esa-sceva/satcom-chunk-collection huggingface dataset:
# Onion Peel Powder's Impact on the Leptin Receptors in the Hippocamp of Obese Rats
###### Abstract  
The bioactive components present in onion peel powder are a promising factor in preventing/treating obesity. Overweight/obesity causes metabolic changes, which can lead to leptin resistance in the central nervous system (CNS) and, thus, to structural and functional changes in the brain. **Objectives**: The presented study focused on evaluating the influence of a diet supplemented with onion peel powder on the immunoexpression of leptin receptors (LepRs) in the hippocampus in obese rats and the potential anti-obesity role of the onion in the brain. **Methods**: To induce obesity, the animals were given a high-energy chow containing lard and sucrose. Onion skin powder was used to modify the standard and high-energy diets (10.5 g per rat/week) of Wistar rats in a 14-week experiment followed by a brain IHC study. **Results**: The effect of the onion diet on the expression of neuronal LepRs and astrocytes in the hippocampus was analyzed. Obese animals receiving onion in the diet showed significant growth in the average number of immunoreactive LepR (LepR-IR) neurons (\(p=0.00108\)) and their average size (\(p=0.00168\)) in the CA1 field of the hippocampus. Meanwhile, in obese rats not given onion peel powder, a significant increase in the average density of astrocytes was observed (\(p<0.0001\)). **Conclusions**: The increased density of astrocytes in the hippocampus of obese animals can probably have a beneficial effect on brain changes in overweight individuals. The inclusion of onion in the diet of overweight/obese individuals may lead to increased hippocampal neuroplasticity, manifested by changes in the immunoexpression of LepRs. It can be speculated that the observed changes have a protective effect on the CNS structures during obesity, but this undoubtedly requires further research.  
onion; bioactive compounds; obesity; leptin receptor; hippocampus Article
## 1 Introduction
### Performance Metrics  
As a result, ONs operate under conditions hostile to data delivery, and there exists the possibility that a message will be lost. Thus, the main performance metrics of an ON are _delivery rate_ and _latency distribution_. The delivery rate is the fraction of emitted messages that reach the destination. The latency is the travel time of the delivered messages. Notice that latency is only computed over messages that reach the destination. Higher delivery rates frequently depend on higher latencies, as harder-to-deliver messages must be held in the network waiting to be delivered. Because links are supported by shared and limited mediums, typically wireless, the _communication overhead_ is also important. Excessive communications utilization can lead to the degradation of the network and thus impact the performance. The number of transmissions also impacts directly power consumption, a scarce resource in many mobile applications.
## 3 Results and Discussion
### Factors Affecting the Removal of 4-CP by the Combined MPHAC/PS System
#### 3.3.3 Effect of PS Dose on 4-CP Adsorption and Degradation  
The dose of PS plays an important role for the production of sulfate radicals and the subsequent degradation of 4-CP. In order to determine the role of PS in 4-CP removal using the MPHAC/PS system, different doses of PS ranging from 50 to 350 mg/L were investigated (Figure 6). The results showed that 4-CP removal factor increased with increasing the PS dose (i.e., 92.5%, 94.72%, 97.9%, and 99.5% for the PS doses 50, 150, 250 and 350 mg/L, respectively, after 90 min).  
As shown in Figure 6, at low doses of PS, the adsorption process plays a major role in removing 4-CP by the MPHAC/PS system. This can be explained by the fact that at low doses of PS, sufficient sulfate radicals are not produced resulting in low 4-CP degradation. This is obvious from the figure corresponding to 50 mg/L of PS dose. Moreover, by increasing the dose of PS from 50 to 350 mg/L, the degradation rate of 4-CP is enhanced, surpassing the adsorption one and inducing a higher 4-CP removal. This is due to the increase of sulfate radicals produced by the activation of PS at the catalyst surface [40]. Similar results were obtained by [PERSON] et al. [44], who claimed an improvement of the removal efficiency of 4-CP and a reduction of the required time to completely remove 4-CP when the concentration of the oxidant in magnetic biochar and the PS system was increased from 5 mM to 10 mM.  
