esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of LGE-MRI in Arrhythmia Ablation
## 2 Use of LGE-MRI for the Detection of Fibrosis  
### Principles of LGE  
LGE-MRI for the detection of myocardial fibrosis was already described in the late 1990s. Its validation was firstly performed with a canine model with controlled myocardial infarction and LGE-MRI was compared with histology [24]. Contrast agents that use mainly gadolinium are able to diffuse freely into the interstitium but are unable to pass through intact cell membranes, leading to their accumulation in the extracellular space.  
Fibrosis replacement produces an expansion of extracellular space so there is an increase in the volume of the distribution of gadolinium. More important, there is a prolonged washout of the gadolinium due the decreased number of capillary vessels in the scar tissue [25; 26]. Regarding T1 sequence, gadolinium contrast agents decrease the T1 relaxation time of adjacent tissue. Therefore, LGE enhancement produces an increased signal intensity in T1-weighted MRI images. In addition, other diseases related to the expansion of the extracellular space can be shown by LGE, such as oedema and inflammation formation. Given the lack of specificity for fibrotic tissue detection, the lesion assessment can be challenging [27].
# Service-Driven Dynamic Beam Hopping with Resource Allocation for LEO Satellites
## 3 A Cooperative Multi-Agent RL for Single-Satellite Beam Hopping
### Cooperative MARL-VDN Algorithm Framework
Update frequency of the target Q-network \(G\). Initialize:  Initialize an independent Q-network for each agent with randomly generated network, parameters \(\omega^{k}\). Initialize the target Q-network, set network parameters \(\omega^{-k}\leftarrow\omega^{k}\). Initialize the environment state \(S_{1}\). Initialize the experience replay buffer \(D\). Training: for episode \(i=1\) to \(MAX\_EpISODE\)do  Reset the environment. for time slot \(t=1\) to \(T\)do for agent \(k=1\) to \(K\)do  Obtain the global state \(S_{t}\) of the agent. Select action \(a_{t}^{k}\) using the \(\epsilon\)-greedy strategy. endfor  Execute the joint action \(A_{t}=(a_{t}^{1},a_{t}^{2},\dots,a_{t}^{K})\). Obtain the global reward \(R_{t}\) and transition to the next state \(S_{t+1}\). Store the experience information \((S_{t},A_{t},R_{t},S_{t+1})\) in the experience buffer. Each agent randomly samples a mini-batch of samples \((S_{t},a_{t}^{k},R_{t},S_{t+1})\) from the experience replay buffer \(D\). for agent \(k=1\) to \(K\)do  Calculate the target value \(y_{t}\) of the mini-batch data via Equation (24). Calculate the mean squared error loss \(L(\omega^{k})\) according to Equation (26). Update the individual Q-network parameters \(\omega^{k}\) of the agent using the Adam gradient descent algorithm. endfor  Update the target Q-network parameters \(\omega^{-k}\leftarrow\omega^{k}\) of each agent every \(G\) time steps. \(S_{t}=S_{t+1}\) endfor endfor
```  
**Algorithm 1** Cooperative MARL-VDN algorithm for dynamic beam hopping
## 2 Materials and Methods  
### Study Site  
South Tyrol is a typical dry inner Alpine region located in the central part of the Alps. Its topography varies between 190 m a.s.l. (southern Adige Valley) and 3905 m a.s.l. (Mount Ortler), with 86% of the area situated above 1000 m a.s.l. [54]. The local climate is shaped by a Mediterranean-Temperate and Oceanic-Continental gradients following the South-North and West-East directions respectively. Precipitation pattern is strongly regionalized. Annual rainfall sums vary between almost 500 mm in the west (Vinschgau Valley), around 700 mm in the central (Adige Valley and Eisack Valley), and 1200-2000 mm observed in the North-East (Pustertal Valley) part of the region. Moreover, local climate variability is shaped by the temperature gradient [12] and insolation [19], both being a result of altitudinal change.  
