esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Results Validation Using Evaluation Metrics
On the other hand, the average sensitivity (recall) of the RVQA process was 79% to 83%. The average sensitivity shows that from 100 known violations that were expected to be detected, 79 to 83 detections managed to be detected. The number of detected confidentiality violations using the RVQA represents the agent confidentiality level and can be used to alert agents when the confidentiality violation level at a particular time is high. By analyzing the agent data confidentiality violations detection results presented, improvement of the results can be performed. New data confidentiality requirements and checking rules to produce more accurate violation detections can be defined. Next, more comprehensive agent-contextual information for assessing the data confidentiality violation conditions could be gathered. The existing agent data confidentiality metrics and checking rules that do not contribute toward achieving the objective of detecting agent data confidentiality violations could be removed. Finally, the existing checking rules could be refined based on new agent data confidentiality requirements and the availability of the supporting contextual information for verifying and assessing agent data confidentiality. The possible theoretical implications of this study include the introduction of a framework that focuses on properties assessment and has the ability for improvement in each process cycle. Smart city initiatives normally involve a significant budget, a wide range of resources, and a long-term commitment. Thus, the framework could benefit smart city developers and decision-makers analyzing their applications when trying to detect the violations of quality properties at an early stage, prior to the real development. The AQM framework would be the suitable framework to be adapted to manage various types of smart city initiatives and projects. Using the AQM framework, standardized indicators for smart city properties, i.e., smartness, sustainability, and resiliency, can be assessed from time to time. From the analysis of previous works, verification of a system is a technique to prove the correctness of a system and to check the functional requirements of a system. This present study, however, has several findings. First, it shows that the assessment and verification stage can be used not only to verify the correctness of specified requirements, but also to detect the violations of the requirements. Second, the verification process can also be used to check not only the functional requirements, but also the non-functional requirements, i.e., quality properties of systems and applications. Another new development is the improvement component in the AQM framework that can be used to recommend a new approach and solutions to continuously enhance the systems along the design and implementation process. The limitation of the present study is that it should have used enhanced reasoning algorithms and enriched rules to increase the confidence and sensitivity levels. Efforts to improve the present study can be considered as potential future work.
# Defense against Adversarial Attacks in Image Recognition Based on Multilayer Filters
## 4 Experimental Results
### The Size of the Filtering Area and Standard Deviation  
Gaussian filters can effectively smooth images and suppress adversarial noise. In the design of Gaussian filters, two key parameters are the size of the filtering area and the standard deviation. They are crucial for the influence of Gaussian filters, therefore, in this experiment, we analyzed these two parameters. Throughout all testing scenarios, the CNN served as the image classification model, while the Adam optimizer was employed, utilizing a consistent learning rate of 0.01.  
The size of the filtering area refers to the size of the area covered when filtering an image. Obviously, the larger the filtering area, the larger the area covered by the filter on the image. This also means that the range of filtering effects will be affected accordingly. In order to analyze the impact of filtering area size on defense models, five different filtering area sizes were used in this experiment, namely 3 \(\times\) 3, 5 \(\times\) 5, 9 \(\times\) 9, 13 \(\times\) 13, and 17 \(\times\) 17.  
The standard deviation is an important parameter of the Gaussian function, which determines the degree of diffusion of the Gaussian function. Adjusting the standard deviation is actually adjusting the degree of influence of surrounding pixels on the current pixel while increasing the standard deviation increases the degree of influence of distant pixels on the central pixel. In other words, the larger the standard deviation, the more pronounced the diffusion of the Gaussian function. Therefore, in practical applications, we can adjust the value of the standard deviation as needed to achieve the desired filtering effect. In this experiment, we used four different standard deviation parameter sigma values, namely 1, 2, 5, and 10.  
Table 5 shows in detail the performance of the model in terms of average accuracy under different filtering area sizes and standard deviation combinations. Through comparative analysis of multiple sets of experimental data, we found that when the filtering area size is 3 \(\times\) 3 and the standard deviation is 1, the average accuracy of the multilayer filter defense model reaches the highest, reaching 77.6%. A reasonable combination of filtering area size and standard deviation can help multilayer filter defense models better improve defense effectiveness.  
Figure 6: The detection results of the classifier model after adding noise to the training set. The black dashed line represents defense methods that have not adopted noise injection mechanisms.
## 5 Integration Options
### Summary of Alternatives  
A number of alternatives have been analyzed depending on where the administrative domain boundary is located in a multi-provider MEC scenario. This will influence the reference points and the MEC components that have to be scoped for multi-domain, potentially by the inclusion of some multi-domain adaptor able to handle the extra functionality needed for multi-domain integration.  
Table 3 summarizes the findings for each scenario, including the interfaces impacted in each case.  