Figure 5: Effect of MPHAC dose on 4-CP removal factor by the MPHAC/PS system (initial 4-CP concentration: 100 mg/L; PS dose: 150 mg/L; and a solution pH of 5).
## 3 Three-State Hidden-Markov Model
### Two-State Hidden-Markov Model  
As benchmark of the three-state HMM presented in Section 3, we now present the more used two-state HMM (Figure 6).  
In the two-state model, we define a cumulative Bad state \(b^{\prime}\), grouping together the Bad and the Medium states used in the three-state model. The probabilities of successful reception given a channel state are computed link-wise by taking the average PERs in each state. The transition probabilities, instead, are computed starting from the three-state model transition probabilities as:  
* \(p_{g{b^{\prime}}}=1-p_{g{g}}\),
* \(p_{{b^{\prime}}{g}}=\frac{p_{{g}{g}}\cdot p_{{g}}+p_{{b^{\prime}}{g}}\cdot p_{ {b}}}{1-p_{{g}}}\),
* \(p_{{b^{\prime}}{b^{\prime}}}=1-p_{{b^{\prime}}{g}}\),  
where \(p_{gg}\) is the probability of not having a transition at time \(n+1\) when a link is in the Good state at step \(n\) for the three-state HMM and \(p_{{s}},{s}\in\{g,m,b\}\) is the generic probability a link finds itself in the Good, Medium, or Bad state, respectively.  
While with the three-state HMM the transition matrix \(P^{n}\) at step \(n\) needs to be computed with matrix exponentiation as presented in Equation (5), in the simple two-state model the transition probabilities at step \(n\) can be obtained via the closed formula [22]:  
\[P^{n}=\frac{1}{p_{g{b^{\prime}}}+p_{{b^{\prime}}{g}}}\binom{p_{{b^{\prime}}{g} }}{p_{{b^{\prime}}{g}}}+\frac{\left(1-p_{{g}{b^{\prime}}}-p_{{b^{\prime}}{g}} \right)^{n}}{p_{{g}{b^{\prime}}}+p_{{b^{\prime}}{g}}}\binom{p_{{g}{b^{\prime} }}}{-p_{{b^{\prime}}{g}}}. \tag{6}\]  
In Figure 7 we report the transition matrices in the two-state HMM for the links under analysis.  
Table 3 shows the relevant averaged BER values for the two-state HMM.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Avg Good** & **Avg Medium** & **Avg Bad** \\ \hline Topology 2, link 4\(\rightarrow\)2 & 0.0051 & 0.0174 & 0.0281 \\ \hline Topology 2, link 3\(\rightarrow\)2 & 0.0048 & 0.0165 & 0.0338 \\ \hline Topology 1, link 1\(\rightarrow\)3 & 0.0066 & 0.0184 & 0.0448 \\ \hline \end{tabular}
\end{table}
Table 2: Average BER values, three-state HMM.  
Figure 6: Two-state channel model.
## 3 Results and Discussion
### The Second Law Efficiency of the Minichannel Heat Sink
The second law efficiency is evaluated by combining \(\dot{E}_{xd}\) and \(\dot{E}_{x}\) in Equation (32). The main trend is a decrement in the second law efficiency with increasing Reynolds number. This trend differs from that reported in [PERSON], et al. [7] for the flow through a rectangular microchannel at similar Reynolds numbers, which displayed a minimum and involved higher numerical values of second law efficiency. The cause of this trend change is unclear. One possible explanation is that the length scale difference between minichannels and microchannels causes a difference in the heat conduction resistance across the thicker minichannel walls. This may cause the second law efficiency minimum to occur at a higher Reynolds number in a minichannel than in a microchannel. The two-phase mixture model with \(\left(a_{nf}=1\%,\Phi=100\%\right)\) predicts a second law efficiency 15.15% higher than that from the single-phase approach at \(Re=93\) and 1.79% higher at \(Re=455\). Using \(\left(\alpha_{nf}\geq 2\%,\Phi\leq 50\%\right)\) this discrepancy reduces to a maximum of 0.0314% and of 0.0115% at Reynolds numbers 92 and 455, respectively. This reaffirms the \(\left(\alpha_{nf}=1\%,\Phi=100\%\right)\) combination not being an appropriate two-phase model setting. Upon close inspection, Figure 14 shows that that two-phase mixture model predictions with \(\left(\alpha_{nf}=2\%,\Phi=50\%\right)\) are second farthest from the single-phase approach prediction and that the two-phase mixture model \(\eta_{II}\) predictions display a monotonic and progressive approach to the \(\eta_{II}\) predicted by the single-phase approach, with increasing \(\alpha_{nf}\). This behaviour is mainly driven by the flow exergy term in the definition of \(\dot{E}_{x}\) and \(\dot{E}_{xd}\), which includes the exergy change due to friction over the channel walls, which is reflected in the inlet to outlet pressure drop in Figure 9. As the contribution from the flow exergy term is relatively small compared to that generated by the heat flux through the minichannel base, its effect on \(\eta_{II}\) is minor, as shown in Figure 14.  
