esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 OTFS Channel Estimation
### DDIPIC Algorithm
```
Input:\(P_{\max}\), \(\epsilon\), \(s_{\max}\), \(e_{\tau}\), \(e_{\upsilon}\), \(m_{\tau}\), \(n_{\upsilon}\), \(\mathbf{y}\) for\(i=1\)to\(P_{\max}\)do \(\tau_{\text{coarse}}^{(i)}\), \(\
u_{\text{coarse}}^{(i)}=\arg\max_{(\tau,\upsilon)\in I_{DD}}\Phi\big{(}\mathbf{ R}(\tau,\upsilon),\mathbf{y}\big{)}\); \(\hat{t}_{\text{fine}}^{(0)}=\tau_{\text{coarse}}^{(i)}\); \(\hat{\
u}_{\text{fine}}^{(0)}=\upsilon_{\text{coarse}}^{(i)}\); for\(s=1\)to\(s_{\max}\)do \(W_{\tau}^{(s)}=\frac{\tau_{\text{fine}}}{m_{\tau}^{s-1}}\): delay search width; \(W_{\upsilon}^{(s)}=\frac{\tau_{\text{fine}}}{\eta_{\upsilon}^{s-1}}\): Doppler search width; \(F_{DD}^{(s)}=\left\{W_{\tau}^{(s)}\Gamma+\hat{t}_{\text{fine}}^{(s-1)}\right\} \times\left\{W_{\upsilon}^{(s)}\Lambda+\hat{\
u}_{\text{fine}}^{(s-1)}\right\}\); \(\hat{\tau}_{\text{fine}}^{(s)}\hat{\
u}_{\text{fine}}^{(s)}=\arg\max_{(\tau, \upsilon)\in F_{DD}^{(s)}}\Phi\big{(}\mathbf{R}(\tau,\upsilon),\mathbf{y}\big{)}\); if\(s=s_{\max}\)or\(\left(\left|\hat{\tau}_{\text{fine}}^{(s)}-\hat{\tau}_{\text{fine}}^{(s-1)} \right|<e_{\tau}\)and\(\left|\hat{\
u}_{\text{fine}}^{(s)}-\hat{\
u}_{\text{fine}}^{(s-1)} \right|<e_{\upsilon}\big{)}\)then break; \(\hat{\tau}[i]=\hat{\tau}_{\text{fine}}^{(s)}\); \(\hat{\
u}[i]=\hat{\
u}_{\text{fine}}^{(s)}\); \(\hat{\mathbf{g}}=\left(\mathbf{R}^{H}(\hat{\tau},\hat{\upsilon})\mathbf{R}( \hat{\tau},\hat{\upsilon})\right)^{-1}\mathbf{R}^{H}(\hat{\tau},\hat{\upsilon })\mathbf{y}\); \(\mathcal{E}^{(i)}=\mathbf{y}-\mathbf{R}(\hat{\tau},\hat{\upsilon})\mathbf{g}\); if\(i>1\)then if\(\|\mathcal{E}^{(i)}\|<e\)then \(\hat{P}=i\); break; else  Refinement procedure (\(i_{\text{ref}}=1,...,i\));
```  
**Algorithm 1**DDIPIC
## 4 Parametric Analysis and Parametric-Sensitivity Analysis
### Effect of Viscosity Coefficient \(\eta\)  
The viscosity coefficient, \(\eta\), is increased by 20%, 40% and 60% and decreased by 20%, 40% and 60%, while other parameters are kept constant. Figure 6 shows the settlements at the centre of the rectangle with time for different \(\eta\). The analysis of viscoelastic solutions in the previous section shows that \(\eta\) does not affect the instantaneous and ultimate settlements. However, Figure 6 shows that the viscosity coefficient, \(\eta\), affects the settlement rate, and the smaller the value of \(\eta\), the less the time it takes for the settlement to reach a steady state. The calculation shows that for \(\eta\) increasing from 400 to 1600 MPa\(\cdot\)d, the time required to reach the ultimate settlement at \(t\to+\infty\) are, respectively, \(4\times 10^{6}\), \(6\times 10^{6}\), \(8\times 10^{6}\), \(1.05\times 10^{6}\), \(1.25\times 10^{7}\), \(1.45\times 10^{7}\) and \(1.65\times 10^{7}\) d. Moreover, the change in \(\eta\) has a lower influence on the change amplitude of settlement than other parameters. When \(t=100\) d, the settlements are 0.0285, 0.0282, 0.0279, 0.0276, 0.0274, 0.0273 and 0.0271 m, with corresponding amplitudes of the change in the settlement of only 1.25%, 1.01%, 0.86%, 0.76%, 0.67% and 0.61%.
