esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Electrochemical Measurements  
The electrochemical tests were performed using an Autolab PGSTAT302N (Metrohm, Zurich, Switzerland) that was controlled with GPES software (4.0) and consisted of a three-electrode cell, using Ag/AgCl (3.0 M KCl), Pt wire and a cork-graphite sensor (geometrical area of approximately 0.45 mm\({}^{2}\), while a real area of 116 mm\({}^{2}\), which was estimated by procedure in [25]) as the reference, auxiliary and working electrodes, respectively. Differential pulse voltammetry (DPV) parameters were as follows--modulation time: (\(\geq\)0.002 s), 0.05 s; interval time: (\(\geq\)0.10 s), 0.5 s; initial potential: 1.0 V; final potential: 1.7 V; step potential: 0.00495 V; modulation amplitude: 0.01995 V; potential scan rate: 100 mV s\({}^{-1}\); and agitation time: 30 s. The optimized parameters were used for all measurements [14]. All analyses were performed in triplicate. All electrochemical analyses were conducted without deaeration, at 25 \(\pm\) 2 \({}^{\circ}\)C. For the identification/determination of HCQ in river water matrices, firstly, the electrochemical sensor's response, in terms of current, was verified by constructing an analytical curve in river water samples. Secondly, the HCQ concentration in HCQ-polluted river water sample was electrochemically treated and the HCQ concentration was determined, at collected electrolysis times, using the standard addition method, where the samples were spiked with a known quantity of a standard solution of HCQ, as it is recommended for diminishing the matrix effect on the current-response sensibility [14]. Potentiodynamic measurements (polarization curves and cyclic voltammetry) were also carried out at 25 \({}^{\circ}\)C in the conventional cell described above with BDD as working electrode. The exposed apparent area of the working electrodes was 1.5 cm\({}^{2}\).
# Predicting Topology Propagation Messages in Mobile Ad Hoc Networks: The Value of History
## 5 Evaluation of OLSR-HBP in Mobile Scenarios
### Performance of OLSR-HBP  
The performance of the implemented predictor was evaluated considering the control traffic decrease and also the memory overhead. These network variables can be positively impacted by the proposed predictor and thus to improve the communication support in highly dynamic interaction scenarios, like urban emergency response processes. Next we describe these evaluations in detail.
## Nomenclature  
\begin{tabular}{l l} \(\mathbf{B_{CO2}}\) & parameter for Langmuir isotherm relation, \(1/\mathrm{kPa}\) \\ \(C\) & Land's constant, dimensionless \\ \(c_{f}\) & rock compressibility, \(k\mathrm{Pa}^{-1}\) \\ \(D\) & diffusion coefficient, \(\mathrm{cm}^{2}/\mathrm{s}\) \\ \(D_{\mathit{eff}}\) & effective diffusion coefficient, \(\mathrm{cm}^{2}/\mathrm{s}\) \\ \(g\) & acceleration due to gravity, \(\mathrm{m}/\mathrm{s}^{2}\) \\ \(H\) & aquifer thickness, \(\mathrm{m}\) \\ \(\mathbf{H_{i}}\) & [PERSON]'s constant at current pressure (\(p\)) and temperature (\(T\)), dimensionless \\ \(\mathbf{H_{i}^{+}}\) & [PERSON]'s constant at reference pressure (\(p^{\star}\)) and temperature (\(T\)), dimensionless \\ \(J\) & Leverett J-function, dimensionless \\ \(k\) or \(k_{k}\) & average horizontal permeability, \(\mathrm{mD}\) [9.869 \(\times\) 10\({}^{-16}\) m\({}^{2}\)] \\ \(\mathbf{k_{v}}\) & average vertical permeability, \(\mathrm{mD}\) [9.869 \(\times\) 10\({}^{-16}\) m\({}^{2}\)] \\ \(k_{rl}\) & relative permeability, dimensionless \\ \(L\) & length of the aquifer, \(\mathrm{m}\) \\ \(L_{w}\) & horizontal well length, \(\mathrm{m}\) \\ \(M\) & mobility ratio, dimensionless \\ \(N_{j}\) & the total moles of mineral \(j\), \(\mathrm{gmol}/\mathrm{m}^{3}\) \\ \(\mathbf{N_{\mathrm{gr}}}\) & characteristic time ratio for fluid to flow in the transverse direction due to gravity, dimensionless \\ \(l\) & identity tensor \\ \(K\) & stiffness tensor \\ \(p\) & pore pressure, kPa \\ \end{tabular}
