esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Another robust approach consists of capturing the electron beam right after the source via strong focusing magnetic fields as close as possible to the plasma-vacuum interface with conventional accelerator techniques (e.g., the use of FODO with quadrupoles) [34; 49; 50]. The required gradient in a compact device is challenging. Plasma lenses (APL) [51; 52; 53; 54] are promising [55], but their use in experiments that run for couple of weeks adds an additional level of risk. Because of the expertise gained in accelerator facilities, the use of permanent magnet quadrupoles (PMQs) appears to be slightly more robust in respect of APL. The required gradient level corresponds to values achieved for prototype systems [49; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68] rather than quadrupoles produced in large amounts. Moreover, special care has to be taken for the design of these PMQs,in particular for their multipolar content in order to minimize the beam spot size while avoiding chromatic aberration effects dominance [56; 69]. As compared to a single pass conventional accelerator, the situation gets more complex due to the combination of chromatic effects and multipoles. In addition, reliability, in particular with the pointing fluctuations, sets a real challenge for the control of the dispersion along the electron beam transport, and there are still very few experiments reporting on a controlled LPA beam transport. A transport preserving the source electron beam properties all along a line has not yet been achieved. A first approach to controlling the electron beam along a transport line downstream to an undulator on the COXINEL line [41] was enabled thanks to a beam pointing alignment strategy, enabling us to separately adjust the beam dispersion and position. In this paper, we investigate how the skew components of the first high gradient quadrupoles required to tackle with chromatic effects can affect the brightness of the transported electron beam with electron beam simulations in the case of the COXINEL line. We examine how the electron beam dynamics is distorted when a skew component is added. We show that the higher the initial beam divergence, the larger the degradation. Experimentally, under conditions of proper electron beam transport, we evidence that non-negligible skew quadrupolar components induce a transversal spread and tilt of the beam, leading to an emittance growth and brightness reduction. We also reproduce the measurements with beam transport simulations using the measured electron beam parameters.
# The Effect of 3D TVs on Eye Movement and Motor Performance
## 3 Results
### Eye Movement
#### 3.1.7 Eye Fixation Accuracy  
The repeated measures ANOVA results in Table 9 shows that there was a significant difference between eye fixation accuracy in screen displays and stereoscopic displays (F\({}_{1,11}\) = 8.559, \(p\)-value = 0.014). Moreover, the Tukey HSD showed a significant difference between screen and stereoscopic displays. Similarly, it shows that there was a significant accuracy difference for six levels index of difficulty (F\({}_{1,11}\) = 13.799, \(p\)-value = 0.000). The Tukey HSD results divided six levels of index of difficulty into three groups (see Table 9). However, there were no significant accuracy differences between depth (F\({}_{2,22}\) = 2.131, \(p\)-value = 0.143), the environment and depth (F\({}_{2,22}\) = 4.785, \(p\)-value = 0.677), the environment and the index of difficulty (F\({}_{5,55}\) = 23.500, \(p\)-value = 0.620), depth and the index of difficulty (F\({}_{10,110}\) = 8.759, \(p\)-value = 0.928), and interactions of the environment, depth, and index of difficulty (F\({}_{10,110}\) = 28.604, \(p\)-value = 0.354).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Saccadic Duration**} \\ \hline  & **Level** & **Mean (s)** & **Group \({}^{\bullet}\)** & **SD** & **F\({}_{\mathbf{n,m}}\)** & \(p\)-Value** \\ \hline \multirow{2}{*}{Environment} & 2D Screen Displays & 0.535 & A & 0.182 & \multirow{2}{*}{F\({}_{1,11}\) = 8.481} & \multirow{2}{*}{\(p\) = 0.014} \\  & 3D Stereoscopic Displays & 0.652 & B & 0.304 & \\ \hline \multirow{3}{*}{Depth} & 210 cm & 0.617 & A & 0.282 & \multirow{3}{*}{F\({}_{2,22}\) = 1.271} & \multirow{2}{*}{\(p\) = 0.300} \\  & 190 cm & 0.581 & A & 0.256 & \\  & 140 cm & 0.546 & A & 0.231 & \\ \hline \multirow{6}{*}{ID} & 2.8 bits & 0.436 & A & 0.173 & \multirow{6}{*}{F\({}_{5,55}\) = 18.512} & \multirow{6}{*}{\(p\) = 0.000} \\  & 3.3 bits & 0.484 & A & 0.182 & \\ \cline{1-1}  & 3.7 bits & 0.665 & B & 0.220 & \\ \cline{1-1}  & 4.2 bits & 0.638 & B & 0.193 & \\ \cline{1-1}  & 5.1 bits & 0.613 & B & 0.286 & \\ \cline{1-1}  & 6.1 bits & 0.724 & B & 0.332 & \\ \hline Environment \({}^{\bullet}\) Depth & & & & & \multicolumn{1}{c}{F\({}_{2,22}\) = 9.915} & \multirow{2}{*}{\(p\) = 0.001} \\ \hline Environment \({}^{\bullet}\) ID & & & & & \multicolumn{1}{c}{F\({}_{5,55}\) = 0.890} & \multirow{2}{*}{\(p\) = 0.494} \\ \hline Depth \({}^{\bullet}\) ID & & & & \multicolumn{1}{c}{F\({}_{10,110}\) = 4.875} & \multirow{2}{*}{\(p\) = 0.000} \\ \hline Environment \({}^{\bullet}\) Depth \({}^{\bullet}\) ID & & & & \multicolumn{1}{c}{F\({}_{10,110}\) = 2.047} & \multirow{2}{*}{\(p\) = 0.035} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Means, SDs, ANOVA, and Tukey HSD test results of saccadic duration.
