esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Sensing Technologies for Cryo-Tank Composite Structural Element Analysis and Maintenance
###### Abstract  
This article focuses on activities addressed in the European project hydrogen lightweight & innovative tank for zero-emission aircraft, H2 ELIOS. The authors propose a preliminary approach oriented to the design of a structural health monitoring SHM system conceived for a cryo-tank liquid hydrogen storage for medium range vehicles. The system was ideated to be installed on board and operating during service, to provide early detection and localization of potential damage, critical both in terms of safety and maintenance. The use of optical fibers for strain measurement is justified, on one hand, by the capability of pure silica fiber to prevent hydrogen darkening effects and, on the other hand, by the absence of metal components, which eliminates the risk of embrittlement. In detail, distributed and fiber Bragg grating FBG sensors designed for this specific application have demonstrated reliable monitoring capabilities, even after exposure to hydrogen and at cryogenic temperatures. Furthermore, another key contribution of this preliminary activity is the analysis of thermoplastic material faults by correlating damage characteristics with static and dynamic response. This is due to the fact that the investigated physics strongly depend on the nature of occurring damage. Achievements lie in the demonstrated ability to assess the health status of the reference composite structure, establishing the first steps for a future qualification of the proprietary system, made of commercial and original hardware and software.  
damage; fiber optic; composite; tank; health monitoring Article
## 4 Performance Evaluation
### Hidden-Terminal Topology
Indeed, a sharp-eyed observer can notice that, for the high load end, the greater \(K\) is, the lower the latency, i.e., the latency when \(K=16\) is slightly smaller than that of \(K=8\), which is smaller than that of \(K=4\), which is smaller than that of \(K=2\), yet the differences are negligible (the difference between \(K=2\) and \(K=4\) is 3 ms; that between \(K=4\) and \(K=8\) is in the order of 1 ms; between \(K=8\) and \(K=16\), the difference is around 0.5 ms.  
Last, we examine a simple four-device topology, in which each device is hidden from the other three devices. We examine the setup under various traffic loads, starting from extremely heavy traffic (mean inter-arrival time 0.05 packet/s), which can represent a situation of an emergency event that requires the devices to send reports frequently (depicted in Figure 18). Figure 17: Hidden-terminal topology: The performance of a topology comprising 50 devices under various mean inter-arrival times (\(\lambda\)) and \(K=2,4,8,16\). (**a**) Sojourn time. (**b**) Duty cycle. (**c**) Latency because both the transmitting device and the sink are awake. Figure 18 depicts similar trends to those of the denser topology, in which, for high loads, the duty cycle of MAR-RiMAC is lower than that of RI-MAC at the expense of a slightly longer sojourn time. For light loads in which the sink can easily cope with the traffic, both the duty cycle and the sojourn time coalesce for both protocols. Note that, for very heavy traffic, MAR-RiMAC manages to cope with the intense load; hence, the devices manage to go to sleep. In contrast, RI-MAC cannot resolve collisions as fast as the arrival rate, i.e., the packet arrival rate is higher than the departure rate. Hence, all of the devices are awake at all times, and the sojourn time of RI-MAC is dramatically longer than the sojourn time of MAR-RiMAC. As soon as RI-MAC can resolve collisions at a faster rate than their arrival time, the sojourn time decreases dramatically, as does the duty cycle, as the devices can go to sleep. In this simulation section, we showed that the MAR-RiMAC amendment improves the performance and the air-time utilization in cases of high contention, when multiple devices are trying to communicate with the same receiver at the same time. It was shown that, in cases of sparse traffic, MAR-RiMAC preserves the performance attained by RI-MAC, despite the slight overhead. We believe that the dense network setup with high contention between devices--in which, even though each device has light traffic to transmit, the aggregated traffic from the numerous devices in the neighborhood causes high contention--is quite prevalent in current wireless sensors and IoT, and is going to be even more prevalent in future networks.