## 3 Results
### CAP-Discharges Temperatures  
In order to determine the rotational temperature of nitrogen during the moment of CAP discharge, high-resolution spectra in the range of 331 nm to 338 nm (0.017 nm resolution) were acquired. The OES data obtained were subjected to analysis using the molecular nitrogen second positive C-B system (band head at 337.28 nm) as a simulation model for data fitting with the MassiveOES software [66]. As a result, using 5 s integration time we were able to monitor the rotational temperature values during the 30 s, 60 s, and 90 s exposures. No significant differences were observed for the different tree exposure times; no temperature increase during exposure was registered. The average value of rotational temperature is 1163 \(\pm\) 24 K, higher than the values observed for diffuse planar dielectric barrier discharges, but comparable with values observed for filamentary discharges or sparks.  
Monitoring the temperature of the solution subjected to CAP discharges, we noted that the exposure only caused minor temperature increases. Starting from the temperature of 16.5 \({}^{\circ}\)C of the control sample, the temperature of the medium exposed for 30 s was 19 \({}^{\circ}\)C, that of the medium exposed for 60 s was 23 \({}^{\circ}\)C, and the highest temperature value of 27 \({}^{\circ}\)C was identified for the medium exposed for 90 s. Additionally, we noted that the exposure had a negligible effect on the temperatures of the other columns except for the first adjacent column, which had a slight temperature increase, as illustrated in Figure 6.  
Figure 5: The optical emission spectrum of cold atmospheric plasma (CAP) at 300–427 nm.  
Figure 6: Temperature of the PAM-RPMI and the medium in the adjacent columns on CAP exposure plate.
## 3 Numerical Results and Discussion
### Engineering Benefits  
The use of a dual-driven MIMO detector in practical applications provides several engineering benefits.  
Enhanced Performance: The dual-driven MIMO detector leverages the power of deep learning and neural networks to achieve improved performance compared to tra  
Figure 6: BER versus layers for the dual-driven MIMO detector and OAMP-Net when the SNR = 20 dB and the MSE = \(-\)30 dB.  
Figure 7: BER versus layers using different learning rates for the dual-driven MIMO detector when the SNR = 20 dB and the MSE = \(-\)30 dB.  
ditional model-driven MIMO detectors or linear MIMO detectors. By capturing complex nonlinear relationships between the correct symbols and the received symbols, dual-driven MIMO detectors can achieve higher accuracy and robustness in MIMO detection. This leads to enhanced system performance in terms of data rates, reliability, and overall communication quality.  
Adaptability to Various Channel Conditions: A dual-driven MIMO detector can adapt to different channel conditions, including varying noise powers and channel estimation errors. Through the training process, the dual-driven MIMO detector learns to extract relevant features from the received signals and detect MIMO symbols effectively. This adaptability makes the dual-driven MIMO detector applicable for a wide range of practical wireless communication environments.  
Flexibility and Scalability: The dual-driven MIMO detector offers flexibility and scalability in terms of system configuration and deployment. It can be adapted to different MIMO system setups, including varying numbers of antennas, spatial streams, and modulation schemes. The neural network architecture of the dual-driven MIMO detector can be customized and optimized to accommodate specific system requirements, making it suitable for a wide range of practical MIMO communication scenarios.  
Real-Time and Near-Real-Time Processing: The dual-driven MIMO detector can be implemented efficiently to enable real-time or near-real-time processing, depending on the hardware and computational resources available. By leveraging parallel computing, hardware acceleration techniques, and optimization algorithms, dual-driven MIMO detectors may be suitable for time-critical applications.  
