esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Selection of the Sorbent Phase  
Before the method optimization, preliminary experiments were performed to select the most suitable sorbent phase for the determination of phenol compounds. For this purpose, a comparison between PS-DVB and AC was performed under similar experimental conditions (extraction: 16 h (1000 rpm); back-extraction: 45 min (ACN) under sonification). While the selectivity of both sorbent phases is pH dependent, AC is a porous solid material that retains the solutes through electrostatic and/or dispersive interactions, according to the textural adsorption properties, surface area and pore dimensions [20,21]. On the other hand, PS-DVB phase is a reversed phase type, retaining the analytes according to the particle size, surface area and mechanisms involved, _i.e._, \(\uppi\)-\(\uppi\), dipole-dipole, hydrogen bonding and ionic interactions [19]. The data obtained are depicted in Figure 1a, in which the efficiency yields obtained by the PS-DVB phase are much higher than for the AC one for the five phenol compounds under study. From the data obtained we can conclude that the reversed phase extraction type seems to be more effective, under similar experimental conditions. Consequently, PS-DVB was chosen as sorbent phase for the subsequent assays.  
Figure 1: A Comparison of the recovery yields obtained by (**a**) BA\(\upmu\)E using AC and PS-DVB as sorbent phases and (**b**) between BA\(\upmu\)E (PS-DVB) and SBSE (PDMS) followed by LD/HPLC-DAD, under similar experimental conditions.
###### Abstract  
Numerical simulation applied to agriculture or wastewater treatment (WWT) is a complementary tool to understand, a priori, the impact of meteorological parameters on productivity under limiting environmental conditions or even to guide investments towards other more relevant circular economic objectives. This work proposes a new methodology to calculate Typical Meteorological Sequences (TMS) that could be used as input data to simulate the growth and productivity of photosynthetic organisms in different biological systems, such as a High-Rate Algae Pond (HRAP) for WWT or in agriculture for crops. The TMS was established by applying Finkelstein-Schafer statistics and represents the most likely meteorological sequence in the long term for each meteorological season. In our case study, 18 locations in the Madrid (Spain) region are estimated depending on climate conditions represented by solar irradiance and temperature. The parameters selected for generating TMS were photosynthetically active radiation, solar day length, maximum, minimum, mean, and temperature range. The selection of potential sequences according to the growth period of the organism is performed by resampling the available meteorological data, which, in this case study, increases the number of candidate sequences by 700%.  
typical meteorological sequence; typical meteorological week; wastewater treatment; high-rate algae pond; solar irradiance; Finkelstein-Schafer statistics +
Footnote †: journal: Applied Sciences
## 3 LED-PHOTON AE Implementation
### MixColumns (MC)  
MC is the final permutation operation in the SPN structure. This operation works in conjunction with the ShiftRows function to ensure data diffusion. For both LED and PHOTON, an algorithm-specific pre-defined matrix is multiplied with each row of the state matrix. Although the structure of the LED's MC operation is inherited from PHOTON, the size of the internal matrix state and the pre-defined matrices are different. The columns of the state in LED are multiplied by a 4 \(\times\) 4 matrix, shown in Equation (1), while the columns in PHOTON are multiplied by a 5 \(\times\) 5 matrix, shown in Equation (2).  
The MixColumns operations in LED and PHOTON share some of the logic resources. The output of the MC is generated by feeding the input into a 100-bit LUT to obtain the relevant multiplication values for the particular cipher.
## 3 Results and Discussion
### Effect of Model Configuration on Detection Performance  
#### 3.2.1 Impacts of the Classifier  
For the hyperparameter-optimized models, we evaluated model performance on the validation dataset using F1 scores. Figure 5 shows the detection performances of the models that utilized class weighting and fine-tuning. Because the data were randomly sorted into five datasets, the classification difficulty could vary slightly from one dataset set to another. However, the significant performance relationships among the models were generally equal for all datasets. For example, the W-FT-SVM-L model consistently had a higher F1 score than the W-FT-FC-L model on each dataset, and the difference passed Student's paired \(t\)-test (\(p<0.05\)).  
