esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Effect of Turbulence
#### 4.2.2 Force Coefficient  
Figure 11 shows the aerodynamic force coefficient of six cases' head and tail cars with respect to different nose lengths. Overall, the effect of turbulence on the aerodynamic coefficient of the train is greater in the tail car than in the head car.  
Turbulence increased drag uniformly for all three heads, increasing by 3% for the 5 m heads and by 2% for the 7.5 m and 10 m heads. Turbulence slightly increases the lift on the 10 m head, decreasing by 0.38% for the 5 m head and by 0.41% for the 7.5 m head. At the same time, it can be seen that the lift coefficients for the 5 m and 7.5 m tail cars slightly decrease with increasing turbulence while the side force coefficients slightly increase. The more obvious changes are the increase of 122.26% in the lift coefficient and the decrease of 46.16% in the side force coefficient for the 10m tail car. Therefore, under the 15\({}^{\circ}\) crosswind, the increase in turbulence enhances the safety of the train, especially the tail car enhances the stability of the train under the effect of turbulence, which is contrary to the expected conclusion.  
Table 4 shows the standard deviation (SD) of the time-average overturning moment coefficient of trains with different streamlined lengths. The variance in the overturning moment coefficient reflects the amplitude of overturning moment fluctuation. For the same nose length, the tail car has the maximum standard deviation, and the head car has the minimum standard deviation. The main reason may be that there is more vortex shedding occurring on the leeward side under the crosswind. In turbulent conditions, the standard deviation gradually decreases as the nose length increases. Compared with Case 4, the standard deviation of the head car's overturning moment coefficient, the middle car and the tail car in Case 6 decreased by 20.4%, 2.7% and 24.3%, respectively. Therefore, in turbulent conditions, the longer the nose of the head car is, the smaller the standard deviation of the tail car's overturning moment coefficient is, and the better the driving stability is.  
The longer the head length, the more pronounced the turbulent changes in the overturning moment of the head car. Turbulent intensity increases from 1% to 5%, and when the nose length increases from 5 m to 10 m, the SD increases by 4.95 times to 5.36 times. It is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Case 1** & **Case 2** & **Case 3** & **Case 4** & **Case 5** & **Case 6** \\ \hline Head car & 0.0196 & 0.0151 & 0.0156 & 0.1165 & 0.1000 & 0.0992 \\ Middle car & 0.0203 & 0.0210 & 0.0197 & 0.0896 & 0.0869 & 0.0989 \\ Tail car & 0.0195 & 0.0181 & 0.0243 & 0.0653 & 0.0634 & 0.0848 \\ \hline \hline \end{tabular}
\end{table}
Table 4: SDs of the overturning moment coefficient.  
interesting to note that the increase in turbulence changes the amplitude of the overturning moment fluctuation for a train with a 10 m head length from the original maximum SD for the tail car to the maximum SD for the head car.
# Resolving Gas Bubbles Ascending in Liquid Metal from Low-SNR Neutron Radiography Images
## 2 Image and Bubble Characterization
### Bubble Flow Properties  
Estimates indicate that Eotvos number is \(Eo\in[2.1;4.1]\) and the Reynolds number near the bubbles is within \(Re\in[10^{3};10^{4}]\). This corresponds to a flow regime wherein bubbles have oscillating elliptic shapes [8; 24; 43; 59]. Bubbles have equivalent diameters of \(d_{\rm b}\in[6;8]\) mm and travel with varying velocities usually in the [20; 40] cm/s range [43]. Such velocities and dynamic phase boundaries dictate the high acquisition frame rate required for physical analysis. A frame rate of 100 FPS was considered an optimal trade-off for the experiments covered herein, in that one already avoids significant motion blur about the bubbles while still maintaining manageable image SNR.  
Bubbles ascend in vertical chains from the bottom of the gallium vessel to the free surface where they exit the liquid metal volume. Bubbles generally exhibit non-uniformly accelerated motion depending on the applied magnetic field and flow rate. Bubble trajectories are mostly planar zigzags (plane parallel to the largest container face) with slight out-of-plane perturbations that intensify with gas flow rate. Bubble collisions, coalescence or breakup do not occur for image sequences considered in this paper, but rather chains with closely packed bubbles manifest for higher flow rates. In the future, the methodology developed herein will also be applied for even higher flow rate cases where bubble collisions do take place. Here we focus on capturing the bubble shape variations and positions.