Woodland occupies around 43% of the area of South Tyrol (namely 3170.5 km\({}^{2}\)). Forest structure is dominated by coniferous stands (90.1%), with mixed coniferous/deciduous (7.3%), and broadleaved forests (2.6%) near the valley floors. Following climate variability, forest forms altitudinal distribution belts with regional variations governed by local-specific conditions. In the valley floors and on low slopes the most common are hardwood stands of Sessile and Downy Oak (_Quercus Petraea_ and _Quercus pubescens_, respectively), Hop Hornbeam (_Ostrya carpinifolia_), Manna Ash (_Frazinus ornus_) and Sweet Chestnut (_Castanea sativa_). With an increase of elevation Downy Oak-Common Beech and Common Beech (_Fagus sylvatica_) forest is introduced, with Scots Pine (_Pinus sylvestris_) growing in the most arid locations. Common Beech, Norway Spruce (_Picea abies_) and Silver Fir (_Abies alba_) stands are the most abundant on higher elevations, but on sunny and drier slopes are substituted by Scots Pine and European Larch (_Larix decidua_). The tree line is formed by European Larch and Arolla Pine (_Pinus cembra_) that gradually transfer into dwarfed trees and shrubs [55].
## 3 Materials and Methods  
### Datasets  
The main color image datasets used in this work are RIM-ONE DL and HUC RGB. The publicly available image set RIM-ONE DL [25] resulted from the combination of the three previous versions of the well-known RIM-ONE dataset [26]. This new version consists of 313 images from healthy subjects and 172 images from patients with glaucoma acquired at three different hospitals. On the other hand, the HUC RGB dataset is a privately accessible set of retinal images collected by the medical specialists of our research team at the Hospital Universitario de Canarias. This set is composed of 191 images of patients with glaucoma and 63 images of healthy patients, and has been acquired with the Topcon TRC-NW8 multifunctional non-mydriatic retinograph (Topcon Healthcare, Spain). Considering the two sets, a total of 739 images have been used: 363 of glaucomatous eyes and 376 of healthy eyes. All these images have been annotated by two experts and include manual disc and cup segmentation.  
Two other publicly available datasets have been used for further validation of the experiments carried out, namely REFUGE and DRISHTI-GS1. The REFUGE challenge dataset [27] is composed of 1200 retinal images, of which 10% (120 samples) correspond to glaucomatous subjects, including primary open-angle glaucoma and normal tension glaucoma. The dataset also contains the ground truth segmentation of the disc and cup. The DRISHTI-GS1 dataset [28] contains 101 fundus images, 31 belonging to healthy subjects and 70 to glaucomatous ones, with different resolutions and ground truth labels for the optic disc and cup.  
Since the area of interest for glaucoma diagnosis is concentrated around the optic nerve head, all images used in this study were cropped squarely around it using the same proportionality criteria as shown in Figure 1b. Table 1 contains a summary of the aforementioned datasets, with the number of healthy and glaucomatous images per dataset and their resolution after cropping them around the optic disc.
# An Enhanced Feature Extraction Network for Medical Image Segmentation
## 4 Experiment
### Datasets and Results
#### 4.2.3 Results on Lesion Boundary Segmentation Dataset from ISIC-2018  
The lesion boundary segmentation dataset is a large-scale dataset which is published by the International Skin Imaging Collaboration (ISIC). It contains 2594 original dermoscopy images and 2594 corresponding binary masks. Lesion boundary segmentation takes its Task 1 from ISIC. It is very useful to analyze dermatoscope images, which can help doctors find potential skin diseases such as skin cancer in advance. Similar to other approaches utilizing this dataset, we randomly partitioned it into three subsets, following the proportions of 80%, 10%, and 10% for training, validation, and testing, respectively. Then, 53,950 training samples and 6734 validation samples are obtained by applying 25 different data augmentation methods. The experimental results of each method in this dataset are shown in Table 4.  
Table 4 shows the results on the lesion boundary segmentation dataset from ISIC-2018. EFEN is compared with the baseline network and recent works with the same settings on the same dataset for evaluation. We observe that EFEN achieves a validation DSC of 89.92%, exceeding U-Net (87.46%) by 2.46%. In addition, our method outperforms DoubleU-Net with 0.3% and 0.13% improvements on DSC and mean IOU, respectively. Of course, it should be noted that our method still has room for improvement compared to the state-of-the-art method BAT, which is based on a transformer and requires a larger amount of training data. We acknowledge this gap and remain committed to further exploring and enhancing our method in future research.  