As can be seen, each of the scenarios has different implications on where the awareness of the multi-domain interaction should reside, at both MEC component and reference points. Different strategies can be considered and their impacts should be evaluated. A primary indication of the implications at both business and technical levels is also included in Table 3. Only in the last case of MEC systems interconnection is there naturally an impact on the external interfaces declared in the MEC architecture, since the other domain is connected at management system level.  
From all the integration options considered, currently, the integration at service level seems to be the more straightforward method to follow since the interfaces involved are subject of current specification in ETSI MEC. In this sense, Mm2 and Mm3 relate to platform management as defined in [27], while Mm4 can be assumed to be an interface from some of the well-known available VIM implementations in the industry (e.g., OpenStack). This greatly facilitates the scoping on the interaction among domains, basically requiring from extensions to manage the multi-domain aspects, such as discovery, monitoring, etc., growing on top of existing specifications.  
All the other options for integration at infrastructure or platform levels show some dependency on not-specified interfaces, e.g., Mp2 in the case of integration at infrastructure level, or Mm5 (and also Mp2 in one case) if the integration is performed at platform level. This lack of definition complicates the integration of different administrative domains, especially if those domains rely on solution implementations from distinct vendors. An extra effort on integration would be required for defining workflows and data models, non-incentivizing to follow these directions unless such interfaces are functionally specified at some point.  
A final case is that of interconnection at the MEC system level. In this case, the situation has not even originally been conceived by ETSI MEC, and in consequence, no current reference point focuses on that. This scenario, however, presents interesting aspects because of being performed at orchestration level. This can abstract the complexity and diversity of the MEC platforms and infrastructures, including their management, and concentrate on the orchestration workflows in an implementation agnostic way. However, this scenario requires standardization of such new reference point to avoid integration of proprietary solutions in a multi-domain environment.
# Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review
## 2 Foundational Concepts
### Machine Learning  
ML encompasses a variety of techniques and methodologies aimed at enabling computers to learn from data without explicit programming. These techniques are broadly classified into three categories: supervised learning, unsupervised learning, and reinforcement learning (RL).  
Supervised learning involves training a model on a labeled dataset, where the correct output is already known. The model learns to map inputs to their corresponding outputs by minimizing the error between predicted and actual values. This method is widely used in applications such as medical diagnosis, where labeled data (e.g., patient symptoms and diagnostic results) are used to train predictive models [23].  
Unsupervised learning, on the other hand, deals with datasets that are not labeled. Here, the goal is to identify underlying structures or patterns in the data, such as clustering similar data points or reducing the dimensionality of large datasets. In the medical field, unsupervised learning is often used for discovering novel patterns in medical imaging data or genomic sequences, where labels are not readily available [24].  
RL is a type of ML where an agent learns to make decisions by interacting with its environment. Through trial and error, it receives feedback in the form of rewards or penalties, which it uses to adjust its actions. This approach is increasingly applied in robotics and personalized medicine, where adaptive systems can optimize treatment plans or assist in surgical procedures [25].  
Deep Learning: Deep learning, a subset of ML, leverages neural networks with multiple layers to model complex patterns in large datasets. In the medical field, deep learning algorithms have shown remarkable success in tasks such as medical image analysis, where convolutional neural networks (CNNs) can automatically detect tumors or abnormalities in radiographs and MRI scans [26].  
Despite its successes, traditional ML faces several key challenges. Scalability remains an issue, as many ML models require large amounts of data and computational resources, making them difficult to apply to larger-scale datasets or real-time applications. Interpretability is another challenge, especially in deep learning models, where the decision-making process can be opaque. This lack of transparency makes it difficult for clinicians to trust or understand the reasoning behind machine-generated recommendations, which is critical in high-stakes fields like healthcare [27]. Addressing these challenges is essential for the widespread adoption of ML in medical decision-making and practice.
# Quantitative Analysis of Retinal Vascular Leakage in Retinal Vascular Using Machine Learning
## 2 Materials and Methods
### Automated Retinal Vascular Leakage Segmentation
#### 2.3.1 Automated Segmentation of Retinal Vessels  
The U-Net network structure was used as a learning machine for segmentation of retinal vessels [15]. The structure of the U-Net is a symmetrical U-shape, with the encoder on the left and the decoder on the right. The down-sampling path consisted of five stages, and the number of convolution filters was 64, 128, 256, 512, and 1024. The convolution layer was repeated twice in each stage, with a kernel size of 3 \(\times\) 3 filters and a stride of 1, and a max pooling operation layer with a kernel size of 2 \(\times\) 2 and stride of 2. The up-sampling path was performed using up-sampling 2D of Keras. The final convolution was performed with a kernel size of 1 \(\times\) 1 and stride of 1. The accuracy was used for assessment of this U-Net network.