Figures 6 and 7 summarize the results for each model and their significance tests. The results of the five-fold cross-validation were expressed as means and standard deviations, while the Student's paired \(t\)-test was used for the significance difference tests. Therefore, significant differences were observed if there was a consistent trend in performance across models in each dataset, regardless of whether variation existed in model performance across datasets. The results for each dataset are shown in Figures  
models. However, in the case without class weighting, there was minimal or no significant performance difference between the FC and SVM. In the case of no class weighting or fine-tuning, the performance improved when nonlinear classifiers were used.  
The superiority of the classifiers depended on whether the F1 score or recall was used as the performance index. The SVM should be used for overall accuracy (high F1 score), and FC for safe performance (high recall). It is consistent with the literature [22; 24; 25; 26] that the SVM shows superior overall performance compared to the FC. Interestingly, however, the model with FC intended to show a higher recall, indicating fewer false negatives. These results indicate the difference between the SVM and FC in characteristics and need for proper use.  
Figure 6: Each model’s F1 score for the validation dataset. Averaged performances and the standard deviation of five-fold cross-validation are shown. (**a**) Models with class weighting and fine-tuning. (**b**) Models with class weighting without fine-tuning. (**c**) Models without class weighting with fine-tuning. (**d**) Models without fine-tuning and class weighting.
# RF Exposure Assessment for Various Poses of Patient Assistant in Open MRI Environment
## 2 Materials and Methods
### Numerical Modeling of RF Coils for Open MRI Systems
#### 2.1.1 RF Head Coil  
As shown in Figure 1, two Helmholtz coils were numerically modeled for the head coil. Helmholtz RF coils are often used because their capacitor configurations are simpler than those of birdcage coils [21]. Helmholtz pairs 1 and 2 produce an excitation magnetic field in the y- and x-directions, respectively, and thus they eventually form a circularly polarized RF field through a 90-degree phase delay between two Helmholtz pairs. Most of all, Helmholtz coils are widely used because of the vertical B\({}_{0}\) direction when considering the RF coil opening to the patient of an open MRI system. The dimensions of the RF coil for the head were 224 mm in width, 260 mm in height, and 280 mm in length. The RF coil was driven at 13 MHz (i.e., the resonance frequency at 0.3 Tesla MRI, 12.77 MHz) using constant-current sources. The steady state of the wave propagation in the x- and y-directions were observed at the same magnitude but with a 90-degree phase delay, indicating the correct formation of the CP B\({}_{1}\) field in the xy-plane, as shown in Figure 2. Note that the B\({}_{1}\) fields shown in Figure 2 are drawn under the unloaded condition; however, we also confirmed that the CP-mode is driven under a loaded condition by observing the magnitude and 90-degree phase delay (not shown here).
###### Abstract  
Light is an important environmental regulator of plant secondary metabolism. Terpenoids, the most abundant secondary metabolites in plants, demonstrate a wide spectrum of biologically significant properties, encompassing antimicrobial, antioxidative, and analgesic activities. _Litsea cubeba_ (Lour.) Pers., a core species within the Lauraceae family, exhibits notable pharmacological potential, including antimicrobial and antitumor effects. Here, we found that darkness treatment significantly suppressed terpenoid accumulation in _L. cubeba_ fruits. To clarify the molecular mechanisms underlying the regulatory effect of light and darkness treatments on terpenoid biosynthesis, we conducted a comparative transcriptome profiling of _L. cubeba_ fruits under light and darkness treatments. A total of 13,074 differentially expressed genes (DEGs) were identified among four sampling time points (L1-L2-L3-L4 vs. D1-D2-D3-D4). These genes were enriched in various pathways, with significant enrichment being observed in the terpenoid and other secondary metabolism pathways. Additionally, the enrichment of DEGs in L2 and D2 stages was further studied, and it was found that nine DEGs were significantly enriched in the monoterpene synthesis pathway. The weighted gene co-expression network analysis (WGCNA) showed that alcohol dehydrogenase (_LDH_), a key enzyme in terpenoid synthesis, had the same expression pattern as _WRKY_ and _NAC_ transcription factors, suggesting their involvement in the biosynthesis of terpenoids in _L. cubeba_. Expression profiling demonstrated that plastid-localized terpenoid pathway genes were markedly downregulated under darkness treatment. qRT-PCR validation of key genes (_LcDXS3_, _LcLHMGST_, _LcMDS_, and _LcTPS19_) confirmed the reliability of the transcriptome data, with _LcDXS3_ exhibiting pronounced declines in expression after 6 h (2.76-fold decrease) and 12 h (2.63-fold decrease) of darkness treatment. These findings provide novel insights into the photoregulatory mechanisms governing terpenoid metabolism in _L. cubeba_.  
