esa-sceva/satcom-chunk-collection huggingface dataset:
# Flexible Phased Antenna Arrays: A Review
## 4 Major Challenges
### Thermal Management  
The inclusion of active components, such as RFICs and amplifiers, introduces significant thermal challenges such as heat dissipation in FPAs. Power amplifiers in particular convert a significant amount of power into heat, leading to localized hot spots. Furthermore, thin and flexible substrates (LCP and PET film) used in FPA have limited thermal conductivity, making heat management critical, impeding heat dissipation compared to rigid PCBs or metal-backed substrates. Such temperature variations can cause mechanical stress on the components due to mismatched coefficients of thermal expansion between components. This phenomenon can lead to the risks of delamination or cracking, especially in laminated or 3D-printed structures. Moreover, electrical performance of RFIC can be significantly affected, which can lead to shift in frequency responses, increase in noise figure, and reduction in linearity. Consequently, without efficient thermal management, long-term exposure to high temperatures reduces component reliability. This issue was addressed with various methodologies. For example, microfluidic cooling channels were embedded to reduce the temperature of the IC in short time interval by using water flow [42, 63]. Thermal management is also critical in high-power configuration of FPAs. For example, in a 256-element FPA system for WPT, with high-power configuration delivering power wirelessly at a 1 m distance, thermal management was achieved through aluminum heat spreaders in high-power system and flat ceramic spreaders in the flexible system [50]. Similarly, for an FPA system with 16 radiators, Power Synthesis and Control Unit (PSCU) was particularly used to ensure stable voltage and current distribution during operation with sufficient temperature control [51]. Thermal insensitivity is a desirable feature. In another work, a thermally and mechanically insensitive antenna array was developed using a PDMS substrate that overcomes the challenge of thermal interference due to the low value of the thermal coefficient of PDMS substrate by reducing the effects of anisotropic thermal expansion [68]. Different thermally resilient materials and integrated cooling channels integrated with dynamic cooling systems can be viable solutions for efficient thermal management.
# Shared Quantum Key Distribution Based on Asymmetric Double Quantum Teleportation
## 3 Double Teleportation as Superposition and Parallel Post-Processing
### Entanglement and Non-Locality Activation  
The double teleportation plus post-processing process depicted at this point has been previously analysed in terms of generation of non-local properties [29]. In fact, after of the measurement of the control state on the basis \(\{|b_{0}\rangle,|b_{1}\rangle\}\), a wide type of entangled states could be generated if each Bob introduces additional local resources. Using the concurrence as entanglement measurement, it was shown that they can range from separable states to maximally entangled ones. In addition, the Clauser-Horne-Shimony-Holt (CHSH) inequality has been used to demonstrate the non-locality activation through the involved quantity \(S(|\psi\rangle,\theta)\equiv|E(S^{A},S^{B})+E(S^{A},S^{B^{\prime}})+E(S^{A^{ \prime}},S^{B})-E(S^{A^{\prime}},S^{B^{\prime}})|\) (there, \(E(S^{1},S^{2})\) is the correlation between the measurements \(S^{1},S^{2}\)). This quantity reaches the Tsirelson's bound in the process, indicating the non-locality activation. Such analysis dealt with the measurement basis settled by the operators \(\{S^{A}=\mathbf{X},S^{A^{\prime}}=\mathbf{Z}\}\) and \(\{S^{B}=\cos\theta\mathbf{X}+\sin\theta\mathbf{Z},S^{B^{\prime}}=-\sin\theta \mathbf{X}+\cos\theta\mathbf{Z}\}\) to get the correlations on a setup testing of the CHSH inequality. It means that the state transference depicted strongly undergoes through a non-local process generating non-locality correlations. Such non-local transference has been also demonstrated between a couple of semiconductor microcavities connected by optical fiber for solid-state physics [38; 39] using geometric quantum discord and concurrence as main non-locality quantifiers. Despite the process presented in that article proposing entanglement to generate quantum states at distance, those works set a certain kind of alternative technology to share or generate quantum states in a second party using non-classical light.