These engineering benefits make the dual-driven MIMO detector a better choice.  
However, the limitation of the dual-driven MIMO detector is that the off-line training process needs to accumulate data and be trained, which means the dual-driven MIMO detector needs extra storage and computing resources.
## 1 Introduction
### Challenges in Space Communication
This premise has been theoretically proven and validated using simulation results by considering packet losses due to the available buffer capacity in DTN nodes [20].
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 2 Artificial Intelligence: An Overview (Fundamentals and Applications in Agriculture)
### Different AI Application in AgroSciences  
#### 2.3.1 Plant Disease Detection  
The advent of AI has revolutionized plant disease detection by enhancing diagnostic accuracy and efficiency. Advanced models, such as those integrating Transfer Learning with Vision Transformers (TLMViT), leverage the architecture of transformers to focus on relevant image parts, achieving high accuracy in disease identification [29]. Studies using large datasets, like PlantVillage with over 54,000 images of diverse crop species, have demonstrated that AI models can achieve identification accuracies above 98%, highlighting  
Figure 1: Key AI technologies.  
the potential of hybrid models in agricultural applications [30; 31]. These advancements are pivotal for precision agriculture and sustainable crop management, providing farmers with powerful tools to detect diseases early and manage crops more effectively. However, although high accuracies have been reported in controlled environments like the PlantVillage dataset, the precision of these models may vary in practical applications, where environmental factors and data variability can affect performance.
## 2 Materials and Methods
### System Architecture
#### 2.3.3 Blood Vessels Orientation Module  
The blood vessels orientation module basically uses the previous vessels approximation as first-degree linear equations, facilitating the computation of their slopes. These slopes are expressed in degrees, and the determined values are stored for further analysis. In order to evaluate the overall distribution of vessel orientations inside the evaluated ROI, the mean square deviation is computed. When the lung circulatory system has radial distribution, in normal conditions, the slopes of the blood vessels vary in a wide range and the standard deviation value is higher. In pathological circumstances such as malignancies, vessel orientations tend to align more tightly, resulting in a narrower range of slopes and a reduced standard deviation. The mean square deviation formula for \(E\) is below, where \(N\)--number of elements; and \(z_{fi}\)--current element:  
\[E=\sqrt{\sum_{i=1}^{N}\left(z_{fi}-z_{oi}\right)^{2}/N} \tag{5}\]  
Figure 2: Blood vessels tortuosity index exemplified on a curly lung vein: (**a**) approximated blood vein pixels; (**b**) approximated blood vein with a green line, first-degree equation; (**c**) computed tortuosity index as a distance (yellow) between the approximated blood vein first-degree equation (green line) and the initial blood vein (white line).
## 3 Results and Discussion
### Accuracy Tests of the Light-Scattering Sensor  
For 3.83% milk, the mean \(\pm\) standard deviation for clean hoses was (3.85 \(\pm\) 0.07)%, for contaminated hoses (3.87 \(\pm\) 0.15)%. For 4.31% fat milk, the mean \(\pm\) standard deviation for clean hoses was (4.34 \(\pm\) 0.11)%, for contaminated hoses (4.36 \(\pm\) 0.18)%. For both stages of the accuracy study, graphs were generated to allow a visual assessment of variations in the measurement accuracy. They are presented in Figures 6  
The mean values and standard deviation differed very little for clean and contaminated (low-quality rinsed and dried hoses). Student's \(t\)-test was used to statistically assess the significance of differences between data sets. The study was carried out for two pairs of data sets--milk with a fat content of 3.83% with clean and contaminated hoses; milk with 4.31% fat content with clean and contaminated hoses. The study determined no significant difference between clean and contaminated hoses at \(p\) < 0.05. It was concluded that low-quality rinsing and drying affect the measurement results only at the very beginning (first three measurements or first \(\sim\)40 s), then the flow of the milk-air mixture was correctly estimated.
