_{K}}\big{[}\bar{\Phi}_{1}^{w}(l_{1},\cdots,l_{K})-\bar{\Phi}_{0}^{w}(l_{1}, \cdots,l_{K})\big{]}\prod_{k=1}^{K}\overline{P}\{C_{k}(t)=l_{k}\} \tag{11}\]
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 3 Modeling Process
### Applications  
The trained D3 RL model focuses on the destroy process of the existing solution and needs to be complemented with a repair process to obtain a viable solution for agile-satellite mission planning. To address this, we designed two applications of the D3 RL model: the DRL method and the DRL-LNS method. The DRL method utilizes the D3 RL model for inference directly, then use the TimeInsert operator to repair and obtain a feasible solution. In the DRL-LNS method, we integrated the D3 RL model as a dynamic destroy operator within the LNS framework, which finds the local optimal solution via heuristic iteration. The specific steps of the DRL-LNS approach are shown in Algorithm 2.  
```
0: Testing Scenarios
0:\(Average(X)\)
1:for scene in Testing Scenarios do
2: Import test Scenario
3: Initial dynamic removal, repair operators \(D\), Random
4: Initial solution: \(x=Initial(x_{0})\)
5:repeat
6:\(x^{i}=Random(D(x))\)
7:if accept \(x^{i}\)then
8:\(x=x^{i}\)
9:endif
10:if\(f(x^{i})<f(x^{*})\)then
11:\(x^{*}=x^{i}\)
12:endif
13:until Terminal condition is met
14: Put \(x^{*}\) into \(X\)
15:endfor
```  
**Algorithm 2** DRL-LNS  
**Remark 3**: _The implementation of the DRL-LNS algorithm can be divided into four steps:_  
1. _Parameter initialization. The initial solution, denoted as_ \(x_{0}\)_, can be arbitrary. The initialization function, Initial, checks the initial solution for compliance with the rules and reorders it according to the constraints to obtain a feasible initial solution._
2. _Destruction and repair process. Tasks are first removed from the current solution, and then new tasks are added to generate a new solution. The repair process employs a Random repair operator, which randomly samples from the set of pending tasks when inserting new tasks. The destruction process utilizes the_ D3 RL_-based destruction operator, denoted as_ \(D\)_._
3. _Parameter update. The obtained solution is evaluated, and if the reward of the new current solution improves, parameter updates are performed. We mainly use simulated annealing for optimal solution selection._
4. _Termination criterion determination. If the termination criterion is met, the algorithm ends and returns to the optimal value. If not, the iteration continues to search for solutions._
# A Study on the Conductivity and Infrared Spectroscopy of Elastic Polymer Composite Materials
## 2 Materials and Methods
### The Analysis of SEM  
The molecular packing of polymers significantly impacts their physical properties. Carbon physisorption reduces the intermolecular distance while substantially increasing intermolecular attraction. The adsorption behavior at different doping concentrations in elastic polymer composite materials was observed through SEM, as shown in Figure 2. These images highlight the secondary bonding forces within the polymer and the high residual forces due to macromolecular weight. When the carbon dopant concentration is too high, it creates a strong attraction, leading to tightly packed molecules arranged in a highly regular structure. However, due to the polymer's macromolecular weight and structural defects, perfect parallel alignment is not achievable.  
Figure 2: SEM images of elastic polymer composite materials with varying levels of carbon powder doping: (**a**) 0 M; (**b**) 4 M; (**c**) 20 M; (**d**) 40 M; (**e**) 80 M.  
Carbon atoms are composed of three sp\({}^{2}\) hybridized orbitals, which interact when in close proximity. In a graphite structure, carbon atoms have an electronic configuration of 2S-2P. Each layer of carbon atoms forms strong \(\sigma\) bonds with three adjacent carbon atoms. The bonding in the vertical direction between layers is facilitated by unhybridized P orbitals forming \(\pi\) bonds, which are relatively weak, resulting in a very narrow energy gap. This narrow gap allows valence electrons to move easily within the \(\pi\) orbitals, providing good electrical conductivity. SEM images from Figure 2a-e show that increasing the concentration of carbon powder enhances the physisorption function. When the distance between carbon particles approaches the van der Waals radius (0.3354 \(\pm\) 0.0001 nm), valence electrons begin to overlap intermolecularly, significantly increasing electrical conductivity.
