esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Security of QBC
### Binding of QBC
#### 5.2.2 Attack with Entangle States  
The entanglement generation and control [63; 64; 65; 66] are the preconditions of the attack with entangle states. Then, we analyze this kind of attacks. In Protocol 1, the states are generated by [PERSON] and sent to [PERSON]. After sending the states, if [PERSON] does not perform the EPR type attack, she can do nothing with the outgoing states. If she prepares entangled states and sends a part of them to [PERSON], she tries to find the local unitary transformation to change the value of commitment, which is actually the no-go theorem attack.  
When [PERSON] commits \"0\" or \"1\", she prepares  
\[|0\rangle=\sum_{i=1}^{2k}\alpha_{i}|e_{i}\rangle_{A}\otimes|\Psi_{r_{i}}\rangle _{B},\hskip 14.226378 pt|1\rangle=\sum_{j=1}^{2k}\beta_{i}|e_{j}^{\prime}\rangle _{A}\otimes|\Psi_{r_{j}}\rangle_{B}, \tag{36}\]  
respectively, where  
\[\bigoplus_{i=1}^{2k}r_{i}=c_{0}\oplus c_{1},\hskip 14.226378 pt\bigoplus_{j=1}^{2 k}r_{j}=c_{0}\oplus c_{1}\oplus 1, \tag{37}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \(\mu\) & \(k\) & \(p(1 round)_{max}\) & \(p(BC)_{max}\) \\ \hline
2 & 131 & 0.6103 & \(5.1\times 10^{-5}\) \\
3 & 172 & 0.6056 & \(4.4\times 10^{-5}\) \\
4 & 210 & 0.6027 & \(4.0\times 10^{-5}\) \\
5 & 236 & 0.6012 & \(3.8\times 10^{-5}\) \\
6 & 259 & 0.6005 & \(3.7\times 10^{-5}\) \\ \hline \end{tabular}
\end{table}
Table 3: The maximum probabilities of [PERSON] breaking the binding of QBC protocol by replacing one state of \(|\Psi_{0}\rangle^{\perp}\) with different \(\mu\) when \(N=800\).  
If [PERSON] wants to change the value of commitment from \"0\" to \"1\", she needs to get state \(|\
u\rangle\) with the same reduced density operator as \(|0\rangle\), which satisfies \(|\langle 1|\
u\rangle|=F(\rho_{0}^{2k},\rho_{1}^{2k})=1-\delta\). Then, she must find out the unitary transformation acting on A alone to transform \(|0\rangle\) into \(|\
u\rangle\). The calculation of unitary transformation is presented in Appendix A. As \(|\
u\rangle\) and \(|1\rangle\) are so similar, [PERSON] can hardly detect the cheating [PERSON].  
However, according to Appendix A, the no-go theorem attack algorithm's time complexity is \(O(2^{3n})\), besides, this algorithm needs at least \(O(2^{2n})\) size of memory space to store the matrix. The entry number of matrix \(U_{A}\) is \(2^{2k}\times 2^{2k}\), according to Table 1 this number is greater than the number of protons on the earth. It means that [PERSON] is unable to get the matrix in practice, and the storage time of quantum states is limited. The bit commitment could be executed over a period of time to prevent [PERSON] from applying transformation with the other part of entanglement states. Therefore, in practice [PERSON] can hardly attack the binding of the bit commitment protocol with this method. Therefore, our protocol achieves the physical security defined in Section 1.