## 4 CW Micro-Doppler MMW Radar  
### CW Micro-Doppler MMW Radar Experimental Setup  
A schematic illustration of the MMW radar is shown in Figure 4. First, an RF signal generator produces a CW signal at 15.67 GHz. Next, the signal frequency is up-converted by an X6 multiplier to the W-band regime, resulting in a 94 GHz CW carrier. The output signal is then coupled to a circulator and a mixer serving as a product detector. The signal is transmitted from the circulator to the target. The received signal, scattered from the moving target, is amplified by a low noise amplifier (LNA) and inserted into the mixer. Finally, the Doppler frequency is transmitted to the scope, where the information is processed into a spectrogram.  
Figure 3: Spectrograms calculated for different window lengths that are (**a**) too short; (**b**) optimal; (**c**) too long.  
Figure 2: Short-time Fourier transform (STFT) calculation: (**a**) chirp signal; (**b**) signal distribution for overlapping windows; (**c**) DFT of each windowed segment.
## 1 Introduction  
Thoracic endovascular aortic repair (TEVAR) can be a life-saving therapy for treating aortic disease. Compared to open surgical repair, TEVAR is a valid therapeutic option for type B aortic dissection (TBAD) because of its lower mortality, morbidity, and paraplegia rate in the past decade [1, 2, 3]. However, management algorithms remain controversial and long-term follow up is required to determine outcomes and reintervention rates [4, 5, 6, 7].  
Computed tomography angiography (CTA) is currently the preferred imaging modality for diagnosis, treatment planning, and follow up for AD patients. With improved spatial and temporal resolution available with modern computed tomography (CT) scanners, CTA plays an important role in the diagnosis and follow up of aortic dissection (AD) [8, 9]. However, the high radiation dose associated with CTA is still a concern for the well-being of patients [10, 11, 12]. To monitor patients after TEVAR, the pursuit of high-resolution and higher-quality images may bring unnecessary radiation doses. Therefore, it is necessary to reduce the radiation dose while maintaining the quality of CT images.  
Finding a balance between image quality and radiation risk has always been a challenging issue in achieving optimal scanning protocols. A number of studies describe different strategies to lower the radiation dose including the use of iterative reconstruction (IR) for reducing image noise, use of low tube kilovoltage peak (kVp), and use of high-pitch protocols with fast speed CT scanners [13; 14; 15]. Despite these studies providing promising results, research is still limited with regard to the reduction of radiation dose for aortic dissection patients who require repeat scanning for many years.  
Compared with preoperative planning when detail is paramount, postoperative patients could receive a lower dose for routine follow-up examinations to reduce cumulative radiation dose. Thus, the purpose of this study was to investigate the optimal CTA protocols for surveillance of TBAD patients after EVAR. We employed a patient-specific three-dimensional (3D) printed TBAD model with a stent-graft TEVAR in situ.  
Patient-specific 3D printed phantoms have been proven to be valuable in multiple medical applications [16; 17; 18; 19; 20; 21]. In our previous study, we described how we developed a patient-specific 3D printed TBAD aortic model, and confirmed its accuracy in resembling the mechanical and radiological properties of in vivo imaging under CT scanning modalities [22]. In this study, we extended our application of a 3D printed aorta model to focus on the study of optimal CT protocols by deploying a bespoke stent graft inside the true lumen of the AD aorta model to simulate the TEVAR process, and then scanning the model with different CT protocols.
# GANBA: Generative Adversarial Network for
## 4 Experimental Setup
### Implementation Details  
Three state-of-the-art anti-spoofing systems were adapted from other works: a light convolutional neural network (LCNN) [2], a residual neural network (ResNet34) [46] and a Squeeze-Excitation network (SENet50) [46]. The softmax layer output of the DNN-based models was directly used to obtain the PAD scores. For ASV, a TDNN model for x-vector feature extraction [21] was trained. Then, two ASV scoring systems were trained: (i) a standard PLDA [22]; and (ii) a b-vector system [28].  
The generator of the proposed GANBA framework is a convolutional neural network (CNN) with five convolutional layers (16, 32, 48, 48, and 3 channels). Furthermore, it uses a kernel size of \(3\times 3\) as well as leaky ReLU activations. The Adam optimizer [47] (with learning rate \(3\times 10^{-4}\)) was used to train the GANBA generator. Moreover, a grid search across the development dataset of the ASVspoof 2019 database was used in order to find the best empirical values of the hyper-parameters \(\alpha=10\) and \(\beta=0.001\). All the deep learning frameworks were trained using the Pytorch toolkit [48].  
