esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Frequency Omnidirectional Antenna Based on a Ring-Shaped Structure
###### Abstract  
A multi-frequency microstrip antenna loaded with a ring-like structure has been proposed. The radiating patch on the antenna surface consists of three split-ring resonator structures, and the ground plate consists of a bottom metal strip and three ring-shaped metals with regular cuts to form a defective ground structure. The proposed antenna works in six different frequency bands covering 1.10, 1.33, 1.63, 1.97, 2.08, and 2.69 GHz and works entirely when connected to 5G NR (FR1, 0.45-3 GHz), 4 GLTE (1.6265-1.6605 GHz), Personal Communication System (1.85-1.99 GHz), Universal Mobile Telecommunications System (1.92-2.176 GHz), WiMAX (2.5-2.69 GHz), and other communications frequency bands. Moreover, such antennas also have stable omnidirectional radiation properties across different operating frequency bands. This antenna meets the needs of portable multi-frequency mobile devices and provides a theoretical approach for the development of multi-frequency antennas.  
multi-frequency; microstrip antenna; split-ring resonator; defective grounding; omnidirectional radiation +
Footnote †: journal:  
0023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], M. A Multi-Frequency Omnidirectional Antenna Based on a Ring-Shaped Structure. _Micomachines_**2023**, 14, 994.
## Appendix A Computational Complexity
### Proposed Scheme  
The computational burden of the proposed LMSE method mainly depends on the operations of the block-by-block correlation in (20), the CFO estimation in (29) and the SFO estimation in (30). For each CP subcarrier, it follows from (20) that \(\tilde{Y}_{l}(k)\) requires one complex multiplication and \(N_{b}-2\) complex additions, which is converted to \(2(N_{b}+1)\) real flops. For the estimation of CFO \(\hat{\epsilon}\) in (29), it is performed for each CP subcarrier, and the summation over \(k\in\mathcal{S}_{\hat{\rho}}\) requires \(N_{\hat{\rho}}-1\) real additions. For \(N_{\hat{\rho}}\) subcarriers, \(\angle\tilde{Y}_{l}(k)\) needs \(N_{\hat{\rho}}(2N_{b}+2)\) real flops. Eventually, \(N_{\hat{\rho}}(2N_{b}+3)\) real flops are used in (29). To estimate the SFO \(\hat{\xi}\) in (30), the summation over \(k\in\mathcal{S}_{\hat{\rho}}\), denoted by \(\sum_{k\in\mathcal{S}_{\hat{\rho}}}k\angle\tilde{Y}_{l}(k)\), requires \(N_{\hat{\rho}}\) real multiplications and \(N_{\hat{\rho}}-1\) real additions. With those calculations in mind, it can be seen that the proposed LMSE scheme requires \(N_{\hat{\rho}}(2N_{b}+5)-1\) real flops.  
In the case of the proposed WLSE method, the computational operations of (39) and (40) are exactly the same as those of (18) and (19), except for the computation of (20). Since the presence of \(|\tilde{H}_{l}(k)|^{2}\) additionally consumes \(4N_{f}\) real multiplications and \(3N_{f}-1\) real additions in the WLSE compared to the LMSE, one can easily find that \(N_{\hat{\rho}}(2N_{b}+12)-2\) real flops are demanded in the proposed WLSE scheme.
## 4 Discussion
The activities of microorganisms are closely related to the availability of soil carbon and nitrogen and are very sensitive to changes in soil moisture. Under extreme drought conditions, the number and activity of microorganisms decrease, and the mineralization rate and flux of soil carbon and nitrogen are clearly decreased [52; 53]. Therefore, the soil water content during drought is the main driving factor controlling carbon exchange in an ecosystem, and the degree of reduction in the plant photosynthetic efficiency depends on the available water content of plants in the soil as well as the related physiological responses and structural changes of the vegetation [47]. The peatland ecosystem, in its natural condition, experiences extended periods of flooding. Additionally, a lot of litter in the soil cannot be decomposed due to the inhibition provided by low temperatures and an anaerobic environment. As such, soil organic matter is continuously accumulated, forming a partially degraded peat layer, which serves as a carbon sink to curb the rise in greenhouse gas concentrations [19; 54]. However, a deficit in soil moisture caused by drought stress accelerates soil drying in peatland, which has a significant effect on greenhouse gas flux in this ecosystem [55; 56]. In this study, it was found that the soil water content at different depths was positively correlated with \(\mathrm{CH_{4}}\) flux, and the maximum emission was reached when the soil water content was greater than 40%, which is consistent with the results of [PERSON] et al. [57]. [PERSON] et al. demonstrated that the absorption of methane is highest at the middle water level, and the relationship between methane flux and soil moisture is a bell-shaped curve. In general, there is a positive correlation between methane flux and soil water content at a depth of 15-45 cm [58]. [PERSON] et al. thought that the decrease of soil water content caused by drainage would reduce \(\mathrm{CH_{4}}\) emission from natural peatland and even transform it into a small sink for \(\mathrm{CH_{4}}\)[59]. With the decrease in soil moisture and the decrease in the degree of anaerobic, the peatland changed from an anaerobic to an aerobic environment, which reduced soil methane production and increased the thickness of the oxidation layer. The methane produced by anaerobic decomposition was oxidized by more methanogens, which reduced the emission flux of \(\mathrm{CH_{4}}\) and even caused a change in the \(\mathrm{CH_{4}}\) source/sink state [55]. It has also been suggested that drought can reduce the supply of microbial respiration [28], decrease the activity of methanogenic bacteria, and thus reduce the production and release of \(\mathrm{CH_{4}}\)[6].