## 4 Discussions
### Potential to be a Fully Remote Sensing Base Carbon Flux Model  
Generally, the RS-CFLUX model performed well in both the Daman superstation and Zhangye wetland station. The model correctly estimated the GPP and NEP of the ecosystem. The model accuracy in term of RMSE and R\({}^{2}\) is as high as the other model used in this area, such as VPM [19]. Based on _in-situ_ data, it is found that correlation coefficient between LST and NEE is much higher than that between air temperature and NEE. Furthermore, high-resolution LST and PAR data could be retrieved from remote sensing data; this will improve the carbon fluxes estimation at the areas with sparse weather stations, such as Northwest China. When using the remote-sensing-derived PAR product and LST product as inputs, RS-CFLUX will simulate the carbon flux using only remote sensing data. However, high temporal resolution PAR and LST products are difficult to generate. A lot of LST value is missing in current MODIS LST product because of the impact of the weather. Some methods are developed in recent years, which can be used to reconstruct the LST product and form a time continuous LST [42]. Polar orbiting and geostationary satellite data can be combined with other remote sensing data to generate continuous daily even hourly PAR data. If the quality of remote sensing data product is improved using these new data and method, then the RS-CFLUX model could easily estimate the carbon flux at regional scale.  
Figure 10: Uncertainties in the NEP caused by parameters in the RS-CFLUX model. N (mu, delta) is a normal distribution with a mean mu and standard deviation delta.
# A Bibliometric Analysis and Visualization of In-Vehicle Communication Protocols
## 3 Results
### Current Status of In-Vehicle Communication Protocol
#### 3.1.5 Most Cited Countries  
When analyzing the most cited nations in research on in-vehicle communication systems, distinct industry influence, and academic leadership disparities emerge. MS Excel was used to generate Figure 5, which shows the most cited countries. China leads the world as the highest contributor with 8746 publications; however, in average citations per publication, China's impact factor (12.8) is moderate. China still lags significantly behind other leading countries. The United States follows China with 2845 published papers, demonstrating a significantly higher impact with average citations at 20.9 per publication. This indicates the higher quality and global relevance of US research. In this regard, South Korea, with 2542 publications and an average of 17.4 citations, demonstrates substantial academic and industrial investment in automotive communication technologies. While Australia published only 1193 papers, it had the highest citation impact, averaging 54.2 citations per publication. This showcases the exceptional quality and international visibility of their research. Malaysia also demonstrates a notable impact at 31.7 citations, surpassing larger contributors such as India (5.5) and Japan (9.2). Active members with balanced research quantity and quality include the United Kingdom (14.2), Italy (16.2), and  
Figure 4: Distribution of published conference papers.  
Canada (23). These figures demonstrate the global balance of impactful research on vehicle communication systems.