Figure 13: Variation of the rate of exergy expended \(\dot{E}_{x}\) in the minchannel with the Reynolds number, over the range \(92\leq\text{Re}\leq 455\). Predictions from the single-phase approach and from the two-phase mixture model.
## 2 Related Works
### Security Requirements for VCC  
In the VCC environment, communications such as inside the vehicle, between vehicles, and between vehicles and the infrastructure utilize wired and wireless communications. Hence, there is a security threat in the existing VANET or cloud environment.  
The essential security requirements for VCC based services are Confidentiality, Integrity, Availability, and Privacy Protection, which we discuss in detail as follows:* **Confidentiality:** In a VCC environment, attackers can easily extort sensitive personal information such as the unique details of a vehicle or email address of driver, phone number, or residential address using man-in-the-middle (MITM) attacks. This is because the unique information of a vehicle or private information are used for application services, and in these cases, the attacker may tap the data using a Sniffing attack in-the-middle. Hence, this information should be encrypted to protect the data from MITM attacks.
* **Integrity:** Integrity should be provided for the information sent in a VCC environment. If the integrity of the driver's personal identification information, payment information, or location information is compromised by message forgery or a falsification attack, it could cause financial damage to the user or have fatal consequences in emergency situations. To defend against attacks on data integrity, important data should have its integrity guaranteed by hash functions and digital signatures.
* **Availability:** In VANET communication, a wireless network is used to communicate between vehicles. In the case of Cluster-based Routing (CBR), a header vehicle and adjacent vehicles form a cluster for mutual communication, and among these vehicles, a malicious user could interrupt or stop the service of the target vehicle with a Denial of Service (DoS) or Distributed Denial of Service (DDoS) attack such as Flooding or Jamming. Additionally, an attack on availability could occur if a malicious user hinders the routing of the vehicle network with a black hole attack [14; 15; 16]. To ensure availability, a vehicle should be authenticated through an authentication mechanism, and only the authenticated vehicle should be able to access the corresponding object.
* **Privacy Protection:** Because various applications are now provided inside the vehicle, privacy invasions can occur. When the black box data inside the vehicle is exposed, privacy is exposed. Additionally, because of the exposure of personal information, vehicle location information, and vehicle route provided by the navigation service, privacy invasion occurs. To prevent privacy invasions, encryption should be applied to important information. In addition, for identification or authentication of a vehicle or user, instead of using a unique ID, a random ID should be used to provide anonymity.
## 2 Tower Capacity
### Limit Capacity
#### 2.2.3 Discussion  
The surface \(\theta_{L}\), shown in Figure 3(a), can be fitted effectively by a cubic polynomial:  
\[U_{\text{lim}}=a_{00}+a_{10}\varphi+a_{01}L_{h}+a_{20}\varphi^{2}+a_{11}\varphi L _{h}+a_{02}{L_{h}}^{2}+a_{30}\varphi^{3}+a_{21}\varphi^{2}L_{h}+a_{12}\varphi L _{h}^{2} \tag{9}\]  
From Equation (9), we see that the limit of the wind speed that the tower structure can sustain has a second-power relation with the horizontal span and a third-power relation with the wind angle of attack. Compared with the horizontal span, the wind angle of attack has more intensive influence in the limit capacity of the tower under winds.  