# Development of Enabling Technologies for Ku-Band Airborne SATCOM Phased-Arrays
## 6 Conclusions  
The reported technological solutions constitute the base of on-going developments for affordable phased-array antennas for commercial aeronautical satellite communications. Similar developments  
Figure 24: Measured pattern after polarization alignment: (**a**) Rx signal (11.7 GHz) impinging with 30 degrees misalignment with the nominal; (**b**) Tx signal (14.0 GHz) transmitted with 15 degrees polarization rotated.  
Figure 25: Measured Rx pattern of the active demonstrator: (**a**) only steering coefficients applied; (**b**) steering and polarization recovery coefficients applied.  
Figure 26: Transmit measured pattern when applying excitation coefficients relevant to \(-10^{\circ}\) steering angle and recovering \(30^{\circ}\) polarization misalignment.  
are currently ongoing at Airbus Italy targeting higher frequencies (i.e., Ka-band) as well as space-borne phased-arrays.  
Conceptualization, A.C., P.R. and M.A.; Validation, A.C. and M.A.; Supervision, G.T. and P.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by ESA/ESTEC ARTES 5.1 \"Advanced Antenna Concepts for Aircraft In-Flight Entertainment\" project, Contract No. 19508/05/NL/JA.  
The authors declare no conflict of interest.
## 3 Network Anomaly Detection
### Header and Payload Analysis
#### 3.2.2 Network Layer  
Time-To-Live (TTL) limits its lifespan to prevent a packet from circulating indefinitely in a network, but it can also be used maliciously. For example, an attacker can send a benign packet with a TTL large enough to reach the victim's network monitor, but not the victim's host. After that, the attacker sends another packet, with the same sequence number, but this time with malicious content and enough TTL to reach the victim. If the monitor ignores the \"retransmitted\" packet, it will not detect the attack, whilst checking every retransmitted packet may overload the monitor.  
Source and destination IP addresses are easily spoofed hiding the source of an attack. One possible way to detect spoofed packets is analyzing the TTL field, which is an indication of how many hops there are between the two hosts. As TTL should not change drastically with time, recording this field for each host helps in the detection of IP spoofed packets. [PERSON] and [PERSON] [66] describe this techniques, among others, to detect spoofed packets that, when combined, achieve a high detection rate.
###### Abstract  
This work aimed to investigate whether automated classifiers belonging to feature-based and deep learning may approach brain metastases segmentation successfully. Support Vector Machine and V-Net Convolutional Neural Network are selected as representatives of the two approaches. In the experiments, we consider several configurations of the two methods to segment brain metastases on contrast-enhanced T1-weighted magnetic resonance images. Performances were evaluated and compared under critical conditions imposed by the clinical radiotherapy domain, using in-house dataset and public dataset created for the Multimodal Brain Tumour Image Segmentation (BraTS) challenge. Our results showed that the feature-based and the deep network approaches are promising for the segmentation of Magnetic Resonance Imaging (MRI) brain metastases achieving both an acceptable level of performance. Experimental results also highlight different behaviour between the two methods. Support vector machine (SVM) improves performance with a smaller training set, but it is unable to manage a high level of heterogeneity in the data and requires post-processing refinement stages. The V-Net model shows good performances when trained on multiple heterogeneous cases but requires data augmentations and transfer learning procedures to optimise its behaviour. The paper illustrates a software package implementing an integrated set of procedures for active support in segmenting brain metastases within the radiotherapy workflow.  
