esa-sceva/satcom-chunk-collection huggingface dataset:
# Knowledge of Quantum Hidden Variables Enables Backwards-In-Time Signaling
## 1 Introduction
In addition, a version of quantum mechanics where the collapse of the wave function happens instantaneously in some preferred reference frame will not allow one to communicate to one's own past [If one assumes relativity, superluminal communication by itself may allow one to communicate to one's own past, under the hypothesis that an observer can communicate to spacelike separated points that are in the \"future\" with respect to the observer's simultaneity hyperplane. Indeed, if [PERSON] communicates superluminally a signal to [PERSON], [PERSON] then accelerates to a large fraction of \(c\) so that his hyperplane of simultaneity intersects [PERSON]'s timeline at a time before she sent the signal and then he resends [PERSON]'s signal along such hyperplane (superluminally in [PERSON]'s new reference frame), then [PERSON] receives her signal back before she sent it]. Retrocausality, as well as weaker variants of the BITS property of ontic quantum mechanical models have been pointed out in the literature under additional hypotheses, e.g., when considering fundamentally time-symmetric versions of quantum mechanics (i.e., even in the presence of collapse) [12; 13; 14; 15; 16; 17; 18; 19]. However, when addressing Bell tests, such retrocausal models only assume the existence of hidden variables that will be measured in the future. Hence these arguments assume communication to the past from the onset. Here, instead, we use plain textbook quantum mechanics together with the hypothesis that one could somehow know quantum hidden variables (as defined above) to prove they must be BITS. Nevertheless, the aforementioned retrocausal frameworks showcase the existence of BITS models which are consistent with standard approaches to quantum mechanics since they strictly prohibit knowledge of the hidden variables. An important interpretation of quantum mechanics that uses hidden variables is Bohmian Mechanics [20; 21; 22]. In Bohmian Mechanics spin hidden variables do not exist, and the only hidden variables are the positions of particles [23], however we show below in which sense our argument also applies to this case.
# Environmentally Stable Chiral-Nematic Liquid-Crystal Elastomers with Mechano-Optical Properties
## 3 Results and Discussion
### Thermal Stability of Optical Properties  
As a representative example, we investigated the thermal stability of the optical properties of the N* LC (2.0) elastomers. Generally, the helically twisted molecular orientation of an N* LC elastomer is sensitive to temperature [31; 50]. The twist of the N* LC helix decreases in the pre-phase transition region upon approaching the phase transition temperature. Therefore, the pitch length increases, and the selective reflection band shifts following thermal treatment. Previous studies have demonstrated that such N* LC elastomers can be utilized as thermochromic materials, as the optical properties show a temperature dependence [50; 51]. However, such temperature responsivity degrades the resolution and accuracy of the mechano-optical response, thus limiting the utility of such N* LC elastomers for sensing applications. Although densely cross-linked LC polymers typically exhibit excellent thermal stability because both their macroscopic and microscopic structures are fixed, they have high glass transition temperatures and low flexibility. The resultant low flexibility and stretchability make these materials incompatible with mechano-optical sensing applications. In contrast, our proposed N* LC (\(x\)) elastomers have high flexibility and stretchability owing to their low density of covalent cross-linking but high thermal stability derived from the noncovalent cross-linking sites of the CN units.  
To confirm the effect of the noncovalent cross-linking sites on the thermo-optical stability of the N* LC (2.0) elastomers, we investigated the thermo-optical properties of N* LC (2.0) elastomers with different HAB/CN ratios by measuring the reflectance spectra at various temperatures. The N* LC (2.0) elastomer with HAB/CN = 40/40 did not exhibit any color change or shift of the reflection peak wavelength with the change in temperature until the isotropic phase transition temperature (Figure 3a,b,e). However, the reflectance intensity decreased spontaneously at the phase transition point (Figure 3d). These results indicate that this N* LC (2.0) elastomer has a high thermo-optical stability. In contrast, an N* LC (2.0) elastomer with a lower concentration of CN units (HAB/CN = 70/10) exhibited a hypsochromic shift in the reflection peak wavelength with increasing temperature (Figure 3c-e). These results clearly suggest that the noncovalent cross-linking sites between CN units in the side chains contribute not only to the thermal stability of the N* LC phase but also to the thermo-optical stability of the N* LC elastomer.  