The results of visual analysis are shown in Figure 5. We need to segment the target with possible skin diseases from dermoscopy images, even when the skin diseases have different characteristics. Although the same disease appears in different people, the size, boundary and color of the affected area may be different, and there may be some other problems such as hair interference. We select some results with obvious contrast effects to show. Through the comparison of visualization results, it is not difficult to find that our method is better than U-Net in segmenting skin diseases with unclear boundaries and unclear targets. For the segmentation of small targets, such as the first column, our method is better than the baseline method. More details are shown in Figure 5. The red box shows the segmentation results of the same area in the same image using different methods.
## Experimental Results
### Impact of Pre-Training  
The above experiments show that the pre-training of SaSLM plays an important role in improving final performance. Therefore, we investigated the impact of pre-training in detail to answer **RQ4**. Specifically, we pretrained the rating prediction module of SaSLM via ARM, VOARM, and ESARM. For comparison, we also train SaSLM without pre-training.  
The statistics in Figures 5 and 6 show that the performance of SaSLM can be improved greatly with pre-training. This is partially because pre-training embeddings decrease the possible instability of the process of learning subsets. Although the two modules, subset learning and rating prediction, are optimized alternatively, the subset learning module is primarily optimized based on the item ratings generated by the randomly initialized rating prediction model. By pre-training with different methods, the results achieved by SaSLM were also slightly different.  
We first analyze the performance in set-rating prediction, as shown in Figure 5. On the RealSet-explicit dataset (Figure 5a,b), pre-training with ESARM led to the best performance. However, on the RealSet-implicit dataset (Figure 5c,d), the situation was the opposite: pre-training by ESARM resulted in worse performance than the other pre-training methods. This is justifiable, since, as shown in Section 6.1, ESARM generates the best set-rating prediction for RealSet-explicit but is outperformed by ARM and VOARM for RealSet-implicit. This shows that the initial performance achieved by the pre-training method has a close connection with the final performance of SaSLM.  
effective is that it considers the diversity of item ratings to avoid the over-training of rating prediction models.  
Figure 5: Performance in set-rating prediction under different settings, using different pre-training methods. (**a**) MF, RealSet-explicit. (**b**) NCF, RealSet-explicit. (**c**) MF, RealSet-implicit. (**d**) NCF, RealSet-implicit.  
Figure 6: Performance in item-rating prediction under different settings, using different pre-training methods. (**a**) MF, RealSet-explicit. (**b**) NCF, RealSet-explicit. (**c**) MF, RealSet-implicit. (**d**) NCF, RealSet-implicit.
## 4 Method
### Encrypted Traffic Preprocessing  
Encrypted traffic processing is illustrated in Figure 4. Using a specific example where a host (\(IP_{1}\)) communicates with two different targets (\(IP_{2}\) and \(IP_{3}\)), three TLS sessions are established between \(IP_{1}\) and \(IP_{2}\), and two TLS sessions between \(IP_{1}\) and \(IP_{3}\), generating a total of 70 network traffic packets. These packets are stored in a PCAP file format. In this paper, we perform the reverse operation to process the PCAP file. First, the network traffic packets are extracted from the PCAP file and reconstructed according to the TCP/IP and TLS protocols. This involves reorganizing the packets from flows that share the same source IP address, destination IP address, source port, destination port, and protocol into a single session. The binary data are then converted into structured  
Figure 4: Preprocessing and feature extraction process for encrypted traffic data. protocol fields. Following this, session-level features are extracted, which are subsequently aggregated into IP-level features to generate a base feature vector for each accessing IP node. In this paper, we utilize Zeek [23] to process encrypted traffic data contained in PCAP files. Zeek can automatically parse the TCP/IP protocol stack and delve into the TLS layer to restore detailed session information. This includes not only basic TCP/IP layer data--such as source and destination IP addresses, source and destination ports, and protocol types--but also TLS-specific information, such as the TLS version, cipher suite, and server name. For each identified session, we extract the following session-level features, as presented in Table 1.  
