esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, a novel approach of studying the interaction of the fluid film, structure, and temperature is provided. Compared to previous models, the present work allows a fast simulation by solving the deformation using a software module. Additionally, the film height effect is particularly considered to improve the prediction of the friction force. A complete and quick fluid-structure-thermal interaction simulation is realized. The finite volume method is used to solve the Reynolds equation and energy equation. COMSOL is introduced to calculate the elastic deformation of the structure. Then, a dynamic model of the piston-cylinder interface integrated the temperature, and elastic deformation effects are developed. The numerical methods are used to the shifting velocity and the position of the piston. Finally, a novel approach for calculating the extra friction force between the piston and the cylinder is provided. Based on the above results, the following conclusions can be obtained:  
Figure 13: Comparison of the leakage of the piston–cylinder interface: (**a**) Condition 1; (**b**) Condition 2; and (**c**) Condition 3.  
1. The temperature greatly influences the lubrication performance. The dynamic viscosity will drop by 73% when the temperature rises from 293.15 K to 323.25 K at the pressure of 0.1 MPa. This will lead to the decrease in the film's load-bearing capacity and the increase in the leakage;
2. Insufficient film height brings extra friction force, which is related to the pressure in the area;
3. A dynamic model of the piston-cylinder interface integrated the temperature and elastic deformation effects can accurately predict the friction force.  
The purpose of the paper is to accurately predict the friction force and the leakage of the piston-cylinder interface with a comprehensive and easy-to-implement model. The model can be used to optimize the structural parameters and shape of the piston in the future.  
Conceptualization, J.Z. and D.W.; methodology, T.L.; software, T.L.; validation, J.Z. and T.L.; formal analysis, J.Z.; investigation, T.L.; resources, J.Z.; data curation, T.L.; writing--original draft preparation, T.L.; writing--review and editing, J.Z. and D.W.; visualization, T.L.; supervision, J.Z.; and project administration, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Not applicable.  
Thanks for the help of the Maha Fluid Power research center of Purdue University for providing the experimental test data.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Electromagnetic Simulation and Analysis for Dielectric Heating Applications
#### 3.2.2 Frequency Response of Power Intensity
At 9 cm, although the energy intensity reduces slightly, it remains sufficient for effective heating, with the added benefit of more evenly distributed energy in both scales and muscle. In contrast, distances of 3 cm and 12 cm are less suitable for dielectric heating applications. At 3 cm, although the energy absorption in fish scales is highly uniform, the overall intensity is excessively high. This increases the risk of overheating muscle tissue, which could lead to structural damage or reduced quality of the fish. Additionally, the extremely focused energy at such a short distance may not adequately reach parasites embedded deeper in the scales. This limitation aligns with the SAR Equation (3), which suggests that excessive energy concentration can lead to non-uniform tissue heating and potential thermal damage. At 12 cm, the energy intensity in both fish scales and muscle is significantly reduced and becomes fragmented in its distribution. This lower intensity compromises the efficiency of energy delivery, making it difficult to generate sufficient thermal energy to effectively target parasites or achieve uniform heating. This is consistent with Equation (2), as the greater distance reduces effective penetration and power absorption, making heating less effective. These findings underscore the importance of precise antenna positioning to balance energy intensity and distribution. Specifically, maintaining a distance within the range of 6-9 cm ensures optimal energy transfer, effective parasite elimination, and uniform heating, making it ideal for dielectric heating applications.
# Joint Optimization of Service Migration and Resource Allocation in Mobile Edge-Cloud Computing
## 1 Introduction
### Our Contributions  
In the rapidly evolving domain of MECC, the critical interplay between service migration and resource allocation commands increasing attention. This paper explores these intricacies by establishing robust service migration and computational models, enhanced by a reinforcement learning-based strategy aimed at minimizing service response times. Addressing gaps identified in previous research, particularly the overlooked aspects of user context migration and the inadequate attention to computational resource allocation, our research introduces an innovative framework. It adeptly manages user mobility and the need for uninterrupted service by migrating unfinished user data and context. Moreover, the framework dynamically adjusts service provisions and resource allocations in real time, significantly enhancing QoS within strict time constraints. This approach not only ensures service continuity but also improves responsiveness across MECC environments.  