# Generative AI in Medicine and Healthcare: Moving Beyond the Peak of Inflated Expectations'
## 3 Applications and Implications of Generative AI and LLMs in Medicine
### Can We Trust Generative AI? Is It Clinically Safe and Reliable?
#### 3.1.1 Regulation, Regulatory Frameworks, and Ethics
However, executing the ideal clinical trial for an AI-based intervention has always proved challenging, and because of this, we have hundreds of medical algorithms (non-generative-AI-based) that received approval on the basis of limited clinical data, which is far from ideal. The testing and evaluation of such interventions is inherently tricky; for example, a perfectly good algorithm can fail if clinicians (or patients) ignore its suggestions. Other evaluation challenges include AI bias and informed patient consent [52]. Generative AI algorithms are no exception. This situation has led [PERSON] and [PERSON] to propose a shift in the focus of generative AI evaluation from reliance on 'premarket assessment' to'real-world postmarket surveillance'. They argue that traditional scientific methods may not be sufficient to evaluate generative AI, and that viewing it as a cybersocial ecosystem rather than as a specific technology may help with its global performance analysis, such as evaluating resilience and sustainability under changing conditions or tasks [53]. With the fast pace of generative AI and LLM developments, the continual mapping ongoing research on their applications in medicine and healthcare has become necessary to inform and regularly update the corresponding ethical frameworks, ensuring that these technologies are adopted responsibly and effectively. [PERSON] et al. discussed the benefits of LLMs in medical research, education, and clinical tasks, emphasizing the associated challenges and ethical concerns, including data privacy, cognitive and automation biases, and accountability. They went on to propose a bioethical framework based on the principles of benefcience, nonmaleficence, autonomy, and justice to ensure responsible use of LLMs in medicine. They highlighted the importance of human oversight, transparency, and the need for regulations to mitigate risks while harnessing the benefits of these advanced AI technologies [54; 55]. [PERSON] and [PERSON] conducted a systematic review on the ethics of ChatGPT and other LLMs in medicine, identifying four broad LLM application categories (covering health professionals and researchers, patient support, clinical applications, and public health) and a number of recurring ethical issues related to epistemic values (reliability, transparency, hallucinations), therapeutic relationships, and privacy, among others. They also noted the recurrent calls for ethical guidance and human oversight in this area, and proposed shifting the focus of the ethical guidance discussion towards establishing clear definitions of acceptable human oversight in various applications, taking into account the diversity of settings, risks, and performance standards that are involved [56].
# Compact SOI Dual-Mode (De)multiplexer Based on the Level Set Method
## 2 Design Principle
### Level Set Method  
The level set method is a technique which represents a 2D region as a matrix by utilizing a plane-section of a 3D surface [30]. In the matrix composed of surface data, points with a value of 0 define the boundaries of the evolving structure. Values larger than 0 and less than 0 represent the interior and exterior of the structure, respectively. Consequently, level set method provides a way to evolve the structure by updating the matrices, eliminating the need to find curve equations.  
As depicted in Figure 2, the initial device structure can be represented as a 2D matrix using the level set method, which is then mapped onto a 3D surface. Each element in the matrix is denoted as \(\Phi(x,y)\), where the silicon material is represented by the set of elements for which \(\Phi(x,y)\geq 0\), while the region where \(\Phi(x,y)<0\) corresponds to SiO\({}_{2}\). During the optimization process, we employ the Hamiltonian-Jacobi equation to iteratively update the matrix \(\Phi(x,y)\), as shown below:  
\[\frac{\partial\phi(x,y)}{\partial t}+\mathbf{V}\cdot\
abla\phi(x,y)=b\kappa\text{Re} |\
abla\phi(x,y)| \tag{5}\]  
where \(t\) represents the virtual time step; \(\kappa\) denotes the curvature of the set of boundary elements where \(\Phi(x,y)=0\); and b is an adjustable parameter that allows us to control the structure radius, ensuring it meets the requirement for minimum feature sizes of at least 200 nm. The vector \(\mathbf{V}=df/d\varepsilon_{r}\) represents the velocity of the curve's points. The structure to be optimized is enclosed by the points where \(\Phi(x,y)=0\). As the optimization progresses, the structure evolves with the points at \(\mathbf{V}>0\) expands outward, while the points at \(\mathbf{V}<0\) shrinks inward.