# Influence of Magnetic Field on Amplification without Inversion Induced by a Femtosecond Pulse Train
###### Abstract  
Taking advantage of the short pulse duration and the high repetition rate of the fs pulse train, amplification without inversion (AWI) can be achieved in two- or three-level atomic systems. A modulation method using a static magnetic field to control such AWI gain in an atomic system is proposed. The schematic model of the \(5^{2}S_{1/2}-5^{2}P_{1/2}\) transition of \({}^{87}\)Rb is adopted, and the results show that the system gain can be significantly enhanced and modulated by the magnetic field, while the population inversion can be almost completely suppressed. Moreover, the pulse number, pulse repetition period, and pulse area can also be utilized to modulate the AWI gain.  
fs pulse train; Zeeman effect; amplification without inversion +
Footnote †: journal: Applied
## 4 Simulation Validation  
This section presents a comprehensive evaluation and comparative analysis of the control results of the proposed ESN-MPC and the traditional FCS-MPC under different operating conditions. Table 2 lists the parameters utilized in the test system. At the first step, a conventional FCS-MPC controller with stable performance is developed for the PMSM drive control. A dataset containing over million samples, obtained through extensive experimental trials, is employed for ESN training and validation. Specifically, 80% of the collected data are used for training, while the remaining 20% serve as a validation set. Importantly, the test dataset comprises samples different from those in the training, allowing effective performance verification of the proposed strategy beyond the training scenario.
## 6 Discussion  
QPI applications including cell counting, migration, and morphology assays have become increasingly popular, but several challenges still persist. The morphological label-free analysis ability of QPI is a fast, automatic, and cost-efficient evaluation tool for analyzing quantitative parameters, including cell area, thickness, volume, population confluence, and cell count. The need for QPI applications in clinical cancer diagnostics and treatments is emerging. There is a demand for tools to classify cells, and to determine cell morphology, differentiation, proliferation, morphological changes of cells transfected with DNA or siRNA, cell death, and effects on cell movement--all in a high-throughput manner. Since QPI is performed on live cells without any labeling, the cells can be investigated with other methods--or the cells can be cultured for longer periods after the analysis. However, extensive image analysis is required due to the richness of information in each image. Analyzing blood samples or tumor biopsies containing small subpopulations of drug-resistant cancer cells is also emerging with the increased need for individualized treatment. Interestingly, by using QPI in combination with machine-learning, the features provide biologically independent information across a variety of mammalian cell state transitions that can be used to standardize holograms for the purpose of kinetic single cell cytometry. The training speed and computer resources can be limitations in automated QPI using machine-learning. Also, it will be important in the near future to share data and to develop algorithms.  
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 721297, the Knowledge foundation in Sweden, Malmo University, and Biofilms--Research Center for Biointerfaces at Malmo University, Sweden.  
Malmo University covered the costs to publish in open access.  
The authors declare no conflict of interest.
## 1 Introduction
To validate the method, the transmission characteristics of one-trace and multi-trace structures are measured, analyzed, and compared to those obtained by VNA directly. Considering the mobility of the probe, the measurement accuracy caused by spatial and perturbance errors are also investigated. The rest of the paper is structured as follows: Section 2 describes the theories of an electric probe for electric field coupling and the computation of transmission characteristics extraction. Subsequently, Section 3 introduces the procedures of transfer function modeling and algorithm programming. Next, Section 4 illustrates the validation of the approach using typical structures, including a printed trace with a via hole, a printed trace with corners, and multi-traces with corners. The spatial and perturbance errors are also analyzed and discussed. Finally, Section 5 summarizes the conclusions of the proposed approach.