The main contributions of this paper are summarized as follows:  
* **Comprehensive optimization of service migration and resource allocation:** We provide a thorough problem formulation that simultaneously optimizes service migration and resource allocation within MECC frameworks. Addressing the challenges of heterogeneous ES environments, our study tackles the intertwined issues of user mobility and fluctuating computational demands. The optimization strategically aims to minimize average response times, substantially enhancing QoS while meeting rigorous temporal constraints.
* **Strategic transformation into a Markov decision process (MDP):** Moving from theoretical models, this paper adeptly transforms the joint optimization challenge into an MDP. We introduce a novel deep reinforcement learning (DRL)-based algorithm to tackle this MDP, autonomously adapting migration and resource allocation strategies without relying on prior knowledge of system states.
* **Rigorous evaluation through simulation:** The efficacy and robustness of our proposed DRL-based dynamic migration and resource allocation strategy are rigorously tested through comprehensive simulations. Performance metrics, including task failure rate and average task response delay, serve as benchmarks. The results demonstrate that our DRL-based approach sustains high service quality and markedly reduces average response delays, thereby outperforming established benchmarks in this paper.  
The remainder of the paper is organized as follows: Section 2 summarizes the related work. Section 3 describes the system model in detail, which includes the migration model, computational model, and problem description. Section 4 provides a detailed description of the proposed A2C-based approach. The results of the simulation experiments are discussed in Section 5. Finally, Section 6 summarizes the paper and outlines future research directions.
## 3 Blockchain-Enhanced Security and Operational Efficiency in VANETs
### Architecture Overview
#### 3.1.3 Infrastructure Layer  
The infrastructure layer constitutes an intricate amalgam of specialized infrastructures and application platforms, which are designed to facilitate a wide spectrum of functionalities essential to vehicular ad hoc networks (VANETs), as can be seen in Figure 7.  
Figure 6: Illustration of the dual-layer blockchain architecture in VANETs.  
Figure 7: Overview of the infrastructure layer.  
This layer predominantly features the following integral components:
* Trust Authority (TA): Operating as the architectural cornerstone of the reputation chain, the trust authority is vested with the task of initializing the chain and orchestrating its ongoing management. By so doing, the TA not only assures the chain's integrity, but also underwrites its resilience against adversarial attacks, thereby fostering a secure and reliable ecosystem for reputation management.
* Emergency Response Units (ERUs): These units serve as indispensable assets in the VANET infrastructure, which are tasked with promptly responding to vehicular incidents based on real-time and historical data. Informed by the event chain, ERUs are capable of executing expeditious countermeasures, as well as formulating postincident strategies to mitigate risk and enhance operational efficiency.  
The blockchain-based reputation management model, as proposed herein, stands as a paradigm of robustness, scalability, and adaptability. It has been meticulously engineered to meet the multifarious requirements intrinsic to vehicular ad hoc networks, thus offering a comprehensive solution to the complex challenges of security and trust in next-generation vehicular communications.
# Nonlinear Vibrations of Low Pressure Turbine Bladed Disks: Tests and Simulations
## 3 Experimental Results  
The experimental tests performed as part of this research, in accordance with the designated setup and procedure, were carried out utilizing an EO = 8 synchronous excitation applied to the disk. The tests were conducted under the following configurations:  
* A tuned bladed disk without UPDs during testing.
* A tuned bladed disk with aluminum UPDs.
* A tuned bladed disk with steel UPDs.  
Each test was performed with both an increase and decrease in the angular velocity. This was conducted to verify the repeatability of the disk output response across various frequency values and to verify the possible presence of nonlinearity producing stiffening or softening effects of the resonance peak. The maximum response amplitude (\(\mu\)m, 0-peak) and the corresponding critical frequency (Hz) are plotted for each of the 48 disk blades in Figures 7 and 8.  
The numerical values are summarized in the following table (Table 1):  
Figure 8: (**a**) The maximum response amplitude and (**b**) critical frequency for each of the 48 blades as well as the averaged one in tuned and tuned + steel UPD configurations.  
Figure 7: (**a**) The maximum response amplitude and (**b**) critical frequency for each of the 48 blades as well as the averaged one in tuned and tuned + aluminum UPD configurations.  