\begin{table}
\begin{tabular}{c c} \hline
**Molar Ratio of HAB/CN 1** & **Phase Transition Temperature 2 (°C)** \\ \hline
70/10 & G 10 N* 80 I \\
60/20 & G 10 N* 83 I \\
50/30 & G 10 N* 86 I \\ \hline \end{tabular} 1  
\end{table}
Table 3: Phase transition temperatures of N* LC (2.0) elastomers with different HAB/CN ratios.
## 2 Materials and Methods
### Phytochemical Analysis
#### 2.4.4 Total Anthocyanin Content  
The anthocyanin content was determined via pH differential, based on a spectrometry test according to the methodology described by [PERSON] et al. [37] with slight modifications. Briefly, two solutions of each sample were prepared: (i) 1 mL of sample extract + 10 mL of ethanol with 0.1% concentrated HCl + 10 mL of buffer solution (Na\({}_{2}\)HPO\({}_{4}\) (0.2 M) with C\({}_{6}\)H\({}_{8}\)O\({}_{7}\) (0.1 M) at pH = 3.5); (ii) 1 mL of sample extract + 10 mL of ethanol with 0.1% concentrated HCl + 10 mL of 2% HCl (pH = 0.6). Absorbance was measured at 520 nm and 700 nm and calculated as Abs = (A\({}_{510\,\mathrm{nm}}-\) A\({}_{700\,\mathrm{nm}}\)) pH\({}_{0.6}-\) (A\({}_{510\,\mathrm{nm}}-\) A\({}_{700\,\mathrm{nm}}\)) pH\({}_{3.5}\) using a molar extinction coefficient for cyanidin 3-glucoside of 26,900. Total anthocyanin content was calculated using the following equation and expressed as mg/g.  
\[\mathrm{Total\ Anthocyanin\ content\ (mg/g)=[(Abs/\varepsilon L)\times MW\times D\times(V/G)]} \tag{1}\]  
where Abs represents the absorbance, \(e\) represents the cyanidin 3-glucose molar absorbance [26,900 mL/(mmol\(\cdot\)cm)], L represents the cell path length (1 cm), MW represents the molecular weight of anthocyanin (449.2 Da), D is a dilution factor, V the final volume (mL), and G represents the sample mass (mg).
# Toward a Modern Last-Mile Delivery: Consequences and Obstacles of Intelligent Technology
## 2 Toward Modernity
The control of the supply chain processes is decisive in improving the productivity, which can be achieved to certain extent by measuring performance. For productive supply chain management, well defined and well controlled processes are needed [69; 70]. Improving the efficiency of LMD routes is critical [6]. Another key part of business continuity is sustainability, which is concerned with the company's care for its business operations, external stakeholders, and the surrounding environment [8]. In LMD, AI is increasingly being utilized to solve problems since customers always expect better service, thus firms must keep up with technological improvements. While many businesses perceive AI as a means to developing new technologies, business models, or data-driven decisions rather than just solving issues, it is gaining popularity. The logistics industry or supply chain, like most industries in the world today, has been affected by the advancement of AI, and this development has revolutionized the field of logistics, and the most important advances in this technology include autonomous vehicles, automation, smart roads, and intelligent analytics and forecasting. This powerful technology has enabled the simplification and automation of countless logistics processes, helping companies save money and time. Despite AI's numerous benefits and limitless potential for future growth, the logistics sector must first overcome its issues before implementing AI. Among the challenges raised are those concerning AI ethics, public policy, and implementation difficulties. Transparency and audit, accountability and legal issues, misuse protection, the digital divide and data deficit, ethics, fairness, and equity are some of the public policy challenges of AI discussed by [PERSON] et al. [71]. The literary canon emphasizes AI's reliance on data collection as well. To begin with, data gathering is complicated by the fact that it must be carried out in a method that is both efficient and lawful all around the world. Some countries permit the acquisition of big data, while others have rules that make it more difficult for the rest of the world to do so [71]. Other AI problems include the threat of AI displacing human work, organizational reluctance towards data sharing, a lack of in-house AI talent/knowledge, and a lack of an AI development strategy [71]. Because of the current e-commerce trend, logistics companies and other industry players must build flexible and efficient LMD. Regardless of the existing challenges, AI, which is capable of upgrading autonomous vehicles as well as collecting and analyzing massive amounts of data, can help to tackle LMD concerns. It considers weather, traffic, and road conditions when determining the optimum route [65]. AI, according to the criteria above, can interact with a wide range of other technologies. The next two sections will concentrate on intangible and tangible AI-based technologies.