From Figure 3(b),c, the region of surface \(\theta_{H}\) is a gradual-changing partial band. The tendency of surface \(\theta_{H}\) approaches to a linear plane, but the non-linear tendency becomes obvious with the proportion of the wind load transferred from transmission wires (i.e., \(H_{w}\)) increasing. The simulation results (see Appendix B) show that the structure fails in a brittle way if the wind load components \(H_{l}\) and \(H_{l}\) are dominant, while the tower structure fails in a ductile way if the wind load component \(H_{w}\) is dominant. Appendix B gives the capacity curves and failure modes of tower ZY under different combinations of three wind load components (Figure A1 in Appendix B). From the failures of tower ZY (Figure A2 in Appendix B), we can further see the damages of the tower are different under different combinations of three wind load components: the buckling damage occurs at the panel over tower legs when the sum of \(H_{l}\) and \(H_{l}\) accounts for the dominate proportion; the buckling damage occurs at the middle of the tower body when \(H_{w}\) accounts for the dominate proportion.  
Surface \(\theta_{L}\) and surface \(\theta_{H}\) show the limit of the wind-resistant capacity of tower from the perspectives of the wind speed and wind load, respectively. Accordingly, the limit state function of the transmission tower under winds can be written as:  
\[g_{1}=-f_{1}(U,\ \phi,\ L_{h};\theta_{S})=U_{\text{lim}}(\phi,\ L_{h};\theta_{S })-U \tag{10}\]  
\[g_{2}=-f_{2}(H_{l},\ H_{l},\ H_{w};\theta_{S}) \tag{11}\]  
The limit state function, as the primary function for fragility analysis, consists of a capacity function and a demand function. Surface \(\theta_{L}\) expresses a capacity function with the limit wind speed \(U_{\text{lim}}\) dependent on the wind angle of attack and horizontal span of transmission line. Surface \(\theta_{H}\) gives a capacity function in terms of three wind load components. The two capacity surfaces are high-dimensional especially when the structural parameters (presented by the vector \(\theta_{S}\)) involve uncertainties. Concerning the structural fragility of towers in a transmission line subjected to winds, the following study focuses on solving the capacity function that expressed by surface \(\theta_{L}\) (as well as by surface \(\theta_{H}\)) using surrogate models instead of high-fidelity models (e.g., finite element simulations).  
Figure 4: Capacity surfaces of tower ZY.
# Microbially Induced Desaturation and Carbonate Precipitation through Denitrification: A Review
## 4 Potential Applications of Denitrification-Based MICP Biotechnology
### MICP-Driven Applications
#### 4.1.4 Remediation of Artwork and Historical Monuments  
Physical, chemical and biological weathering is a common deterioration mechanism in various types of artwork, such as stone materials, lithiod materials, paintings, and frescoes [67; 68]. As a result, the surfaces of artwork and monuments are damaged and altered by nitration, sulfation, black crust, and the accumulation of dust and residual hydrocarbons [67]. Moreover, the mineral matrix of stonework can dissolve causing an increase in the material porosity and a decrease in mechanical properties [69]. Compared to the traditional physical and chemical approaches, biophysical solutions are considered to be more sustainable for the remediation and restoration of artwork and historical monuments because they provide powerful, inexpensive, gentle and environmentally friendly solutions and pose a low risk to human health [44]. [PERSON] et al. [44] successfully used a denitifying bacteria (_Bacillus cereus_) to induce carbonate precipitation to protect and regenerate the limestone, which is the base material used in statuary, and also in buildings of historic patrimony.  
Some other strains of denitfying bacteria, such as _Pseudomonas stutzeri_ and _Pseudomonas aeruginosa_, have also been employed in the removal of nitrates, mineral salts, and organic matter from the stones, frescoes and wall paintings of cultural heritage monuments under laboratory conditions [45; 46; 47; 49]. [PERSON] et al. [50] reported the potential of MICP for ornamental stone biorestoration and protection by using the isolated microorganism (i.e., _Pseudomonas chlororaphis_) from marble stones. The results demonstrated that the entire surfaces of the marble were covered with vaterite after 10 days and they were stable throughout the experimental runs.