# Fault Detection in Active Magnetic Bearings Using Digital Twin Technology
## 7 Classification Algorithm
In order to implement the fault diagnostic procedure utilizing the suggested algorithm, the signals are acquired under various fault conditions during the operation of the Active Magnetic Bearing (AMB) system. These signals are then separated into two distinct sets: a training set and a testing set. The calculation of the vibration image is based on the signals present in each individual sample. The input for training the network parameters of the CNN consists of vibration images, while the output corresponds to the fault category  
Figure 12: The structure of the CNN. of each sample. Subsequently, the vibration images obtained from the testing samples are fed into the convolutional neural network (CNN) that has been previously trained, resulting in the determination of fault categories. The AMB system includes four sensors that have the capability of collecting six vibration images. The integration of the data from these six vibration images allows for a comprehensive representation of the fail-related information. It is necessary to develop a new network structure and integrate the data obtained from the vibration images. The redesigned topology of the CNN contains many branches and combinations. Every branch relies on other branches and includes multiple conventional layers and pooling layers. In total, there exist six branches. The structure of these branches consists of a sequence of densely connected layers. Figure 13 illustrates the structure of the multi-branch network. The integration of many branches results in a significant amount of information derived from vibration images, which is then collected by merging the signals from all sensors using a sequence of fully connected layers. The fault representation acquired has a greater amount of information and possesses a higher level of discriminability. In cases when obtaining and labeling experimental data is challenging, the digital twin model may be used to replicate the data after introducing a defect. The data that has been simulated is used to train the model, and the variables of the model that was trained are transmitted. Subsequently, the transfer model is fed with the real-time operating data of the AMB in order to identify its condition. This approach successfully resolves the issues of insufficient defect data and computational resources. Figure 14 illustrates the implementation method. Figure 14: The state recognition procedure relies on the use of the CNN and digital twin technology. Figure 13: The structure of the multi-CNN.
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
###### Abstract  
Electric vehicle (EV) fast charging systems are rapidly evolving to meet the demands of a growing electric mobility landscape. This paper provides a comprehensive overview of various fast charging techniques, advanced infrastructure, control strategies, and emerging challenges and future trends in EV fast charging. It discusses various fast charging techniques, including inductive charging, ultra-fast charging (UFC), DC fast charging (DCFC), Tesla Superchargers, bidirectional charging integration, and battery swapping, analysing their advantages and limitations. Advanced infrastructure for DC fast charging is explored, covering charging standards, connector types, communication protocols, power levels, and charging modes control strategies. Electric vehicle battery chargers are categorized into on-board and off-board systems, with detailed functionalities provided. The status of DC fast charging station DC-DC converters classification is presented, emphasizing their role in optimizing charging efficiency. Control strategies for EV systems are analysed, focusing on effective charging management while ensuring safety and performance. Challenges and future trends in EV fast charging are thoroughly explored, highlighting infrastructure limitations, standardization efforts, battery technology advancements, and energy optimization through smart grid solutions and bidirectional charges. The paper advocates for global collaboration to establish universal standards and interoperability among charging systems to facilitate widespread EV adoption. Future research areas include faster charging, infrastructure improvements, standardization, and energy optimization. Encouragement is given for advancements in battery technology, wireless charging, battery swapping, and user experience enhancement to further advance the EV fast charging ecosystem. In summary, this paper offers valuable insights into the current state, challenges, and future directions of EV fast charging, providing a comprehensive examination of technological advancements and emerging trends in the field.  
electrical vehicles; DC fast charging; power electronics converters; control strategies +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON] [PERSON]; [PERSON] A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology. _Appl. Sci._**2024,** 14, 4728. [[https://doi.org/10.3390/app1414728](https://doi.org/10.3390/app1414728)]([https://doi.org/10.3390/app1414728](https://doi.org/10.3390/app1414728))
### Motivation  
The purpose of almost multipath routing protocols is to use multipath as a substitute route when the network link is broken [6]. However, if large amounts of data are being transmitted, transmission over a single alternative route can be difficult to attain using only bandwidth management and queuing techniques. In addition, advanced attacks such as unplanned on-off are still difficult to detect and prevent the achievement of targeted missions. For these reasons, the requirements for mission-critical data may not be met, which can have the worst consequences for mission operations.  