## 5 Conclusions
\begin{tabular}{l l} AGL & Above ground level \\ ASD & Analytical spectral device \\ BRDF & Bidirectional reflectance distribution function \\ ChLF & Chlorophyll fluorescence \\ DN & Raw digital counts \\ ESA & European Space Agency \\ FLD & Fraunhofer Lines Depth \\ FLEX & FLuorescence EXplorer \\ Flox & Fluorescence box \\ FOV & Field of view \\ FWHM & Full Width at Half Maximum \\ GNSS & Global navigation satellite system \\ GOME-2 & Global Ozone Monitoring Experiment-2 \\ GOSAT & Gases Observing Satellite \\ GPP & Gross primary production \\ IMU & Inertial measurement unit \\ JSON & JavaScript Object Notation \\ LIFT & Light induced fluorescence \\ NDVI & Normalized difference vegetation index \\ NIR & Near infrared \\ O\({}_{2}\)-A & Oxygen-A band \\ O\({}_{2}\)-B & Oxygen-B band \\ OCO-2 & Orbiting Carbon Observatory-2 \\ PRI & Photochemical reflectance index \\ RGB & red-green-blue \\ SCIMACHY & Scanning Imaging Absorption Spectrometer for Atmospheric Cartography \\ SFM & Spectral fitting method \\ SIF & Solar induced chlorophyll fluorescence \\ SNR & Signal to noise ratio \\ SpecLab & Environmental Remote Sensing and Spectroscopy Laboratory \\ SVD & Singular vector decomposition \\ TanSat & Tropospheric Monitoring Instrument (TROPOMI) and the Exploratory Satellite for Atmospheric CO\({}_{2}\) \\ TRuStEE & Training on Remote Sensing for Ecosystem Modelling \\ UAS & Unmanned aerial systems \\ VNIR & Visible and near-infrared \\ \end{tabular}
## 3 Results and Discussion
### Parameter Study of the Adsorption of DR-81 onto the Cockleshell Powder  
#### 3.2.1 Effect of Adsorbent Dose  
It is important to know the mass transfer phenomenon of DR-81 removal by the CS, in the range of 0.3-1.5 g. Figure 8 depicts the effect of biosorbent dosage on the DR-81 removal efficiency and adsorption capacity.  
According to Figure 8, the removal efficiency data present two stages: In the first stage, in the adsorbent dose range of 0.3-1 g/L, there is an increase in removal efficiency from 39 to 88.06%. This phenomenon could be attributed to the presence of a large number of unsaturated active sites [40]. The second stage marks an increase in the CS dosage in the range 1-1.5 g/L, with a decrease in removal efficiency from 88.06% to 84.73%. In  
Figure 8: Effect of CS dosage on removal efficiency and adsorption capacity for DR-81 (C\({}_{0}\) = 10 mg/L, V = 100 mL, T = 22 \({}^{\circ}\)C, pH = 7.09, stirring speed = 300 rpm).  
Figure 7: EDX of cockleshell powder (**a**) before and (**b**) after adsorption of DR-81.  
this stage, the vacant sites of CS are totally charged by dye. Simultaneously, the capacity of adsorption of DR-81 by CS decreases from 4.52 to 1.20 mg/g with the increase in the biosorbent dosage from 0.3 to 1.5 g/L. This result could be explained mathematically in the expression of the adsorption capacity (q\({}_{\mathrm{e}}\)) cited in Equation (1). When the biosorbent dose rises, q\({}_{\mathrm{e}}\) automatically decreases.
# Research on Interference Mechanism of 25 Hz Phase Sensitive Track Equipment from Unbalanced Current
## 2 Cause Analysis of Abnormal Red Ribbon
### Influence of Unbalanced Current Intrusion on Relay
#### 2.4.2 Interference Analysis of Unbalanced Impulse Current on Relay Coil
When the DC component reaches the maximum value of 2.5 A, the core loss is 92 W. At this time, the core loss is serious, which makes the core temperature rise, then affects the operation state of the transformer. If it is in long-term operation, the service life of the choke transformer will be reduced due to the increase of temperature stress. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Winding Number**} & \multicolumn{1}{c}{**Winding Turns**} & \multicolumn{1}{c}{**Winging**} & \multicolumn{1}{c}{**Number of**} & \multicolumn{1}{c}{**Winding**} \\  & & **Diameter** & **Parallel Branches** & **Material** \\ \hline traction coil & 2 & 8 & 10.4 \(\times\) 30 mm & 1 & flat steel \\ signal coil & 1 & 48 & 10.4 \(\times\) 30 mm & 1 & electrolytic copper \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Choke transformer parameters. Figure 8: Excitation current of choke transformer under different DC components. For the influence of unbalanced impulse current on the secondary side of the choke transformer, it can be found from Figure 11 that the secondary side voltage of the choke transformer decreases with the increase of the DC component, and the two are negatively correlated. This is due to the increase of the excitation current, the choke transformer produces DC magnetic bias, so that the core accelerated magnetic saturation, resulting in a secondary side voltage drop. For a binary two-bit relay, the voltage drop of the track coil will cause the wing plate to fall, resulting in misoperation.
