esa-sceva/satcom-chunk-collection huggingface dataset:
# Device Reliability Analysis of NNBI Beam Source System Based on Fault Tree
## 5 Fault Tree Analysis  
### Data Selection for Reliability Analysis  
Since the initial plasma discharge achieved by NNBI (Neutral Beam Injection), over 40,000 discharge experiments have been conducted. The facility employs a periodic maintenance strategy, where systematic maintenance is performed after a certain period of operation. Among the datasets accumulated from multiple maintenance operations, the data records generated from the most recent maintenance work are the most complete and comprehensive. Meanwhile, benefiting from the continuous maturity and development of technology, the quality of this batch of data is also optimal. The results obtained from the analysis based on this data are more representative; therefore, this study has selected the data from the most recent maintenance work as the analysis object. The most recent maintenance cycle spanned from November 2024 to April 2025, with a continuous operation duration of 166 days. This study selects fault events occurring within this cycle for reliability analysis (fault tree bottom events without actual occurrences but with potential risks are retained but excluded from analysis).  
During the experimental cycle, the system experienced 76 failures, specifically: 30 failures in the Negative Ion Accelerator, 27 in the Plasma Expansion Chamber, and 19 in the RF Exciter. Based on this, the following Formula (1) is applied [19]:  
\[\lambda_{T}=\frac{n}{T} \tag{1}\]  
where \(\lambda_{T}\) = failure rate, \(n\) = number of failures (unitless), \(T\) = total operational time (hours). The failure rates of each component are calculated using Formula (1), representing the probability of system failure within a fixed time period. Below are the failure rates of each subsystem (due to system complexity, calculations are limited to Level 3 of the fault tree) [22].
## 3 Results and Discussion
### Going from NIPS to VIPS: A Parametric Study  
As an attempt to combine the advantages of both VIPS and NIPS processes, a series of parametric studies to understand the role of several parameters on structure, CA and BP, were performed. The main objective is to obtain membranes which are highly porous with a surface morphology in favor of high CA, while keeping an acceptable BP (0.4-0.6 \(\upmu\)m).
# Plant-Derived Phytobiotics as Emerging Alternatives to Antibiotics Against Foodborne Pathogens
## 3 Mechanisms of Action of Phytobiotics Against Foodborne Pathogens
### Disruption of Central Metabolism by Phytobiotics  
Pytobiotics also exert antibacterial effects by interfering with the central metabolism of microorganisms. Key bacterial metabolic pathways--such as glycolysis, the tricarboxylic acid (TCA) cycle, and the pentose phosphate pathway--are disrupted by phytobiotics through altered substrate availability and modulation of enzyme activities involved in these processes [148; 149]. For instance, changes in pH can influence bacterial metabolism by affecting ATP levels and the NADH/NAD+ ratio, both of which are critical for microbial survival [154]. Some phytocompounds directly inhibit ATP synthase. Phenolic constituents of ginger, such as 6-gingerol and 6-shogaol, inhibit ATP synthase activity in _E. coli_, thereby reducing ATP production [155]. Similarly, resveratrolol and piceatannol--polyphenolic phytochemicals--bind to specific inhibitory sites on ATP synthase, enhancing its inhibition [156]. Certain phytobiotics also affect the proton motive force required for ATP synthesis. Extracts from _Donella welwitschii_ have been shown to disrupt the activity of the H+-ATPase pump, leading to reduced ATP production in bacterial cells [157]. These findings suggest that phytobiotics can effectively impair bacterial metabolic processes, thereby reducing both microbial growth and pathogenic potential. In the context of phytobiotics, energy spilling (overflow metabolism) is also worth mentioning. This phenomenon leads to inefficient energy utilization by bacteria, resulting in ATP loss that could otherwise be allocated for cell growth and maintenance [158]. Phytobiotics--including plant extracts and bioactive compounds--demonstrate the ability to modulate bacterial metabolism and energy use. For instance, clove essential oil and its active component--eugenol--have been shown to effectively reduce methane production and ammoniacal nitrogen waste in the rumen by inhibiting methanogenic and deaminating bacteria [159]. This activity may promote more efficient energy utilization by minimizing losses associated with methanogenesis and deamination [159, 160]. It is important to note that while phytobiotics exhibit antimicrobial activity by inducing oxidative stress, their presence may also trigger resistance mechanisms in microorganisms. Bacterial cells possess redox sensors that detect oxidative stress and activate detoxification pathways. Phytobiotics can influence these sensors, leading to altered gene expression and the activation of defense mechanisms. For instance, redox-responsive transcriptional sensors can oxidize thiol groups in cysteine residues, thereby regulating the transcription of genes involved in detoxification [161]. Moreover, phytobiotics may act at an additional regulatory level by modulating the expression of genes encoding antioxidant enzymes, thereby enhancing bacterial detoxification capacity against ROS [162, 163]. A summarized mechanism of action of phytobiotics on pathogenic microorganisms is presented in Table 4.