The PGD method uses \(N=30\) training iterations for generating the adversarial _spoofing_ attack, and for evaluation the number of iterations is set to \(N=100\)[49]. The magnitude of the perturbation is configured with the \(\epsilon\) parameter. In the experiments, we do a grid search between \(\epsilon=0.1\) and \(\epsilon=5.0\) in order to find the optimal perturbation of the FSGM and PGD techniques. However, the magnitude of the GANBA perturbation is not restricted to any specific value. This is one of the main advantages of the proposed GANBA technique since it is in charge of finding the optimal perturbation value by itself in order to fool the PAD system without being detected by the ASV system, i.e., without changing the speaker information of the utterance.
# NeuChain+: A Sharding Permissioned Blockchain System with Ordering-Free Consensus
## 3 NeuChain+ Design
### System Overview  
As shown in Figure 3, NeuChain+ is design based on the sharding-EV architecture. There are three types of components in NeuChain+: client proxy, epoch server, and block server. Client proxy is responsible for batching transactions into blocks and block replication. Epoch server provides a global consistent batch timeout to prevent clock skewing among client proxies. Block server executes blocks deterministically and updates its local ledger. These components are discussed in detail below.  
Figure 3: Overall system architecture.  
Client proxy.The client proxy receives transactions involving its shard from clients. To determine which block the transactions belong to, it batches them and requests an epoch number from the epoch server. Each transaction is then assigned a globally unique transaction ID _tid_ for deterministic intrablock ordering. The client proxy then uses atomic broadcast (e.g., PBFT or Raft) to exchange its transactions with other intrashard client proxies, ensuring the integrity and consistency of transactions. Notably, the client proxy acts as both the leader of the broadcast instance initiated by itself and as the follower of other broadcast instances initiated by other client proxies within the same shard.  
Epoch server.The epoch server generates monotonically increasing epoch numbers and assigns them to transaction batches generated by client proxies. The epoch server can be a trusted single node or a fault-tolerant cluster containing multiple epoch servers. Notably, only the increment of the epoch number requires consensus, and the epoch number assignment can be performed concurrently with this consensus to reduce latency.  
Block server.The block server only stores transactions that access records on its local shard, forming a subchain. A block server corresponds with a client proxy that accepts transactions. It uses deterministic concurrency control to execute transactions concurrently to generate read-write sets for validation. Each block server maintains a shared read-write reserve table, which only reserves transactions that can be committed in the current _epoch_, ensuring a serializable isolation level. After execution, the cross-shard transactions use _cross-reserve_ (detail in Section 5) to guarantee atomicity.
## 3 The Proposed Fuzzy Relevance-Based Cluster Head Selection Algorithm
### FSV Structure for Clustering  
FSV (Fuzzy State Viewing) structure clusters adaptively and is efficient when the size of networks varies according to the mobility of nodes. In the FSV structure, a node transmits not only packets but the fuzzy value [11] to neighbor nodes. The determined fuzzy value is used to prevent interferences and attacks from other nodes. A cluster is composed of a CH, CH candidate, gateway, and CMs, where CH is Cluster Head and CM is Cluster Member. Cluster nodes, classified as CH, CM, gateway node, and CH candidate according to their roles, broadcast packets shown in Figure 2 to neighbor nodes.  
The parameters of the packet are explained as follows:  
\(\bullet\)_Identifier (ID)_: ID is assigned for identifying each node and used to avoid interference and attacks from other nodes during the selection of cluster head.  
\(\bullet\)_Fuzzy Relevance Degree ( \(\mu\))_: Fuzzy Relevance Degree (FRD) is a fuzzy value \(\mu\) (\(0\leq\mu\leq 1\)), determined by available power, distance, and mobility. To reduce the computational complexity, we set \(\mu\) a fuzzy value between 0 and 1, _i.e._, ranging in {0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1}. FRD is used for selecting the cluster head and construction of clusters.  
\(\bullet\)_Level_: Each node has a level assigned according to the FRD of each node. Three levels are proposed: low level (Level-0) with \(\mu\leq 0.4\), middle level (Level-1) with \(0.5\leq\mu\leq 0.7\), and high level  
Figure 1: Flat structure and Hierarchical structure. (**a**) Flat structure. (**b**) Hierarchical structure.  
Figure 2: Packet structure of FSV.  
(Level-2) with \(\mu\geq\) 0.8. The assigned levels are used in the selection of CH, CM node, and CH candidate nodes, and they are also are used to avoid the complexity of cluster management.  
\(\bullet\)_M-hop (Multi-hop)_: M-hops controls the management and generation of the 1-hop cluster and 2-hop cluster according to FRD. In large scale networks, the 1-hop cluster and 2-hop cluster generate too many cluster heads. Thus, the M-hops Adjustment adjusts the size of clusters according to the network size.  
\(\bullet\)_Balance_: Each cluster head is selected according to FRD ( \(\mu\) ). The balance parameter is used to balance the number of nodes in clusters for achieving fair management of the attached cluster members (CM).