esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Recognition of Road Internal Void Using Ground-Penetrating Radar
## 1 Introduction
Although image quality enhancement research has been conducted in other fields, such as remote sensing image enhancement based on relation cross-attention modules [30], hybrid CNN for fruit classification [31], exposure difference network-based enhancement [32], DEANet for low-light image enhancement [33], underwater image enhancement [34], and wavelet-based MRI enhancement [35], these techniques are difficult to directly apply to GPR void image quality enhancement due to the unique characteristics of GPR images, necessitating targeted research. In recent years, research on GPR image enhancement has gradually developed. Researchers have proposed image enhancement methods based on non-linear technology, using LIP model and CDF-HSD functions to improve target signal contrast while suppressing background noise [36]; data enhancement methods based on AutoAugment have improved the performance of intelligent recognition models [37]; deep learning methods based on diffusion models have achieved simultaneous optimization of image resolution enhancement and clutter removal, improving the quality of deep GPR images [38]. However, although existing enhancement methods have achieved good results in single tasks, they lack in-depth exploration and effective modeling of the feature correspondence relationship between high-quality and low-quality images. This limitation makes it difficult to accurately capture and maintain key feature information during the enhancement process, affecting the reliability of enhancement results. Therefore, this study aims to conduct research on the intelligent recognition of road internal voids using 3D GPR. By proposing a quality enhancement model specifically designed for GPR images of internal road voids, this study aims to reduce the impact of noise interference and complex media materials during GPR image acquisition on image quality, thereby improving the accuracy of intelligent recognition models for internal road voids. The research assumes that the proposed image enhancement model can effectively improve the feature representation of void areas in GPR images and enhance image quality. Additionally, the enhanced images are expected to provide clearer void features for the intelligent recognition model, thus increasing the detection accuracy. Initially, internal road void data were collected using 3D GPR. For the collected images, an image quality enhancement model was proposed specifically for void images, which integrated the advantages of the Unet model, Multi-Head Self-Attention (MHSA) mechanism, and Multi-Head Cross-Attention (MHCA) mechanism. Finally, intelligent recognition models of road voids were constructed using images before and after enhancement to clearly determine the impact of image enhancement on model performance. This research aims to provide an accurate method for recognizing internal road voids, rapidly assessing internal void areas, and providing technical support for comprehensive road maintenance.
# Conceptual and Technical Design Aspects of Accelerators for External Injection in LWFA
## 3 Production of Ultra-Short e-Bunches
### Uncorrelated Energy Spread  
The longitudinal beam distribution of an electron bunch can be expressed using the sigma-matrix:  
\[T=\left(\begin{array}{cc}t_{11}&t_{12}\\ t_{12}&t_{22}\end{array}\right) \tag{7}\]  
where the elements \(t_{11}=\sigma_{t}^{2}\) and \(t_{22}=\sigma_{E}^{2}\) are the Root Mean Square (RMS) pulse length squared and the RMS energy spread squared respectively. The RMS longitudinal emittance is defined as \(\epsilon_{long}=\sqrt{t_{11}\cdot t_{22}-t_{12}^{2}}\) and \(\pi\cdot\epsilon_{long}\) is the area of the ellipse describing the particle distribution in the longitudinal phase space. The uncorrelated energy spread is defined as the RMS width of the ellipse along the energy axis in the absence of correlation terms (i.e., when the semi-axis of the ellipse coincides with the principal axis E and \(t\)). After rotation of the beam phase space through bunch compression, this quantity influences the charge density in the central peak of the current distribution, as qualitatively illustrated in Figure 2.  
The uncorrelated energy spread is determined by the cathode and laser properties at the photo-injector as well as by the space charge and the RF field:  
* The dependance of the uncorrelated energy spread on the RF fields is due to the fact that in the RF gun the electrons are accelerated from rest and therefore the energy gain weakly depends on the radial offset of the electron with respect to the longitudinal axis. This contribution is typically small, e.g., it was estimated to be <1 keV for 1 mm offset for the Linac Coherent Light Source (LCLS) RF gun [33].  
Figure 2: Example of the e-bunch phase space and relative charge profile versus time at the RF gun exit (**left**) and at its maximum compression point (**right**). The limitation caused by phase space non-linearities and uncorrelated energy spread is illustrated. The simulations have been performed using the code ASTRA [32].  
