esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Coherent \(\pi\)-Electron Angular Momentum and Current of a Molecule Having Two Aromatic Rings
### Quantum Switching of Coherent \(\pi\)-Electron Rotations in a Nonplanar Chiral Aromatic Molecule
#### 3.2.2 Simulation Results of Angular Momentum Switching  
Figure 13a shows a 3D plot of the resultant angular momentum switching based on the sequential four-step scheme, which demonstrates that \(\pi\)-electron rotations are successfully controlled by the pulses depicted in Figure 13b. Control at each switching step was carried out by using pump and dump pulses with specific polarization directions. The pulsed laser with amplitude of 1.2 GV/m (= 1.9 \(\times\) 10\({}^{11}\) W/cm\({}^{2}\)) [14] was used at the second and fourth steps [86].  
The pulses shown in Figure 13b have two features: firstly, the pump (dump) pulse for each step has \(\textbf{e}_{\alpha\beta}^{(+)}\) [\(\textbf{e}_{\alpha\beta}^{(-)}\)] or \(\textbf{e}_{\alpha\beta}^{(-)}\) [\(\textbf{e}_{\alpha\beta}^{(+)}\)] polarization. Each pulse has a bandwidth that is sufficient to coherently excite two quasi-degenerate electronic excited states. Secondly, the pump and dump pulses partially overlap. For the first step, i.e., creation of CC rotation, for example, the electric field of the pump pulse was given as \(\textbf{e}_{bil2}^{(+)}(t)=\textbf{E}_{bil2}^{(+)}E_{bil2}^{0}\sin^{2}(\pi t/T_{ b1b2})\sin(\omega_{c,b1b2}t)\), while that of the dump pulse was \(\textbf{E}_{b1b2}^{(-)}(t)=\textbf{e}_{bil2}^{(-)}E_{bil2}^{0}\sin^{2}\!\left[ \pi\Big{(}t-t_{bil2}^{pd}\Big{)}/T_{b1b2}\right]\sin(\omega_{c,b1b2}t+\pi/2)\)[86]. Here, \(E_{bil2}^{0}\) is the amplitude of the pulse, \(T_{b1b2}\) (= 60.9 fs) is the oscillation period between the two excited states, \(b_{1}\) and \(b_{2}\) states,  
Figure 13: (**a**) Sequential four-step switching of \(\pi\)-electron rotations in (\(P\))-2,2\({}^{\prime}\)-biphenyl. (**b**) Electric fields of the sequence of overlapped pump and dump pulses. Reprinted from [86] with permission of the American Chemical Society.  
\(\omega_{c,b1b2}\) is the central frequency between the two excited states, and \(t_{b1b2}^{pd}\), the time interval between the pump and dump pulses, was set to \(T_{b1b2}/2\). The angle between the two polarization directions, i.e., \(\mathbf{e}_{b1b2}^{(+)}\) and \(\mathbf{e}_{b1b2}^{(-)}\), is \(113.5^{\circ}\).  
For the present ultrafast quantum switching, overlap between the pump and dump pulses is essential: the resultant electric field is rotated as an elliptically polarized one in the overlapped region, and the electric field forces the rotating \(\pi\) electrons to induce the reverse rotation that occurs in this region. As a result, the electronic angular momentum of the \(\pi\) electrons is erased.
