esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Architecture
### System Implementation
#### 3.2.2 Data Plane Implementation  
Once the control plane determines the candidate computing nodes for each service at different levels, it issues flow tables with state area information to the data plane. As shown in Figure 5 above, the data plane contains two types of flow tables: the first flow table is the user requirements match table, and the second is the forwarding table. The two tables are executed sequentially to complete the data plane data packet scheduling process. Flow tables consist of two parts: match fields and actions. The match fields correspond to fields in the packet, and the matching methods include exact match, range match, longest prefix match,etc. This article adopts exact matching, that is, the fields in the data packet must be completely equal to the matching fields for the match to be successful. The actions are composed of a series of instruction sets, and by logically organizing these instructions, corresponding scheduling strategies can be implemented. When executing actions, input parameters can be obtained from the packet or the local state area, and the packet fields or state area can be modified.  
Figure 6 shows the detailed design of the two flow tables. Figure 6a shows the design of the user requirements match table, where the matching fields are the service identifier (SID) and the delay level, and the action set is the corresponding scheduling strategy. The state area is a structured storage area that stores the IDs of candidate clusters with different success rates at the same latency level in order. It is divided into K intervals according to the success rate level K. There are r candidate clusters in each interval. The smaller the success rate level, the fewer the candidates, and the higher the success rate guarantee. Figure 6b shows the design of a common ICN routing table, with the cluster ID as the matching field.  
The data packet processing steps on the data plane are as follows: When a data packet carrying a user request arrives, the SID, delay level requirement d, and success rate requirement s of the data packet are extracted. When the SID and d match successfully, the sizes of K and s are first determined. When s is greater than K, it means that all current candidate nodes can meet the requirement, and then a destination node is randomly selected in level K; when s is less than K, there may be a situation where no level s can be met. In this case, the requirement can be appropriately relaxed, the interval closest to level s is selected, and a destination node is randomly selected from it. If the match fails, the packet is processed according to the default instructions: it is forwarded to the upper layer or directly discarded. If the packet is not discarded, it will be matched in the forwarding table and forwarded from the corresponding port according to the destination ID.
# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 3 Background: Challenges in Detecting Three Behaviors  
As mentioned above, our system deals with commonly found abnormal behaviors to provide flexibility of detection in different environments (indoor and outdoor) to enhance the level of scene understanding with minimal fine-tuning. Therefore, to develop a system that classifies abnormal behaviors with high accuracy, two main types of challenges must be recognized and overcome. The first type of difficulty is related to the variance in specific movement patterns entailed in each behavior. So, the distinctive movement patterns involved in each act must be specified in detail and ample examples of each kind must be provided to the classification system so that it learns features related to each behavior correctly. The second challenge relates to the essential similarity in the acts of different behaviors leading to wrong predictions. Though such problems are inherent in the joint detection of various behaviors, they must be alleviated as much as possible by specifying the overlaps and providing examples that enable the system to distinguish features in apparently similar activities. In the following, each behavior is defined by specifying the representative movement patterns involved in it followed by the definition of overlapping patterns.
# Construction of Color Prediction Model for Damaged Korla Pears during Storage Period
## 3 Variation Laws of Colors of the Damaged Korla Fragrant Pears during the Storage Period
### Variation Laws of b* Value  
The b* value expresses the yellow-blue degree. It is yellow if the b* value is positive, and the color intensity is positively related to the b* value. On the other hand, it is blue if the b* value is negative, and the color intensity is negatively related to the b* value. The variation laws of b* with the storage time are displayed in Figure 7. The b* value increased with the increase in the storage time. The mean b* value of the fragrant pears with different degrees of damage increased from 39.0444 at 0 d to 43.3331 at 40 d. This meant that the pericarp color of fragrant pears was yellowing with the increase in the storage time. This effect could be explained as follows. As the storage time was prolonged, the chlorophyll was quickly degraded, but the carotenoid content was negatively related to chlorophyll degradation, and it quickly increased [34; 35]. With the increase in the ethylene content after harvest, the total carotenoid content accordingly increased [36] to intensify the yellow of the pericarp.  