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
## 4 Numerical Simulation
### Verification of Finite-Element Model  
The numerical simulation outcomes closely resemble the pull-out damage patterns observed in perfobond connectors, as depicted in Figure 13. In the case of notched T-perfobond connectors, an increase in pull-out force intensifies the concrete's constraint on the flange, predisposing the perfobond rib and flange to a wedge-shaped inverted pyramid damage mechanism, as illustrated in Figure 13a. Conversely, for T-perfobond connectors, the larger area of the flange in contact with the concrete during upward pull-out results in enhanced anchorage. This robust anchorage ultimately culminates in the fracture of the perfobond connectors, as shown in Figure 13b. Specifically, the peak stress in the perfobond rib reached 667.99 MPa, a threshold sufficient to confirm the rib's fracture. In the notched T-perfobond connectors, the shallow embedment depth coupled with the flange's influence means that the perforated rebar's loss of bearing capacity plays a limited role, predominantly in anchoring.  
In Figure 14, the load-separation curves derived from the finite-element model (FEM) are juxtaposed with the outcomes of the pull-out tests. The experimental curves for T-perfobond connectors comprise three distinct phases, namely the elastic phase, the nonlinear parabolic phase, and the descending branch. With the concrete dilation angle set at 30\({}^{\circ}\), the finite-element analysis predicted pull-out forces of 106.51 kN for the notched T-perfobond connectors and 111.52 kN for the T-perfobond connectors. These values correspond to 106.0% and 107.5% of the average experimental results, respectively. The initial stiffness observed in the numerical simulation's load-separation curves exceeds the experimental values, possibly due to the idealized boundary conditions in the simulation, which do not account for the deformations present in the experimental setup. Additionally, discrepancies may arise from the inherent variability in the mechanical properties of the steel fiber-reinforced UHPC (ultra-high-performance concrete) material used in the tests. Therefore, the stiffness of the loaded steel beam was adjusted between 4 and 800 kN/mm by trial calculation so that the load-separation curves of the simulations were in better agreement with the model test results.  
Figure 12: Material constitution of steel.
## 3 Results and Discussion
### Adsorption of Reactive Orange 16 (RO 16) as an Indicator for Fabric Sorption Properties
#### 3.3.1 Optimization of the Initial Solution's pH to Achieve Maximum RO 16 Removal
The key reason for this lies in the chitosan structure characterized by the abundance of hydroxyl and amino groups and high content of amorphous regions. Increasing the solution pH value causes deprotonation of a certain number of the chitosan ammonium groups and obtaining -NH\({}_{2}\) groups. Both -OH and -NH\({}_{2}\) groups are able to participate in the effective multiple interactions with the dye molecules, such as hydrogen bonds (dipole-dipole and Yoshida), and n-\(\pi\) stacking interactions between lone pairs of the oxygen and nitrogen atoms and aromatic rings of RO 16 [39; 40; 41]. Dipole-dipole hydrogen interactions involve hydrogen atoms of -OH and -NH\({}_{2}\) groups interacting with the oxygen (carbonyl, sulfone, or sulfonyl groups) and  
Figure 6: The impact of the solution’s initial pH (pH\({}_{\text{i}}\)) on the removal (\(R\)) of RO 16 by different adsorbents (\(c_{0}\) = 25 mg/L, \(t\) = 21 h, \(T\) = 25 \({}^{\circ}\)C, the adsorbent mass of 0.50 g). nitrogen atoms (azo bridge), while Yoshida hydrogen bonding includes interactions of hydrogen with RO 16 aromatic rings. Furthermore, the higher \(R\) or RO 16 by J2.0% CS with respect to other adsorbents could also be ascribed to the higher content of amorphous regions present on its surface originating from chitosan that facilitates the trapping of dye molecules. Further, an increase in the solution pH value causes the decline of the dye removal for all samples since the adsorbent surface becomes neutralized and carries an excess negative charge and thereby repelling the anionic dye. Since the \(R\) of RO 16 was found to be highly pH dependent, further experiments were conducted at a pH of 2.0 (for J, J0.5% CS, and J1.0% CS) and 4.0 (for J2.0% CS).