## 5 Performance Analysis and Discussion
### Asymptotic Performance Analysis for Non-Identical Local Detection Performance Indices  
For non-identical local detection performance indices, _i.e._, \(P_{d_{i}}\
eq P_{d_{j}}\) for \(i\
eq j\), the closed-form expression for the system detection performance is difficult to derive. Here, we resort to the asymptotic performance analysis. The statistic \(\Lambda_{\text{BER}}\) is the sum of independent random variables. This allows direct application of the CLT approximation, and the limiting distribution under the \(H_{0}\) and \(H_{1}\) hypotheses is Gaussian with respective mean and variance. The mean and variance under the \(H_{1}\) hypothesis are:  
\[E(\Lambda_{\text{BER}}\mid H_{1})= \sum_{i=1}^{N}[g(y_{i}=1)((1-P_{e})P_{d_{i}}+P_{e}(1-P_{d_{i}}))\] \[\qquad+g(y_{i}=0)(P_{e}P_{d_{i}}+(1-P_{e})(1-P_{d_{i}}))]\] \[\overset{\Delta}{=} \sum_{i=1}^{N}f(y_{i},P_{d_{i}})\overset{\Delta}{=}\mu_{\text{ BER}1} \tag{33}\]  
and:  
\[V_{\text{ar}}(\Lambda_{\text{BER}}\mid H_{1}) =\sum_{i=1}^{N}\left[E(g^{2}(y_{i})\mid H_{1})-f^{2}(y_{i},P_{d_{i} })\right]\] \[\overset{\Delta}{=}\sigma_{\text{BER}1}^{2} \tag{34}\]  
where \(g(y_{i})\) is defined as:  
\[g(y_{i})=\log\left[\frac{P_{d_{i}}+(P_{e}/(1-P_{e}))^{2y_{i}-1}(1-P_{d_{i}})}{ P_{fa_{i}}+(P_{e}/(1-P_{e}))^{2y_{i}-1}(1-P_{fa_{i}})}\right] \tag{35}\]  
and \(f(y_{i},P_{d_{i}})\) is defined as:  
\[f(y_{i},P_{d_{i}})=g(y_{i}=1)((1-P_{e})P_{d_{i}}+P_{e}(1-P_{d_{i} }))\] \[\qquad+g(y_{i}=0)(P_{e}P_{d_{i}}+(1-P_{e})(1-P_{d_{i}})) \tag{36}\]  
\(E(g^{2}(y_{i})\mid H_{1})\) denotes:  
\[E(g^{2}(y_{i})\mid H_{1})=g^{2}(y_{i}=1)((1-P_{e})P_{d_{i}}+P_{e }(1-P_{d_{i}}))\] \[\qquad+g^{2}(y_{i}=0)(P_{e}P_{d_{i}}+(1-P_{e})(1-P_{d_{i}})) \tag{37}\]  
Similarly, the mean and variance under the \(H_{0}\) hypothesis are:  
\[E(\Lambda_{\text{BER}}\mid H_{0})\overset{\Delta}{=}\sum_{i=1}^{N}f(y_{i},P_ {fa_{i}})\overset{\Delta}{=}\mu_{\text{BER}0} \tag{38}\]and:  
\[V_{\text{ar}}(\Lambda_{\text{BER}}\mid H_{0}) =\sum_{i=1}^{N}\left[E(g^{2}(y_{i})\mid H_{0})-f^{2}(y_{i},P_{fa_{i}} )\right]\] \[\triangleq\sigma_{\text{BER0}}^{2} \tag{39}\]  
where \(E(g^{2}(y_{i})\mid H_{0})\) is defined as:  
\[E(g^{2}(y_{i})\mid H_{0})= g^{2}(y_{i}=1)((1-P_{e})P_{fa_{i}}+P_{e}(1-P_{fa_{i}})) \tag{40}\] \[+g^{2}(y_{i}=0)(P_{e}P_{fa_{i}}+(1-P_{e})(1-P_{fa_{i}}))\]  
By using the CLT approximation, the system false alarm probability can be written as:  
\[P_{\text{FA}}=P\{\Lambda_{\text{BER}}\geq\Gamma\mid H_{0}\}=Q\left(\frac{ \Gamma-\mu_{\text{BER0}}}{\sigma_{\text{BER0}}}\right) \tag{41}\]  
where \(\Gamma\) is the threshold of the fusion statistic. The corresponding detection probability is:  
\[P_{\text{D}}=P\{\Lambda_{\text{BER}}\geq\Gamma\mid H_{1}\}=Q\left(\frac{ \Gamma-\mu_{\text{BER1}}}{\sigma_{\text{BER1}}}\right) \tag{42}\]  
It is interesting to find that the accuracy of CLT approximation depends not only on the number of sensors, but also on the local detection performance indices and received SNRs [18].