* The space charge effect plays the dominant role since its longitudinal component depends on the radial offset of the electrons [33]. This effect also explains the typical _butterfly shape_ of the longitudinal phase space that can be often observed in numerical simulations [34].
* Finally the uncorrelated energy spread scales linearly with the surface charge density up to the space charge limit [35].  
Many experiments have so far characterized the uncorrelated energy spread [36; 37; 38; 39] but its evaluation is typically limited by the resolution of the diagnostics device or by perturbations to the measurement given, for example, by the space charge force in the gun region.  
Sometimes the minimization of the uncorrelated energy spread is limited by the necessity of damping the microbunching instability phenomena associated with acceleration and compression in Free Electron Lasers (FELs) [40; 41].
# Research on the Influence of Dust Suppressants on the Coupling Behavior of Dust-Mist Particles
## 1 Introduction
Previous studies, such as those by [PERSON] [16] and [PERSON] [19], demonstrated the precision and reliability of the PDPA system in capturing key parameters influencing dust suppression mechanisms. These developments have provided a theoretical foundation for exploring the finer details of particle interaction and settling behavior within mist systems. This study aims to investigate the impact of dust suppressants on the coupling behavior of dust-mist particles in spray dust removal technology, and this research seeks to establish correlations between the properties of spray solutions and the coupling efficiency of dust-mist particles. By investigating the effects of surface tension, solution viscosity, and the wettability of spray solutions, we aim to enhance the understanding of how these solution parameters influence the dust removal efficiency of spray technology.
# A Study on the Effects of Vertical Mass Irregularity on Seismic Behavior of BRBFs and CBFs
## 4 Seismic Performance  
### Drift  
As a major step in the seismic design of structures, estimating and controlling of deformation increase the overall stability of buildings during earthquakes. Limiting the lateral displacement of a structure maintains its overall stability and prevents damage to its non-structural components such as mechanical equipment and architectural components. In this study, the maximum mean drift was observed in the irregular 10-and 15-story frames with mass irregularity BRBs under the effect of 12 far-field earthquakes. The minimum mean drift was recorded in the regular 10-and 15-story frames with regular CBFs. Using BRBs respectively increased the mean drift by 48% and 77% in the four irregular 10-story and four irregular 15-story frames compared to using CBFs, as shown in Figure 4. The Iranian code of practice for seismic resistant design of buildings [23] requires that the seismic drift be limited to 0.004 and 0.0057 in frames with BRBs and CBFs, respectively. Figure 4 compares the drift of the floors with its permissible value for both BRBFs and CBFs systems. Mass differences between two floors were found to insignificantly affect the average drift in the regular and irregular frames with the same lateral resisting system; for instance, the insignificant difference in the average drift between the regular (0.005) and irregular (0.006) 10-story frames with BRB (Figure 4) can be explained by the high stiffness of this kind of bracing. Two floors with different masses from their adjacent floors, one in the upper half and the other in the lower half of all the four irregular frames, imposed higher levels of inter-story drift on the intermediate floors and caused the damage to be concentrated in these floors, see Figure 5.  
Figure 4: Average drift comparison of (**a**) 10-story frames and (**b**) 15-story frames. Figure 3: Process of scaling for the 10-story (R-BRBF). In response to the #EQ5 (Duzce/Turkey Kocaeli) record, the maximum drift was observed in all the four types of the 10-story frames irrespective of their regularity or irregularity and type of bracing. This record was the highest in magnitude (7.5) among the 12 records selected in this study. According to Table 5, the maximum drift was equal to 0.022 in both the regular and irregular 10-story BRBFs and 0.011 in the regular and irregular 10-story CBF. In regular and irregular 10-story frames, the maximum drifts associated with BRBs were generally twice the maximum drift related to CBF. As in the 10-story frames, the maximum drift was observed in all the four types of 15-story frames under the effect of the #EQ5 (Duzce/Turkey Kocaeli) record. The maximum drift of the four types of the 15-story frames was higher in those with BRBs compared to in those with CBF irrespective of their regularity or irregularity (see Table 5). Figure 5: Inter-story drift of (**a**) 10-story regular frames, (**b**) 10-story mass irregularity frames, (**c**) 15-story regular frames and (**d**) 15-story mass irregularity frames.