Figure 6: Variation laws of the a* values of fragrant pears at different damage degrees with storage time (the mean values \(\pm\) standard deviations).
## 7 Discussion
### Comparative Advantages and Application Scenarios  
To highlight the trade-offs involved, we provide a comparative assessment of each variant's strengths and weaknesses in terms of solution quality, execution time, and robustness. A detailed comparison among the three algorithm variants--Standard GA, GA-AM, and GAAM-TS--reveals distinct performance profiles. The Standard GA, while computationally efficient, lacks mechanisms for dynamic adaptation and local refinement, leading to suboptimal routes with higher travel time and distance. GA-AM improves significantly upon this by introducing adaptive mutation, which helps maintain diversity and escape local optima. This variant consistently produced better results with only a moderate additional computational cost. The GAAM-TS configuration, which combines adaptive mutation with Tabu Search, demonstrated the best performance in terms of fitness, distance minimization, and convergence speed. However, its superior results come with a substantial increase in execution time, especially in larger problem instances. Overall, the comparative analysis confirms that GAAM-TS is best suited for applications where optimality is prioritized over time constraints, GA-AM excels in real-time scenarios with acceptable trade-offs, and Standard GA remains useful for lightweight tasks or initial approximations.  
The conditions under which each variant performs best depend largely on the trade-off between solution quality and computational efficiency. GAAM-TS is well suited for scenarios where route optimization is performed offline and quality is the primary concern. In contrast, GA-AM strikes a practical balance and is preferable for near-real-time  
Figure 7: Best routes obtained for the 25-location problem using different genetic algorithm variants. (**a**) The Standard GA route contains multiple crossings and inefficient detours, indicating difficulty in escaping local optima. (**b**) GA-AM produces a more structured and efficient route with fewer unnecessary deviations. (**c**) GAAM-TS achieves the best balance, significantly reducing redundant loops and optimizing the client visit sequence.  
applications where fast decision-making is needed without sacrificing much accuracy. The Standard GA may still be useful in constrained environments or when an initial solution is required quickly for further refinement. In practical deployments, the choice of algorithm should align with the application's operational requirements. GAAM-TS is ideal for centralized logistics systems that can afford longer computation times to obtain high-quality routing solutions, such as batch planning for e-commerce deliveries. GA-AM is more appropriate for dispatch systems that require dynamic adjustment in response to new orders or traffic events, where solutions must be computed within seconds to minutes. Standard GA remains relevant in lightweight mobile applications or in embedded systems where computational resources are limited and approximate solutions are acceptable.
## 1 Introduction
### Cholesterol and Atherosclerosis
The CD36 receptor and toll-like receptors (TLR4-TLR6) capture oxLDL, resulting in intracellular cholesterol crystals [27]. Additionally, vascular smooth muscle cells (VSMC), see [28], present in the intima, can internalize oxLDL independently of scavenger receptors. Notably, at least 50% of the foam cells in human coronary intima are derived from VSMCs rather than monocytes, underlying the role of VSMCs in the propagation of atherosclerosis. miRNAs regulate VMSC proliferation and are also related to endothelial dysfunction [29; 20]. One way miRNAs affect endothelial dysfunction is through their role in exacerbating endothelial cell senescence, a cellular condition that has been implicated as making atherogenesis more likely. Several miRNAs, including miR-34a, miR-217, and miR-146a, have been associated with regulating mechanisms involved in endothelial dysfunction; for a better review, see [30]. One of the key miRNAs involved in lipid phagocytosis is miR-33, downregulating key transcription effectors and activators, reducing phagocytosis and lysosomal activity in macrophages. In addition, miR-33 also targets ABCA1, which reduces lipid residues' metabolism and cholesterol efflux from macrophages, potentially affecting the growth of atherosclerotic plaques [31; 32]. The mentioned complexity is the answer to why it has not been possible to treat atherosclerosis effectively so far. This is a crucial issue since CVD is still the number one cause of death worldwide. It is well recognized that plasma cholesterol concentration increases the risk of a cardiovascular (CV) event, especially on cumulative prior exposure to LDL-C [33]. Therefore, elevated LDL-C levels are considered the main therapeutic target. Sttains competitively inhibiting HMGK have been developed to lower plasma LDL-C levels and reduce the risk of CV events. Many clinical trials have shown that statins can reduce the number of new adverse CV events and CV mortality by 35%. It turns out, however, that even aggressive statin therapy cannot wholly eliminate CV risk. Approximately 65% of statin patients continue to experience adverse cardiovascular events. Hence, additional therapeutic interventions besides statins are needed to reduce CV risk. The scheme of the crucial metabolic pathways of the cholesterol involvement in atherosclerosis development is shown in (see Figure 2). Figure 2: The role of cholesterol in atherosclerotic plaque formation. Parts of the figure were created using images from Servier Medical Art. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License accessed on 2 April 2023 ([[https://creativecommons.org/licenses/by/3.0/](https://creativecommons.org/licenses/by/3.0/)]([https://creativecommons.org/licenses/by/3.0/](https://creativecommons.org/licenses/by/3.0/))).