## Materials and Methods
### Numerical Simulation Model Construction
#### 2.3.2 Hydraulic Parameter Fitting
The two sides of the model are known as the impervious boundary, as shown in Figure 8c. In order to reduce the impact of accidental factors, the model of each condition was simulated at least five times. The initial joint hydraulic aperture of the \"contact\" group joint was \(a_{0}=7.02\)\(\upmu\)m. The initial joint hydraulic aperture of the \"fracture\" group joint was \(R\times a_{0}\). At the same time, the joint hydraulic aperture of the \"tensile damage\" and \"shear damage\" groups was reassigned, and the initial joint hydraulic aperture of the \"tensile damage\" and \"shear damage\" groups was also \(R\times a_{0}\). These values were used to simulate the permeability of the rock sample after secondary failure. Values of \(R=1\), 2, 3, 3.0625, 3.125, 3.1875, and 3.25, respectively, were used. The change curve of the seepage flow rate produced by changes in the \(R\) value is shown in Figure 10. When \(R\) was less than 3, the \(R\) value had almost no response to the joint seepage flow. When \(R\) was greater than or equal to 3, there was a quartic polynomial relationship between the flow rate and the \(R\) value. The correlation was above 99%, and the relationship between the \(R\) value and the joint average flow was obtained with the following formula. \[Y=a*R^{\prime}4-b*R^{\prime}3+c*R^{\prime}2-d*R+e. \tag{7}\]  
In the formula,  
\(Y\): flow rate (m\({}^{3}\)/s)  
\(R\): ratio of FHA to CHA  
\(a=0.00017147221333246\)  
\(b=0.002086868991998950\)  
\(c=0.009523375562661920\)  
\(d=0.0193137834439905\)  
\(e=0.01468718676499290\)  
The value of \(Y=4.05721705398765\times 10^{-8}\) was converted into a function relation, and a \(R\) value of 3.17492492492493 was obtained, and the joint hydraulic opening of the crack was shown to be about 22.29 \(\upmu\)m.  
Figure 9: Microscopic crack structure of rock samples.
## 4 Monte Carlo Simulation
### Geant4 Simulation Results and Analysis
As shown in Figures 12 and 13, the 20-\(\mu\)m-thick spherical shell is divided into four equal layers of 5 \(\mu\)m, with inner to outer layers a, b, c, and d, respectively, and radionuclides are set uniformly in each layer. The materials of the spherical shells were set as Sn\({}_{63\%}\)Pb\({}_{37\%}\), and SAC305, and their densities were \(\rho\)Sn\({}_{63\%}\)Pb\({}_{37\%}\), = 7.34 g/cm\({}^{3}\) and \(\rho\)SAC = 8.84 g/cm\({}^{3}\), respectively. One million \(\alpha\)-particles with an energy of 5.3 MeV were randomly ejected within each layer. Figure 11: Emission energy spectra of 5.3 MeV \(\alpha\)-particles on the surface of two kinds of tin spheres obtained by simulation. Figure 10: Schematic diagram of the simulation model of tin sphere. The energy spectra of the ejected \(\alpha\)-particles in each layer were probed at the surface, as shown in Figures 14 and 15:  
Figure 14: Emission energy spectra of \(\alpha\)-particles at 5.3 MeV obtained from simulation on the surface of the SAC305 tin sphere. There are four cases, a represents the emission energy spectrum of radionuclides on the surface within the a-layer, b represents the emission energy spectrum of radionuclides on the surface within the b-layer, c represents the emission energy spectrum of radionuclides on the surface within the c-layer and d represents the emission energy spectrum of radionuclides on the surface within the d-layer. Figure 12: Simulation model of the SAC305 tin sphere. Figure 13: Simulation model of Sn\({}_{63\%}\)Pb\({}_{37\%}\) tin sphere. (1) Very few alpha particles are emitted from the innermost layers of the tin sphere, and the energy distribution is in the range of 0 to 1 MeV;  
(2) The energy of the \(\alpha\)-particles emitted from the b-layer is concentrated in the range of 0-2.5 MeV, and converges to 0 at energies higher than 2.5 MeV. (3) The energy of the \(\alpha\)-particles emitted from the c-layer is concentrated in the range of 0-4 MeV, tends to zero at energies higher than 4 MeV, and the number of \(\alpha\)-particles emitted from 0-1 MeV is similar to that of the d-layer;  
(4) The d-layer emits the largest total number of \(\alpha\)-particles and all \(\alpha\)-particles with energies higher than 4.1 MeV originate from the d-layer. A combination of simulation and experimental results is analyzed. In the emission energy spectrum, \(\alpha\)-particles with energies higher than 4 MeV all originate from the contribution of radionuclides within 5 \(\upmu\)m of the surface layer. Therefore, about 60% of the \(\alpha\)-particles in the energy spectrum of the Sn\({}_{63\%}\)Pb\({}_{37\%}\) tin sphere in Figure 2 originate from the contribution of radionuclides within 5 \(\upmu\)m of the surface layer of the tin sphere, and about 62% of the \(\alpha\)-particles in the energy spectrum of the SAC305 tin sphere in Figure 4 originate from the contribution of radionuclides within 5 \(\upmu\)m of the surface layer of tin sphere.
# Optimum System for Diagnosing Power Quality in Electrical Microgrids
###### Abstract  
An electrical measurement network designed for analyzing power quality within microgrids is presented in this paper. It is very portable and easy to install across various types of microgrids. Data collected by the system meet the standards for measuring electrical parameters, calculating waveforms spectra and comparing results from different microgrid nodes. The measurements provided by the network are useful for both utility and consumer sides. The system's effectiveness is verified through two experimental setups, specifically built ad hoc: one for testing the accuracy of the measurements obtained and the other for assessing the suitability of these measurements for power quality analysis.  
electrical measurement network; harmonic sources; microgrids; power quality disturbances +
Footnote †: journal: Applied Sciences
# Two-Dimensional Full-Waveform Joint Inversion of Surface Waves Using Phases and Z/H Ratios
## 4 Inversion Experiments  
The complementary sensitivity of the phase and Z/H ratio to the vs. perturbation makes the joint inversion an important method of constraining S-wave velocity structures. Here, we designed two synthetic experiments to demonstrate the successful usage of this method on the large- and small-scale inversions.