# Meta-Heuristic Optimization and Comparison for Battery Pack Thermal Systems Using Simulink
## 2 Numerical Methods for Simulink
### Battery Configuration  
In Simulink, the equivalent circuit model (ECM) utilizes a consolidated resistance-capacitance (RC) unit, as recommended by [PERSON] and colleagues [5]. Displayed in Figure 2 is a schematic of a battery cell and its corresponding Simulink implementation. This schematic features a voltage source labeled Em, an RC segment composed of a capacitor (\(C_{1}\)) and a resistor (\(R_{1}\)), in addition to a separate resistor in series (\(R_{0}\)). The values for each component within this analogous circuit are affected by the battery cell's temperature (\(T\)) and its state of charge (SOC), as outlined in Equations (1)-(3) referenced from sources [28; 29; 34].  
\[V-E_{\text{m}}=IR_{0}+(I-I_{1})R_{1}, \tag{1}\]  
\[I_{1}=C_{1}\frac{dV_{1}}{dt}, \tag{2}\]  
\[R=R_{0}+R_{1}\Big{(}1-e^{-t/C_{1}R_{1}}\Big{)}, \tag{3}\]  
Within the ECM framework in Simulink, the thermal assessment is integrated, as shown in Figure 3b. The temperature of the cell is calculated using a thermal equation that takes into account the uniform heat transfer between the cell body and its environment.  
\[c_{\text{p}}\frac{dT}{dt}=-\frac{T-T_{a}}{R_{\text{t}}}+Q, \tag{4}\]  
\(C_{\text{p}}\) denotes the specific heat capacity and \(R_{\text{t}}\) refers to the resistance to heat transfer [5]. The calculation of temperatures can be performed through a Laplace transform, as indicated in Equation (4). Information on the characteristics of Li-ion batteries was obtained from MathWorks' open database, and these characteristics are detailed in Table 1. The focus is on temperatures ranging from 5 \({}^{\circ}\)C to 40 \({}^{\circ}\)C. For temperatures outside of this interval, Simulink allows for linear extrapolation.  
Figure 3: Basic configuration of a battery pack in Simulink. (**a**) ECM of a battery cell. (**b**) ECM in Simulink.  
With the assumption that battery materials are homogeneously isotropic, they exhibit a consistent specific heat capacity of 800 J/K. This assumption, together with the values for specific heat and thermal conductivity, confirms the applicability of the lumped system analysis [10; 35]. The precision of the model generally enhances with the incorporation of more RC network elements. Yet, for cells with a high coulombic efficiency, a single RC (1 RC) network model proves to be adequate [3; 5; 29; 33; 34]. Upon configuring the equivalent circuit model (ECM) block as shown in Simulink, its schematic is represented in Figure 1b. The model's construction can proceed, drawing on the aspect of geometric resemblance. Temperature-dependent variations in physical properties are managed by interpolating values over time using data from Table 1. The setup includes four battery packs, each comprising twenty cells wired in series, which aligns with the configuration in the example model. The starting temperature for the cells is not specified, making it an adjustable parameter to be optimized in line with the external conditions experienced during the optimization trials.
# Performance Evaluation of Carbon Black Nano-Particle Reinforced Asphalt Mixture
## 2 Experimental Work
### Tests Performed
#### 2.4.4 Bitumen Bond Strength Test  
Bitumen aggregate bond strength was studied by a Pneumatic Adhesion Tensile Testing Instrument (PATTI) in accordance with ASTM D 4541. Margalla limestone aggregate plate (15\({}^{\prime\prime}\)\(\times\) 6\({}^{\prime\prime}\)\(\times\) 1.5\({}^{\prime\prime}\)) was heated at 150 \({}^{\circ}\)C in the oven for one hour before the start of the test to remove all absorbed moisture. F-4 pullout stubs (0.5 inch diameter) were also heated for 30 min at 65 \({}^{\circ}\)C. Samples were tested after 24 h of dry conditioning and moisture conditioning. Pneumatic pressure was applied on the samples and burst pressure was recorded at which the stub was detached. The burst pressure was converted to pull-off tensile strength (_POTS_) by the equation given below. Five samples were tested for each category.  
\[POTS=\frac{(BP\times Ag)-C}{Aps} \tag{1}\]  
where;  
\(BP\) = Burst pressure, \(Ag\) = Contact area of the gasket with the reaction plate, \(C\) = Piston constant and \(Aps\) = Pullout stub area.  
For F-4 stub type; \(Ag\) = 4.06 in\({}^{2}\), \(C\) = 0.286 in\({}^{2}\) and \(Aps\) = 0.193 in\({}^{2}\).
## 4 Discussion  
We developed the finite element model, which includes perforation channels, casing, rock, and cement stone. The numerical simulation of the stress-strain state near the well allows us to draw the following main conclusions:  
1. In order to exclude stress concentration at the boundaries of adjacent media, the present model includes the contact elements between the casing-cement stone and cement stone-rock. As a result, it is possible to distribute stresses both in the rock and in the structural elements of the well more accurately. The reservoir rock was modeled as a poroelastic media with permeability varying due to the effective stresses.