## Results and Discussion
### Synthetic Studies
Conventional heating greatly lengthened the reaction time (12 h), although the yield of the product (**6a**) was only slightly lower (85%) than in the MW-induced method. In the \({}^{1}\)H NMR spectrum of **6a** with a phenyl (Ph) group directly attached to the pyrazole-N, the signals of the aromatic protons were identified between 7.0 and 7.6 ppm with characteristic multiplicities, while the singlet of 5'-NH\({}_{2}\) appeared at around 5.0 ppm. According to the \({}^{13}\)C NMR (J-MOD) spectra of **6a**, the negative signal of C-17 was found at around 170 ppm, consistent with the chemical shift of a carbon in a pyridine-like environment (C = N) for similar pyrazole derivatives [6]. \begin{table}
\begin{tabular}{c c c c c}
**Entry** & **Ar-NH-NH\({}_{2}\)** & **Ar** & **Product** & **Yield 1 (\%)** \\ \hline
1 & 5a & Ph & 6a & 90 \\
2 & 5b & 4-CH\({}_{3}\)-C\({}_{6}\)H\({}_{4}\) & 6b & 89 \\
3 & 5c & 4-OMe-C\({}_{6}\)H\({}_{4}\) & 6c & 92 \\
4 & 5d & 4-F-C\({}_{6}\)H\({}_{4}\) & 6d & 94 \\
5 & 5e & 4-Cl-C\({}_{6}\)H\({}_{4}\) & 6e & 80 \\
6 & 5f & 4-Br-C\({}_{6}\)H\({}_{4}\) & 6f & 84 \\
7 & 5g & 4-CN-C\({}_{6}\)H\({}_{4}\) & 6g & 85 \\
8 & 5h & 4-NO\({}_{2}\)-C\({}_{6}\)H\({}_{4}\) & 6h & 52 \({}^{\text{\text{\textdegree}}}\), 88 \({}^{\text{\text{\textdegree}}}\) \\ \hline \end{tabular}  
* After purification by column chromatography. \({}^{2}\) Reaction time (**a**) 10 min; (**b**) 20 min.  
\end{table} 
Table 1: Syntheses of D-ring-fused 5-amino-1-arylpyrazole in the androstane series. The reaction is considered to involve a nucleophilic addition of the terminal nitrogen of the arylhydrazine (**5**) to the carbonyl-C17 of **3** and subsequent dehydration to afford hydrazones, which then undergo intramolecular ring-closure by the attack of the internal nitrogen on the nitrile carbon to provide 5-aminopyrazole **6** (Table 1) [14]. According to the proposed mechanism, the rate of both consecutive reaction steps depends on the nucleophilicity of the reagent, which may be affected by the electron demand of the substituent of the aromatic ring. Therefore, similar reactions of 3 were also performed with para-substituted phenylhydrazine hydrochloride (**5b-h**) in order to investigate the possible substituent effect on the heterocyclizations. All MW-assisted reactions were carried out in a closed vessel under identical conditions at 120 \({}^{\circ}\)C for 10 min. However, due to the elevated temperature and efficient heat transfer provided by the MW conditions, a marked substituent effect could not be observed, except for the reaction of 3 with para-nitrophenylhydrazine (**5h**) where the strong electron-withdrawing effect of the NO\({}_{2}\) group hampered the ring-closure to occur, and substantially decreased the yield of the desired product **6h** (Table 1, entry 8). However, complete conversion was also achieved in this case by prolonging the reaction time to 20 min. The novel D-ring-condensed arylpyrazoles (**6b-h**) were purified by column chromatography and characterized by NMR spectroscopy similarly to **6a**.
## 3 Results
### Regression Analysis of Eye-Movement Outcomes and Subjective Evaluation  
In order to further measure the interactive experience and extract valuable eye movement indicators to predict the interactive experience of the navigation interface, the study used the PLS (partial least squares) method to establish the regression equation between subjective evaluation and eye movement metrics. PLS is a data optimization analysis technique that finds the best function match for a set of data by minimizing the sum of the squares of the errors [35].  
The eye movement indicators with significant effects in two experimental modes were respectively used as independent variables, and the corresponding subjective scores were used as dependent variables. The PLS regression was performed using MATLAB software. The results showed the regression equation between the subjective scores and the eye movement indicators.  
Free browsing mode: y\({}_{1}=126.5607-0.0418\) x\({}_{1}-0.8541\) x\({}_{4}-5.3526\) x\({}_{5}\)  
Task-oriented mode: y\({}_{2}=38.1893+0.0611\) x\({}_{1}-0.0216\) x\({}_{2}+3.2734\) x\({}_{3}+0.1231\) x\({}_{4}\)  
Figure 4: The heat map of two experimental modes.  
where \(y_{1}\) and \(y_{2}\) stand for the subjective score in free browsing mode and task-oriented mode, \(x_{1}\)-\(x_{5}\) stand for the eye movement indicators, which respectively are fixation count, first fixation duration, dwell time ratio, saccade count, and average saccade amplitude.  