_Litsea cubeba_; terpenoid; biosynthetic pathway; expression patterns
## 3 Results and Discussion
### Mesh Independence Verification
#### 3.1.1 Verification of Time Step Independence  
Four different time steps under the fixed initial size of gas parcels are chosen as follows: 50, 100, 200 and 300 time steps per cycle. The variations of inlet velocities at the cold end are obtained and shown in Figure 3, serving as the selection criteria of time steps.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Geometry** & **Parameters** \\ \hline length & 65 mm \\ inner diameter & 17.5 mm \\ porosity & 0.7 \\ mesh number & 400 \\ hydraulic diameter of mesh wire & 43 \(\mu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical geometry parameters of the regenerator in the mesh independent verification.  
Figure 2: Schematic of the numerical algorithm.  
As shown in Figure 3, the red solid lines represent the specified velocity at the warm end, and the blue lines are the predictions of velocities at the cold end. From these 4 figures, it can be seen that the calculated value of cold end velocity is unstable, with large fluctuations when a cycle is divided into 50 time steps; the fluctuation of the calculated cold end velocity becomes smaller under the condition of 100 time steps per cycle; a division of 200 time steps per cycle shows even better stability and lower fluctuations; the choice of 300 time steps per cycle enhances the stability of the calculated velocity at the cold end and minimizes the fluctuation to a great extent. As shown in Figure 4, the cold end velocity after several cycles converges with little fluctuations under the condition of 200 time steps per cycle, which is almost the same as under 300 time steps per cycle. At the early stage, the initial velocity, which is inconsistent with actual condition, produces a large fluctuation. Therefore, a division of 200 time steps per cycle is adopted in the consideration of both the calculation accuracy and computational cost.  
Figure 4: Predictions of cold end velocity after several cycles when time steps = 200 per cycle.  
Figure 3: Velocity variations at the cold end of the regenerator predicted using different time steps per cycle: (**a**) 50; (**b**) 100; (**c**) 200; (**d**) 300. The velocity at the warm end is also shown in each figure as reference.
# Bonding Behaviors of GFRP/Steel Bonded Joints after Wet-Dry Cyclic and Hydrothermal Curing
## 3 Results and Discussion
### Load-Displacement Curves  
Figure 6 shows the load versus joint displacement behaviors and normalized tensile capacities of the WD series bonded specimens. Based on the load-displacement curves in Figure 6a, approximately linear relationships of tensile load and displacement were found for all the specimens until they reached their tensile capacities, i.e., ultimate tensile loads. Once the ultimate tensile load was reached, the load was sharply reduced without increasing the joint displacement, corresponding to a brittle fracture of the bonded specimens. The tensile capacities and load-displacement curves are presented in Table 2 and Figure 6a. According to the debonding failure of the WD series in Figure 5, the tensile behaviors of the specimens of the WD series can be attributed to the premature debonding of the adhesive-steel interfaces that was caused by the degradation of wet-dry curing. Table 2 shows that when compared with the reference specimen (22.48 kN), a significant reduction of tensile capacities occurred for the specimens exposed to wet-dry cyclic curing, especially for the specimen WD-90-60 (10.26 kN). This phenomenon may be attributed to the moisture diffusion and heat transfer mechanisms in the cured specimens [21].  