###### Abstract
Although the algorithm proposed by [PERSON] [9] to estimate the human posture of students in schools has improved significantly in model performance, it has many parameters and the model training process is complicated. The DNN model proposed by [PERSON] et al. [10] uses the AlexNet network to capture different joint features; however, the weight parameters obtained by this method are more coupled with the training data distribution. In contrast, graph convolutional networks are more adept at handling typical non-Euclidean structured data such as human skeleton information. For example, the ST-GCN algorithm based on human skeleton joints proposed by [PERSON] [11] combines a graph convolutional network (GCN) [12] and a temporal convolutional network (TCN) [13]. It extends to the spatial-temporal graph model, which forms a hierarchical representation of the skeleton sequence through a spatial-temporal graph. The GCN module is used to learn the local features of adjacent joints in space, and the TCN module is used to learn the local features of joint changes with time. The space-time-separable graph convolutional network (STS-GCN) proposed by [PERSON] et al. [14] includes the temporal evolution and the spatial joint interaction within a single-graph framework, which favors the cross-talk of space and time, while bottleneck the space-time interaction allows to better learn the fully-trainable joint and time interactions. A novel graph-based method was proposed by [PERSON] et al. [15] to tackle the problem of 3D human body and 3D hand pose estimation from a short sequence of 2D joint detections. The method first uses a pre-trained cascaded pyramid network [16] for 2D pose prediction and then feeds it into a spatial temporal graph convolution network. The spatial temporal graph convolution connects each joint to its corresponding joint in adjacent frames in the time dimension and connects the joint points in each frame that have direct and indirect kinematic dependencies (adjacent and symmetric) in the spatial dimension. The joints are also classified into six classes, and different convolution kernels are used for training for different node classes. The manuscript structure is shown in Figure 1 and the rest of this paper is organized as follows. In Section 2, we introduce the work related to data preprocessing and model structure design. In Section 3, we introduce the improved algorithm in detail including the model structure, the model configuration strategy, and the model training strategy. Section 4 is the experimental part, including an introduction to the dataset used in the improved algorithm, model evaluation indicators, comparison experiments, ablation experiments, and experimental results analysis. Furthermore, finally, we conclude the paper in Section 5.
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 2 Problem Modeling
### Problem Formulation  
According to the interference and coverage model, the average communication capacity of the system is maximized to define the optimization problem.  
To circumvent the interference from the LEO system to the GSO system, it is necessary to make the off-axis angle of each beam in the inline scenario larger than the isolation angle threshold \(\mu_{th}\). In order to ensure seamless coverage of the LEO satellite, a certain overlapping coverage area needs to be guaranteed between adjacent satellites.  
Under the constraints of interference avoidance and seamless coverage, the maximized average communication capacity of the LEO satellite system can be expressed as the following optimization problem.  
\[\mathbf{P}1:\underset{\{X(\psi),K(\psi)\}}{\text{maximize}} \int_{0}^{90}\sum_{i=K(\psi)}^{N}c_{i}(\psi)d\psi\] (18) subject to: \[0\leq\chi(\psi)\leq\chi_{max}, \tag{18a}\] \[0\leq K(\psi)\leq N,\] (18b) \[K(\psi)\in Z^{+},\] (18c) \[\psi_{up}(\psi)-\psi_{down}(\psi+\delta)\geq\epsilon,\] (18d) \[\psi+\delta\leq 90,\] (18e) \[\psi\geq 0,\] (18f) \[\mu_{min}^{i}(\psi)\geq\mu_{thr},\forall i\in\{1,2,\cdot,N\}. \tag{18g}\]  
In the above optimization problem, the constraints include constraints on the maximum pitch angle (18a) and the maximum number of closed beams (18b), in addition to constraints on the seamless coverage (18d) and the harmful interference (18g). In (18g), \(\mu_{min}^{i}(\psi)\) equals to \(min_{|\sigma|<\sigma_{max}}\mu_{i}(\psi)\). For the progressive pitch scheme, both the LEO satellite pitch angle and the number of off beams increase as the satellite latitude decreases.