## 2 Methodology
### Case Study in Two Cities with Different Solar Radiation  
#### 2.2.1 Weather Data  
Solar radiation and its utilization will directly affect buildings' energy consumption, indoor thermal comfort, etc., thereby affecting their carbon emissions and costs during operation, so the optimal design scheme formed under the multi-objective optimization will vary. For the purpose of this study, considering the regionality and design consistency of residential buildings, Xi'an (34\({}^{\circ}\)30\({}^{\prime}\) N) and Yulin (38\({}^{\circ}\)23\({}^{\prime}\) N), two cities in the same province, were selected for comparative analysis. Moreover, according to the Chinese standard \"GB 50176-2016 Code for thermal design of civil building\" [40], both cities are located in cold areas, where the average temperature ranges from \(-\)10 \({}^{\circ}\)C to 10 \({}^{\circ}\)C in the coldest month, and the number of days below the average temperature of 5 \({}^{\circ}\)C is between 90 and 145 days.  
Figure 2 shows the monthly average dry-bulb temperature, and the annual average temperature is 14.1 \({}^{\circ}\)C in Xi'an and 8.5 \({}^{\circ}\)C in Yulin, which has a slightly colder climate. However, the cities' solar energy resources have great differences. As presented in Figure 3, the annual total solar radiation of Xi'an is 4262.1 MJ/m\({}^{2}\), while Yulin's is 6093.6 MJ/m\({}^{2}\) (\(-\)43% higher), which may reflect the influence of solar radiation.
## 3 Architectural Layers and Digital Twin Integration for Headlamp Systems
### Architectural Layers  
To further clarify the functional content of the system and software architecture layers depicted in Figure 3, the system layer includes physical subsystems, such as ambient light sensors, camera-based perception units, LED arrays, stepper motors for beam adjustment, and the headlamp management ECU. These components interact via CAN FD communication and are powered through monitored supply lines.  
Figure 3 presents an integrated view of the system and software architecture for an automotive headlamp system, illustrating the interaction between physical components, embedded logic, and their virtualization via the digital twin.  
A practical example of how this layered architecture operates can be seen when a vehicle enters a short, unlit tunnel. In this scenario, the system layer detects the ambient light drop via onboard sensors and relays the signal to the lighting control ECU. The software layer evaluates this input and activates the headlamps through CAN FD-based control logic. The DT replicates the sensor signal and software response in a virtual environment, allowing engineers to verify system behavior, switching time, and compliance without physical testing.
## Materials and Methodology
### Index Assessment  
Several parameters including the root mean square error (RMSE), mean absolute error (MAE), root mean absolute percentage error (RMAPE), and R\({}^{2}\) were used to assess the SWI. These indexes are regularly employed in model evaluation studies and their expressions are as follows [40; 41; 42]:  
\[\delta_{RMSE}=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{(y_{i}-\hat{y}_{i})^{2}}}} \tag{2}\]  
\[\delta_{MAE}=\frac{1}{n}{\sum\limits_{i=1}^{n}{|y_{i}-\hat{y}_{i}|}} \tag{3}\]  
\[\delta_{RMAPE}=\frac{1}{n}\sqrt{\sum\limits_{i=1}^{n}{\left[{(y_{i}-\hat{y}_{ i})\left/{y_{i}}\right.}\right]^{2}}} \tag{4}\]  
\[R^{2}=\left({\sum\limits_{i=1}^{n}{\left({\hat{y}_{i}-\overline{y}}\right) \times(y_{i}-\overline{y})}}\right/{\left({\sqrt{\sum\limits_{i=1}^{n}{(\hat{ y}_{i}-\overline{y})^{2}}}}\times\sqrt{\sum\limits_{i=1}^{n}{(y_{i}-\overline{y})^{2}}} \right)}}\right)^{2} \tag{5}\]  
where \(n\) is the total number of samples; \(y_{i}\) represents the observation value; \(\hat{y}_{i}\) represents the estimated value; \(\overline{y}\) represents the average observation value; and \(\overline{y}\) represents the average estimated value. When RMSE, MAE, and RMAPE are low and \(R^{2}\) is high, then the best-fit equation is achieved.  