## 3 Security Attacks in Smart Healthcare Systems
### Data Breach Attacks  
A data breach is a coordinated security incident where a cognizant insider or external malicious user obtains unauthorized access to the authorized information (e.g., confidential or protected data). This attack could be an accidental event, where an insider (employee) accidentally reveals the sensitive information, or it could be a nefarious event, where an attacker explicitly performs the privilege escalation approaches to extract the sensitive information. In the context of smart healthcare systems, patients' medical records, personal information, social security number, and financial information is the most sensitive information that an attacker attempts to acquire. Most healthcare facilities have electronic healthcare record databases where the patient's information is centrally stored; these are the prime target of cybercriminals. Once they acquire the central databases, they extract patients' critical information and sell it to third-party intermediaries, resulting in the healthcare sector having to sustain financial loss and reputational damage loss. In May 2022, Partnership HealthPlan and Shields Health Care Group organizations of California and  
Figure 3: Taxonomy of different security attacks on a smart healthcare system.  
Quincy disclosed a data breach that affected \(\approx\)2 million individuals and 50 small facilities. In a cyberattack, individuals' data, such as date of birth, patient medical data, addresses, patients health insurance, and social security numbers, where accessed by the attackers [60]. Later, in August 2022, Yuma Regional Medical Center was exposed to a ransomware attack that stole the social security numbers of \(\approx\)700,000 individuals [61]. [PERSON] et al. [62] proposed a blockchain-based architecture to secure and preserve the patient's electronic health records by executing a smart contract. Furthermore, they focused on confirming the user's identity utilizing the zero-knowledge proof and proxy re-encryption safeguard the healthcare systems against data breaches.
## 3 Results
### Number of User Interactions and Time for Use Scenario  
Figure 8 shows the time required by the participants to complete the individual test scenarios. For the \"introduction tasks\" scenario, the VR drilling experts group needed \(548\) s \(\pm\) 185 s, which is approximately the same time as the group of real drilling experts with 543 s \(\pm\) 191 s. The novices (\(t_{\text{{\tiny{Non-VR}}}}\)) needed significantly more time than the experts (\(t_{\text{{\tiny{EX-VR}}}}\)) for the \"tripping-1\" test scenario (\(p\) < 0.001; \(t_{\text{{\tiny{Non-VR}}}}\) = 622 s \(\pm\) 140 s; \(t_{\text{{\tiny{EX-VR}}}}\) = 361 s \(\pm\) 65 s). Using the real prototype, the experts were able to perform this scenario most quickly (\(t_{\text{{\tiny{EX-Real}}}}\) = 305 s \(\pm\) 140 s). The \"top drive connection\" scenario was also performed fastest with the real prototype (\(t_{\text{{\tiny{EX-Real}}}}\) = 118 s \(\pm\) 40 s). For the virtual prototype, the experts were significantly slower (\(p\) = 0.02; \(t_{\text{{\tiny{EX-VR}}}}\) = 182 s \(\pm\) 47 s).  
A comparison of the two tripping simulations shows that all of the groups improved significantly (\(p_{\text{{\tiny{Real-Experts}}}}\) = 0.01; \(p_{\text{{\tiny{VR-Experts}}}}\) < 0.001; \(p_{\text{{\tiny{VR-Novices}}}}\) < 0.001). In the second test, the experts required only 147 s \(\pm\) 42 s with the real prototype. The experts who tested the VR prototype were able to perform the second tripping simulation in 203 s \(\pm\) 53 s. Novi  
Figure 8: Mean time and 95% confidence intervals for completing the different test scenarios. Significant differences (\(p\) < 0.05) are marked with an asterisk (*).  
Figure 7: Mean scores and 95% confidence intervals of the individual UEQ dimensions achieved by the different prototypes. Significant differences (\(p\) < 0.05) are marked with an asterisk (*).  
showed the greatest improvement. The participants required 262 s \(\pm\) 61 s for tripping in the second trial using the VR prototype.  