## 2 Results and Discussion
### Validation, Matrix Effect and Recovery Using Fiber pD14  
The analytical performance of the most efficient fiber, pD14, is represented in Table S2. As shown, large linear regions were obtained, with correlation coefficients varying from 0.987 to 0.999. Comparing the limits of detection (LODs) of pD14 with those of the PDMS7, it can be observed that they vary from analyte to analyte, in the range of 2 \(\times\) 10\({}^{2}\) nanograms for the lowest LODs, where pD14 showing always lower LODs than PDMS7. When comparing the overall LODs obtained for pD14 with those reported in the literature for other PIL-based fibers [12; 38; 39], they fall in the same detection limits range. As for fiber's reproducibility, they were studied using two different situations: using the same fiber to extract similar solutions in different days, and extracting the same solution with different fibers. In terms of the total detectable area, 6% and 17% difference was obtained for the first and second case, respectively. The recovery studies were performed using pD14 fiber in human urine samples. Since no signals of the compounds under study were detected in the urine sample, recoveries were accessed by performing extractions from the urine sample spiked at two different analytes concentrations, 65 mg\(\cdot\)L\({}^{-1}\) and 6.5 mg\(\cdot\)L\({}^{-1}\). Recoveries ranging from 72.0% to 104.1% for the sample with the highest analyte concentration, and from 60.2% to 93.5% for the sample with the lowest analyte concentration, were obtained.
## 3 Results and discussion
### Simultaneous Determination of the Eight Marker analytes in HPYJT  
The developed and validated HPLC analytical method was applied for the quantitative analysis of the eight analytes in the lyophilized HPYJT sample. Quantification was performed simultaneously by scanning a wavelength range of 190-400 nm with a PDA detector. The wavelength monitored for the quantitative analysis of each component was: 214 nm for AMY, 254 nm for GA, 275 nm for LIQA and LIQ, 280 nm for NAR and HES, 325 nm for MULA, and 330 nm for RA. In the lyophilized HPYJT sample, the eight marker compounds were found to be present in 0.43 mg/g to 2.83 mg/g and, among these, it was confirmed that MULA was the most abundant in the HPYJT sample at 2.83 mg/g (Table 4).
## 3 Artificial Intervention in the Degradation of Antibiotics
### New Type of Catalyst
#### 3.5.2 Metal-Organic Frameworks (MOFs)  
MOFs possess high specific surface areas, tunable pore structures, abundant active sites, and an excellent chemical stability, which provide significant advantages in the adsorption and photocatalytic degradation of pollutants, including antibiotics. The degradation of antibiotics by MOFs primarily occurs through two mechanisms: adsorption and photocatalytic degradation. In terms of adsorption, iron-based MOFs, such as MIL-101 (Fe), acquire a positive charge on their surface when the pH exceeds 7. This property enables them to adsorb negatively charged tetracycline molecules (TC\({}^{-}\)) [119]. ZIF-8@TA adsorbs tetracycline through \(\pi\)-\(\pi\) interactions [120], while hydrophobic MOFs, such as ZIF-8, attract hydrophobic antibiotic molecules via hydrophobic interactions [121]. Additionally, Lewis acid sites in MOFs, such as zirconium-oxygen (Zr-O) clusters, interact with the basic groups of antibiotics, such as amino groups, through acid-base interactions [122]. In the field of photocatalytic degradation, MOFs absorb photons from light sources, including visible light. This absorption causes electrons to transition from the valence band (VB) to the conduction band (CB), resulting in the formation of electron (e\({}^{-}\))-hole (h\({}^{+}\)) pairs. This process generates reactive species, such as superoxide radicals (-O\({}_{2}\)\({}^{-}\)), hydroxyl radicals (-OH), and sulfate radicals (SO\({}_{4}\)\({}^{-}\)), which are produced in the presence of persulfate (PMS/PDS). These reactive species attack antibiotic molecules, disrupting their structures through mechanisms such as ring opening and decarboxylation, ultimately mineralizing them into CO\({}_{2}\) and H\({}_{2}\)O [123].
# Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs
## 6 Experimental Results  
In this section, we first give a brief introduction to the database of German and Chinese traffic scenes. Then, we evaluated the accuracy and computing time consumption of the traffic sign detection and classification through experiments. Finally, we analyzed the reasons that caused false results in some of the test samples.
# Buffeting Response Prediction of Long-Span Bridges Based on Different Wind Tunnel Test Techniques
###### Abstract  
The traditional method for calculating the buffeting response of long-span bridges follows the strip assumption, and is carried out by identifying aerodynamic parameters through sectional model force or pressure measurement wind tunnel tests. However, there has been no report on predicting the buffeting response based on the sectional model vibration test. In recent years, the author has proposed a method, based on the integrated transfer function, for predicting the buffeting response of long-span bridges through theoretical and full-bridge tests. This provided an idea for predicting the buffeting response based on the sectional model vibration test. Unfortunately, the effectiveness and accuracy of this method have not been proven or demonstrated through effective tests. To solve this problem, a long-span suspension bridge was taken as a background. Parameters such as aerodynamic admittance were identified through a sectional model force measurement test and the integrated transfer functions were identified through a sectional model vibration test. A taut strip model test was also conducted. Furthermore, the buffeting response prediction results based on three kinds of wind tunnel test techniques were compared. The results showed that if the strip assumption was established, the results of the three methods aligned well, and that selecting a reasonable model aspect ratio for the test could effectively reduce the influence of the 3D effect; moreover, identifying the integrated transfer function by the sectional model vibration test could effectively predict the long-span bridge buffeting response. Furthermore, when the strip assumption failed, the results of the traditional calculation method using 3D aerodynamic admittance became smaller. A larger result would be obtained by neglecting the influence of aerodynamic admittance.  
long-span bridge; buffeting response; wind tunnel test; sectional model; aerodynamic admittance; integrated transfer function +
Footnote †: journal: Applied Sciences
# Deep Learning (Fast R-CNN)-Based Evaluation of Rail Surface Defects
## 2 Rail Damage Inspection  
To analyze the correlation between the types of rail surface defects and the characteristics of rail internal defects, the damaged rails on the operating lines were examined. Furthermore, the damaged rails were used as specimens for the scanning electron microscopy (SEM) testing. The damaged rails on the concrete track section of the tunnel were sampled. Figure 1 shows the rail damage site.  
Figure 1: Photograph of the rail damage site.  
An investigation was conducted on a site where many rail surface damages occurred on the concrete track in the tunnel section. The damaged rails were sampled based on the on-site investigation.  
The investigation revealed the occurrence of various rail surface damages in a railway system that is currently in service. Headchecks, spallings, and shells were observed to occur, as shown in Figure 2.  
The current status of rail surface damage observed during a field investigation is shown in Figure 2. The damage pattern observed on the rail is caused by rail contact fatigue (RCF), where fine cracks extend to the front of the rail head. This phenomenon occurs when the rail is subjected to continuous loads, such as impacts from wheel load and speed, as well as the inner and outer wheel load ratios, resulting in the contact between the wheel and rail. As shown in Figure 2a, the headcheck can be easily detected. However, the evaluation  
Figure 2: Rail surface damages observed during field investigation of urban railway: **(a)** headcheck; **(b)** spalling; **(c)** shell.  
of the crack depths or the extent of damage is quite challenging. Microcracks primarily occur in the range of relatively large curve radii, 1500-2000 m. Microcracks are primarily caused by the longitudinal forces generated by the rolling contact between the wheels and the outer rail, and also by slip. They mainly form in the gauge corner of the outer rail at an angle of 30\({}^{\circ}\)-60\({}^{\circ}\) in a thin, linear pattern ranging from 2 to 7 mm. Spalling refers to the localized detachment of base metal from the rail head, and it is typically caused by repetitive loading and high contact stresses, as shown in Figure 2b. It further indicates the breaking off of shallow pieces of metal from the surface of a rail when cracks originating from the surface are connected by other similar cracks at a shallow depth in the rail head. The investigation demonstrated the occurrence of localized detachment resulting from the expansion of fatigue cracks in the case of shell damage.