# The Shared Bicycle and Its Network--Internet of Shared Bicycle (IoSB): A Review and Survey
## 3 Key Technologies of IoSB
### Energy Harvesting  
In IoSB, bicycle nodes are battery-powered and often dispersed in the city that is hard to access for a battery replacement. For overcoming the problem of partial node death due to exhaustion of energy, energy-harvesting technology is particularly important. A comprehensive introduction of ambient sources for energy-harvesting has been given in [72].  
Solar energy is richly available in nature which is green and free of cost. A solar cell can convert the sunlight energy into electrical energy and stored in battery in form of chemical energy for the further use [73]. In [74], a solar energy-powered bicycle system was designed for the monitoring of immediate solar degree of illumination and ambient temperature. Existing shared bicycle like Mobike and Hellobike, are all using this kind of energy-harvesting technology. However, it has the disadvantage of being able to generate energy only when there is sufficient sunlight or artificial light.  
Vibrational, kinetic and mechanical energy generated by movements of objects can also be harvested. One method of harvesting mechanical energy (e.g., mechanical energy of cycling) is through the use of a dynamo installed in bicycle hub, which has a very high energy conversion rate but poor user cycling experience. In the classic Mobike, the installation of dynamo results in heavy bicycle body and hard riding.  
Ambient RF energy has a relatively low energy density compared to other energy sources [75]. However, owing to the rapid development of cellular networks, the ambient RF power density in city is getting higher and higher. The RF energy-harvesting technologies is especially useful in charging a battery or powering up electronics by wireless in scenarios of the deployed wireless networks, just like IoSB. However, some of its problems, such as low energy conversion rate and low coverage, remain to be solved [76].
# Assessment of Appropriate Geometry for Thermally Efficient CO\({}_{2}\) Adsorption Beds
## 4 Conclusions  
Three beds with different cross-sectional area shapes (circular, square, and triangular) are investigated to enhance thermal characteristics (heating and cooling) during the sorption processes of carbon capture. The cross-sectional area and bed length are the same for systematic comparison. Mg-MOF-74, as an excellent adsorbent, is used in this study. The CFD model is developed by a user-defined function coupled with commercial ANSYS-Fluent software. The model is validated against the experimental work completed previously by the authors. Based on the applied conditions, we present the following conclusions:  
Temperature values along the bed are lower for triangular cross-sectional beds than others, improving the adsorption and desorption processes. About 6 K is the reduction in temperature when a triangular cross-sectional bed is used compared to a circular one.  
Due to an enhancement in temperature cooling and heating of the triangular bed, the CO\({}_{2}\) adsorption uptake has higher values for this bed than others.  
The pressure drop is lower for the square bed, about 17% less than the circular one. The triangular bed has slightly lower values compared to the circular beds.  
Based on the above points, triangular cross-sectional beds could be an excellent candidate when pumping power is not important for temperature swing adsorption plants in which a bank of tubes is compact together with proper cooling between them. The circular beds suggested in the literature are less compact. The square beds could be suitable for pressure swing adsorption as the pressure drop is low. Furthermore, the square bed is the correct choice when considering both pumping power and CO\({}_{2}\) uptake.  
Conceptualization, N.A.A.Q.; Data curation, R.B.-M.; Formal analysis, N.A.A.Q.; Funding acquisition, R.B.-M.; Methodology, N.A.A.Q.; Project administration, R.B.-M.; Software, N.A.A.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by KFUPM grant number SB191021.  