MRI brain segmentation; brain metastases; machine learning; features extraction; convolutional neural network; medical software +
Footnote †: journal: Medical Image Analysis
# A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET
## 3 An RL-Based Framework of Congestion and Awareness Control
The vehicle's goal is to maximize the total reward it receives. This means maximizing not the immediate reward, but the cumulative reward in the long run. If the sequence of rewards received after time step \(t\) is denoted as \(R_{t+1}\), \(R_{t+2}\), \(R_{t+3}\), \(\ldots\), the maximized reward, where the return is \(G_{t}\), can be the sum of all the rewards at each time step until the final state. However, the BSM transmission is a continuous task without a time limit. To avoid the return being infinite, a discount rate \(\gamma\) is used to determine the  
Figure 1: Typical RL Cycle in V2V communication. present value of future rewards: a reward received \(k\) time steps in the future is worth only \(\gamma^{k-1}\) times the reward. Then when a vehicle selects an action \(A_{t}\) to send the BSM, the expected return can be expressed as the following formula:  
\[G_{t}=R_{t+1}+\gamma R_{t+2}+\gamma^{2}R_{t+3}+\ldots=\sum_{k=0}^{\infty}\gamma ^{k}R_{t+k+1} \tag{2}\]  
In Equation (2), \(G_{t}\) is the total return and \(R_{t+i}\) is the reward at each timestamp, where \(i\in\mathbb{N},i\geq 1\) and \(0\leq\gamma\leq 1\). When \(\gamma\) is close to 1, it means we take the future rewards into account more strongly. With RL, vehicles will learn by estimating how good it is to be in a given state or how good it is to perform a given action in a given state in terms of return. The higher the return, the better the state or the action just taken is. The mapping from each state to the probabilities of selecting each possible action is called a policy, denoted as \(\pi(a|s)\), which indicates the probability that \(A_{t}=a\) if \(S_{t}=s\). Solving an RL task means, roughly, finding a policy that achieves a high reward value over the long run [13]. At each state, a vehicle can have many actions to choose from, which means it can use different policies to choose an action. The value of taking action \(a\) in state \(s\) under a policy \(\pi\), denoted \(q_{\pi}(s,a)\), is the expected return starting from \(s\), taking action \(a\), and following policy \(\pi\). \(q_{\pi}(s,a)\) is called the state-action value function. The learning target is to find the optimal state-action value function which tells the vehicle the maximum reward it is going to obtain if it is in state \(s\) and takes action \(a\) under policy \(\pi\); it can be denoted as follows:  
\[q_{*}(s,a)=\underset{\pi}{max}\,q_{\pi}(s,a) \tag{3}\]  
After finding the optimal policy by \(q_{*}(s,a)\), the vehicle can then pick the action that gives it the optimal state-action value function as follows:  
\[\pi_{*}(a|s)= \begin{cases}1&if\ a=\underset{a\in\mathcal{A}}{argmax}\,q_{*}( s,a)\\ 0&\text{otherwise}\end{cases}  \tag{4}\]  
In Equation (4), when the agent is in state \(s\), it can just simply select action \(a\) which maximizes the value of \(q_{*}(s,a)\) and ignores other optional actions.
## 1 Introduction
### IPv6 Vehicular Networks  
eHealth is one application example that can be supported by ITS. Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) settings include several other examples of eSafety and infotainment application support. These applications can be roughly classified into two major types: safety-oriented or user-oriented [10]. Safety applications are clearly time-critical tasks, where message delivery with short delay guarantee is the first design goal. In these use cases, including safety on the road, non-IP communication technologies are often considered for their reliability [11]. In contrast, non-time-critical user-oriented applications include infotainment and other preventions on road applications. The use of IP (best effort) to extend the supported geographic area for these applications is possible [12].  
The use of IPv6 in current standardization work for vehicular communications technologies guarantees a better integration in the future Internet. For example, LTE technology supports IPv6 [13], which opens new V2I service perspectives [14]. In recent European Telecommunications Standards Institute (ETSI) activities, a geographic networking protocol combined with an IPv6 stack layer hasbeen experimented upon and standardized [12]. GeoBroadcasting safety messages by relaying messages through a V2V mode in the same geographic zone using IEEE 802.11p has also been experimented upon.