2. Analysis of the stress distribution in the casing based on the safety factor shows that, in the main part of the casing, the value of this parameter represents 3-4 units, which indicates its high stability. The exception is small parts near the perforation channels, where this value is close to one unit.
3. The safety factor for the main part of the cement stone reaches 2-3 units, which also indicates its sufficient strength. The safety factor of the cement stone reaches the minimum value under the maximum pressure drawdown. The parts with its minimum value are located near the perforations.
4. The permeability dependence on effective stresses, previously determined on core samples, proves that areas of increased (in the lateral parts) and decreased (in the upper and lower parts) stresses arise on the perforation channels' surface. As a result, these areas have both an increase (up to 20%) and a decrease (up to 25%) in permeability from the initial values caused by variable effective stresses.
5. With an increase in pressure drawdown, the areas with increasing permeability diminish, while the areas with decreasing permeability grow, which is caused by an increase in effective stresses.  
Analysis of the destruction areas of reservoir rocks, based on the Mohr-Coulomb criterion and the impact of effective stress, shows that areas of destruction due to both compressive and tensile stresses appear near the perforations. With an increase in pressure drawdown, tensile stress areas decrease and then disappear completely, while compressive stress areas increase, which is also explained by an increase in effective stresses.  
Due to a decrease in permeability and an increase in pressure drawdown to 12 MPa, the well productivity index can decrease by up to 15%, which indicates the need to optimize well production in order to prevent intensive reservoir compaction due to a decrease in bottom hole and reservoir pressures.
# Remote Sensing and Machine Learning for Safer Railways: A Review
## 4 Discussion
### Future Directions  
RS-ML-based railway inspection research pushes the boundaries of the current applied methods. It inspires researchers to develop new models and algorithms. For instance, [PERSON] et al. (2022) modified a CNN model to work with fewer training images and to provide a more precise segmentation [75]. [PERSON] et al. (2022) improved the Mask R-CNN model [50]. [PERSON] et al. (2021)'s study was inspired by three backbone network structures [40]. Therefore, the new models may lead to the development of more robust and adaptable systems capable of handling complex, real-world scenarios in railway inspection and beyond.  
Regarding the efficiency and discriminative capabilities of different ML models, GANs can be more robust through the generation of high-quality synthetic data, enhancing model training where real-world datasets are incomplete or biased. While GANs are invaluable for data augmentation and simulation, their performance in direct defect detection tasks is not necessarily superior to task-specific models like CNNs or YOLO. These models, particularly CNNs and fully convolutional networks, leverage the spatial hierarchy in images, making them more efficient for the high-precision localization required in railway inspection. In contrast, YOLO's integrated approach to bounding box prediction and class probability makes it exceptionally fast and suitable for real-time applications, though sometimes at the cost of precision when compared to CNNs.  
The objective of most of the reviewed publications was to ensure railway safety and reliability. Using RS-ML methods in railway inspection can enhance public safety by ensuring more reliable and frequent inspections, potentially reducing the risk of accidents. These methods also offer the potential for cost reductions in maintenance and repair, passing on the savings to both service providers and users [36]. Moreover, the ability of these technologies to pinpoint defects more accurately and quickly than traditional methods could lead to longer-lasting infrastructure, reducing the environmental impact through the less frequent need for raw materials and construction activities [43].
## 3 Underwater Communication Experiment and Discussion
### Analysis of FPGA Resource Utilization
The receiver state machine effectively reduces decoding delay, particularly for error-free data, thereby significantly minimizing decoding time and enhancing decoding efficiency. As shown in Figure 19, at 100 MHz clock frequency, the design outputs the decoding result \"_decode_y_\" at 2655 ns, which is 2090 ns earlier than the 4745 ns timing required by the official IP for its \"_decode_y1_\" output. This improvement is equivalent to a reduction of 209 clock cycles and results in a substantial decrease in overall decoding time. Figure 17: FPGA resource utilization and power consumption. (**a**) Resource utilization, and (**b**) power distribution.