Not Applicable.  
The authors thank the support received from the Deanship of Research Oversight and Coordination, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran, Saudi Arabia, under Project SB191021.  
The authors declare no conflict of interest.
# Optimisation of 2D U-Net Model Components for Automatic Prostate Segmentation on MRI
## 2 Background
### Structure  
Here, we will refer to structure as the overall shape of the architecture. For the task of segmentation, the deep learning model structures that are often adopted are FCN and U-Net type structures. U-Net consists of two FCN-like structures that are cascaded in the form of an encoder-decoder (autoencoder) structure. The encoder is used for feature extraction and the decoder is used for feature mapping to the original input resolution. The main difference between the FCN and U-Net structures is that the FCN does not learn the mapping of the high-level features to the original input resolution in a step-by-step manner as it relies only on the feature extraction part of the network to make the final classification. Although both these structures are able to produce good results on different organ segmentation tasks [26], many works on prostate segmentation show success using U-Net as their base model [21; 22; 23; 24; 25]. Moreover the basic U-Net has been used successfully for the segmentation of different parts of the prostate [27].
# Computer-Aided Diagnosis of Alzheimer's Disease via Deep Learning Models and Radiomics Method
## 1 Introduction
### Deep Learning and Convolution Neural Networks  
Recently, deep learning has become a hot research topic in the field of machine learning, and has made many achievements in computer vision and speech recognition [13; 14; 15]. Varied deep learning methods such as deep neural network (DNN), convolution neural network (CNN), and recurrent neural network (RNN) have been implemented to diagnose human neurological disorders [14].  
Deep learning is derived from artificial neural network, whose essence is the establishment of simulation structure of human brain neural networks and form a high-level feature representation through the combination of low-level features, in order to achieve efficient processing of complex data [15]. In 1962, [PERSON] [16] first proposed the concept of a receptive field; in 1980, [PERSON] [17] proposed a neural perceptron based on receptive field, and implemented the first CNN model. Subsequently, [PERSON] et al. [18] applied Back Propagation (BP) algorithm to CNN training, and successfully completed the image recognition. CNN has attracted extensive attention because of its parallelism, distortion, and robustness. One example shows the application of deep learning diagnosis [19]. Additionally, CNN and RNN are found to present better results than other deep learning methods in diagnosing [PERSON]'s disease [14].  
CNN is a feedforward neural network with local connection and weight sharing. Each layer of neural networks consists of a pair of two-dimensional planes, and each  
Figure 3: (**a**) T2-weighted MRI image. (**b**) PET image. (**c**) Fusion result of pixel-level fusion method. (**d**) Pixel-level image fusion flow chart.  
pair of two-dimensional planes is composed of a convolution layer and a lower sampling layer. Convolutional layer is also called feature extraction layers, which consist of multiple independent neurons. The function of convolutional layer is to extract the input data characteristics, and this approach is closer to the treatment of the human brain vision system. Figure 4 shows the basic structure of convolution neural network.
# Dual-Frequency Microwave Plasma Source Based on Microwave Coaxial Transmission Line
###### Abstract  
A dual-frequency plasma source has many advantages in applications. In this paper, a dual-frequency microwave plasma source is presented. This microwave plasma source is based on a coaxial transmission line without the resonator, and it can be operated in a wide band frequency region. Two microwaves are inputted from two ports into the plasma reactor: one is used firstly to excite the plasma and the other one is used to adjust plasma characteristics. Based on the COMSOL Multiphysics simulation, the experiment is carried out. In the experimental investigation, the plasma electron density and electron temperature can be controlled, respectively, by feeding in different frequencies from the second port, causing the particles at different energy levels to present different frequencies. This exploratory research improves the operation frequency of dual-frequency microwave plasma sources from RF to microwave.  
microwave plasma; dual-frequency plasma; electron temperature; electron density +
Footnote †: journal: Medicalfrom low-frequency wave to millimeter wave, could be inputted and propagated. The 2450 and 915 MHz microwaves, which are widely used in industry, are adopted in this investigation. There are two ports to input the microwaves. The 2450 MHz microwave is inputted from one port to excite plasma, and the 915 MHz microwave is inputted from another to adjust the plasma characteristics. To investigate the characteristics of dual-frequency microwave plasma, the microwave is inputted from two ports to excite the plasma, and a comparison is conducted between single-frequency microwave plasma and dual-frequency microwave plasma in the same reactor.