Figure 9 shows the mean number of interactions required by the participants to complete the different test scenarios. In all four scenarios, the experts with the real prototype required the fewest interactions to complete a task. The novices required an average of 55 \(\pm\) 12 interactions, while the drilling experts completed the test scenario \"Tripping-1\" with an average of 42 \(\pm\) 9 interactions using the VR prototype. The observed difference is statistically significant (\(p\) = 0.035).  
Similar to the results for task completion times, all of the subject groups showed improvements in the second tripping process. For the second tripping trial, the novices needed significantly fewer interactions, specifically 38 \(\pm\) 9, compared to their initial performance (\(p\) = 0.002).
# Study on Corrosion Behavior of Waterborne Polyurethane Coating with High Thermal Conductivity
## 1 Introduction
Subsequently, the thermal conductivity and corrosion resistance performance are evaluated through the plate heat source method and salt spray experiment, respectively. Electrochemical impedance spectroscopy (EIS) and the scanning Kelvin probe (SKP) technique are used to analyze the corrosion failure mechanism of modified waterborne polyurethane coating.
# Evaluation of the Acceleration Vibration Signal for Aggregates of the Horizontal Drilling Stand
## 4 Experimental Results and Analysis
### Vibration Signal Processing in the Time Domain
The individual values of the modes do not differ significantly. Measured and processed vibration signals from the pump and hydro-generator, i.e., modes II.-III.-VI. are classified as non-harmonic, compound periodic, or quasi-periodic with the weak stochastic component. The fact that there are periodic components in individual signals will be proved by processing and evaluation in the frequency and time-frequency domain. The processing of the measured vibration acceleration signals by time analysis assumes that the generated vibration emissions are a possible reference source of information about the current state of the observed aggregates and the disintegration process. The vibration signal is an indicator of faultless operation of the units and a vibration generator in the working environment. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Operating Mode** & **Peak** & **Peak-to-Peak** & **Average Value** & **RMS** \\ \hline
1. motor & 12.753 & 25.836 & \(-\)0.276 & 3.831 \\
2. water pump & 1.646 & 3.787 & \(-\)0.261 & 0.979 \\
3. hydro-generator & 1.931 & 4.326 & \(-\)0.249 & 1.032 \\
4. motor and water pump & 13.2325 & 26.016 & \(-\)0.232 & 3.697 \\
5. motor and hydro-generator & 12.065 & 26.657 & \(-\)0.267 & 3.576 \\
6. water pump and hydro-generator & 1.542 & 3.622 & \(-\)0.245 & 0.904 \\
7. motor, water pump and hydro-generator, tool spindle without the bit & 13.247 & 27.752 & \(-\)0.2767 & 3.552 \\
8. motor, water pump and hydro-generator, tool spindle with the bit & 9.939 & 20.343 & \(-\)0.292 & 2.948 \\
9. andesite drilling & 71.1702 & 159.4525 & 0.14856 & 21.1312 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical characteristics of operating modes. Figure 8: Histogram of probability distribution of measured signals ((**a**) normal Gaussian distribution, (**b**) non-Gaussian distribution). Obviously, the time characteristics are not entirely sufficient to analyze the properties of the vibration signal at steady-state modes. However, in the case of dynamic changes in the drilling mode, such as a change in revolutions, pressure force, disintegrating tool, or a change in the type of disintegrating rock, monitoring the ongoing changes in the frequency domain is effective.
