esa-sceva/satcom-chunk-collection huggingface dataset:
# Demonstration of High-Speed Optical Transmission at 2 \(\upmu\)m in Titanium Dioxide Waveguides
## 2 Design and Fabrication of the Photonic Structure
### Fabrication  
The fabrication process relies on traditional techniques. Titanium dioxide layers are deposited on a glass substrate by reactive direct current (DC) magnetron sputtering of a \(99.9\%\) pure titanium target under argon and oxygen control atmosphere. Electron-beam lithography, followed by thermal gold evaporation (here, \(3\) nm of chromium are used as an adhesion layer) and a lift-off process is used to fabricate the gold gratings. Then the top layer of TiO\({}_{2}\) is deposited, followed by overlay electron-beam lithography. After metallic mask evaporation, reactive ion etching is performed to make the waveguides. Finally, wet etching removes the mask to obtain the final device to be tested.  
As mentioned previously, we fabricated two structures of interest (Figure 3): an MM waveguide (with a width of \(8\)\(\upmu\)m) and an SM waveguide (slightly overexposed leading to a width of \(1.65\)\(\upmu\)m).  
These waveguides have the same length fixed at \(575\)\(\upmu\)m but, for the SM waveguide, two \(85\)-\(\upmu\)m long tapers (with a maximal width of \(30\)\(\upmu\)m) are used at the input and output. The parameters measured on the fabricated device differ slightly from the targeted one: we measured a \(304\)-nm total thicknesss with \(h_{\text{bottom}}=69\) nm and \(h_{\text{top}}=235\) nm. Moreover, the waveguide width is actually \(1.65\)\(\upmu\)m, which induces the existence of the TE\({}_{0,1}\) with an effective index close to \(1.47\). For each structure, two widths of grating lines (related to filling factor) have been fabricated. The two targeted widths were the best values for which TE and TM were equal in Figure 1b (i.e., \(550\) and \(700\) nm). However, due to overexposure, we measured both widths to be \(630\) and \(860\) nm.  
Figure 2: (**a**) Sketch of the cross-section of the \(304\)-nm strip waveguides. Corresponding mode profile of the electric field and associated effective index for (**b**) a multimode waveguide (width, \(8\)\(\upmu\)m) and (**c**) a subwavelength waveguide (width, \(1.6\)\(\upmu\)m). Subplot (**b1**) corresponds to the fundamental TE mode, whereas subplot (**b2**) is related to fundamental TM mode.
## 4 Results and Analysis of the Experimental Results
### BERT-BiGRU-Attention
#### 4.2.4 Experimental Results  
In order to verify the effect of the relationship extraction of the BERT-BiGRU-Attention model proposed in this paper, this paper uses different models to conduct experiments on the same dataset. The comprehensive comparison results of each model are shown in the table below. From the results in the table below, it can be seen that the model proposed in this paper has improved the precision, recall, and F1 values in the Duie and Chinese literature text discourse-level relationship extraction datasets. For datasets with a small number of relations, the effect of lifting is higher. The experimental results under the Duie dataset are shown in Table 8.  
The experimental results under the Chinese-Literature-RE-Dataset dataset are shown in Table 9.  
\begin{table}
\begin{tabular}{c c} \hline
**Name** & **Configuration Information** \\ \hline CPU & AMD Ryzen 5 5600X 6-Core Processor 3.70 GHz \\ RAM & 32 G \\ Memory & 1 T \\ Operating System & Windows 11 22000.1574 \\ Development Languages & Python 3.6 \\ Development Framework & PyTorch 1.7.1 \\ \hline \end{tabular}
\end{table}
Table 6: Environment configuration for relational extraction model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter Name** & **Parameter Information** \\ \hline TorchSize & [x, 1, 128] \\ Self Attention Hidden layer dimension & 768 \\ Dropout & 0.5 \\ Learning Rate & 0.001 \\ Epoch & 10 \\ Batch Size & 8 \\ \hline \end{tabular}
\end{table}
Table 7: Experimental parameters of the relational extraction model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Experiment Number** & **Model Name** & \multicolumn{3}{c}{**Metrics**} \\ \cline{3-4}  & **Precision** & **Recall** & **F1** \\ \hline
1 & BiLstm-Attention & 86.15 & 85.20 & 85.67 \\
2 & PCNN-Attention & 87.32 & 87.16 & 87.24 \\
3 & BERT & 94.20 & 92.71 & 93.45 \\
**4** & **BERT-BiGRU-Attention** & **94.60** & **94.61** & **94.62** \\ \hline \end{tabular}
\end{table}
Table 8: Experimental results under Duie dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Experiment Number** & **Model Name** & \multicolumn{3}{c}{**Metrics**} \\ \cline{3-5}  & **Precision** & **Recall** & **F1** \\ \hline
1 & BiLstm-Attention & 88.90 & 88.23 & 88.56 \\
2 & PCNN-Attention & 95.73 & 67.74 & 89.96 \\
3 & BERT & 91.78 & 90.63 & 91.20 \\
**4** & **BERT-BiGRU-Attention** & **93.35** & **91.69** & **92.51** \\ \hline \end{tabular}
\end{table}
Table 9: Experimental results under the Chinese-Literature-RE-Dataset dataset.  