Figure 9: The average value and standard deviation of the maximum response amplitude for (**a**) tuned (no UPDs), (**b**) tuned + aluminum damper, and (**c**) tuned + steel damper configurations. The average value and standard deviation of the critical frequency for (**d**) tuned (no UPDs), (**e**) tuned + aluminum damper, and (**f**) tuned + steel damper configurations.  
As can be noticed from the results obtained, there is a slight increase in the critical frequency of the ND8 mode with respect to that obtained by modal hammer testing, specifically from 503.2 to 509.8 Hz. This phenomenon occurs due to centrifugal stiffening.  
Furthermore, it is possible to evaluate the effectiveness of under-platform dampers (_d_) by comparing the average response amplitude in the presence of UPDs (_A damp_) with respect to those in the nominally tuned configuration (_Anom_).  
\[d=\frac{A_{damp}-A_{nom}}{A_{nom}}100 \tag{2}\]  
The results obtained are as follows:  
* Aluminum dampers: \(d=9.85\%\);
* Steel dampers: \(d=26.27\%\).  
As anticipated from the preceding analysis, it is evident that the steel UPDs exhibit a greater damping capacity compared to the aluminum ones. This experimental damping will subsequently be used for a comparison against the corresponding percentage derived from the numerical analysis, aiming to estimate the accuracy of the simulated model relative to the actual case tested in the laboratory.
## 4 Joint Message-Passing and Convex Optimization Framework for UAV Scheduling
### Algorithm Details
#### 4.2.2 Joint Optimization for Charging Matching and Wireless Energy Transfer
\[\sum_{\forall u_{j}\in\mathcal{U}}e_{(i,j)}(t)\leq C_{i}^{\max}(t ),\forall c_{i}\in\mathcal{C}, \tag{26}\] \[\sum_{\forall u_{j}\in\mathcal{U}}x_{(i,j)}(t)\leq n_{i},\forall c _{i}\in\mathcal{C},\] (27) \[\sum_{\forall c_{i}\in\mathcal{C}}x_{(i,j)}(t)\leq 1,\forall u_{j} \in\mathcal{U},\] (28) \[e_{(i,j)}(t)\leq\min\left\{E_{j}^{\max}-e_{j}(t+\Delta t_{(j\to i )}),\delta_{j}-E_{j}^{tr}(\Delta t_{tr})\right\}\cdot x_{(i,j)}(t),\forall c_ {i}\in\mathcal{C},\forall u_{j}\in\mathcal{U},\] (29) \[e_{(i,j)}(t)\geq 0,\forall c_{i}\in\mathcal{C},\forall u_{j} \in\mathcal{U},\] (30) \[e_{(i,j)}(t)\leq\min\left\{E_{j}^{\max}-e_{j}(t+\Delta t_{(j\to i)}),\delta_{j} -E_{j}^{tr}(\Delta t_{tr})\right\}\cdot\underbrace{0}_{x_{(i,j)}(t)=0}=0, \forall c_{i}\in\mathcal{C},\forall u_{j}\in\mathcal{U}, \tag{22}\]0  
The equations (i.e., (25)-(31)) in this mathematical program are all linear combinations by Theorems 1 and 2. The program determines the charging matching of \(u_{j}\) to \(c_{i}\) and the charging energy transfer from \(c_{i}\) to its associated \(u_{j}\) in each \(t\). At the end of each period, the remaining energy of each UAV can be calculated, as follows: XXX  
\[e_{j}(t+1)\triangleq\left\{ \begin{array}{ll}e_{j}(t)-E_{j}^{hc}(t),&\text{ UAV $u_{j}$ for task (i): $hovering$ for surveillance,}\\ e_{j}(t)+e_{(i,j)}(t)-E_{j}^{tr}(\Delta t_{tr}),&\text{ UAV $u_{j}$ for task (ii): $ round-trip traveling$ to charging tower,}\end{array} \right. \tag{32}\]where the energy of hovering and communications (i.e., \(E_{j}^{hc}(t)\) in (2)) is considered on top of current energy status \(e_{j}(t)\), in the case of task (i). For the case of task (ii), the energy of traveling (i.e., \(E_{j}^{tr}(\Delta t_{tr})\) in (4)) is expended and, moreover, the amount of charging energy (i.e., \(e_{(i,j)}(t)\), which is the optimal solution of mathematical program (25)-(31)) is recharged. For this convex programming problem (25)-(31), we use an off-the-shelf software package, called CVX and MOSEK, for solving the mixed-integer convex optimization problem [71; 72]. Consequently, we propose a polynomial-time optimization framework for the joint optimization of the charging matching of UAVs to charging towers and the wireless energy transfer between UAVs and charging towers, as shown in our final mathematical program (25)-(31).