The need to aggregate session-level features into IP-level features arises from the fact that malicious IPs often engage in both benign and malicious activities. Not all sessions generated by an attacker's IP are necessarily malicious. Thus, relying solely on the behavior of individual sessions to determine malicious intent can lead to inaccurate conclusions. To provide a more accurate evaluation of an IP's behavior, it is essential to aggregate all sessions associated with that IP, thereby capturing its comprehensive behavioral characteristics and improving the identification of attackers. Session-level features consist of two types: numerical features and categorical features. Accordingly, the following two aggregation methods are applied:  
* Numerical Feature Aggregation: For each IP, we calculate statistical metrics for the numerical features of all associated sessions, including the mean (avg), maximum (max), minimum (min), and standard deviation (std). As a result, each session-level numerical feature produces four new aggregated statistical features. * Categorical Feature Aggregation: For categorical features, such as TLS version and cipher suite, we apply the TF-IDF (Term Frequency-Inverse Document Frequency) method for feature extraction. In this context, each enumeration value is treated as a \"word\", and each IP as a \"document\". To control the feature space's dimensionality
# Design of Mega-Constellations for Global Uniform Coverage with Inter-Satellite Links
## 2 Mega-Constellation Design Model
### Mega-Constellation Configuration Design
Step7: Output the number of satellites contained in each sub-constellation, \(N_{C_{1}},\;N_{C_{2}},\;\dots,\;N_{C_{\frac{m}{2}}}\), respectively. Figure 2: Schematic diagram of sub-constellation generation according to the sub-satellite point density of constellation in iterative order. The configuration of a Walker constellation depends on the three parameters (\(N\), \(P\), and \(F\)). Through the above steps, we obtain the \(N\) of all sub-constellations in the mixed Walker constellation and ensure that the sub-satellite points of the mega-constellation are evenly distributed along latitude. The next work is to determine the \(P\) of each sub-constellation and guarantee that the sub-satellite points of the mega-constellation are relatively uniformly distributed along the longitude direction. When the number of satellites is huge, the \(F\) has little impact on the constellation configuration. Therefore, the \(F\) can be selected within a reasonable range. For the Walker constellation with a given \(N\), the smaller the deviation between the right ascension of ascending node (RAAN) difference of the adjacent orbits and the phase difference between the adjacent satellites in the same orbit plane, the more evenly the constellation's sub-satellite points spread globally. Meanwhile, considering collision avoidance between satellites, the constraint must be satisfied that the RAAN difference of any two satellites in the same Walker constellation cannot be equal to \(180^{\circ}\). As a result, the orbital plane number of the \(j\)-th sub-constellation is determined by:  
\[P_{C_{j}}=\Bigg{\{}P|\min\left(2\pi\left|\frac{1}{P}-\frac{P}{N_{C_{j}}}\right| \right),mod\left(N_{C_{j}},P\right)=0,mod\left(\pi,\frac{2\pi}{P}\right)\
eq 0\Bigg{\}}\]  
The parameter \(\Omega_{0}\) defines the constellation's positional datum. Once the \(\Omega_{0}\) has been determined, the absolute position between sub-constellations can be established. To make each sub-constellation uniformly distributed, set the \(\Omega_{0}\) of the \(j\)-th sub-constellation to \(4\pi j/m\).
## 4 Simulations and Analysis
### Receiving Depth  
When sending control signals, transmitting device is usually located below the surface of the sea while the depth of receiving device is not fixed. The first simulation is designed to model UWA channels with different receiving depths and examine the influence to OFDM channel estimation. In this simulation, the energy converter is 10 m in depth and the acoustic signal is evenly emitted from a 30-degree emission angle. The depth of receiving device is set to 10 m, 30 m, 50 m, and 70 m, respectively. In order to control the effect of sea surface and sea bottom in this simulation, UWA channels are modelled in the sea area of scenario A (flat bottom boundary) with flat sea surface boundary.  