# Special Issue \"Artificial Intelligence in Complex Networks\"
## 1 Introduction  
Artificial intelligence (AI) in complex networks has made revolutionary breakthroughs in this century, and AI-driven methods are being increasingly integrated into different scientific research [1, 2, 3]. The scientific research of complex networks can be traced back to two aspects. Firstly, the main mathematical subjects of graph theory and statistical physics. One of the major breakthroughs in graph theory is the idea of random graph theory. Complex topologies arise from simple random rules. Random graph theory is often used in conjunction with percolation theory to describe random network modeling. Secondly, complex systems and statistical physics gave birth to a few important theoretical models, such as the Ising model [4, 5, 6, 7, 8], mean-field theory, nonequilibrium thermodynamics and dissipative structure theory, synergetic theory, and self-spinning glass model [8, 9].  
Moreover, AI plays a crucial role in improving the performance of network dynamics, key node mining, community detection, and recommendation behaviors in complex networks [10, 11]. The social impact of artificial intelligence is becoming increasingly prominent. On the one hand, as the core force of a new round of scientific and technological revolution and industrial reform, artificial intelligence is promoting the upgrading of traditional industries, driving the rapid development of an \"unmanned economy\", and having a positive impact on people's livelihoods, such as intelligent transportation, smart homes, and intelligent medical care. On the other hand, issues such as personal information and privacy protection, intellectual property rights of AI-created content, possible discrimination and bias of AI systems, traffic regulations for driverless systems, and the scientific and technological ethics of brain-computer interfaces and human-machine symbiosis, have emerged and need to be urgently provided with solutions [12, 13, 14, 15].  
Despite the transformative potential of AI in complex networks, there are challenges such as data privacy, user privacy protection, data sample scarcity and diverse network structure, and so on. As these technologies continue to evolve, addressing these issues is paramount for ensuring their responsible and ethical implementation. In the future, further developments in AI technologies are expected to refine and expand their applications in social networks, social computing, transportation and finance networks, large model applications, etc.
# Performance Evaluation of a Long-Span Cable-Slayed Bridge Using Non-Destructive Field Loading Tests
## 2 Non-Destructive Field Loading Test for Pingnan Xiangszhou Bridge
### Structural Features of Pingnan Xiangszhou Bridge  
Pingnan Xiangszhou Bridge spans over the Xun Jiang River in China, and it is the cable-stayed bridge with the largest span in Guangxi Province. The structure of Pingnan Xiangszhou Bridge is shown in Figure 1. The main span is a semi-floating cable-stayed structure with two towers and two cable planes. The total length of the bridge is 870 m (i.e., 40 m + 170 m + 450 m + 170 m + 40 m). The bridge deck is divided into four two-way lanes, with a full width of 33.50 m. The design speed is 120 km/h. The main structural features of the bridge are as follows:  
* The stay cables of the bridge are fan-shaped, with two cable planes in space; 20 pairs of cables are arranged on both sides of the main tower, with a total of 80 pairs of cables on the whole bridge. The stay cables are anchored by steel anchor beams on the towers, and by anchor plates on the beams. All stay cables are tensioned on the tower;
* The bridge tower is diamond shaped. An upper beam is set on the top of the tower connecting the two tower columns. In order to strengthen the lateral stability of the cable tower, a middle beam is set. The elevation of the top of the bearing platform is 21.20 m, the elevation of the tower base is 23.20 m, and the elevation of the top of the tower is 170.50 m. The total height of the cable tower above the tower base is 147.30 m. The heights of the lower, middle, and upper tower columns are 26.186 m, 51.375 m, and 69.739 m, respectively;
* The bridge deck is paved with asphalt concrete with a thickness of 10 cm. Column-type crash barriers are set on both sides of the carriageway. Sidewalks and maintenance railings are set on both sides of the bridge deck.