## 2 Current Research of Cyber-Physical Power System (CPPS) and Co-Simulation Platforms
### Application Scenarios of Co-Simulation Platforms
#### 2.2.1 Wide-Area Security Monitoring  
The current physical security theory and information security theory are integrated in a unified CPPS framework where the network attributes, equipment statuses, and exterior environments are taken into consideration to evaluate the safety, interoperability, and controllability of the wide area system. A general electric power and communication synchronizing simulator called EPOCHS, developed by a research team at Cornell University, has been utilized to simulate the agent-based special protection process and verify the impact of network delay on the real-time protection action [18]. The research team at Virginia Tech built a global event-driven simulator called global event-driven co-simulation (GECO) [19]. Wide area backup distance protection scenarios were simulated to prove that the communication delay between the agents satisfied the power protection requirements. Overall, this kind of application is usually used to explore the influencing factors, cascading effects, and optimization methods of wide area security and stability from the perspective of complex dependent networks.  
Figure 1: Overall structure of cyber-physical power system (CPPS).
# Old-Fashioned, but Still a Superfood--Red Beets as a Rich Source of Bioactive Compounds
## 1 Introduction  
_Beta vulgaris_ (beet) is an edible plant in the subfamily _Betoidae_ of the family _Amaranthaceae_. It has several cultivar groups such as sweet beet, garden beet, chard, and mangoldwurzel. Red beetroots (_Beta vulgaris_) have been cultivated for many hundreds of years in all temperate climates. Surprisingly, the green parts of the beetroot were consumed initially in the form of chard [1]. The dish was so popular that the ancient Greeks and Romans developed a method of growing beetroot also during the hot summer months. It should be emphasized that this variety had only thin and fibrous roots, which were occasionally used in medicine. Consumption of underground beet parts was first reported in Germany and Italy in 1542 [2]. However, that beet variety was significantly different from the one we know today and rather closely resembled a parsing. The beetroot variety that is still cultivated today appeared in the late 16 th century, and is believed to have originated from a prehistoric North African root vegetable. Currently, most beetroots are consumed as a vegetable and also as a juice. Beetroots can also be processed into a powder for use as a food colorant E162 and additive to cosmete [3; 4; 5; 6].  
Although beets have been known for centuries, understanding of their value is still limited. New reports about bioactive compounds in beets suggest that they fit the definition of a functional food, as they offer health benefits that extend beyond their nutritional value [7; 8; 9]. In addition to proteins, carbohydrates, fat, amino acids, fatty acids, phytosterols, minerals, and fibres, beetroot contains also vitamins, nitrate, polyphenolic compounds, and betalains, a class of red and yellow natural pigments found in plants. Betalains are considered to be strong antioxidant agents as they can decrease oxidative stress by efficiently removing the reactive oxygen species [9; 10; 11]. Other health benefits of betalains include antimicrobial, antiviral, and anti-inflammatory activities [12; 13; 14]. Several studies showed the tumour-chemoprereentive effects of beetroot extracts in laboratory animals [15; 16; 17]. This is probably a synergistic effect of both betalains and other compounds present in beetroot,e.g., polyphenolic compounds. The scheme of the main bioactive compounds present in beetroot is depicted in Figure 1.  