Figure 6 illustrates the eigenrays path of modelled channels during propagation. The color of lines indicates whether eigenrays hit surface boundary or bottom boundary of the sea, and basically there are four cases: The black line represents eigenray hitting both boundaries; the blue line represents eigenray hitting bottom only; the green line represents eigenray hitting surface only; and the red line represents eigenray hitting neither bottom nor surface. As can be seen in the eigenrays figure, the main eigenray type is black line. In shallower depth, the number of blue lines is greater than that in deeper depth.  
Different types and lengths of eigenrays lead to different transmission losses and time delay. The normalized CIR is calculated by accumulating all eigenrays. Figure 7 shows the normalized CIRs  
Figure 6: Eigenrays of channel with different receiving depth. The color of lines indicates whether eigenrays hit surface boundary or bottom boundary of the sea. Receiving depth of each figure: (**a**) 10 m; (**b**) 30 m; (**c**) 50 m; (**d**) 70 m.  
of channel with different receiving depth. Comparing the four normalized CIRs, the normalized CIR with 10 m receiving depth is more concentrated in low time delay and has a maximum amplitude of 0.9415. Whereas the other normalized CIRs are scattered and have maximum amplitudes under 0.5.  
Figure 7(a) shows BER performance of channels in different receiving depths. It is obvious that the performance of channel estimation in 10 m depth channel is much better than others. The gap of performance between 10 m depth channel and other channels is gradually increasing as SNR increases. With a 30 dB SNR, the gap in LS algorithm reaches around 20 dB and the gap in LMMSE algorithm reaches around 30 dB. Comparing the performance of LS and LMMSE algorithms in different channels, the difference between LS and LMMSE algorithms is larger in 10 m depth channel. MSE performance of channels in different receiving depth is illustrated in Figure 7(b). The curves of LS algorithm have the same performance due to the principle of algorithm. Furthermore, the performance of MSE has similar features to that of BER in LMMSE algorithm.  
Figure 7: Normalized channel impulse responses (CIRs) of channel with different receiving depth. Receiving depth of each figure: (**a**) 10 m; (**b**) 30 m; (**c**) 50 m; (**d**) 70 m.
## 1 Introduction  
A fault is a discrepancy between the actual and expected value of a parameter. Fault tolerance refers to a system's capacity to continue operating under malfunctioning conditions [1]. Any real-world system can develop faults, lowering the system's dependability and performance [2]. Fault-Tolerant Control (FTC) is a technique that may be used to improve the dependability of important systems such as nuclear power plants and airplanes [3]. In faulty conditions, the system's performance can be lowered, although failure can be tolerated to some amount, provided stability is ensured. Fault-tolerant control systems are defined as systems with a high level of dependability [2]. FTC approaches are currently being applied in important industrial processes, such as combustible fuel and gas, petrochemicals, and fertilizers, where output losses cannot be allowed, and constant system efficiency is essential [2]. The active and the passive form are the two main typesof FTC [4]. The Active Fault-Tolerant Control System (AFTCS) is responsible for recognizing and isolating the system's defective components. Fault Detection and Isolation (FDI) employs the observer theory, in which a plant parameter is compared to a predetermined normal value to produce a residual [5]. If the residual is below the permissible limits, it means the machine is free of defects. If the residual exceeds the required threshold, the FDI unit reports faulty conditions. The controller is then reprogrammed to meet the current operational requirements.  
Internal Combustion (IC) engines are common in the process sector and maintaining adequate Air-Fuel Ratio (AFR) control in their fuel system is vital for improved engine performance, fuel efficiency, and environmental safety. Because faults in the sensors of the AFR system cause the engine to shut down, fault tolerance is required. The fault tolerance in IC engines can be introduced with the help of analytical redundancy. The main principle of analytical redundancy is to compare real system behavior to the model [6]. The estimated sensor values can be computed by using several optimization and machine learning techniques. In this paper, we have used three techniques, namely, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and a Nonlinear Regression (NLR) model, to compute the estimated sensor values in the case of a fault. The advantages of using these techniques and the results obtained are discussed in the upcoming sections.  
The rest of the paper is organized as follows: the literature review is presented in Section 2, research methodology is presented in Section 3, estimation techniques used are discussed in Section 4, and the results are discussed in Section 5, followed by a conclusion at the end.