# Modeling and Resolving Uncertainty in DIKWP Model
## 2 Problem Description
### Uncertainty of Internal Conditions
#### 2.2.1 System Uncertainty  
* **Network latency (_e10_):** In HFT, even milliseconds of delay can lead to significant losses, as market conditions can change drastically within extremely short periods. For instance, consider a trading firm that relies on the fastest network connection from New York to London to execute arbitrage strategies. However, due to the cross-geographical nature, the risk associated with network connectivity is much higher compared to intra-geographical risks. If this network connection experiences delays due to technical issues, the firm may miss out on executing lucrative trades, or worse, they may fail to withdraw in time before market conditions deteriorate, resulting in losses.
* **Processing latency (_e11_):** The impact of processing latency on HFT is significant, as in this trading mode, the advantages of every millisecond or even microsecond can determine profits or losses. For example, a company encounters technical issues during the development of its trading system, resulting in a 5-millisecond delay in the execution of trade orders. Although seemingly insignificant, in the world of HFT, such delays can have substantial effects. Due to execution latency, when the company's algorithms identify an arbitrage opportunity and attempt to execute trades, market prices have already adjusted, causing the arbitrage opportunities to vanish. Thisimplies that the company may have missed out on numerous potentially profitable trading opportunities.
* **System failures (\(e_{12}\)):** Defects introduced during software updates or modifications are common issues in HFT systems. Even with rigorous testing, defects may remain undetected, especially those that manifest only in actual trading environments. For instance, a financial services company in 2012 updated its trading software one day, and a flaw in the new software resulted in abnormal behavior of the trading system, erroneously executing millions of orders at high speed that should not have been executed. Within less than an hour, this system failure incurred hundreds of millions of dollars in losses for the company. This event underscores the importance of software updates and defect management in HFT systems. When new code runs in an actual trading environment, even after rigorous testing, undiscovered software defects may exist.
## 2 Materials and Methods
### Phase One: Development of Seismic Microzonation Map
#### 2.1.1 Time Histories  
In this study, time histories refer to the ground motion measurements due to seismic events, recorded by selected seismic stations in Malaysia. Seismic time histories were obtained from the Malaysian Meteorological Department (MMD). Six seismic events were selected for this study comprises of three near-fields (seismic events occurred in Malaysia) and three far-field (seismic events from Sumatera, Indonesia). The seismic events for far-field are Sumatera (25 July 2004), Sumatera (1 December 2006), and Sumatera (2 March 2016) known as F1, F2, and F3, respectively. Seismic events for near-field are Beluran, Sabah (23 May 2005), Kunak, Sabah (28 May 2012), and Ranau, Sabah (1 February 2014), noted as N1, N2, and N3, respectively. One of the criteria for the seismic time history selection is that the earthquake magnitude reach 5 or above for far-field seismic events (since lower values are considered as non-harmful to structures). Only secondary waves in the east-south (E) direction were used in this study with noise reduction due to the assumption that the recording station would capture all vibrations, including vibrations from surroundings other than the seismic. Figure 5 shows seismic time histories used in this study.
# Shiga Toxin Therapeutics: Beyond Neutralization
## 9 Targeting the RSR and Inflammation during Shiga Toxicosis  
Limited research has been performed to evaluate the impact of targeted anti-inflammatory therapy on susceptibility to STX-mediated tissue injury, morbidity, and mortality. [PERSON] et al. hypothesized that the enhanced inflammatory response documented in vitro in cells costimulated with STXs and LPS could be blunted with anti-LPS antibodies in vivo. Mice immunized with _E. coli_ O111:B4 were challenged with LPS and STXs following the confirmation of circulating anti-LPS IgG antibodies. There was no difference in mortality in immunized mice following STX + LPS challenge; however, the immunized mice failed to suppress circulating TNF\(\alpha\) following intravenous LPS challenge compared to naive mice, suggesting that the immunization did not prevent pattern recognition receptor activation by circulating LPS [125]. Mice pretreated with immunosuppressive doses of dexamethasone exhibited greater survival following STX2 challenge, reduced numbers of Gb3-positive CNS cells, reduced damage to the blood brain barrier, and a reduction in the number of activated astrocytes compared to controls [126]. The vasoactive drug anisodamine, an inhibitor of TNF\(\alpha\) secretion, improved the survival of mice injected with lethal doses of STX. The improved survival was reversed via a concurrent injection of recombinant TNF\(\alpha\) with STX, suggesting that the suppression of TNF\(\alpha\) by anisodamine provided significant protection from STX-induced disease [127]. The pretreatment of mice with MCP-1 MIP-1\(\alpha\)-, or RANTES-neutralizing antibodies protected against renal fibrin deposition following STX + LPS injections [128]. [PERSON] et al. found that germ-free mice pretreated with anti-TNF antibodies and infected with STEC were protected from clinical signs of morbidity, reduced renal pathology on histology, and reduced renal IL-18, TNF\(\alpha\), and IL-6 protein concentrations despite similar levels of intestinal colonization and fecal STX content compared to controls [129].  