It can be seen from the experimental results that the model performs well in the Chinese relation extraction task, and the performance of the model in the Chinese relation extraction task does not mean that it performs equally well in other datasets. Therefore, a comprehensive evaluation of the model is required in the next work to determine its actual effect on other datasets. In addition, continuous efforts are needed to improve the performance of this model to meet the ever-changing demands of Chinese relation extraction tasks.
###### Abstract  
The performance of a wind turbine generator (WTG) is highly dependent on the interaction of a rotor blade with complex fluid behaviors, especially the induced vortex structure. In this paper, vortical flows around a blade were first investigated by the unsteady Reynolds averaged Navier-Stokes (RANS) simulation with shear stress transport (SST) turbulence model. It showed that the vortical flows were strongly formed at the blade tip due to the 3D behavior of the boundary layers dominated by pressure gradient. The strong secondary flow was also formed at the near hub due to the Coriolis force and the centrifugal force. At the interacting region of the rotating blade with the tower, the power production was reduced by 22.1% due to the high-pressure fluctuation of the 3P frequency. Based on the close investigation, methods for enhancing the power performance of a WTG were explored, which included the optimization of winglet and ogee design for the blade tip and optimal layout of the nacelle anemometer. The optimized winglet achieved the increase of aerodynamic performance with 0.54%, and the optimal location of the nacelle anemometer was found with a low-turbulence intensity level of 0.003 normalized by the rotor tip speed. The results showed that the traditional anemometer needs to consider the intrinsic flow angle of 11.43\({}^{\circ}\) to avoid the loss of aerodynamic performance caused from yaw error.  
CFD (computational fluid dynamics); vortical flows; wind turbine rotor blade; blade tip shape optimization; optimal nacelle anemometer layout +
Footnote †: journal: Applied  
Acticle  
Kwangtae Ha
## 4 Conclusions  
Directionally solidified Er-doped aluminosilicate glasses were manufactured by the laser floating zone in a controlled oxidizing atmosphere to provide them with a high optical transmission in the visible spectral range. The infrared and visible emissions were assessed at room temperature. Infrared emission corresponding to the \({}^{4}\)I\({}_{13/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{15/2}\) transition at 1528 nm presented an effective bandwidth nearly 30 nm broader than other silicate glasses, which makes them suitable for broadband amplifiers. The visible emission was dominated by the green emission corresponding to the \({}^{2}\)H\({}_{11/2}\) and \({}^{4}\)S\({}_{3/2}\) levels. Lifetimes from levels \({}^{4}\)S\({}_{3/2}\) and \({}^{4}\)F\({}_{9/2}\) at room temperature under excitation at 488 nm were found to be similar for both glasses, whereas lifetime from the \({}^{4}\)I\({}_{13/2}\) level was found to be shorter for the glass with the higher content of Er\({}^{3+}\) ions.  
NIR-to-visible upconversion of Er\({}^{3+}\) ions in these glasses under excitation at 800 nm presented intense green emissions corresponding to \({}^{2}\)H\({}_{11/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{15/2}\) and \({}^{4}\)S\({}_{3/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{15/2}\) transitions placed at 525 nm and 550 nm which were attributed to a two-photon process. Nevertheless, red upconversion emission for the \({}^{4}\)F\({}_{9/2}\)\(\rightarrow\)\({}^{4}\)I\({}_{15/2}\) transition was not observed due to the high maximum phonon energy. Excitation spectra of the upconverted luminescence from the \({}^{4}\)S\({}_{3/2}\) level suggests the energy transfer upconversion as the mechanism responsible for the upconversion process in these glasses.  
[PERSON] conceived and planned the experiments. [PERSON], [PERSON], and [PERSON] carried out the experiments. [PERSON], [PERSON] and [PERSON], [PERSON] contributed to the interpretation of the results and provided critical feedback. [PERSON] wrote the paper with input from all authors. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the PIT2 program of the University of Murcia's own research plan. Fundacion Seneca grant No 20647/JLI/18 and European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie IF No 795630 are also acknowledged.  