# Comparative Analysis of Different Model-Based Controllers Using Active Vehicle Suspension System
## 4 Numerical Simulation Results and Discussion
Although the ride comfort is enhanced by using LQR and ISMC, but the output obtained by STA-based HOSMC yields better performance. When active control signals are applied to the system, the suspension deflection reduces, as seen in the latter figure. The performance index \((PI)\) which is used to calculate various algorithms is stated as follows:  
\[PI=\frac{1}{2}\int_{0}^{t}H_{p}^{T}ZH_{p}dt \tag{42}\]  
Figure 10: Control Efforts. Figure 9: Wheel deflection. where \(H_{p}\) represents displacement vector, \(Z\) is an identity matrix. \(H_{p}^{RMS}\) is the root mean square value of displacement sprung and unsprung masses of the system, which are calculated as follows:  
\[H_{p}^{RMS}=\sqrt{\frac{1}{2}\int_{0}^{t}\left[h(t)\right]^{2}dt} \tag{43}\]  
\[\dot{H}_{p}^{RMS}=\sqrt{\frac{1}{2}\int_{0}^{t}\left[\ddot{h}(t)\right]^{2}dt} \tag{44}\]  
where \(h(t)\) and \(\ddot{h}(t)\) show the vertical displacement of the sprung mass of the system. The performance indices values of different proposed active controllers are shown in Table 3.  
Nonlinear control techniques, FOSMC and ISMC have an edge over linear control techniques i.e., PID and LQR. It is evident from the results that STA-HOSMC addresses all the problems and yields better performance as compared to other control algorithms.
# Artificial Intelligence to Reshape the Healthcare Ecosystem
## 4 Other Related Challenges
### Network and Computing Infrastructure  
In the ever-evolving landscape of AI healthcare, all the components of the Internet of the future are expected to have a significant contribution in terms of advanced data transfer, flexibility, and efficiency. This contribution can be declined based on the needs of health services. For example, the availability of digital twins requires the presence of a widespread, pervasive, and continuously available network. For these applications, the 5G system has already represented a technological turning point due to its performance, flexibility and adaptability [170; 171; 172]. As regards image-based diagnostic services, access to the applications available in the cloud must be constant, broadband, and secure. Furthermore, to adequately support remote surgery, in addition to the aforementioned characteristics, it is essential to have an adequate latency in data transfer, on the order of ms. Many other examples could be given, but it seems clear that the presence of AI is also indispensable in the creation and provision of network services. In this case, it is necessary to mention both research in Beyond 5G (B5G) and 6G [15]. It is also necessary to consider the considerable research effort in the intelligent management of edge computing services to reduce latency and improve data privacy by enabling local processing of sensitive data before they are sent to the cloud [173; 174].  
Concerning computing power, _quantum computing_ is considered a game-changer. In fact, due to the increasing computation complexity of medical computing, for handling complex situations computation can still be a significant bottleneck [175]. For this reason, a quantum computing service could make a difference [176]. For example, since DNA information enables personalized medicine through the development of new therapies and drugs, being able to achieve this quickly, at speeds that are orders of magnitude faster, would make the level of detail of models much higher. Even in medical imaging and digital twins, quantum computers have the potential to create efficient rendering systems that can provide doctors with greater fine-grained clarity in real time [176].