This review aims to collect the latest literature on the content of biologically active compounds in beets and beet products. It also provides an overview of the positive health effect associated with beet consumption.
# Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things
## 4 Constrained Stochastic Game  
To maximize the aggregated data rate while keeping a low energy outage probability, we formulate a constrained stochastic game model [24; 25]. It is assumed that IoT devices are the players, and therefore, in our formulation, player \(i\) and IoT device \(i\) have the same meaning. Table 1 summarizes the notations used in the model.
## 2 System and Channel Models  
As shown in Figure 1, in the FSO communication in maritime environments, both Tx and Rx have the characteristic of being mobile platforms. Because of these characteristics, the pointing error that continues to occur even after beam alignment, especially the effects of jitter, is the major factor affecting performance. As the distance between these mobile platforms is not fixed, its impact is also a major factor affecting performance. Furthermore, in the typical case of applying FSO communication in terrestrial environments, the main consideration is the horizontal link between fixed platforms or the vertical link between fixed mobile platforms, which has been extensively studied recently, whereas, in maritime environments, the horizontal link between mobile platforms is the main consideration. Furthermore, as the occurrence of fog is higher than in terrestrial environments owing to the nature of the ocean, fog acts as the major factor in performance degradation compared to terrestrial environments. Based on these considerations, we assume that the irradiance can be modeled as \(I^{\prime}=I_{l}I_{a}I_{p}\), where \(I_{l}\), \(I_{a}\), and \(I_{p}\) denote the pathloss, foggy channel, and pointing error, respectively, \(I_{a}\) and \(I_{p}\) are random variables discussed below [8], and \(I_{l}\) is deterministic.
## 2 Methods
### Soil Model Parameter
Soft soil such as very soft clay, normally consolidated clay, silt, and peat, is modeled using the soft soil model (SSM) in finite element modeling (FEM). Unlike Mohr-Coulomb, which assumes linear behavior and constant stiffness of soil throughout soil depth, soft soil developed a nonlinear behavior of soil. Soft soil model considers soil nonlinearity via a linear stress dependency of stiffness. There are two key model inputs that define the compressibility of the soil in isotropic loading, unloading, and subsequent reloading. The first is the modified compression index lambda (\(\lambda\)) for modeling the compression of soil and the second is the modified swelling index kappa (\(\kappa\)). This parameter can be calculated automatically in the software using the compression index (\(C_{C}\)) and swelling index (\(C_{S}\)). The calculation formula for \(\lambda\) and \(\kappa\) is given by [32] as  
\[\lambda^{*}=\frac{\lambda}{1+e_{0}}=\frac{Cc}{2.3\ (1+e_{0})} \tag{5}\]  
\[\kappa^{*}=\frac{\kappa}{1+e_{0}}=\frac{2 Cs}{2.3\ (1+e_{0})} \tag{6}\]  
The yield failure criterion of the Mohr-Coulomb theory serves as a basis for the soft soil model yield function, except that the yield function of SSM defines an ellipse with \(P_{P}\), isotropic pre-consolidation pressure defines the length of the ellipse, and \(M\) is the height of the ellipse in the \(p^{\prime}\) plane. \(M\) also represents the ratio of horizontal to vertical stress under primary loading and is used to determine the earth pressure at rest (\(K_{\textit{o}}\)nc). Input parameters for the soil modeling in Plaxis 2D Version 21.01.00.479 (build 479), based on the requirements from PLAXIS Version 21 [33], are displayed in Figure 4. Each soil layer has a color shown in Figure 4. This color will be displayed in the Plaxis modeling schematic shown in Figure 5.  
Figure 4: Soil input parameter.