## 3 Results and Discussion
### Validation of Vaporisation Enthalpies Using Structure-Property Relationships
#### 3.3.5 Quantum Chemistry: Theoretical Enthalpies of Formation of Iso-Propyl- and Tert-Butyl-Benzenes
The reaction enthalpies for the methyl-tert-butyl- and di-tert-butyl-benzenes are very close at the common level of \(-\)64 to \(-\)66 kJ mol\({}^{-1}\)/(H\({}_{2}\)). However, a positive finding of this study is that 1,3,5-tri-substituted alkylbenzes (except for 1,3,5-tri-methyl-benzene) exhibit remarkably low hydrogenation enthalpies at the level of -60 kJ mol\({}^{-1}\)/(H\({}_{2}\)) as shown in bold in Table 12. The findings of this study indicate that the low absolute values for enthalpy of reaction for crowded aromatic rings, which could be expected from data published so far, cannot be backed in a comprehensive and consistent thermochemical study. As a consequence, it should not be expected that alkylation will lead to a strong decrease in heat demand for hydrogen release. However, a slight decrease in the enthalpy of dehydrogenation due to alkylation can be observed. The reason for this is most likely the lowest possible strain of the perhydrogenated 1,3,5-trisubstituted alkyl-cyclohexanes as a hydrogen-rich LOHC compound. Hence, such a low enthalpy of reaction could significantly improve the reaction thermodynamics and enable lower dehydrogenation temperatures in such 1,3,5-trialkyl-substituted LOHC systems. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Compound** & \(\Delta_{\mathbf{f}}H_{\mathbf{m}}^{\mathbf{o}}\)**(liq)HL b** & \(\Delta_{\mathbf{f}}H_{\mathbf{m}}^{\mathbf{o}}\)**(liq)HR c** & \(\Delta_{\mathbf{f}}H_{\mathbf{m}}^{\mathbf{o}}\)**(liq) d** & \(\Delta_{\mathbf{f}}H_{\mathbf{m}}^{\mathbf{o}}\)**(liq) d** & \(\Delta_{\mathbf{f}}H_{\mathbf{m}}^{\mathbf{o}}\)**/H2 e** \\ \hline hexa-methyl-benzene & \(-144.3\pm 2.8\) & \(-310.1\pm 2.5\) & \(-165.6\) & \(-\)**55.2** \\
1,3,5-trimethyl-benzene & \(-63.4\pm 1.3\) & \(-258.9\pm 2.5\) & \(-195.5\) & \(-65.2\) \\ iso-propyl-benzene (liq) & \(-41.1\pm 1.0\) & \(-194.9\pm 2.5\) & \(-197.8\) & \(-65.9\) \\  
5-tetra-tert-butyl-benzene & \(-252.6\pm 2.1\) & \(-410.9\pm 2.8\) & \(-241.7\) & \(-80.6\) \\ \hline \end{tabular}  
* The uncertainties are given as twice the standard deviation; b Enthalpies of formation of fully dehydrogenated species (HL = hydrogen-lean) from Table S16; e Enthalpies of formation of fully hydrogenated species (HR = hydrogen-rich) from Table S17; d Calculated according to Hess’s Law for the hydrogenation reaction (see text); e Calculated per mole of hydrogen.  
\end{table} 
Table 12: Calculation of the liquid-phase enthalpies of formation, \(\Delta_{\mathbf{f}}H_{\mathbf{m}}^{\mathbf{o}}\)**(liq), of the hydrogen-lean (HL) and hydrogen-reach (HR) counterparts of the LOHC systems based on alkylbenzes (at \(T\) = 298.15 K, \(p^{\circ}\) = 0.1 MPa, in kJ-mol\({}^{-1}\)) a.
# Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image +
## 5 Conclusions  
Herein, we proposed a gimbal control system, which can make drones capture effective frames from a thermal camera for detecting deteriorated areas of PV modules by locating a target object in the center of an image. This system involves real-time image processing and PID signal generation. In order to calculate proper angles (pitch, roll, yaw), blob analysis and the Hough transform are mainly used for center detection and angle calculation, respectively. These position and angles are converted into a PID signal. These processes can give feedback to each other and make a target PV module stable. Finally, experimental results showed that target modules can be kept in the center area under limited conditions. In the near future, we will develop a fully automatic drone system to detect deteriorated areas using this gimbal control system after verifying its performance in real environments such as hard wind, higher trees, cloudy and rainy weather, etc.  