The tensile capacity of each joint of the WD series under different curing conditions was normalized by that of the specimen cured in the air environment (i.e., the reference specimen), as shown in Figure 6b. By comparing the results of WD-[D]-20 (i.e., WD-30-20, WD-60-20, and WD-90-20), the lowest tensile capacity was found in the specimen cured the longest (WD-90-20), indicating that a more severe degradation of bonding performance (i.e., interfacial and adhesive properties) was introduced by a longer wet-dry cyclic curing duration. A similar phenomenon was observed in the series of WD-[D]-40 and WD-[D]-60. For WD-30-[T] (i.e., WD-30-20, WD-30-40, and WD-30-20) with the same wet-dry duration under different temperatures, a lower tensile capacity was found with a higher curing temperature. Therefore, the adhesive-steel interface and adhesive may deteriorate at a high temperature. The smallest normalized tensile capacity, 46%, was observed in specimen WD-90-60, which was cured for 90 days (wet-dry) at 60 \({}^{\circ}\)C, showing the combined negative effect introduced by longer curing time curing and higher temperature.  
For HY series testing, a similar reduction of tensile capacity was observed in Figure 7, which shows that the degradation of the bonded joints could be caused by the hygothermal pretreatment. However, according to the delamination failure of GFRP in HY series testing (as shown in Figure 4), the GFRP material properties could degrade after the hygothermal pretreatment, and the degradation would become more severe with longer exposure durations and higher curing temperature.  
Figure 6: Specimens of wet–dry (WD) series. (**a**) Load–Displacement Curves; (**b**) Normalized Tensile Capacities.
## 3 Assessment of Precise MGEX Orbit and Clock Products
### SLR Validation  
The optical SLR technique measures the distance between an SLR observatory and a satellite equipped with laser retroreflector arrays (LRAs). This distance can also be computed using satellite orbits and observatory coordinates. The differences between the computed distance and the distance measured by SLR, namely SLR residuals, can be used as an external criterion to validate GNSS satellite orbits independently, especially radial components [38]. In this work, SLR observations were collected from the International Laser Ranging Service (ILRS) [39] to validate MGEX orbit products. Offsets of the laser retroreflector array [40] with respect to the satellite centers of mass were used in our SLR analysis, drawing from data released by the CAO [21; 22; 23; 24], EGSC [20] and China Satellite Navigation Office (CSNO) [41]. The mean bias and STD values of the SLR residuals were introduced as quality indicators.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **COD** & **ESM** & **GFZ** & **SHA** & **WUM** \\ \hline \multirow{2}{*}{COD} & BDS-2 IGSO & - & 0.28 & 0.45 & 1.11 & 0.23 \\  & BDS-2 MEO & - & 0.17 & 0.46 & 1.12 & 0.17 \\ \hline \multirow{4}{*}{ESM} & BDS-2 IGSO & - & 0.47 & 1.03 & 0.34 \\  & BDS-2 MEO & - & 0.42 & 1.10 & 0.16 \\  & BDS-3 MEO & - & - & - & 0.25 \\ \hline \multirow{4}{*}{GFZ} & BDS-2 GEO & & - & 1.21 & 1.06 \\  & BDS-2 IGSO & & - & 1.28 & 0.48 \\  & BDS-2 MEO & & - & 1.31 & 0.42 \\ \hline \multirow{4}{*}{SHA} & BDS-2 GEO & & & - & 1.26 \\  & BDS-2 IGSO & & & - & 1.14 \\ \cline{1-1}  & BDS-2 MEO & & & - & 1.12 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Mean STD of BDS-2 and BDS-3 clock differences among different ACs (units: ns).  
Figure 7: Clock comparisons for BDS with respect to WUM. The horizontal axes represent the satellite PRN, and the vertical axes represent the STD of clock differences.