It can be seen from the above regression equation that the fixation count, saccade count, and average saccade amplitude in the free browsing mode all have a negative effect on the interactive experience. While in the task-oriented mode, the first fixation duration, the dwell time ratio, and the saccade count have a positive effect on the interactive experience and the fixation count has a negative effect on the interactive experience. The results of PLS analysis were consistent with the eye movement measurements, which further validate the feasibility of eye movement technology for measuring the interactive experience of the navigation interface.  
In order to ensure the accuracy of the regression equation, another four subjects were selected by the previous experimental procedures to conduct a new experiment. The experimental data were compared with the calculated data by the regression equation, and the paired sample \(t\)-test was conducted on the actual values and the predicted values. The test results are shown in Tables 4 and 5, respectively.  
According to the data in Tables 4 and 5, the significance level between the actual value and the predicted value was greater than 0.05, indicating that there was no significant difference between them. Therefore, the regression equation can ensure the accuracy and validity of the prediction.
## 5 Case Study of Developing Liver Cancer Diagnosis
### Implementing the HCC Diagnosis System
#### 5.4.1 Hardware Environment for Training and Running ML Models  
The hardware environment required for training the six machine learning models in the CECT-based diagnostic process depends on several factors, including the number of  
Figure 12: Integrating CNN models and LSTM models for HCC diagnosis.  
CT scans, the average number of slices per CT scan, and the complexity of the models. The training set used for the liver cancer diagnosis case study consists of 130 CT scans, with an average of 447 slices per CT scan.  
The below-mentioned hardware environment was utilized for training the models. We encountered system crashes when the hardware configuration was not sufficient to meet the required computational demands.  
* Processor: AMD Ryzen Threadripper 7970X with 32 Cores and 64 Threads
* GPU: Two NVIDIA RTX 4090 GPUs, each with 24 GB VRAM
* RAM: 256 GB DDR4  
The computing power required for running the six machine learning models is generally lower than that for training the models. In our case study, we used the same processor and RAM, but only a single GPU of NVIDIA RTX 4090.
## 2 Materials and Methods  
This Section describes the design and implementation workflow for deploying VGG16 and VGG19 on FPGA for image classification. The methodology encompasses CNN architecture adaptation, model training, hardware-specific optimization, and deployment. The methodology integrates transfer learning, resource-aware model optimization, and deployment using the Xilinx Vitis-AI library 2.5.0 and TF2 frameworks, with an emphasis on real-time performance and resource efficiency.  
Figure 1 illustrates the workflow proposed in this study for deploying pretrained CNN models, specifically VGG16 and VGG19, on FPGA using the Xilinx Vitis-AI framework. The process begins with data preprocessing and augmentation to enhance the performance of the model. In model training, pretrained models from TF2 are adapted for the CIFAR-10 dataset through transfer learning and fine-tuning. These steps are performed using an Integrated Development Environment (IDE) such as Jupyter Notebook. The trained models are then subjected to quantization, reducing precision to 8-bit integers to optimize resource usage and inference latency. Next, the models are compiled using the Vitis-AI compiler to generate FPGA-compatible binaries. These binaries are deployed to the FPGA's Deep Learning Processing Unit (DPU), where real-time inference is performed. Finally, the model is tested on 10,000 images of the CIFAR-10 test set, and critical parameters are measured. The workflow ensures an efficient balance of accuracy, latency, and resource utilization, tailored for edge applications. This process, which avoids the complexities of custom implementations, streamlines FPGA deployment for advanced deep learning models.