## 3 Results
### Three-Factor Eating Questionnaire (TFEQ-13) Results
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{2}{c}{**High Adherence to UE**} & \multicolumn{2}{c}{**Moderate Adherence to UE**} & \multicolumn{2}{c}{**Low Adherence to UE**} \\ \cline{2-5}  & **n** & **OR (CI 95\%),** & **n** & **OR (CI 95\%),** & **n** & **OR (CI 95\%),** \\  & **p** & **n** & **p** & **n** & **p** \\ \hline Emotional Stability & & 1.36 (0.96; 1.91), & & 1.51 (1.06; 2.14), & & 0.51 (0.36; 0.72), \\
3 rd tercile 2  & 99 & \(p=0.08\) 3  & 88 & \(p<0.05\) * 3  & 73 & \(p<0.001\) * 3  \\ Emotional Stability & & 0.80 (0.57; 1.12), & & 0.67 (0.47; 0.96), & & 1.75 (1.26; 2.43), \\
1 st tercile (Neuroticism) 1  & 80 & \(p=0.20\) 3  & 63 & \(p<0.05\) * 3  & 117 & \(p<0.001\) * 3  \\ Emotional Stability & & \(p=0.30\) 4  & & \(p=0.06\) 4  & & \(p<0.01\) * 4  \\ Emotional Stability & & 0.80 (0.57; 1.12), & & 0.67 (0.47; 0.96), & & 1.75 (1.26; 2.43), \\
1 st tercile (Neuroticism) 1  & 80 & \(p=0.20\) 3  & 63 & \(p<0.05\) * 3  & 117 & \(p<0.001\) * 3  \\ Emotional Stability & & \(p=0.30\) 4  & & \(p=0.06\) 4  & & \(p<0.01\) * 4  \\ Emotional Stability & & 1.06 (0.76; 1.48), & & 0.87 (0.62; 1.23), & & 1.07 (0.77; 1.48), \\
3 rd tercile 1  & 100 & \(p=0.73\) 4  & & \(p=0.44\) 3  & 111 & \(p=0.70\) 3  \\ Openness to Experiences & & 1.08 (0.77; 1.51), & & 0.87 (0.61; 1.23), & & 1.05 (0.76; 1.47), \\
3 rd tercile 2  & 100 & \(p=0.66\) 3  & 80 & \(p=0.43\) 3  & 111 & \(p=0.76\) 3  \\  & & \(p=0.69\) 4  & & \(p=0.48\) 4  & & \(p=0.76\) 4  \\ \hline  & \multicolumn{2}{c}{High Adherence to CR} & \multicolumn{2}{c}{Moderate Adherence to CR} & \multicolumn{2}{c}{Low Adherence to CR} \\ \hline Extraversion & & 1.02 (0.70; 1.49), & & 0.82 (0.58; 1.16), & & 1.19 (0.85; 1.67), \\
3 rd tercile 1  & 65 & \(p=0.93\) 3  & 73 & \(p=0.27\) 3  & 88 & \(p=0.32\) 3  \\  & & \(p=0.93\) 4  & & \(p=0.79\) 4  & & \(p=0.79\) 4  \\ Extraversion & & 1.00 (0.74; 1.35), & & 0.83 (0.58; 1.17), & & 1.21 (0.86; 1.69), \\
3 rd tercile 2  & 65 & \(p=0.99\) 3  & 73 & \(p=0.28\) 3  & 88 & \(p=0.28\) 3  \\  & & \(p=0.99\) 4  & & \(p=0.71\) 4  & & \(p=0.71\) 4  \\  & & 0.93 (0.65; 1.31), & & 1.05 (0.76; 1.46), & & 1.02 (0.71; 1.45), \\
3 rd tercile 1  & 92 & \(p=0.67\) 3  & 118 & \(p=0.76\) 3  & 121 & \(p=0.92\) 3  \\  & & \(p=0.89\) 4  & & \(p=0.93\) 4  \\  & & 0.91 (0.64; 1.29), & & 1.06 (0.76; 1.48), & & 1.03 (0.74; 1.43), \\
3 rd tercile 2  & 92 & \(p=0.59\) 3  & 118 & \(p=0.72\) 3  & 121 & \(p=0.88\) 3  \\  & & \(p=0.80\) 4  & & \(p=0.90\) 4  & & \(p=0.94\) 4  \\ Conscientiousness & & 0.67 (0.47; 0.96), & & 0.95 (0.69; 1.32), & & 1.49 (1.07; 2.07), \\
3 rd tercile 1  & 75 & \(p<0.05\) * 3  & 106 & \(p=0.77\) 3  & 126 & \(p<0.05\) * 3 [FOOTIntensified extraversion increased the probability of a high adherence to emotional eating (EE) by 76%, while agreeableness increased it by 61%. Conscientiousness and emotional stability successively increased the chance of a high adherence to the emotional eating behaviour by almost 3 times and 2 times, respectively. Moreover, increased neuroticism reduced the likelihood of a high adherence to emotional eating (EE) by 39%.