We attempt to ensure trustworthiness and QoS by transmitting the data through all multiple possible routes that satisfy the requirements of each packet. Our mechanism ensures the successful detection of malicious nodes using flexible methods in MC-WSN and guarantees the trustworthy transfer of data. The proposed technology can provide clearer and more accurate trust evaluation through Q-learning in MC-WSN and transmit mission-critical data. As a result, The MC-WSN gateway quickly and correctly gather mission-critical data vital for situational awareness and operations.
# Towards Incidence Management in 5G Based on Situal Awareness
###### Abstract  
The fifth generation mobile network, or 5G, moves towards bringing solutions to deploying faster networks, with hundreds of thousands of simultaneous connections and massive data transfer. For this purpose, several emerging technologies are implemented, resulting in virtualization and self-organization of most of their components, which raises important challenges related to safety. In order to contribute to their resolution, this paper proposes a novel architecture for incident management on 5G. The approach combines the conventional risk management schemes with the Endsley Situational Awareness model, thus improving effectiveness in different aspects, among them the ability to adapt to complex and dynamical monitoring environments, and countermeasure tracking or the role of context when decision-making. The proposal takes into account all layers for information processing in 5G mobile networks, ranging from infrastructure to the actuators responsible for deploying corrective measures.  
5G; incidence management; information security; SDN/NFV; Situational Awareness +
Footnote †: journal: Future Internet
## 1 Introduction  
Ball bearings are important machine components that are used in vehicles, machines, airplanes, appliances, and precision equipment [1; 2]. Defects of ball bearings are the main cause of failure of a rotating machine [3; 4]. A ball bearing failure caused a mechanical machine to stop working, resulting in economic losses. Therefore, fatigue life prediction for bearing has an important significance and practical value [5]. Currently, bearing manufactures offer their products with a reliability based on the \(L_{10}\) life percentile, that is obtained through tests performed on static loads and at a certain rotation speed [6]. Therefore, machine designers and people who use machines need to know the actual reliability that corresponds to their own application. In various articles, [PERSON] explained how the \(L_{10}\) life equation has been analyzed in different models such as the [PERSON] fatigue life model, Lundberg-Palmgren model, Ionnides-Harris model, and Zaretsky model [7; 8; 9]. In 1962, The Lundberg-Palmgren life equation has been incorporated into the International Organization for Standardization (ISO). Over the years, the formula has been questioned for various reasons, one of which is that it was obtained by testing bearings of the same model and under laboratory conditions. The aim of this article is not to criticize the formula, but rather, to make use of it in a new methodology to obtain the reliability in a current ball bearing application. The actual reliability must be different from the reliability offered in the \(L_{10}\) life equation.  
Consequently, in this article, a new methodology to determine the reliability of a ball bearing in its actual application is given. The proposed method is based on the generated Hertz contact stresses values, which are present on the fixed outer ring race.  
Moreover, this proposed method is based on the traditional method to select a ball bearing, and in Hertz theory, both methods let us determine the magnitude of the generated contact stresses. Then, based on these Hertz stresses values, the parameters shape beta (\(\beta\)) and scale eta (\(\eta\)) parameters of the Weibull distribution are determined. Finally, by using them, the corresponding actual ball bearing reliability is determined. Consequently, since the actual reliability index is based on the generated stresses, then the proposed methodology can also be used to determine the reliability of a single-row deep groove ball bearings application.  
The present work is organized as follows. Section 2 explains the Hertz theory. Section 2.1 describes the steps to determine the contact stresses and the \(L_{10}\) life of a ball bearing, while Section 2.2 describes the steps to determine the contact area of a ball bearing. In Section 2.3, the steps to determine the principal stresses are given. Section 2.4 shows how to determine the Weibull shape \(\beta\) and scale \(\eta\) parameters, and Section 2.5 shows the steps to determine the actual reliability of the selected ball bearing. In Section 3, an application of the new methodology is presented. Finally, in Section 4, the conclusions are given.
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 4 Discussion and Future Work  
The growing reliance on advanced communication technologies, such as 5G, UAVs, and GNSSs, has introduced significant challenges for aviation safety and system performance. RFI poses risks that require a systematic and collaborative approach to mitigate. One of the key challenges is the lack of real-world data on 5G interference mitigation near airports. While theoretical models exist, comprehensive field trials and real-time monitoring are essential to understand the actual impact of 5G signals on radar altimeters and aviation navigation systems. Establishing exclusion zones and validating their effectiveness through data-driven methodologies can help minimize risks.