## 2 Airborne Air-to-Air Channel Laser Communication Link Analysis and Modeling
The photon azimuth is \(\varphi_{aci}\) in rad and is described by a uniform distribution of random numbers in the range \([0,2\pi]\). The iterative analytical model of the occurrence of continuous Mie scattering is as follows (a photon exiting the pupil into the null channel when the first Mie reflection occurs, \(n=0\)) [13]:  
\[ \begin{split} u_{x,n+1}&=\frac{\sin\theta_{scan}}{ \sqrt{1-u_{z,n}^{2}}}\left(u_{x,n}u_{z,n}\cos\varphi_{aci}-u_{y,n}\sin\varphi_ {aci}\right)+u_{x,n}\cos\theta_{scan}\\ u_{y,n+1}&=\frac{\sin\theta_{scan}}{\sqrt{1-u_{z,n} ^{2}}}\left(u_{y,n}u_{z,n}\cos\varphi_{aci}+u_{x,n}\sin\varphi_{aci}\right)+ u_{y,n}\cos\theta_{scan}\\ u_{z,n+1}&=-\sin\theta_{scan}\cos\varphi_{aci} \sqrt{1-u_{z,n}^{2}}+u_{z,n}\cos\theta_{scan}\end{split}  \tag{4}\]  
In this formula, the mean free path between scattering events is denoted as \(\Delta s=-\ln\xi_{s}/\mu_{t}\), \(\xi_{s}\) represents a uniformly distributed random number in the range \([0,1]\) and \(\mu_{t}\) signifies the atmospheric attenuation coefficient. Let \(\xi\) be a uniformly distributed random number in the range \([0,1]\). The photon scattering angle \(\theta_{scan}\) is then modeled as [12]:  
\[\cos\theta_{scan}= \begin{cases}\frac{1}{2g}\left[1+g^{2}-\left(\frac{1-g^{2} }{1-g+2g\cdot\xi}\right)^{2}\right]&(g\
eq 0)\\ 2\cdot\xi&(g=0)\end{cases}  \tag{5}\]  
The analytical formula for the asymmetric factor \(g\) is calculated as follows [14]:  
\[g=\frac{4}{x^{2}Q_{sca}}\sum_{n=1}^{\infty}\left[\frac{n(n+2)}{n+1}\text{Re} \big{(}a_{n}a_{n+1}^{*}+b_{n}b_{n+1}^{*}\big{)}+\frac{2n+1}{n(n+1)}\text{Re} \big{(}a_{n}b_{n}^{*}\big{)}\right] \tag{6}\]  
where \(x\) is the scattering element scale factor \(x=2\pi r/\lambda\); the asterisk (*) is the complex conjugate; \(a_{n}\) and \(b_{n}\) are the Mie scattering coefficients; \(r\) is the scattering element particle radius, whose unit is \(\upmu\)m; and \(\lambda\) represents the wavelength, measured in \(\upmu\)m.  
The relationship between the particle size and concentration of the scattering element can be characterized as obeying a lognormal distribution [15]:  
\[\frac{dN}{dr}=\sum_{i=1}^{3}\frac{N_{i}}{\sqrt{2\pi}\ln\sigma_{g,i}}\exp\left[ -\frac{\left(\ln r-\ln r_{g,i}\right)^{2}}{2\ln^{2}\sigma_{g,i}}\right] \tag{7}\]  
where \(N_{i}\) is the concentration of the particle in mode \(i\) in cm\({}^{-3}\); \(r_{g,i}\) is the geometric mean particle size of the scattering element in \(\upmu\)m; and \(\sigma_{g,i}\) is the geometric standard deviation of the scattering element in \(\upmu\)m.  
The scattering element scattering efficiency \(Q_{sca}\) is calculated as follows [15]:  
\[Q_{sca}=\frac{2}{x^{2}}\sum_{n=1}^{\infty}(2n+1)\Big{(}|a_{n}|^{2}+|b_{n}|^{2 }\Big{)} \tag{8}\]  
The Mie scattering coefficient was chosen from the Bohren computational model below [15].