## 3 Fault Feature Extraction Technique
### Feature Extraction with MFEVS  
Local faults in rotating bearings usually generate an array of periodic impulses in vibration signal. Theoretically, the impulses will excite the system resonance in the form of quasi-periodic or periodic damping oscillation waveform. Besides, MDE_rms_ shows a remarkable ability in measuring and quantifying the complexity of a time series. Regular and periodic signals are often characterized by a lower entropy value, whereas interference random signals usually correspond to a higher entropy value. Then, it can be concluded that the short sliding signal segment hidden local fault features is relatively less complex and corresponds to a lower entropy value. Hence, the proposed method will divide the original vibration signal into an array of short sliding segments and separately explore the fault signature by calculating the MDE_rms_ of each part.  
Having in mind the introduction mentioned above, the fusion feature _MFEVS_ can effectively characterize the complexity and irregularity of the corresponding short sliding segments. If the short sliding segment contains the fault impulse or part, the corresponding entropy value will be relatively small. Then, minimum MDE_rms_ is achieved as the following equation:  
\[En(j)=\ \ \min(fe_{(i)})\ \ \ (i=1,2,\ldots n) \tag{23}\]  
where \(j\) is the number of the short sliding signal with minimum MDE_rms_.  
As mentioned above, the corresponding optimal short sliding signal is reconstructed as follows:  
\[Y_{j}=[y((j-1)s+1),y((j-1)s+2),\cdots,y((j-1)s+Nw)] \tag{24}\]  
According to the mechanism of damped oscillation, the short sliding reconstructed signal theoretically characterizes an obviously quasi-periodic oscillation waveform. Besides, in the frequency spectrum of the short sliding reconstructed signal, the dominant frequency part can be regarded as the main resonance frequency band of original vibration signal and used as the optimal coefficient of the FIR bandpass filter.
# Laboratory Evaluation of Wear Particle Emissions and
## 2 Research Method
### Experimental Method  
#### 2.2.1 Experimental Equipment and Dust Measurement Method  
The TRWP test device used in this study is the laboratory tire-road-wear particle generation and evaluation tester (LTRWP tester) developed by the Korea Expressway Corporation. This device is designed to simulate the wear and friction between tires and road surfaces to generate and evaluate TRWPs.  
Due to the simulation with small-scale experimental equipment, the actual tire-wear conditions were assumed to be represented by the side wear of the cylindrical specimen. This approach ensures the effective generation and evaluation of TRWPs under laboratory conditions.  
The LTRWP tester simulates road and tire wear in the field by rotating and making the cylindrical PSMA-mixture specimen and tire inside the chamber come into contact with each other (Figure 4). Considering the size of the LTRWP tester, three passenger car tires of size 155/70, R13 were placed around the cylindrical specimen at 0\({}^{\circ}\), 120\({}^{\circ}\), and 240\({}^{\circ}\), and the load of each tire was set to 200 kgf.  
To measure the dust generated due to the friction between the mixture and the tires, a sensor for measuring suspended dust was installed inside the tester, and the PM concentration was measured at 10 s intervals. The cumulative TRWP emissions generated due to the rotational friction between the tires and the mixture were collected directly from the  
Figure 3: Visual inspection of specimen deformation by test’s starting temperature.  
Figure 2: Wear test and dust measurement method.  
bottom of the chamber after completing the test, and the collected TRWP emissions were weighed (Figure 5).  
The size of TRWPs found on roads ranges from 4 \(\mu\)m to 264 \(\mu\)m [15]. In this study, the dust measured using the LTRWP tester was recorded by particle size by referring to the standards of the United States Environmental Protection Agency (EPA) [16]. For TRWP emissions (g), the wear particles accumulated at the bottom of the chamber were collected and weighed after the test was completed. The total suspended particulate (TSP) was recorded as the average total suspended dust concentration (\(\mu\)g/m\({}^{3}\)) with a particle size of 10 \(\mu\)m or less, and the PM was recorded as the average suspended dust concentration based on the range defined in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Classification**} & \multicolumn{1}{c}{**Particle Size (\(\mu\)m)**} \\ \hline \multirow{5}{*}{TSP} & PM\({}_{10}\) & 5–10 \\  & PM\({}_{5}\) & 2–5 \\  & PM\({}_{2}\) & 1–2 \\  & PM\({}_{1}\) & 0.5–1 \\  & PM\({}_{0.5}\) & 0.3–0.5 \\  & PM\({}_{0.3}\) & \(<\)0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Particle size classification.  
Figure 4: Laboratory tire–road-wear particle generation and evaluation tester.  
Figure 5: TRWP emissions.