A broad range of diseases are either driven through inflammatory responses or are complicated by inflammatory dysfunction. Anti-cytokine and chemokine therapeutics are being developed for use in diseases ranging from rheumatoid arthritis to inflammatory bowel disease, with multiple commercially approved drugs already available in the United States [130; 131; 132; 133]. General anti-inflammatory drugs, such as glucocorticoids and NSAIDs, have been available for decades to treat acute inflammatory diseases. Further preclinical studies are necessary to characterize the role of inflammation in the pathogenesis of STX-induced HUS, as the role of the host response to STEC infection in the pathogenesis of HUS remains unclear [10; 11; 12]. Further work is necessary to determine if specific host cytokine responses are necessary for the development of HUS to determine the suitability of RSR elements as therapeutic targets for clinical study (Figure 3).
# An Optimal Scheduling Model for Connected Automated Vehicles at an Unsignalized Intersection
## 3 Methodology
### Solving Algorithm  
#### 3.2.1 Algorithm Analysis  
As indicated in Section 3.1, the number of decision variables in this model is equal to the total number of vehicles within the optimization time, hence the number of decision variables is \(\sum_{i=1}^{4}\mathcal{N}_{i}\). The constraints are given by Equations (4), (5), (7), and (8), with their total number being \(\sum_{i=1}^{4}\mathcal{N}_{i}+\sum_{i=1}^{4}(\mathcal{N}_{i}-1)+2\sum_{\forall (i,i^{\prime})\in\mathcal{C}}(\mathcal{N}_{i}\mathcal{N}_{i^{\prime}})\). Upon analysis, it is evident that when traffic demand is high and the number of vehicles per unit time increases, the constraints will grow exponentially, resulting in longer solution times that cannot meet the timeliness requirements. Further analysis reveals that the primary constraints between vehicles are time-based. Moreover, for vehicles arriving at the intersection control area, if the time difference between their arrival and that of other vehicles is significant, they will not be affected by each other. Therefore, the original large-scale optimization problem can be transformed into several consecutive small-scale optimization problems by considering the time dimension (setting an optimization time window) to reduce the solution time of the original model. However, when decomposing the problem, it is necessary to consider the boundary constraints between subsequent subproblems in the time dimension. Specifically, the first vehicle in the next optimization time window must satisfy the safety time constraint with the last vehicle in the previous optimization time window. If they are from conflicting directions, the time difference for entering the conflict zone must be no less than \(\omega\); if they are from the same direction, the time difference must be no less than \(\tau\). This can be represented by Equation (9),  
\[t_{i1}^{i+}\geq\begin{cases}t_{i|\mathcal{N}_{i^{\prime}}|}^{+}+\omega,& \text{if }\forall(i,i^{\prime})\in\mathcal{C}\\ t_{i^{\prime}|\mathcal{N}_{i^{\prime}}|}^{+}+\tau,&\text{if }i=i^{\prime} \end{cases}, \tag{9}\]  
where \(t_{i1}^{i+}\) represents the time when the first vehicle from the \(i\)-th direction in the next optimization time window enters the conflict zone. \(t_{i^{\prime}|\mathcal{N}_{i^{\prime}}|}^{+}\) represents the time when the last vehicle from \(i^{\prime}\)-th direction in the current optimization time window enters the conflict zone.