To examine the applicability of the SWI-fitted equation in different periods and regions, 190 and 225 pairs of testing data (SWI and MNDWI, respectively) were selected randomly from S1A and OLI images acquired on 2 February 2016, in the Taihu Lake region, China, and 9 September 2015, in the Poyang Lake region, respectively, to examine the fitted SWI using the indexes mentioned above.
## 3 Results
#### 3.1.3 Matching of the Asymptotic Approximation Derivatives  
Taking into account the expansions in Equations (13) and (14), we can represent the conditions for matching derivatives (Equation (16)) as  
\[\sum_{i=0}^{n}\varepsilon^{i-2}H_{i}^{u}(p,q,\mathbf{y})=O(\varepsilon^{n-1}),\ \ \sum_{i=0}^{n}\varepsilon^{i-1}H_{i}^{v}(q,\mathbf{y})=O(\varepsilon^{n}),\ \ \mathbf{y}\in\gamma, \tag{25}\]  
where the functions \(H_{0}^{u}(p,q,\mathbf{y})\), \(H_{0}^{v}(p,\mathbf{y})\) are defined by the expressions in Equation (10):  
\[H_{1}^{u}:=\frac{\partial M_{1}^{(-)}u}{\partial\sigma}(0,\mathbf{y})+\frac{ \partial Q_{0}^{(-)}u}{\partial\tau}(0,\mathbf{y})-\frac{\partial M_{1}^{(+)} u}{\partial\sigma}(0,\mathbf{y})-\frac{\partial Q_{0}^{(+)}u}{\partial\tau}(0, \mathbf{y});\]  
\[H_{1}^{v}:=\frac{\partial M_{2}^{(-)}v}{\partial\sigma}(0,\mathbf{y})+\frac{ \partial Q_{1}^{(-)}v}{\partial\tau}(0,\mathbf{y})+\frac{\partial\psi^{(-)}}{ \partial n_{\gamma}}(\mathbf{y})-\]  
\[-\frac{\partial M_{2}^{(+)}v}{\partial\tau}(0,\mathbf{y})-\frac{\partial Q_{1 }^{(+)}v}{\partial\tau}(0,\mathbf{y})-\frac{\partial\psi^{(+)}}{\partial n_{ \gamma}}(\mathbf{y}),\]  
and similarly for \(H_{i}^{u}\), \(H_{i}^{v}\), \(i=\overline{2,n}\).  
The conditions for matching of the derivatives in Equation (16) in the zeroth order are satisfied due to Proposition 4. The coefficients \(p_{k}(\mathbf{y})\) and \(q_{k}(\mathbf{y})\) of the expansions in Equation (15) are determined from the equations obtained from the equalities in Equation (25):  
\[\frac{\partial H_{0}^{v}}{\partial q}(q_{0}(\mathbf{y}),\mathbf{y})q_{k}=G_{k} ^{v}(\mathbf{y}),\ \ \frac{\partial H_{0}^{u}}{\partial p}(p_{0}(\mathbf{y}),q_{0}(\mathbf{y}), \mathbf{y})p_{k}=G_{k}^{u}(\mathbf{y}),\ \ \mathbf{y}\in\gamma.\]  
These equations are solvable due to Proposition 4. Here, \(G_{k}^{v}(\mathbf{y})\), \(G_{k}^{u}(\mathbf{y})\) are known functions; in the first order these functions are as follows:  
\[G_{1}^{v}(\mathbf{y})=-H_{1}^{v}(p_{0}(\mathbf{y}),\mathbf{y}),\ \ G_{1}^{u}( \mathbf{y})=-H_{1}^{u}(p_{0}(\mathbf{y}),q_{0}(\mathbf{y}),\mathbf{y})-\frac{ \partial H_{0}^{u}}{\partial q}(p_{0}(\mathbf{y}),q_{0}(\mathbf{y}),\mathbf{y} )q_{1}.\]
# Quantum-Inspired Neural Network Model of Optical Illusions
## 1 Introduction
However, despite the advent of quantum mechanical models of perception and the previous attempts to study ambiguous figures using neural network algorithms, there have been no demonstrations of quantum neural network models of optical illusions. The concept of quantum neural networks has become an active topic of scientific research relatively recently owing to increasing maturity of quantum technologies [57; 58; 59; 60; 61]. Nevertheless, quantum neural network architectures have immediately demonstrated a number of advantages over the classical networks, including higher accuracy of outputs produced using just several qubits [62; 63; 64; 65]. Thus, it is plausible that quantum neural network models of optical illusions will also outperform their classical counterparts. Figure 2: (**a**) Projective measurement of a qubit. (**b**) Projective qubit-like measurement applied to the Necker cube. The