## 3 Results and Discussion
### Frequency Tunability of Graphene Plasmonic Trimers  
To demonstrate the tunable advantage of graphene, we keep the identical geometry of the graphene trimer and change the chemical potential to 0.6 eV. The calculated resonance energy diagram and the corresponding scattering spectra are plotted in Figures 7 and 8. It is seen that the general trend is that the energy diagram and the scattering spectra are identical with those of graphene trimer with a chemical potential of 0.5 eV. The difference is the blue-shift effect in the corresponding spectra and the energy diagrams. This comes from the higher frequency of the eigenmodes of the trimer with a higher chemical potential. Also, the corresponding quality factors are higher due to the lower absorption loss. This property enables the graphene trimer to be flexibly used in a wide spectral range by changing the chemical potential of graphene. This structure will benefit development of multiband graphene nanosensors. It should be noted that the refractive index of the SiO\({}_{2}\) affects the positions of the plasmonic peaks of the scattering spectra. However, it does not influence the mechanism of mode evolution and frequency tunability.  
Figure 6: Scattering extinction cross-section as a function of wavelength ranging from 4.5 to 5.5 \(\upmu\)m. (**a**) The incident light is longitudinally polarized, (**b**) the incident light is vertically polarized.  
Figure 7: (**a**) Plasmonic energy diagram of graphene plasmonic trimers. (**b**) Corresponding quality factors of the plasmon modes as a function of vertex angle. The chemical potential of graphene is 0.6 eV. Compared with trimers of 0.5 eV graphene, the energy diagram shows blue shift, and the corresponding \(Q\) factors increase.
# A Risk Assessment Analysis to Enhance the Security of OT WAN with SD-WAN
## 3 Background
### OT Risk Management Frameworks
Lastly, even though ISO/IEC 27001 is globally accepted and has a structured approach, its best application is when IT environments are addressed alone. In turn, NERC CIP is more sector-specific, relating solely to the energy sector, and it lacks such broad applicability as NIST CSF and ISA/IEC 62443. Table 2 summarizes the differences between the above-mentioned frameworks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **NIST CSF** & **ISO/IEC 27001** & **ISA/IEC 62443** & **NERC CIP** \\ \hline Focus & Broad, includes IT and OT & Primarily IT, with some OT applications & Specific to ICS and OT & Specific to the energy sector \\ \hline Core Components & Five Functions: Identify, Protect, Detect, Respond, Recover & Plan-Do-Check-Act (PDCA) cycle & Various parts for different aspects of ICS security & CIP Standards \\ \hline Industry Adoption & Widely adopted across multiple industries & Widely adopted, especially in IT & Increasing adoption in industrial sectors & Mandated for North American electric utilities \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the different frameworks.
# A Novel Approach for Fraud Detection in Blockchain-Based Healthcare Networks Using Machine Learning
## 6 Conclusions  
Smart healthcare is a health service that uses technology and data to improve the quality of healthcare services, increase efficiency, and reduce costs. Blockchain technology plays an important role in smart healthcare by providing secure storage for sensitive patient information, such as medical records and personal identification details. Despite the benefits of integrating smart healthcare with blockchain, but are still many challenges. The major challenges are privacy and security issues because of the attacks and vulnerabilities of the blockchain network. This article proposes a novel approach for detecting and protecting against fraud in a blockchain-based smart healthcare network using machine learning techniques. The proposed system uses six machine learning algorithms to identify abnormal transactions within the blockchain network, enabling timely detection and prevention of fraudulent activities.  