Not applicable.  
Figure 9: Excitation spectra of upconverted green emission from the \({}^{4}\)S\({}_{3/2}\) level and square of the one photon absorption spectra of the aluminosilicate glass doped with 4 wt% Er\({}_{2}\)O\({}_{3}\).  
**Data Availability Statement:** All data generated or analyzed during this study are included in this published article. Presented data are also available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Method to Remove Tilt-to-Length Coupling Caused by Interference of Flat-Top Beam and Gaussian Beam
## 3 Theoretical Analysis
### Diffraction
However, when the two beams in the model or only flat-top beam used in the model are diffracted, the value of \(LPS_{LPF}\) can be below the picometer order. This is similar to the situation shown in Figure 2a. To explain why the TTL noise is eliminated in Figure 3a,b, the propagation path of flat-top beams is reset as shown and described in Figure 4a. The dynamic relationship between \(\alpha\) and \(d_{offset}\) is given by:  
\[d_{offset}=s\tan\alpha \tag{18}\]  
Then the propagation paths of the flat-top beams in Figure 4a and Figure 1 are the same and there is only a difference \(\Delta S\) between them. We denote the coordinate system by \(\vec{r}_{ref}\), the location of the pivot by \(\vec{p}_{pivot}\), the translation vector by \(\vec{M}_{trans}\), the rotation matrix for a rotation around the y-axis is \(M_{rot}\)  
\[\vec{r}_{ref}=(x_{1},y_{1},z)^{T},\vec{p}_{pivot}=(d_{offset},0,s)\]  
\[\vec{M}_{trans}=(d_{offset},0,0)^{T},M_{rot}=\left( \begin{array}{ccc}\cos \alpha&0&-\sin\alpha\\ 0&1&0\\ \sin\alpha&0&\cos\alpha\end{array} \right) \tag{19}\]  
Figure 3: Numerically computed \(LPS_{LPF}\) results for interference of the diffracted flat-top beam with (**a**) a non-diffracted Gaussian beam and (**b**) a diffracted Gaussian beam. The analytically result \(LPS_{LPF}\) obtained when both beams are non-diffracted is shown in (**c**). Then the transformed coordinate system \(\vec{r}_{meas}\) is given by:  
\[\vec{r}_{meas}=M_{rot}^{-}(\vec{r}_{ref}+M_{trans}-\vec{p}_{pivot})+\vec{p}_{pivot }=\left( \begin{array}{c}x_{1}\cos\alpha+(z-s)\sin\alpha+d_{offset}\\ y_{1}\\ (z-s)\cos\alpha-x\sin\alpha+s\end{array} \right) \tag{20}\]  
Combining Equations (14), (18) and (20), The field distribution of the diffracted flat-top beam on detector in Figure 4 can be given by:  
\[E(\rho,\psi)_{flat-diff}=\frac{1}{i\lambda}\int_{0}^{2\pi}\int_{0}^{R}\frac{ \exp(ikr)}{r}\frac{1+\cos\theta}{2}\exp[-ik(s-\rho_{1}\cos\psi_{1}\sin\alpha- s\cos\alpha)]\rho_{1}d\rho_{1}d\psi_{1} \tag{21}\]  
where \(r=\sqrt{s^{2}+[\rho\cos\psi-(\rho_{1}\cos\psi_{1}\cos\alpha-s\sin\alpha+s\tan \alpha)]^{2}+(\rho\sin\psi-\rho_{1}\sin\psi_{1})^{2}}\), \(\cos\theta=\frac{s}{r}\). Then Equations (16) and (17) are used for numerical integration. The range of \(\alpha\) is set also to \(-400\)\(\upmu\)rad-400\(\upmu\)rad in the calculations. The two calculations present the same result, so just one \(LPS_{LPF}\) is shown in Figure 4b. Simultaneously Equation (12) is used for calculating the geometric optical path difference. The results shows that while there is no geometric optical path difference between the two interference beams, the pathlength signal, which caused by the initial offset and the relative angle, has same magnitude as \(LPS_{OPD}\) and a different sign. Therefore the reasons of the negligible TTL noise shown in Figure 3a,b can be regarded as the offset of the geometric optical path difference and the pathlength signal caused by lateral offset with the tilt between the two beams.