## 4 Discussion
### Limitations Analysis and Future Research Directions  
Despite the significant progress achieved in this study, several limitations remain:  
Limited Dataset Coverage and Applicability: A primary limitation of the current study is its reliance on the SGCBP dataset. Consequently, the presented PSCDR-BMPNet method was specifically validated for the agricultural crops included in this dataset, namely, wheat and barley, and within the geographical and environmental context of New South Wales, Australia, where the data was collected. While the proposed methodological innovations in contrastive learning and network architecture are designed to be generalizable, the direct applicability and performance of the trained model on other crop types (e.g., maize, rice, soybean) or in different agro-ecological regions require further dedicated validation and potential fine-tuning. Future research efforts should prioritize expanding the evaluation to more diverse datasets to ascertain the broader applicability and robustness of the PSCDR-BMPNet.  
Computational Efficiency Challenges: BMP_Net exhibits high computational complexity when processing large-scale point cloud data, making it challenging to deploy on edge devices such as drones or field robots.  
Need for Improved Multimodal Data Fusion and Noise Robustness: The model relies solely on LiDAR point cloud data and does not incorporate multimodal information such as RGB images, soil moisture, or meteorological data. Additionally, its ability to filter point cloud noise in extreme environmental conditions (e.g., rain, fog, or varying lighting) is limited.  
Based on the above analysis, future research can explore the following directions:  
Dataset Expansion and Diversity: Expanding the dataset scale to include a broader range of crop types (e.g., maize, rice) and diverse ecological conditions would enhance the model's robustness and generalization ability.  
Model Optimization and Lightweight Design: [PERSON] et al. [40] utilized deep learning to complete sparse point clouds, addressing occlusion issues in forests. In the future, such methods could be integrated to further enhance the robustness of BMP_Net in complex environments. Optimizing the network architecture to reduce computational costs or employing knowledge distillation to transfer BMP_Net's knowledge to a lightweight framework could maintain accuracy while lowering computational complexity.  
Multisource Data Fusion: Integrating multispectral and thermal infrared remote sensing data could improve the model's perception of crop physiological states, further enhancing prediction accuracy.
###### Abstract  
Onion is a well-known health-beneficial vegetable. However, fresh onion is high in FODMAPs (fermentable oligosaccharides, disaccharides, monosaccharides, and polyols) which may be problematic for IBD. Fermentation of onion may help to lower FODMAP problems and increase the availability of bioactive compounds, especially quercetin. We investigated the effect of fermented onion on DSS-induced IBD in rats. Rats were divided into six groups and treated orally with saline as a control and negative control (DSS), probiotics, low and high doses of fermented onion, or fresh onion extract for 3 weeks. After two weeks, rats were given drinking water containing 0.2% DSS for 5 days, except for the control followed by two days of regular water. The colonic histomorphology, immunity, oxidative stress, short-chain fatty acids, and biochemical analysis showed improved IBD conditions in the fermented onion groups. In contrast, the consumption of fresh onion appeared to exacerbate the IBD condition. These results suggest that the consumption of a high dose of fermented onion can ameliorate IBD symptoms.  
Keywords:inflammatory bowel disease; dextran sodium sulfate; fermented onion; onion +
Footnote †: journal:  
overall health, changes in diet and food consumption are used to improve, control, or even cure IBD. Numerous studies have shown that dietary components impact gut microbiota and may also have an effect on gut homeostasis directly [5].  
Onion (_Allium cepa_), a monocotyledon, is one of the oldest fructan-containing vegetables. Onions are known to be high in polyphenols, the most abundant of which is quercetin [6]. According to research, quercetin has antihypertensive, antibacterial activities, and anti-inflammatory properties [7]. Furthermore, studies have shown that quercetin helps to suppress lipid peroxidation, inhibit the initial process of inflammation, and improve the immune system [8].  
Onions are high in fructan, one of the FODMAPs (fermentable oligosaccharides, oligosaccharides, oligosaccharides, monosaccharides, and polyols) that has been shown to have negative effects on IBD due to its poor absorption in the small intestine and is rapidly fermented by bacteria in the large intestine, leading to symptoms of irritable bowel syndrome (IBS) in IBD patients. On the other hand, the fermentation of onions enhances antioxidant activity, and the availability of bioactive compounds such as flavonoids and short-chain fatty acids (SCFAs) [9; 10]. A recent study showed that a diet containing a high content of fermented food increased microbiome diversity and decreased inflammatory signals and activity [11]. Therefore, fermentation of onion may reduce onion's FODMAP characteristics and improve IBD. Thus, this study aims to evaluate the effect of fermented onion on gut health in DSS-induced IBD rats.