# ODGNet: Robotic Grasp Detection Network Based on Omni-Dimensional Dynamic Convolution
## 3 Method
### Network Architecture  
The overall architecture of the ODGNet is shown in Figure 2. It consists of an input layer, an encoder, a grasping region enhancement fusion layer, a primary feature fusion layer, a decoder, and an output layer. In the input layer, the input data are multimodal data consisting of RGB images and depth images, where the dimensions of the input RGB image are 224 \(\times\) 224 \(\times\) 3 and the shape of the input depth image is 224 \(\times\) 224 \(\times\) 1. Aiming to make full use of the information of the RGB image and the depth image, we concatenate the RGB image and the depth image in the channel dimension to obtain the RGB-D image, which have the dimensions 224 \(\times\) 224 \(\times\) 4.  
Figure 2: Overall architecture of ODGNet.  
The encoder consists of three omni-dimensional dynamic convolution layers for feature extraction; the size of the convolution kernel for the first omni-dimensional dynamic convolution is 9 \(\times\) 9, and the size of the convolution kernel for the second and third omni-dimensional dynamic convolution is 4 \(\times\) 4.  
After going through the encoder, a feature map \(\text{F}_{\text{in}}\) with the dimensions 56 \(\times\) 56 \(\times\) 128 is obtained. In the grasping region feature enhancement fusion stage, three successive GRFFE modules are passed. On the one hand, this enhances the ability of the network to learn the features of the grasping region, and on the other hand, it suppresses the features of the non-grasping region, and outputs the 56 \(\times\) 56 \(\times\) 128 feature map \(\text{F}_{\text{out}}\).  
In the feature reconstruction stage, the primary pixel features of the encoder are fused with the high-level semantic features of the decoder through the primary feature fusion layer. This further improves the grasping region feature representation. Afterwards, the size of the feature map is restored to the input image size by transposed convolution, and the feature map \(\text{D}_{3}\) with the dimensions 224 \(\times\) 224 \(\times\) 32 is output.  
Finally, in the output layer, the reconstructed feature \(\text{D}_{3}\) is used as the input to output the grasping quality map \(Q\), the grasping angle map \(\Phi\), and the grasping width map \(W\). The final grasp \(\overset{\sim}{g}\) was obtained through three grasping maps.
# Minimization of Average Peak Age of Information for Timely Status Updates in Two-Hop IoT Networks
## 8 Conclusions  
In this paper, we addressed the minimization of the average PAoI in a two-hop IoT network employing joint packet coding, with the aim of achieving timely status updates. To this end, we derived a closed-form expression for the average PAoI and jointly optimized the blocklengths and the group size. Numerical results demonstrate that the proposed optimization reduces the average PAoI compared to the existing approach, with more substantial reductions observed under lower SNR conditions.  
We validated our analysis using Monte Carlo simulations in MATLAB 2024. While this confirms the accuracy of our analytical results, it serves as an initial step and requires a further extension to adequately capture real-world system behaviors. As a next step, we plan to evaluate the proposed scheme using dedicated network simulators (e.g., ns-3) that account for factors such as protocol stack operations, traffic patterns, and channel fading.  