# Heterogeneous Electro-Fenton-like Designs for the Disposal of 2-Phenylphenol from Water
## 2 Materials and Methods
### Sample Preparation and Analytical Methods
#### 2.5.2 Hydrogen Peroxide Concentration
On the other hand, the current efficiency proved a better efficiency in the hydrogen peroxide production at a lower intensity (9.36 and 1.93 at 60 min for 100 and 300 mA, respectively). Based on these results a current intensity of 100 mA was selected to evaluate the ability of the different transition metals (nickel and zinc) in the degradation of 2-phenylphenol. In the electro-Fenton the optimal concentration of used iron catalyst is lower than the conventional Fenton process, thus in the Fenton process is required catalyst concentration in a range of g/L, while in the electro-Fenton process is mg/L [44]. Moreover, the catalyst concentration is one of the key parameters to control the production of hydroxyl radical and the efficiency of the electro-Fenton process [31]. For this reason, as an attempt to optimize the electro-Fenton process, three concentrations (1, 3 and 5 mM) of each transition metal ions were tested. Figure 2a shows the degradation profiles of 2-phenylphenol in the three electro-Fenton tests carried out in comparison to the anodic oxidation test, used as control experiment. BDD electrodes have great characteristics that distinguish them from other materials, such as high oxygen evolution potentials, inert surface with low adsorption and very low double layer capacitance and background current. Thanks to its properties, BDD anodes attain higher oxidation rates and mineralization efficiencies, producing \({}^{\bullet}\)OH from water  
Similarly, the electro-Fenton-like test using zinc as homogeneous catalyst showed also better results than anodic oxidation (Figure 3). Concerning the zinc concentration, in this case, the differences among them are minimal, achieving the highest reaction rate at a zinc concentration of 3 mM (Table 2). However, the maximum removal was around 84.88% with a corresponding 64.36% TOC reduction after 120 min, values that were lower than the obtained using nickel as a catalyst with 90.12 and 69.08% of pollutant removal and TOC reduction, respectively. Thus, the best performance was attained when the electro-Fenton process was performed with nickel and the next assays were carried out using this transition metal ion. \begin{table}
\begin{tabular}{c c c c c} \hline
**Transition Metal** & **Concentration (mM)** & **k (min\({}^{-1}\))** & **R\({}^{2}\)** & **TOC Reduction (\%)** \\ \hline \multicolumn{2}{c}{Anodic oxidation} & 0 & 0.0098 & 0.9989 & 44.62 \\ \hline \multirow{3}{*}{Nickel} & 1 & 0.025 & 0.9924 & 69.08 \\  & 3 & 0.0218 & 0.9829 & 66.67 \\  & 5 & 0.0152 & 0.9855 & 64.60 \\ \hline \multirow{3}{*}{Zinc} & 1 & 0.0189 & 0.9952 & 60.37 \\  & 3 & 0.0208 & 0.9954 & 64.36 \\ \cline{1-1}  & 5 & 0.0186 & 0.9942 & 60.22 \\ \hline \end{tabular}
\end{table} 
Table 2: Parameter of first order reaction kinetic model (Equation (9)) fitting to the experimental data and TOC reduction after 2 h of treatment. Figure 3: SEM images of (**a**) perlite, (**b**) biochar and (**c**) carbon nanofibers.
## 3 Test Results
### Modulus of Elasticity Test--MW1 Sample
#### 3.2.1 Time Evolution of the Modulus of Elasticity  
Figure 8 shows the time evolution of the average values of the modulus of elasticity E as a function of the curing time of the MW1 specimen. The last point (in red; not considered for the trend line) represents the rupture of the sample. In the same figure, the trend line of the results can be seen (assumed 2 nd degree polynomial; trend line that best fits the values). Starting from this line, the result at T = 96.6 h is about 10% away.  
Once more, several questions were formulated in advance of the tests. First, the initial age of the specimen in the 1 st test, due to the time interval until the minimum resistance is reached and the time required to instrument the specimen, i.e., to bond the strain gauges. For this sample, it was possible to start the tests after 40 h. When evaluating the results, other questions arose: what is the best way to describe the evolution of the modulus of elasticity of the material over the time, and when does the positive evolution of this value end? For this sample, it was decided to focus the tests on the initial phase of curing. It was not possible to determine the point in time after which it would no longer be worthwhile to continue the tests.
# PLC-Controlled Intelligent Conveyor System with AI-Enhanced Vision for Efficient Waste Sorting
## 5 Discussion
### Future Extensions  
The modular design of the system lays a robust foundation for scalability and versatility, facilitating seamless expansion to accommodate larger and more complex operational demands. This design supports straightforward upgrades to the AI deep learning modeland adjustments to the PLC programming, enabling the system to meet diverse waste management requirements. Beyond MSW, the flexibility of the system extends to the classification and sorting of a wide range of recyclables, including organic waste, electronic components, and specialized industrial or chemical materials. The modular design further allows the integration of additional sorting stations customized to bio-based materials or other specific waste streams, paving the way for a comprehensive and versatile waste management solution capable of addressing the evolving demands of recycling industries.  