## 3 Illustration of the Proposed Approach: Results of Case Studies
### Tensile Test
This trend in the \(f_{m}\) behaviour is confirmed in Figure 15--as long as plastic deformation is uniform, the average \(f_{m}\) value increases steadily. However, when the deformation begins to localise in a neck, the local strain rate increases drastically relative to that outside the neck. In response to this, \(f_{m}\) rises steeply, which is in qualitative agreement with the linear strain rate dependence of the AE median frequency proposed in [124]. The behaviour of the strain hardening rate indicates that uniform plastic deformation proceeds through different stages (the yield point followed by the Luders band propagation, and stages II and III can be discerned from the \(\theta(\varepsilon_{T})\) behaviour). The same stages can be seen, in principle, in the evolution of the average total mechanical power \(p_{M}\). The dissipated \(p_{D}\) and stored \(p_{S}\) powers exhibit opposite trends in the necking region: while the former shows a sharp increase, the latter changes its trend from ascending to descending. The smooth gradual increase in \(p_{D}\) and \(p_{S}\) with strain is observed in parallel with the progressive rise in the AE median frequency changing with the evolution of the dislocation microstructure. The overall strain hardening behaviour is nicely reflected by the AE power, which behaves in general agreement with the predictions of the phenomenological model of AE caused by dislocation-based strain hardening proposed in [125]. Specifically, the increased AE activity is noteworthy at approximately 0.07 strain, where the transition  
Figure 14: Distribution of the expended mechanical power density for the tensile test at three different points of interest indicated in Figure 13, corresponding to the initiation of the Lüders band (**a**,**b**), uniform deformation stage (**c**,**d**), and necking (**e**,**f**). between Stages II and III can be seen in the behaviour of \(\theta(\varepsilon_{T})\) and \(p_{M}(\varepsilon_{T})\). The in-depth discussion of the observed trends requires a bridge to be built between the thermodynamics of plastic deformation and the underlying microstructural evolution, which is beyond the scope of the present paper and will be discussed elsewhere. Nonetheless, importantly for the purpose of the present work, the proposed methodology, based on a comprehensive in situ monitoring strategy, follows the evolution of the microstructure with strain hardening, and provides the grounds for the progress in the thermodynamic analysis of plastic deformation.
## 4 Case Study
### Design of the Number of Preset Seams
#### 4.3.2 Lateral Pressure Coefficient Greater than One  
Figures 10 and 12 compare the SDEG and seam opening displacements of the lining for different preset seam configurations when \(\lambda\) = 1.50. These configurations include zero, two, three, four, six, and eight preset seams. For the monolithic cast concrete lining, diffuse cracking is still observed. In contrast, the damage factor within a 15\({}^{\circ}\) range at the seams of the segmented lining approaches zero. The SDEG at the far end of the lining is further reduced compared to the case when \(\lambda<1\), and crack formation also improves. When \(\lambda>1\), the horizontal compressive stress on the lining increases, and cracks tend to concentrate near the crown. Additionally, both the single seam opening displacement and total seam opening displacement decrease significantly compared to the case where \(\lambda<1\).  
The reinforcement stress and the maximum crack widths of the lining for each configuration are shown in Figures 11 and 13. It can be observed that the reinforcement stress for all preset seam configurations is below 200 MPa, and the corresponding maximum crack width of the lining is less than the design code limit of 0.3 mm. The maximum tensile stress significantly decreases compared to the case when \(\lambda>1\). For configurations with four, six, and eight preset seams, the maximum tensile stress in the reinforcement does not differ greatly. However, when six or eight preset seams are used, the reduction in the overall quality of the lining results in a slight increase in the maximum tensile stress in the reinforcement. Therefore, considering the SDEG of the lining and the maximum tensile stress of the reinforcement for different preset seam configurations, four, six, and eight preset seams meet the design requirements when \(\lambda>1\). Additionally, it can be observed that increasing the lateral pressure coefficient positively affects the lining's performance, and under higher lateral pressure coefficients, reducing the number of preset seams can still satisfy the design requirements.