The proposed system achieved high levels of accuracy, precision, recall, F1-score, execution time, ROC-AUC, and scalability, suggesting its effectiveness in detecting and preventing fraud within the blockchain network. It presents the results of experiments conducted on two datasets. Although all the algorithms performed well after the preprocessing and feature selection, the Random Forest achieved the highest accuracy and execution time. Moreover, it is scalable.  
The proposed system includes some limitations. In fact, it can be used only in the healthcare field, and it needs many adaptations to be implemented in other fields. Moreover, many novel attacks are emerging, and we cannot predict the reaction of our proposal to these attacks. Overall, the proposed system has the potential to enhance the security and reliability of blockchain-based healthcare applications and networks by detecting and preventing fraudulent activities using machine learning. Future work includes investigating the use of deep learning algorithms to improve the proposed system's performance further. Additionally, we inspect using the proposed system in other BC-based applications to provide solutions for security issues in the blockchain network.  
Finally, the article presented a promising solution for secure and efficient data transmission in IoT healthcare applications using AI and blockchain technologies. It also provided insights into the potential of these technologies to address security and privacy concerns in various domains beyond healthcare.  
M.A.M.: Conception and design of this study, Acquisition of data, Analysis and/or interpretation of data, writing--original draft, Writing--review and editing. M.B.: Conception and design of this study, analysis and/or interpretation of data, writing--review, and editing. M.A.: Conception and design of this study; writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Improving the Efficiency of Energy Consumption in Buildings: Simulation of Alternative EnPC Models
## 2 State of the Art
### First-Out Contracts
#### 2.3.3 Analysis of risks in EnPCs  
The implementation of EnPCs by public authorities is seen as a means to influence changes in local energy systems, while allowing these entities to achieve policy objectives on this topic ([PERSON] and [PERSON], 2015). Therefore, it is essential to account for the trade-off between exposure to risk by public authorities with regard to energy projects, and the control they can exercise over the development of the projects in question [15]. With the right contract design, EnPCs can mitigate the risks related to the energy savings provided for in the contracts [5]. This is a problem that falls within typical contract design theory, as extensively discussed by [33].  
Despite growing worldwide, energy services markets are still far from realizing their potential. Previous studies show that some key barriers such as technological barriers (such as lack of knowledge and awareness), financial barriers (difficulty in finding adequate financing solutions), and political barriers (e.g., policy measures) must be broken down so that energy services markets can reach their full potential [7].  
The risks associated with the EnPCs have to be properly considered and studied by both parties in the conclusion of these contracts, because the greater the knowledge about the risks, the easier the mitigation of the same. [20] identified critical risks in energy efficiency improvement projects, such as a possible lack of payment by the EU after project execution, errors in baseline measurement, and increased installation cost. In turn, [34] highlighted that the risks and uncertainties in these projects are largely related to energy prices, equipment utilization, unexpected events, and contractual risks, among others. In addition to the points mentioned above, it is important to highlight that the low knowledge and awareness in the development of this type of project, the technical barriers and risks, and insufficient political support for the implementation of this type of project are other risks that deserve consideration by decision-makers [35].  
Reference [36] state that risks arising from such agreements can be grouped into five major groups (Figure 3): financial and market risks, operational and management risks, technological risks, client risks, and external environmental risks. Table 1 presents the main risks associated with the alternative contracting models.  
Unlike the traditional acquisition model, the use of alternative acquisition models for energy efficiency projects must be justified by various analytical techniques, with value-for-money (V/M) being one of the most recurrent techniques for this purpose [2; 37; 38]. V/M analysis is an analytical tool where all the values considered are in the form of \"single unit rate\", which helps to compare different options, including in its calculation all the costs of possible energy efficiency projects throughout the life cycle [39].