# Dynamic Responses of Asphalt Concrete Waterproofing Layer in Ballastless Track
## 5 Numerical Model
### Track Structure Model  
The geometric parameters track structure model was obtained from the \"Code for Design of High Speed Railway\" in China (TB 10621-2014) [25]. The CRTS III track structure system consists of rail, fastener, track slab, self-compacting concrete (SCC), base plate, ACWL, roadbed, subgrade and soil foundation from top to bottom. The Chinese 60 kg/m rail with a standard gauge of 1.435 m and WJ-8B fastener with a spacing of 0.63 m were adopted in the model. The standard CRTS III P5600 track slab has a length of 5.6 m, a width of 2.5 m and a thickness of 0.2 m. The SCC has the same length and width as the track slab and the thickness is 0.1 m. The length, width and thickness of base plate is 16.99 m, 3.1 m, 0.3 m, respectively. The expansion joints are taken into consideration in the model and the width of the expansion joint for the track slab and base plate is 70 mm and 20 mm, respectively. So, there are three track slabs on a base plate. The double line subgrade with a surface width of 13.6 m for the ballastless track structure is used in the model and the gradient of subgrade slope is 1:1.5. The standard thickness of surface layer and bottom layer of roadbed is 0.4 m and 2.3 m, respectively. The thickness of ACWL is included in the surface layer of roadbed. The thickness of subgrade and soil base is set as 3 m and 5 m, respectively, to reduce the influence of the bottom boundary condition on the analysis results.  
The linear elastic constitutive relationship is used to simulate the track structure model except the ACWL and the material parameters are listed in Table 4. The ACWL is considered as a linear viscoelastic material in the model according to the results in Table 3. The Rayleigh damping determined by modal analysis is also considered in the model [26]. The eight-node hexahedral solid element is used to mesh the track structure model. The Cartesian connector element is used to simulate the fasteners. The elastic stiffness and damping coefficient of fasteners is 30 kN/mm and 60 kN\(\cdot\)s/m. The structural layers in the track structure system are considered to be completely continuous. The encastre boundary condition is set at the bottom of model and the symmetry boundary condition is set at both the longitudinal and lateral ends. To eliminate the influence of boundary conditions, especially the dynamic wave, on the calculation results, the distance of five base plates along the driving direction is used in the model. The calculated results of dynamic responses are obtained from the region at the middle base plate. Finally, the entire finite element model for the CRTS III ballastless railway with ACWL is illustrated in Figure 12.  
Figure 11: Wheel–rail contact model.
## 2 Related Works
### Lane Detection
#### 2.2.2 Deep Learning-Based Methods  
Deep learning has gained significant traction across various fields, including lane detection. Recent advancements in deep learning-based lane-detection methods can be systematically classified and presented based on different criteria. Here are two main categories:  
1. Encoder-decoder segmentation: In this approach, encoder-decoder convolutional neural networks (CNNs) are commonly used for pixelwise segmentation. SegNet is a deep fully convolutional neural network architecture specifically designed for semantic pixelwise segmentation [26; 27]. LaneNet is another model that focuses on end-to-end lane detection and consists of two decoders: a segmentation branch for lane detection in a binary mask and an embedding branch for road segmentation [28]. Another variant replaces the skip connection in U-Net with a long short-term memory (LSTM) layer, shown in Figure 3[29]. LSTM is a type of recurrent neural network (RNN) that addresses the challenge of modeling long dependencies in sequential data [30; 31]. By incorporating an LSTM layer, this method effectively preserves high-dimensional information from the encoder and transmits it to the decoder. Also, there is a method to replace the standard convolution layer in the traditional U-Net with depthwise and pointwise convolutions to reduce computational complexity while maintaining the detection rate. This U-Net structure is referred to as DSUNet (depthwise and pointwise U-Net) [32].
2. GAN model: The GAN is composed of a generator and a discriminator [33]. Lane detection can be performed using GAN-based models [34]. One specific method proposed the embedding-loss GAN (EL-GAN) for semantic segmentation. The generator predicts lanes based on input images, while the discriminator determines the quality of the predicted lane using shared weights. This approach produces thinner lane predictions compared to regular CNN results, allowing for more accurate lane observation. It also performs well in scenarios where lanes are obscured by obstacles such as vehicles.