Conceptualization, H.-C.P. and H.J.; methodology, H.-C.P.; software, H.-C.P.; validation, H.-C.P., H.J., and S.-W.L.; formal analysis, H.J.; investigation, H.-C.P.; resources, S.-W.L.; data curation, H.J.; writing--original draft preparation, H.J.; writing--review and editing, H.-C.P.; visualization, H.J.; supervision, S.-W.L.; project administration, H.J.; funding acquisition, H.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (NO. 2017-0-01712) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019R1F1A1062075).  
The authors declare that there are no conflicts of interest regarding the publication of this paper.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Case No.** & **(a)** & **(b)** & **(c)** & **(d)** & **(e)** & **(f)** & **(g)** \\ \hline Vertical position [pixel] & 124 & 158 & 234 & 190 & 358 & 430 & 272 \\ Ground truth & 96 & 168 & 240 & 192 & 350 & 404 & 273 \\ Error & 28 & \(-\)10 & \(-\)6 & \(-\)2 & 8 & \(-\)26 & \(-\)1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment result for vertical position cases.  
Figure 16: Gimbal control response according to left and right angle change.
## 4 Simulation Analysis of Holdback Bar Release Process
### Simulation of Holdback Bar Release Process  
The dynamics simulation of the holdback bar release process is performed by the dynamics simulation analysis software, and the load applied on the holdback bar is the hold back force in the simulation analysis model. The traction force of the catapult is applied to the nose landing gear through the launch bar, and then acts on the holdback bar through the joint on the torsion arm of the nose landing gear. When the load on the holdback bar reaches the specified value, the holdback bar lock mechanism is unlocked and the holdback bar is discharged from the nose landing gear joint, at which time the holdback load on the holdback bar becomes zero. The release process of the holdback bar can be divided into two stages.  
In the first stage, when the traction force applied to the holdback bar is less than the unlocking force of the lock mechanism, the lock mechanism and the release mechanism move with the front joint under the application of the traction force. With the increasing traction force, the gap between the lock mechanism and the load transfer bar gradually decreases, and the lock mechanism of the holdback bar remains locked when the traction force acting on the holdback bar does not reach the released load.  
In the second stage, as the traction force acting on the holdback bar increases, the lock mechanism is brought into contact with the load transfer bar. When the load acting on the load transfer bar reaches the release load, the release mechanism unlocks the lock mechanism. After the lock mechanism is unlocked, in a very short period of time, the front joint of the holdback bar discharges from the nose landing gear torque arm joint, the holdback bar releases the aircraft, and the aircraft achieves catapult-assisted takeoff.  
Based on the theory of tribology and contact mechanics, a simulation model is established to analyze the holdback bar release process. From the start of tensioning of the lock mechanism to the phase before release, there is a small axial displacement between the lock mechanism and the load transfer bar due to the gap between them. After the lock mechanism finishes unlocking, the lock mechanism moves rapidly and reaches the maximum axial displacement, as shown in Figure 7.  
The load is applied to the holdback bar front joint to simulate the aircraft catapult load on the holdback bar, as shown in Figure 8.  
When the loading time counts from 0 to 0.55 s, the load increases from 0 to 19.5 tons, and the load is stabilized at 19.5 tons from 0.55 to 0.7 s, and then will be unloaded after 0.7 s. When the holdback bar is unlocked, the nose landing gear torque arm joint of the aircraft will be unloaded after the separation from the front joint of the holdback bar.
## 2 Materials and Methods
### Biomethane Potential (BMP) Tests  
BMP tests (Table 2) were performed in triplicate under mesophilic conditions using a self-developed method [23; 24], basically compliant with UNI/TS 11703:2018 (the Italian standard procedure for BMP tests). Tests were performed using glass bottles (1.1 L volume) placed in a thermostatic cabinet at 35 \(\pm\) 0.5 \({}^{\circ}\)C and mixed by using a magnetic stirrer. The inoculum was mixed with the substrate (substrate to inoculum ratio in terms of vs. was set equal to 0.3) and nutrient solutions (prepared and dosed according to UNI/TS 11703:2018).  