For future work, we also plan to extend the system model to more practical scenarios, including heterogeneous sensors located at varying distances from the sink node and full-duplex operation at the sink node. In addition, we aim to extend the model to allow a different number of sensors per group, which, in turn, raises fairness considerations. We expect the analysis in this paper to provide a foundation for these extensions.  
Conceptualization, Y.K.; methodology, J.-H.C. and Y.K.; software, Y.K.; validation, J.-H.C.; formal analysis, Y.K.; investigation, J.-H.C. and Y.K.; writing--original draft preparation, Y.K.; writing--review and editing, J.-H.C.; visualization, J.-H.C.; funding acquisition, J.-H.C. and Y.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1F1A1060743 and No. RS-2023-00279714).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The original data presented in this study are openly available in FigShare at [[https://doi.org/10.6084/m9.figshare.29322113](https://doi.org/10.6084/m9.figshare.29322113)]([https://doi.org/10.6084/m9.figshare.29322113](https://doi.org/10.6084/m9.figshare.29322113)).  
**Conflicts of Interest**: The authors declare no conflicts of interest.
## 2 Materials and Methods
### OLE Characterization  
A high-performance liquid chromatography analysis (HPLC) was carried out to identify and quantify the major phenolic compounds of the OLE using a slightly modified method developed in a previous study [24]. The retention times and UV absorbance spectra of phenolic compounds present in OLE were compared with those of the commercial standard and quantified at 278 nm, using p-hydroxyphenyl acetic acid as the internal standard, according to the previously reported method [9]. Sample concentrations were determined by linear regression. For each calibration curve, the correlation coefficients were >0.99. HPLC analysis was performed with a HPLC instrument (Thermo Finnigan-Spectra System SCM1000) equipped with a Spectra System P2000 (Pumps), Spectra System UV2000, set to 280 nm, and using a Phenomenex Gemini reverse-phase C18 column (250 \(\times\) 4.6 mm, 5 \(\upmu\)m particle size; Phenomenex, Castel Maggiore, Italy). The mobile phase was a mixture of H\({}_{2}\)O/acetic acid (97.5:2.5 \(v/v\)) (A) and ACN (B). A linear gradient was run from 5% (B) to 25% (B) in 20 min; it changed to 50% (B) during 20 min (40 min total time); in 10 min it changed to 80% (B) (50 min total time), after re-equilibration in 5 min (55 min, total time) to initial composition. The flowrate was 1 mL/min, and the injected volume was 50 \(\upmu\)L. Samples were injected as a mixture of methanol/PBS (1:1 \(v/v\)). To assess the phenol content, 10 mg of OLE powder was dissolved in PBS and therefore analyzed with HPLC. The amount of phenolic compounds incorporated inside the polymer fibers was investigated using 4 cm\({}^{2}\) square meshes dissolved in MeOH, and the resulting solution was analyzed by HPLC.  
The total phenolic (TP) content was determined using the Folin-Ciocalteu method and gallic acid as the standard equivalent (ug GAE /mg), following a reported procedure [25]. About 10 mg of OLE were dissolved in 10 mL of CH\({}_{3}\)OH-H\({}_{2}\)O (80:20 \(v/v\)), and then, an aliquot of this solution (1 mL) was mixed with 0.25 mL of Folin-Ciocalteu reagent and 1.5 mL of Na\({}_{2}\)CO\({}_{3}\) (20% \(w/v\)). After purified water was added to reach the volume of 10 mL, the resulting mixture was kept for 45 min at the controlled temperature of 25 \({}^{\circ}\)C. Spectrophotometric analysis was performed at \(\uplambda=725\) nm.
## 3 Results
### Experimental Comparison
#### 3.3.2 Model Evaluation
Finally, to more clearly demonstrate the effectiveness of each model, we selected data from the temperature sensor points that had the most corrupted data for evaluation, as shown in Figure 15, which displays 500 data points. It can be observed that several basic models performed averagely and exhibited significant fluctuations, indicating high sensitivity to data and inability to adapt to new test data. In contrast, Transformer, GRU, CNN-BiLSTM-Attention, and the proposed model in this paper all achieved relatively good results. Particularly, the proposed model shows a more appropriate prediction of the detailed trend of the data. The results indicate that our proposed model has better generalization performance and practicality.