two-dimensional shadows of the cubes are identical and perceived by an observer as an ambiguous Necker cube. Considering the shadows as a qubit-like superposition of \(|0\rangle\) and \(|1\rangle\), we virtually project the shadows back to the three-dimensional space to obtain an unambiguous cube that corresponds to one of the basis states. Hence, in this present work, we construct a deep neural network that uses a quantum random generator to define the weights of the neural connections and we exploit it to model the perception of ambiguous figures. We demonstrate that the so-designed computational algorithm reproduces the hypothesised superposition of the possible perceptual states of the Necker cube. We also show that these results qualitatively agree with the predictions of a recently proposed quantum oscillator model of optical illusions [14]. Broadly speaking, the results presented in this paper may be used to enhance the ability of quantum neural networks to recognise and generate images of similarly looking human faces and fashion items [66; 67], also being useful in the analysis of patterns of financial behaviours [68]. For example, optical illusion called #theDress has become a subject of scientific research in the fields neuroscience and vision science [69] following a viral online dispute between people who believed that the dress was blue and black and people who perceived its colours as white and gold. The quantum neural network model presented in this work should be applicable to #theDress because recent research works have also revealed that both #theDress and Necker cube are ambiguous figures [70]. The remainder of this paper is organised as follows. In Section 2, we present a detailed theoretical and technical discussion of the deep neural network algorithm proposed in this work. Then, in Section 3 we introduce a quantum oscillator model of perception of ambiguous figures. Then, in Section 4 we conduct a detailed comparative analysis of the results obtained using the neural network and the quantum oscillator model. Finally, we demonstrate the potential of our model to solve a wide range of practical problems.
## 6 Simulation Results
Restricted backhaul capacity leads to a low throughput per TP, as shown in Figure 6(a), since fewer transmissions are scheduled in this case. However, the tendency is different in terms of energy efficiency. Figure 6(b) shows that the energy efficiencies of the proposed algorithms under different backhaul limits are almost the same. This is explained by the fact that the power consumed by transmissions is also reduced when backhaul capacity is restricted. At last, we conduct simulations when \(R_{\text{thres}}\) is 180 kbps, 360 kbps and 540 kbps, respectively, with the fixed backhaul capacity of 100 Mbps. This shows that the throughput of our proposals grows as the increase in \(R_{\text{thres}}\), as shown in Figure 7(a). This is because more resources are assigned to the UEs with better channel conditions, which are possible to achieve for quality transmissions. Figure 7(b) illustrates that the value of \(R_{\text{thres}}\) hardly affects the energy efficiency of the system. Since those transmissions estimated to be inferior to the given \(R_{\text{thres}}\) are closed, no (or little) power is wasted. Therefore, that energy efficiency of the system can be maintained at a high level. Figure 6: System performance under different strategies (40 Watts, infinite \(C_{m}\), \(R_{\text{thres}}=180\) kbps). Figure 7: System performance under different backhaul capacity (20 Watts, \(R_{\text{thres}}=360\) kbps).