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 5 MIMONet: A DL-Based MIMO Detector
### MIMONet: Training and Testing
#### 5.2.3 Testing
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* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] ADMM-Based Infinity-Norm Detection for Massive MIMO: Algorithm and VLSI Architecture. _IEEE Trans. Very Large Scale Integr. (VLSI) Syst._**2021**, _29_, 747-759. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON] On the Design of Massive MIMO-QAM Detector via \(\ell_{2}\) Box ADMM Approach. _arXiv_**2022**, arXiv:2208.13122. * _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] A Model-Driven Deep Learning Network for MIMO Detection. In Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA, 26-29 November 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 584-588. * _[PERSON] et al. (2020)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Model-Driven Deep Learning for MIMO Detection. _IEEE Trans. Signal Process._**2020**, _68_, 1702-1715. [CrossRef]
* _[PERSON] et al. (2024)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] A low-complexity AMP detection algorithm with deep neural network for massive MIMO systems. _Digital Commun. Netw._**2024**, _10_, 1375-1386. [CrossRef]

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# Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
## 5 Conclusions  
In this study, we proposed the automated classification of COVID-19 from normal and pneumonia CXRs on an unbalanced dataset. The images were first preprocessed using a median filter and CLAHE to suppress the noise and enhance the contrast. The lung regions were then segmented using the DeepLabv3+ semantic method and subsequently cropped. The cropped lungs were fed as inputs to deep CNN models. The dataset was heavily unbalanced; therefore, to tackle this concern, we employed five different approaches: (i) substituting weighted loss as the loss of CNNs; (ii) increasing the number of CXRs for the minority class using image augmentation; (iii) undersampling; (iv) oversampling; and (v) hybrid sampling for fine-tuning CNN models. The top five models from each approach formed the ensemble classifier to improve the prediction performance. We achieved the highest accuracy of 99.23% and an AUC of 99.97% using an ensemble classifier of XceptionNet, MobileNetV2, DensetNet201, InceptionResNetV2 and NasNetMobile with image augmentation. Finally, Grad-CAM was used to identify the indicative regions of COVID-19 biomarkers. We envision the proposed algorithms in this study to be a step toward the development of automated screening or triage systems to facilitate medical experts in the diagnosis of COVID-19. The promising results of our algorithms require further validation for robustness using real-time clinical data. As this study shows, deep learning with image augmentation can improve COVID-19 detection accuracy; therefore, we are keen to apply advanced image augmentation methods such as generative adversarial networks (GAN) to obtain more synthesized CXRs. We also hope to develop integrated algorithms of COVID-19 detection using X-rays and CT scans.  
**Author Contributions:** Conceptualization, N.M. and K.Y.W.; methodology, N.M. and K.Y.W.; experiments, K.Y.W.; validation, N.M. and K.Y.W.; formal analysis, K.Y.W. and S.S.; investigation, N.M.; resources, N.M.; data curation, K.Y.W. and S.S.; writing--original draft preparation, K.Y.W. and S.S.; writing--review and editing, N.M., K.Y.W. and S.S.; visualization, K.Y.W.; supervision, N.M., K.H.; project administration, N.M.; funding acquisition, N.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was fully funded by JICA Technical Cooperation Project for ASEAN University Network/Southeast Asia Engineering Education Development Network (JICA Project for AUN/SEED-Net) under Special Program for Research against COVID-19 (SPRAC) 2020-2021.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data and code presented in this study are available upon reasonable request from the corresponding authors.  
**Acknowledgments:** The authors greatly appreciate AUN/SEED-Net for funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 1 Introduction  
With the recent developments of mobile networks, users profit from ubiquitous connectivity, ever-increasing data rates, and a wide range of emerging applications. On the other hand, our dependence on communication technology can lead to increased, or completely new, security risks. Cellular networks such as the Fourth Generation (4G) and Fifth Generation (5G) systems of mobile communications are among the most widespread and commonly used communication systems nowadays. As such, they are often used for managing various private systems as well as critical infrastructure, making them potential and attractive targets for cyberattacks [1, 2]. Attacks on mobile networks usually aim to compromise at least one of the requirements of secure communication, such as confidentiality, integrity, accountability, availability, or privacy. On top of that, several unprecedented risks may arise from the use of currently very popular Open Radio Access Network (RAN) architecture, as mentioned in the current report of the German Federal Office for Information Security on security related to Open RAN [3].  