# Industrial Metaverse: A Comprehensive Review, Environmental Impact, and Challenges
## 5 Environmental Impact and Sustainable Development
* Reduction in pollution generated by activities: To evaluate the effects of various scenarios on an entity's energy consumption, such as a city or factory, one can use the Metaverse. To evaluate the effects of various scenarios on energy usage, an entity like a factory can be created using the IM. For instance, the IM's digital twins can be used to replicate real-world performance conditions cost effectively and safely. Microsoft's implementation of IM capabilities for Hellenic, one of the biggest Coca-Cola bottlers, is an example. With over 55 locations in Europe, Hellenic services 29 local markets. Ninety thousand Coca-Cola bottles are produced per hour on a single production line in Greece. Microsoft used sensor data to create digital twins that allowed factory workers to immerse themselves in the models. The factory reportedly reduced its energy consumption by more than 9% percent in 12 weeks. Furthermore, physical objects like diesel generators account for CO2 emissions, amounting to 1,091,618 kg/yr of pollutants [104], and this is costly to mitigate. 4. Reduction in the consumption of physical objects: It is important to think about the possible challenges of virtual consumption and how the environment might be affected. Although virtual environments have the potential to be more environmentally friendly than real ones, it is still unclear how this will impact energy consumption and carbon emissions [105]. Realizing the possibility of much less materialistic consumption can be facilitated by the IM. It is stated that 21% of consumers expressed their willingness to engage in digital activities in the future, which is expected to reduce the need for physical items [106]. 5. Precise assessment of pollution generated and improvement in reward and enforcement: Finding out how much pollution a company produces can help with processes related to rewards and enforcement, as well as encouraging the adoption of eco-friendly practices. While tracking carbon in the real world is difficult, it can be done in the Metaverse by using blockchain technology to create fungible digital assets. Tokenization makes it easier to transfer carbon credits and establishes a market for voluntary carbon credit exchange. The credits might be used to offset emissions that have been reduced as a result of conservation efforts in the forestry industry and participation in carbon sequestration initiatives like improved soil and altered land use planning. This is exemplified by Reseed company's platform, which utilizes blockchain technology to ensure the validity of carbon stock management, from registration through validation and verification, enabling farmers to receive additional income while providing a potential return to investors [107]. To be ready for sustainability within the IM, enterprises may consider utilizing renewable energy sources and cloud services, in addition to developing a culture of examining the effects of products on the environment, as well as creating a circular economy.
# Design and Implementation of a Contextual-Based Continuous Authentication Framework for Smart Homes
## 3 Framework Design
### Framework Components  
This section defines some of the terminology used in the rest of this paper as well, as the major components upon which the framework is built.  
1. A Home Device: A home device might be any device, including an IP camera, thermostat, or smart lock, which can be accessed wirelessly by users through the Gateway. Access request will be  
Figure 1: A high-level architecture of the proposed framework.  
granted if the user is either in the boundary area of the physical home space or via the Internet. This access will depend on a combination of traditional authentication with context-aware attributes such as location, schedule, and time.
2. End-User's Devices: An end-user's device is any smart device (e.g., smartphone or tablet) that can be used to access protected devices or services that are resident on that device, through an application programming interface (API) installed during the registration stage. This API can collect sensor data and send it to the home server for user behavior analysis. Every access request or command will be accompanied with sensor data measurements for user pattern analysis and evaluation.
3. Home Gateway (Local Server): The Gateway acts as an intermediary between the user and the connected home devices. It is responsible for the authentication process, and protects access to these devices. The Gateway, which collects the required context information and determines whether the access request satisfies the predefined requirement, is also responsible for verifying identity information that is received or transferred for authentication purposes.
4. Bluetooth Sensor: The embedded Bluetooth sensor in the Raspberry Pi is used for the Gateway and utilized to collect information about the user's location in real-time.
5. The Gateway Database: This contains all the following tables. * Access Control Policies: This contains the assigned policies and roles that dictate a user's privileges and the security levels of the various smart devices. * Login Access Profile: The system saves a copy of all authentication attempts for each user with related information, such as time, session duration, the number of attempts, access decisions, and any other provided contextual information that is related to every authentication or access attempt. These data will help in interpreting the system's usage, learning usage behavior, and checking for malicious usage. * General User Information: This contains the information that describes each user's personal information, such as name, age, and any other identifiers used in authentication. * Calendar: This caches the events retrieved from users' calendars that are used in determining access schedules, and periodically compares them against the online copies to check for new events or tampering.
# Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards
## 2 Materials and Methods
### Energy Consumption  
Each node of the wireless system (Coordinator and End-nodes), was powered by a 6600 Ahr rechargeable battery, connected to solar panel providing 3 Watt a voltage of up to 7.14 V, depending on the available solar energy to be harvested. The energy consumption and charging capacity were tested under unfavorable conditions (shaded to simulate cloud covered conditions) to evaluate the limits of the system, assuring unsupervised, uninterrupted operation of the nodes in real field conditions.