# Throughput Analysis of IEEE 802.11 WLANs with Inter-Network Interference
## 4 Throughput Analysis of String and Grid Topology Considering Inter-Network Interference  
### Analysis of String Network Topology  
In a 1 \(\times\) n string topology as in Figure 3, only one network is within the carrier-sensing range of the leftmost or rightmost network. When an adjacent network of the leftmost one is in the idle state, the network does not have a carrier sensing or transmission events. Hence, the probability of the leftmost network in the idle state is 1. That happens similarly to the rightmost network. Except for those cases, Network \((i-1)\) and Network \((i+1)\) are within carrier-sensing range of Network \(i\). Hence, at Network \(i\), the carrier-sensing network number is two.  
From (14), assume that Network \((i-1)\) is in the idle state, Network \(i\) is never in the transmission state. Therefore, there will be two possibilities of Network \(i\)'s state: idle or carrier-sensing. The carrier-sensing state of Network \(i\) can be caused by the transmission of either Network \((i-1)\) or Network \((i+1)\). That is because the two networks are in the carrier-sensing range of Network \(i\) in the string topology. However, we have assumed that Network \((i-1)\) is in the idle state; therefore, the carrier-sensing state of Network i is only caused by the transmission state of Network \((i+1)\). The accurate expression of the probability can be  
\[U_{i,i-1}=1-\frac{X_{i+1}}{1-X_{i}}. \tag{16}\]  
\(U_{i,i+1}\) is the probability that the state of Network \(i\) is in the idle state, when Network \((i-1)\) is in the idle state. It can be derived in the similar way.  
The carrier-sensing airtime is the time-share when the network is in the carrier-sensing state. Basically, the carrier-sense airtime of Network \(i\) is the total transmission airtime of the carrier-sensing nodes. From (2), the carrier-sensing airtime of Network \(i\) could be obtained accurately.  
\[Y_{i} = X_{i-1}(1-\gamma_{i,i-1})+X_{i+1}(1-\gamma_{i,i+1}) \tag{17}\] \[- \frac{X_{i-1}(1-\gamma_{i,i-1})X_{i+1}(1-\gamma_{i,i+1})}{1-X_{i }}.\]  
Figure 3: Carrier-sensing range of each network in string-topology.  
Note that, \(X_{i-1}\) and \(X_{i+1}\) equal to 0 when Network \(i\) is the leftmost, rightmost network, respectively. The final component in the right-hand side of (17) denotes the overlap time between Network \((i-1)\) and \((i+1)\) when Network \(i\) does not transmit.
# Study on Lightweight Model of Maize Seeding Object Detection Based on YOLOv7
## 3 Results and Analysis
### Comparison between CloU and SIoU  
To assess the efficacy of the SIoU loss function, this experiment conducted a comparative analysis with the CIoU and DIoU loss functions. The results, as presented in Figure 11, demonstrate several important findings.  
Firstly, it is observed from the figure that the coordinate loss stabilizes after approximately 80 epochs during the training phase. This indicates that the network has reached a point of convergence where further training may not significantly improve performance. Based on this observation, the training epoch was set to 100. Secondly, the SIoU loss function exhibits a significantly faster convergence speed compared to the CIoU and DIoU loss function. This implies that the SIoU loss function allows for more efficient optimization and training of the model, leading to quicker convergence toward the desired performance. Lastly, the error ratio between the ground truth and the predicted bounding box decreases by 9.49% to 45.78% when using the SIoU loss function. This reduction in the error ratio suggests that the SIoU loss function improves the accuracy of the predicted bounding boxes, leading to better object-detection performance.  
To summarize, the comparative analysis reveals that the SIoU loss function outperforms the CIoU and DIoU loss functions in terms of convergence speed and error reduction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Networks** & **Precision** & **Recall** & **mAP** & **Parameters** & **FPS** \\  & **(\%)** & **(\%)** & **(\%)** & **(M)** & **(f/s)** \\ \hline YOLOv7+MobileNet-V2 & 89.6 & 80.7 & 90.9 & 49.6 & 93 \\ YOLOv7+MobileNet-V3 & 87.5 & 82.1 & 89.8 & 47.3 & 94 \\ YOLOv7+ShuffleNet-V2 & 85.3 & 83.2 & 89.1 & 46.1 & 94 \\ YOLOv7 & 90.9 & 82.5 & 92.7 & 74.8 & 121 \\ LW-YOLOv7 & 89.3 & 85.5 & 93.2 & 59.4 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of lightweight backbone network models.  
Figure 11: Comparison of loss values.  
These findings indicate that the SIOU loss function is more effective for assessing the accuracy of object-detection models, particularly in the context of maize detection at the seedling stage.