To improve the performance efficiency of the system, several optimization strategies are being explored. These include the incorporation of advanced machine and deep learning algorithms to improve object recognition and sorting accuracy, particularly under challenging conditions such as varying lighting or overlapping objects. Integrating additional sensors, such as infrared or ultrasonic technologies, could provide additional data to refine the identification of materials, improving the precision and reliability of the sorting process.  
Future extensions will emphasize increasing the system's ability to manage overlapping objects, a critical challenge in automated sorting. Advanced image processing algorithms are being investigated to improve segmentation and edge detection, facilitating more accurate differentiation between stacked or adjacent items. Depth-sensing technologies, such as stereo cameras or LiDAR, are also being considered to provide spatial information that can further improve the system's capacity to handle complex waste configurations. These enhancements aim to refine the sorting process, reduce error rates, and improve operational efficiency in demanding and dynamic environments.  
Advancements in the model training process are also a key priority, with efforts focused on minimizing classification errors and improving generalization across diverse operational scenarios. These improvements involve expanding the training dataset to include a wider variety of waste types and conditions, as well as employing more rigorous validation techniques to ensure robustness and reliability in real-world applications.  
Emphasizing its scalability, adaptability, and ongoing optimization, the system is presented as a proactive approach to addressing the issues associated with modern waste management. These planned extensions and improvements will ensure their relevance and effectiveness in addressing a wider spectrum of waste types and recycling needs while simultaneously contributing to the advancement of intelligent sorting technologies.
# A Multidisciplinary Approach for Improving Resource Efficiency in the Indian Surimi Supply Chain
## 2 Methodology  
A multidisciplinary research approach was applied with expertise from supply chain management, life cycle assessment, biotechnology, and energy and process engineering, to propose several solutions to improve the overall resource efficiency in the surimi supply chains in India. The surimi supply chain was mapped using value stream mapping methodology by industry visits and semi-structured interviews to understand the critical points where losses can occur and their potential causes. The study focussed on the supply chain from fishing boats to the surimi processor with an intermediary transportation stage.  
Industry visits and interviews were conducted during 2018-2019 in the Mumbai area in India with surimi supply chain stakeholders. A non-vertically integrated representative supply chain of a surimi processing plant in Mumbai was selected as the case study. The surimi processing plant operates between August and June with a production capacity of 24 MT/day. The plant sources fish from 15 suppliers in the Mumbai coastal area. The RRM generated are supplied to various companies for further utilisation. The research team of ReValue spent two weeks at the fishing dock, processing side of surimi, and RRM, to gain a complete understanding of the supply chain. The process mapping and other information were collected through semi-structured interviews with the processing plant and fishers supplying fish to surimi processing plants.  
An industrial refrigeration system was simulated as a part of ReValue project. The intention of this work was both to contribute with solutions that can improve fish quality compared to today's products and promote more climate-friendly refrigeration systems. The research in ReValue project was mainly divided into the following three parts:  
1. Mapping of the cold chain and an industrial refrigeration system simulation.
2. Development of valorisation options for improving resource efficiency and developing value-added products. These valorisation options were selected based on the chemical composition, quantity, and quality of RRM.
3. Review of the regulatory frameworks for utilisation of RRM in India and Europe. Conversion of RRM from the fish supply chains is governed by a set of regulations both in India and Europe. These regulations were reviewed, and their implications on the utilisation of the rest of the raw materials were analysed.  
The impacts associated with the SDGs and the effect that lack of regulatory standards can have on the environmental and economic prosperity of the industry are also presented in this paper. Finally, the market exploitation potential of derived solutions is discussed.  
Main results and discussion derived from the ReValue project are presented in the next section. Impact of ReValue project associated with Sustainable Development Goals are presented in Section 4.