## 3 Classification of MPPT
### Incremental Conductance Method  
The Incremental Conductance (INC) method is faster when compared to the HC and P&O method. The logic of INC method is at MPP, the derivative of power of PV with respective voltage is zero, negative on right of MPP and positive on left of MPP, on the \(P\) vs. \(V\) curve as expressed in Equation (5)  
\[\frac{dP}{dV}=\frac{d(IV)}{dV}\approx I+V\frac{\Delta I}{\Delta V} \tag{5}\]  
In INC method \(\Delta V\) term play important role to decide the tracking speed [40]. The flow chart of INC method is shown in Figure 6.
# Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI
## 2 Background
### Skip Connections  
Skip connections were originally implemented by ResNet [53] to address the degradation problem of training accuracy in deep networks. They help to prevent the deep model from having a high training error when compared to a shallow counterpart, as it simplifies the training when the additional complexity introduced by redundant layers in the network is not required [53]. An element-wise summation layer is used at the end of a skip connection hence keeping the dimension of the output layer fixed such that it adds neither additional parameters nor computational complexity [53].  
In U-Net [19], the skip connections are used to pass the features from one layer to another layer which are then combined with a concatenation instead of summation. The idea is to maintain the preceding layer information and re-use it in the later layer to achieve better performance.
# Plant-Derived Phytobiotics as Emerging Alternatives to Antibiotics Against Foodborne Pathogens
### Alkaloids  
Alkaloids are a diverse group of nitrogen-containing secondary metabolites that play several crucial roles in plants. Alkaloids can be classified based on their chemical structure, biosynthetic pathway, and natural origin. Within the major classes of alkaloids, there are several groups, each with distinctive structural features and specific biological properties (Table 3). Primarily, they serve as a defense mechanism against herbivores, pathogens, and competitors. Their toxic nature helps plants repel insects, animals, and microorganisms. In addition, they act as reservoirs of nitrogen, which is essential for plant growth and development. Some alkaloids act as growth regulators due to their structural similarity to known plant hormones [63, 64, 65].  
Pyrrolidines are alkaloids containing a five-membered ring with one nitrogen atom. An example of this class is prolines, which have important functions in plant metabolism. Pyrrolizidines are alkaloids with a bicyclic structure that contains nitrogen. This group includes, for example, lasiocarpine, known for its toxic properties. Quinolizidines are also characterized by a bicyclic structure with the presence of nitrogen atoms. This group includes compounds such as anabasine, which is used in the pharmaceutical industry and exhibits toxic effects in high concentrations. Tropanes, which include compounds such as atropine and cocaine, are known for their medicinal properties but also for their toxicity when used improperly. Atropine, used in medicine, has the ability to dilate pupils, while cocaine, despite its use in anesthesiology, is an addictive substance. Piperidines are alkaloids containing a six-membered ring with one nitrogen atom. An example of this group is piperine, an alkaloid found in pepper, which has been shown to stimulate the digestive system. Pyridines, such as nicotine, which is derived from the pyridine ring, are one of the more recognizable groups of alkaloids. Nicotine, found in tobacco, acts on the nervous system and is responsible for smoking addiction. Isoquinolines are a group of alkaloids with a complex structure, characterized by significant pharmacological activity. An example from this class is morphine, an alkaloid found in opium, which has strong analgesic but also addictive effects. Indoles are alkaloids that contain an indole ring. Compounds such as strychnine and reserpine are examples from this group, exhibiting a variety of biological activities. Strychnine, known for its neurotoxic properties, acts as a potent nervous system stimulant, while reserpine is used to treat hypertension [64, 65, 66, 67, 68].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Class** & **Examples** & **Sources** & **Properties** \\ \hline \multirow{3}{*}{Pyridine} & Nicotine, Anabasine, & Tobacco plants, & Stimulant effects, \\  & Coninine & _Anabasis aphylla,_ & addiction potential, \\  & Conium _macultatum_ & antimicrobial activity \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classes of plant alkaloids, their sources, and biological properties.