## 2 Electric Field Characteristics of Insulators under Complex Operating Conditions
### Establishment of Insulator Model  
The XWP2-70 double-umbrella porcelain insulator string was modeled by SolidWorks software, and specific parameters are shown in the following Tables 1 and 2.  
During modeling, the influence of tower cross arm, transmission line, and other factors are considered; models are then combined into an assembly and imported into COMSOL finite element software for simulating the calculation of the space electric field of insulators. In the simulating calculation, the electric field solution of the insulator model surface and its surrounding air needs to satisfy the following electric field equation:  
\[\
abla\times E=0 \tag{1}\]  
\[\
abla\cdot\epsilon E=\rho \tag{2}\]  
where \(E\) is the electric field strength of the medium area (V/m), \(\epsilon\) is the dielectric constant of the medium, and \(\rho\) is the electric charge volume density (C/m\({}^{3}\)). After the influence of the space charge on the simulation is ignored in the electric field simulation, the potential function \(\varphi\) obtained satisfies the following requirements:  
\[\
abla^{2}\varphi=0 \tag{3}\]  
During the simulating calculation, type 1 boundary conditions adopted for the test model are:  
\[\varphi|_{\Gamma_{0}}=0 \tag{4}\]  
\[\varphi|_{\Gamma_{1}}=U_{0} \tag{5}\]  
where \(\Gamma_{0}\) represents the boundary and ground of the air domain; in this paper, the infinite element domain is used as the infinite boundary of the air domain for the problem of the open field domain. \(\Gamma_{k}\) represents the high-potential simulation model, and \(U_{0}\) represents the potential of the conductor, which is set as the peak phase voltage of 220 kV, that is, 179.63 kV. Equations (3)-(5) constitute the boundary conditions of the electric field simulation model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Parameter**} \\ \cline{2-5}  & **Height/mm** & **Umbrella Diameter/mm** & **Creepage Distance/mm** & **Outline/Structure** \\ \hline XWP2-70 & 146 & 255 & 400 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure parameters of insulator.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Dielectric material & Air & Porcelain & Cementing agent \\ Relative dielectric constant & 1 & 5.71 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The relative dielectric constant of each dielectric material.
# A Survey on Cyber Risk Management for the Internet of Things
## 5 Cyber Risk Treatment For IoT Survey Results
### IoT Risk Control
#### 5.1.2. Consider Resources for Risk Control  
Various resources are needed to implement security controls, with resources including time, money, people, device capabilities, etc. Within the work of [PERSON] [56], the most suitable mitigation strategies are not only those that decrease risk but strategies that are feasible based on the available resources. Concerning this, [PERSON] et al. [55] characterise the security of a smart infrastructure by using integral security indicators that can be used to choose security controls based on risk scores and the resources that are available.  
[PERSON] et al. [72] consider the number of resources that need to ensure that a risk is minimised as much as possible based on the nature of the associated vulnerability. The number of available resources for IoT will greatly differ depending on the environment. For an organisation that uses IoT, it is likely that a group of employees would carry the responsibility for cyber risk management, having a designated budget and mitigation mechanisms. However, IoT domains like smart homes will not have the same people power, budget, and mitigation mechanisms that an organisation has. [PERSON] et al. [67] consider that there are fewer resources within smart homes, which means that other assessments and controls would be needed to accommodate this. Not only this but IoT devices in general have a limited set of security capabilities. An assessment that [PERSON] et al. [40] suggest before controls are implemented is a cost-benefit analysis that is used to understand the cost of implementation and impact. When it comes to financial resources for organisations, the estimated value of an IoT control can be determined by the cost of implementation, threat event occurrence rate and expected losses. Considering the cheapness of IoT devices, some controls may not be suitable with the control cost, outweighing the risk [44].  
_Insight 14:_ IoT risk responses need to be determined based on accessible resources, with money being the most discussed resource. As part of answering RQ3, the consideration of resources is limited within the surveyed papers. For unregulated personal IoT domains, the types of controls may be limited, for example, smart home residents may not want to learn about and buy an intrusion detection system due to the amount of money and time required to use this control. This means that there needs to be an assessment of control benefits and drawbacks, with no access to certain controls affecting the acceptable risk threshold or requiring another control.