BMP tests were performed to evaluate the potential methane production from AS, MW, and from a mixture of both. The mixture was prepared with the aim to obtain a C/N in the substrate equal to 25, which can be considered a well-balanced value. To obtain the desired C/N, a proportion of 1:19 (5-95) on the TS basis of AS and MW, respectively, was necessary.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Anchovy Sludge** & **Market Waste** & **Inoculum (Batch Tests)** & **Inoculum (Semi-Continuous t.)** \\ \hline TS [\%] & 20.1 & 19.4 & 3.9 & 3.1 \\ VS [\%TS] & 66.7 & 93.3 & 66.6 & 66.7 \\ pH & 6.85 & 5.26 & 8.13 & 8.04 \\ C/N & 3.4 & 36.3 & - & - \\ \(d\)-limonene [g/g] & 0.125 1 & - & - & - \\  & 0.160 2 & & & \\ \hline \hline \end{tabular}  
* A sample of AS used for batch and semi-continuous tests (days 1–56); 2 A sample of AS used for semi-continuous tests (days 57–80).  
\end{table}
Table 1: Substrates and inocula characteristics.  
In addition to BMP bottles, blanks (containing inoculum and a nutrients solution, used to assess a non-specific biomethane production) and cellulose-fed reactors (used as control) were also prepared. About three times per week, the biogas produced was transferred in a bottle filled with a NaOH solution (3M) for CO\({}_{2}\) adsorption, and the methane amount in the produced biogas was then measured by an audiometer (a water displacement method). The pH was measured at the beginning of the tests, while TS, VS, COD, ammonium ion and chloride concentration, VFAs, and FOS/TAC were measured at the end of them. VFAs and FOS/TAC allow verification of the stability of the digestion process since, when a high level of them is registered or when they tend to increase over time, an unbalance of the process, due to an overloading or an inhibition of the methanogenesis, is possible [25; 26]. TS, VS, and pH were measured using standard methods [22]; COD, ammonium ion, and chloride concentration were evaluated thanks to a photometric method (Photometer WTW Photolab S12 and appropriate pre-dosed cuvettes), whereas VFAs were determined through a three-point titration method, and then the FOS/TAC was calculated [25; 26].
## 4 Analysis of Bending Stiffness Calculation Coefficients
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & **Diameter, \(d_{c}\)** & **Height,** & **Experimental** & **Equation (1)** & **Equation (1)** & **Equation (27)** \\  & **(mm)** & **\(h_{c}\) (mm)** & **Result** & \(\beta=6.54\times 10^{7}\) & \(\beta=5.00\times 10^{7}\) & **Equation (27)** \\ \hline
1 & 130 & 60 & 2.97 & 3.06 (+3.05\%) & 2.34 (\(-\)21.21\%) & 3.02 (\(+\)1.83\%) \\
2 & 130 & 55 & 2.84 & 2.86 (+0.62\%) & 2.18 (\(-\)23.07\%) & 2.76 (\(-\)2.69\%) \\
3 & 140 & 60 & 3.36 & 3.30 (\(-\)1.90\%) & 2.52 (\(-\)25.0\%) & 3.35 (\(-\)0.31\%) \\
4 & 160 & 65 & 4.61 & 4.42 (\(-\)4.10\%) & 3.38 (\(-\)**26.68\%**) & 4.61 (\(+\)0.03\%) \\
5 & 205 & 85 & 9.16 & 9.69 (+5.75\%) & 7.41 (\(-\)19.15\%) & 9.80 (**\(+\)6.99\%**) \\
6 & 245 & 100 & 20.95 & 21.36 (+1.98\%) & 16.33(\(-\)22.04\%) & 19.85 (\(-\)5.26\%) \\
7 & 275 & 110 & 27.70 & 29.02 (+4.75\%) & 22.18 (\(-\)19.9\%) & 27.97 (\(+\)0.99\%) \\
8 & 375 & 170 & 57.60 & 78.75(**\(+\)36.72\%**) & 60.21 (\(4.53\%\)) & 56.40 (\(-\)2.08\%) \\
9 & 500 & 230 & 133.0 & 172.98(+30.06\%) & 132.25(\(-\)0.56\%) & 134.59(\(+\)1.20\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Bending stiffness, \(K_{c}\) (Unit: \(\times 10^{6}\) N-m\(\cdot\)rad\({}^{-1}\)). Figure 7: Fitted curve of the elasticity coefficient, \(\mu\). Equation (27) was derived in this study and considers the physical and mechanical properties of the joint material. For all specimens, the maximum error of the bending stiffness calculated with Equation (27) is less than 6.99%. Compared with the current code Equation (1), the equivalent bending stiffness equation established in this study is more consistent with the experimental results.