# PV Benefits Assessment for PV-Powered Charging Stations for Electric Vehicles
## 3 Conclusions
**Abbreviations**  
AGM  
Absorbed glass mat  
EV  
Electric Vehicle  
HOMER  
Hybrid Optimization Model for Multiple Energy Resources  
OR  
Occupancy Rates  
PV  
Photovoltaic  
PVCS  
PV-powered Charging Stations  
PVGIS  
Photovoltaic Geographical Information System  
SOC  
State of Charge  
STC  
Standard Test Conditions  
**Parameters**  
\(A\)  
Available area  
\(A_{p}\)  
Conventional surface of a parking spot  
\(A_{p0}\)  
Area of one PV panel  
\(A_{PV}\)  
Area of all PV panels  
\(\alpha\)  
PV panels inclination  
\(C_{max}\)  
Maximum capacity of stationary storage (batteries)  
\(C\)  
Stationary storage capacity (batteries)  
\(E_{Best-day}\)  
Total energy produced during the best sunny day  
\(E_{green-day}\)  
Energy required for charging an EV with the green profile  
\(E_{rec-red}\)  
Energy required for charging an EV with the red profile  
\(G\)  
Solar irradiation  
\(G^{\prime}\)  
Corrected solar irradiation  
\(N_{p}\)  
Number of parking spots  
\(N_{pv-max}\)  
Maximum number of PV panels  
\(N_{PV}\)  
Number of PV panels  
\(N_{terminal-green}\)  
Number of terminals used per 2-hours' time slot  
\(N_{terminal-red}\)  
Number of fast charging terminals  
\(N_{terminal-used}\)  
Number of slow charging terminals  
\(NT_{max}\)  
Theoretical maximum number of charging terminals  
\(NT_{max}^{\prime\prime}\)  
Real maximum number of charging terminals  
\(N_{terminals}\)  
Number of terminals  
\(\eta\)  
PV conversion efficiency  
\(\gamma\)  
PV panels orientation  
\(P\)  
PV power of panels  
\(P_{demand}\)  
Total demanded power  
\(P_{diff}\)  
Difference between the power requested by all the used terminals  
and the power supplied by the PV panels  
\(P_{G}\)  
Available maximum public grid power  
\(P_{p}\)  
Total peak power of PVCS  
\(P_{\tt STC}\)  
PV panel power at standard test conditions  
\(P_{s}\)  
Power exchanged with storage system or power grid  
\(P_{T}\)  
Maximum power delivered by each terminal  
\(P_{T-green}\)  
Maximum power of the slow charge  
\(P_{T-red}\)  
Maximum power of the fast charge  
\(t_{rec-green}\)  
EV charging time for the green profile  
\(t_{rec-red}\)  
EV charging time for the red profile
## 2 Thematic Review and Analysis
### System-Level Optimization
#### 2.5.2 Optimization Dilemmas  
In the context of system-level optimization, addressing the operational temperature is critical; however, it is equally important to consider other factors that contribute to the overall efficiency and effectiveness of the DH system. While optimizing the supply temperature to achieve a lower return temperature is a primary objective, several additional considerations must be integrated into the optimization process.  
[PERSON] [82] underscores the necessity of optimization by addressing the conflict between radiator oversizing and the temperature-boosting activities of peak plants, each of which influences the return temperature differently. Furthermore, [PERSON] et al. [124] emphasize the impact of boundary conditions on the optimal design of space heating systems, noting that the relative effectiveness of different configurations may vary depending on the specific boundary conditions.  
[PERSON] et al. [125] highlight that the additional electricity required by instantaneous heaters for domestic hot water production underscores the need for system-level optimization. This optimization should assess whether the benefits of improving the DH operation  
Figure 14: Influence of the return temperature reduction as the ratio of (i) reduction as obtained at the pump flow, pump electricity (EL) consumption (considering both equipped at DH and combined heat and power (CHP) cycle), DH network heat loss, and heat output by the CHP condenser, and (ii) increase as obtained at the CHP heat output by the flue-gas condensation and at the turbine electricity output, reproduced from the source data given in [45].  
temperature with these heaters can be outweighed by advancements in the DH network and heat source. Similarly, [PERSON] et al. [PERSON] et al. (2016) identify another optimization challenge related to add-on fans, noting that their power consumption might exceed the surplus electricity generated from reduced return temperatures at the DH heat production plant. Consequently, it is recommended to design an on-off fan controller that deactivates the fan when consumption surpasses excess generation. For a comprehensive analysis of add-on fan operations, see [PERSON] et al. (2016); [PERSON] et al. (2016).  
[PERSON] (2018) indicates that optimization is necessary for cases of radiator or space heating heat exchanger oversizing. The reduction in the heat transfer coefficient due to oversizing can negate the benefits of increased heat transfer surface area, thus underscoring the need for careful optimization.