In the last decade, several studies of 4G and 5G cellular networks have dealt with their security risks, e.g., [4] they have focused on the threats to voice and short message services. The survey paper [5] presented techniques for physical layer authentication, but only from the general methodological perspective. Study [6] was aimed only at the deep-learning techniques for physical layer security themselves. In contrast to [2], we include up-to-date 5G-related findings in the domain of physical layer security, provide an overview of machine learning, localization, and behavior-based methods to detect malicious basestations and include an overview of hardware tools and open RAN architectures suitable for practical experimentation.  
Although the focus of this study is on the techniques related to the Physical (PHY) layer, prior to diving deep into PHY and corresponding threats, we first provide an overview of the possible attack entry points. In Figure 1, the topology of a 4G and 5G network defines possible attack vectors for network breaches. While both Non Stand Alone (NSA) and Stand Alone (SA) architectures aim to deliver the benefits of 5G, they differ significantly in their approach to network infrastructure and security. The 5G NSA networks leverage existing 4G Long Term Evolution (LTE) infrastructure, sharing the 4G core network Evolved Packet Core (EPC) for the control plane, with the 5G RAN providing enhanced user plane capabilities. In contrast, 5G SA networks are built from the ground up, with all functions, including the control and user planes, residing in the 5G Core Network (5 GC). Depending on the network architecture, whether its 4G, 5G NSA, or 5G SA, potential entry points for threats can vary. These entry points can generally be categorized into four groups: the compromised mobile device, the access network, the backhaul network, and external or third-party networks, where each category can implement various technologies.
# Analysis of Dynamic Response of a Two Degrees of Freedom (2-DOF) Ball Bearing Nonlinear Model
###### Abstract  
Often the input values used in mathematical models for rolling bearings are in a wide range, i.e., very small values of deformation and damping are confronted with big values of stiffness in the governing equations, which leads to miscalculations. This paper presents a two degrees of freedom (2-DOF) dimensionless mathematical model for ball bearings describing a procedure, which helps to scale the problem and reveal the relationships between dimensionless terms and their influence on the system's response. The derived mathematical model considers nonlinear features as stiffness, damping, and radial internal clearance referring to the Hertzian contact theory. Further, important features are also taken into account including an external load, the eccentricity of the shaft-bearing system, and shape errors on the raceway investigating variable dynamics of the ball bearing. Analysis of obtained responses with Fast Fourier Transform, phase plots, orbit plots, and recurrences provide a rich source of information about the dynamics of the system and it helped to find the transition between the periodic and chaotic response and how it affects the topology of RPs and recurrence quantificators.  
ball bearings; nonlinear mathematical model; shape errors; radial internal clearance; diagnostics; recurrence analysis +
Footnote †: journal: Applied sciences
## 4 Results and Discussion
### Discussion Summary  
The integrated framework successfully optimized the tube making process, identifying 18 Pareto optimal solutions that improve the baseline performance (87.2% efficiency, 2007.5 kg CO\({}_{2}\)/day). The optimal balanced solution (P9) achieves the following:  
* 5.1% improvement in operational efficiency (87.2% \(\rightarrow\) 92.3%)
* 12.4% reduction in carbon emissions (2007.5 \(\rightarrow\) 1758.2 kg CO\({}_{2}\)/day)
* Maintained throughput above minimum requirements (>94 units/day)
* Reduced bottleneck impact through workload redistribution The practical value of the framework is demonstrated by the following:
* Real-time integration of process mining with environmental metrics
* Identification of viable trade-off solutions for different strategic priorities
* Quantitative guidance for bottleneck mitigation (Stations 202-205)
* Actionable recommendations for reducing rework loops (205 \(\rightarrow\) 204)  
These results validate the framework's capability to navigate the complex trade-offs between operational efficiency and environmental sustainability in real-world manufacturing systems, providing decision makers with data-driven insights for sustainable process improvement.