## 2 Solar Photovoltaic Power System Description Under Study  
Figure 1 depicts the photovoltaic (PV) power system in which the PV modules are interconnected through the boost converter to the DC load. The system consists of three series-connected PV modules (249 W each) exposed to varying irradiance levels (Ir1, Ir2, Ir3), a boost converter, and an MPPT controller employing CSO and PSO algorithms. Voltage (V\({}_{\text{PV}}\)) and current (I\({}_{\text{pv}}\)) are measured using high-precision voltage and current sensors, respectively, positioned at the PV array's output. The voltage sensor measures the array's output voltage across the terminals, while the current sensor, placed in series with the array, captures the output current, enabling real-time computation of the power-voltage (P-V) characteristic. Both sensors are interfaced with an MPPT controller. A signal-conditioning circuit with a low-pass filter minimizes noise from environmental variations and the boost converter's switching transients, which consist of an inductor (L), diode (D), capacitor (C\({}_{\text{out}}\)), and switch. The MPPT controller processes the data, calculates instantaneous power (P = V\({}_{\text{PV}}\times\) I\({}_{\text{pv}}\)), and adjusts the boost converter's duty cycle (D) using the CSO and PSO algorithms to track the MPP.  
In the scenario in which the series PV modules have the same irradiances as inputs, the I-V and P-V for one PV module is shown in Figure 2. Table 1 provides the specifications of the Tata TP250 MBZ PV module. Table 2 presents the specifications of the boost converter. The converter is adjusted based on the computed D [19], enabling efficient following of the global peak power by dynamically adjusting its operation.  
Figure 1: Photovoltaic power system modeling.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Module Type & Polycrystalline \\ Configuration & Single-sided \\ \(\mathrm{P_{max}}\) (W) & 249 W \\ \(\mathrm{V_{Open,circuit}}\) (V) & 36.5 V \\ Cells (number of each module) & 60 \\ \(\mathrm{I_{Short,circuit}}\) (A) & 8.83 A \\ \(\mathrm{V_{max}}\) (V) & 30.2 V \\ \(\mathrm{I_{max}}\) (A) & 8.30 A \\ \(\mathrm{I_{Light,generated}}\) (A) & 8.83 A \\ \(\mathrm{I_{Diode\,saturation}}\) (A) & 1.0132 \(\times\) 10\({}^{-10}\) \\ Diode factor of ideality & 0.94812 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The PV module specifications under study.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline \(f_{Switching}\) & \(f\) \\ Inductor & \(L\) \\ Output capacitor & \(Co\) \\ Input capacitor & \(Cin\) \\ Load & \(R\) \\ \(\mathrm{I_{max}}\) (A) & 8.30 A \\ \(\mathrm{I_{Light,generated}}\) (A) & 8.83 A \\ \(\mathrm{I_{Diode\,saturation}}\) (A) & 1.0132 \(\times\) 10\({}^{-10}\) \\ Diode factor of ideality & 0.94812 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The specifications of the DC–DC boost converter.  
Figure 2: The characteristics curve of each module under study.
# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 4 Results
### Comparison Results with Non-Lightweight State-of-the-Art Methods  
To further validate the computational efficiency of the proposed methods, we conduct a comprehensive complexity comparison between DAFEN and DAFEN-S and several state-of-the-art non-lightweight super-resolution methods, including RCAN [14], SwinIR [36], and HAT [18]. The complexity evaluation is primarily conducted from three dimensions: model parameters (Params), multiply-add operations (Multi-Adds), and inference times (Times), with specific results shown in Table 5. In terms of model parameters, DAFEN and DAFEN-S require only 0.422 M and 0.192 M parameters, respectively, which are significantly lower than those of RCAN (15.67M), SwinIR (11.55 M), and HAT (20.53 M). This indicates that the DAFEN series models significantly reduce the storage requirements while maintaining high performance, making them more suitable for deployment on resource-constrained devices. In terms of computational complexity, the Multi-Adds of DAFEN and DAFEN-S are 0.038 T and 0.017 T, respectively, which are about 1-2 orders of magnitude lower than those of RCAN (1.492 T), SwinIR (2.883 T), and HAT (3.871 T). The low  
Figure 7: Visualization results of several SR methods and our proposed networks (DAFEN and DAFEN-S) on BSD100 and Urban100 datasets for \(\times\)4 SR.  
computational complexity not only reduces energy consumption but also significantly improves the inference speed of the models. Experimental results show that the inference times of DAFEN and DAFEN-S are 0.017 s and 0.012 s, respectively, while those of RCAN, SwinIR, and HAT are 0.12 s, 0.23 s, and 0.32 s, respectively. This demonstrates that the DAFEN series models have significant advantages in scenarios with high real-time requirements. In summary, DAFEN and DAFEN-S excel in model complexity, computational efficiency, and inference speed, not only significantly outperforming other lightweight methods but also demonstrating their unique performance-efficiency trade-off advantages when compared with non-lightweight state-of-the-art methods. These results further validate the potential of the DAFEN series models in practical applications, especially in resource-constrained environments.