# Operationalizing Dyadic Urban Traffic Interaction Studies: From Theory to Practice
## 5 Discussion
### Regional and Cultural Differences  
One objection that might be raised about traffic interaction studies is that the outcomes are likely not to be \"generalizable\". It would be surprising if the outcomes of a traffic interaction study run in Dallas, Texas would yield the same outcomes as the same study run in Mumbai, India. However, we point out that this is not a weakness of traffic interaction studies, but rather a strength; traffic interactions in Mumbai are highly different from those in Dallas, Texas, and it is important that AV designers know about and can plan for the difference (see e.g., [65; 66; 67]. The methodological framework we proposed is designed to focus on the behavioral dynamics inherent to specific \"classes\" of encounters rather than the contextual specifics of road geometry or cultural norms. For example, while the layout of an intersection may vary between urban settings in North America, Europe, and Asia, the fundamental dynamics of yielding behavior, right-of-way negotiation, and space-sharing conflicts remain consistent within the same class of interaction scenarios [68; 69; 70; 71]. By prioritizing these behavioral dynamics over contextual details, the framework ensures that the novelty effects observed are attributable to the interaction itself rather than to variations in context. Furthermore, the scenarios presented in the framework are intentionally abstracted to be recognizable across diverse cultural and regional settings. This abstraction facilitates cross-contextual applicability while allowing researchers to adapt specific elements--such as road signage, vehicle types, or traffic density--to better reflect the local conditions being studied. Future studies could validate the framework by comparing its outcomes across multiple cultural contexts, exploring how localized adaptations influence interaction behaviors. By emphasizing behavioral universality within distinct encounter classes and supporting regional customization, the framework provides a flexible yet robust tool for studying traffic interactions globally. This approach ensures its relevance and applicability in diverse cultural and geographic settings.
## 3 Results and Discussion
Hence, by obtaining the CARS signal for O\({}_{2}\) at various nozzle positions it becomes possible to visualize the flow of nitrogen from the nozzle. One can see that the nitrogen flow stays almost symmetric about the zero of the Y-axis as it propagates away from the nozzle, where the slight slope can be due to a tilt of the nozzle. The CARS signal from oxygen in the central part of the flow increases very slowly, and the nitrogen diffuses into the surrounding air. Figure 4: N\({}_{2}\) flow as it displaces air: (**a**) Setup schematics; (**b**) the CARS signal from O\({}_{2}\) in front of the nozzle. Darker regions correspond to higher concentrations of nitrogen. Figure 3: CARS signal dependence on O\({}_{2}\) partial pressure at constant total pressure in the cell (black circles, solid line), and at constant gas mixture at different pressures in the cell (red diamonds, dashed line). Both fittings are performed using “power1” fit in MATLAB R2016b (MathWorks, Natick, MA, USA). 95% confidence bounds are provided for each fitting parameter. We then add a thin metal plate (50 \(\times\) 20 \(\times\) 0.5 mm) in front of the nozzle to examine the resulting flow disturbance (Figure 5). The plate is placed at a distance 5.9 mm away from the nozzle surface in such a manner that the top half of the nozzle is blocked by the plate. The thin plate acts as an impenetrable barrier/obstacle for the gas and laser beams. The flow is disturbed and no longer symmetric; points with zero CARS signal mark the plate location. Moreover, the signal decreases in front of the plate as the nitrogen flow is partially redirected along the surface, while another portion of the flow is deflected by the plate downward. Right behind the barrier, the signal is restored to its value in ambient air since the nitrogen flow cannot penetrate through the plate; i.e., the air in this region stays undisturbed.
# Tabular Data Models for Predicting Art Auction Results
## 3 Results
### CatBoost  
CatBoost demonstrated stable performance across all the auction datasets. The results of this model are presented in Figure 5.  
CatBoost's key strength lies in its target-based encoding for categorical variables that auction data consist of. Moreover, CatBoost uses ordered boosting, a method that addresses the prediction-shift problem often observed in gradient boosting. In ordered boosting, each tree is trained on a historical view of the data, preventing overfitting and leakage by making sure the model only uses past data for training. This can be useful in auction datasets, where past auction results influence price predictions. In addition, CatBoost incorporates L2 regularization, which helps control the model's complexity by penalizing large weights, leading to more stable predictions. This is especially important in datasets like auctions, where market volatility can introduce noise. The robust regularization, combined with CatBoost's ability to efficiently handle categorical data, results in models that generalize well, even with noisy auction prices.