## 4 The Architecture of IoT-Enabled Smart City
### Sigfox Protocol  
Sigfox [97] adopts an ultra-narrowband (UNB) technology where devices communicate directly to the base station in a star-like topology. Multiple base stations are available for data collection, making it a reliable network. Most devices are used for uplink communication, and tiny downlink channels control the device from gateways. Sigfox network supports a message payload capacity of up to 12 bytes and employs a technique where the same payload message can be transmitted multiple times for robust reception. The frequency of messages a device can transmit daily can be customized based on the application's specific requirements. The network coverage of Sigfox is estimated to reach up to 10 km in urban areas and 50 km in rural areas. Building, installing, and maintaining the low-powered network lies with Sigfox Network Operators (SNO). They are also responsible for business development and maintaining/upgrading the reference architecture if necessary [97].
## 3 Material and Methods
### Modeling  
In this study, K-means clustering was used to group 11 clusters according to the characteristics of the data, and each cluster was augmented to 85 data samples using VAE to solve the class imbalance problem (refer to Section 3.2).  
To determine the significance of utilizing data samples that solved the class imbalance problem and the optimal number of augmentations, linear regression was used to analyze the change in performance according to the number of augmentations and method of augmentation (refer to Table 3). The changes in performance based on regularization terms were compared and analyzed using linear regression, Lasso regression, Ridge regression, and Elastic-Net regression. We also evaluated the performance with ensemble models, such as AdaBoost regression, Extra Trees regression, Random Forest regression, and XGBoost regression.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Variables** & **VIF** \\ \hline Climate & Cooling degree day & 1.1 \\ \hline \multirow{2}{*}{Prices} & Dubai crude oil prices & 8.7 \\  & International gasoline (95 RON) prices & 8.8 \\ \hline \multirow{2}{*}{Stocks} & FFR & 1.4 \\  & USDX & 4.4 \\  & S\&P 500 \\ \hline \multirow{2}{*}{Economy} & PPI fluctuation rate & 1.8 \\  & NSI & 2.1 \\ \hline \multirow{2}{*}{Policy} & GPR & 1.8 \\  & Fuel tax & 1.7 \\ \hline \multirow{2}{*}{Management} & Gasoline inventory at the gas station & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Significant independent variables derived from VIF.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Variables** & **Point** \\ \hline Climate & Cooling degree day & \(t-12\) \\ \hline \multirow{2}{*}{Prices} & Dubai crude oil prices & \(t-3\) \\  & International gasoline (95 RON) prices & \(t-1\) \\ \hline \multirow{2}{*}{Stocks} & FFR & \(t-2\) \\  & USDX & \(t-1\) \\  & S\&P 500 & \(t\) \\ \hline \multirow{2}{*}{Economy} & PPI fluctuation rate & \(t-2\) \\  & NSI & \(t-1\) \\ \hline \multirow{2}{*}{Policy} & GPR & \(t-1\) \\  & Fuel tax & \(t\) \\ \hline \multicolumn{2}{c}{Management} & Gasoline inventory at the gas station & \(t-1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The optimal time points for impact of each variable from VIF, used as independent variables for predicting gasoline orders at gas stations in South Korea.
###### Abstract  
Purpose: This study aims to create a deep learning (DL) model capable of accurately delineating the ventricles, and by extension, the periventricular space (PVS), following the 2021 EPTN Neuro-Oncology Atlas guidelines on T1-weighted contrast-enhanced MRI scans (T1 CE). The performance of this DL model was quantitatively and qualitatively compared with an off-the-shelf model. Materials and Methods: An nnU-Net was trained for ventricle segmentation using both CT and T1 CE MRI images from 78 patients. Its performance was compared to that of a publicly available pretrained segmentation model, SynthSeg. The evaluation was conducted on both internal (N = 18) and external (n = 18) test sets, with each consisting of paired CT and T1 CE MRI images and expert-delineated ground truths (GTs). Segmentation accuracy was assessed using the volumetric Dice Similarity Coefficient (DSC), 95 th percentile Hausdorff distance (HD95), surface DSC, and added path length (APL). Additionally, a local evaluation of ventricle segmentations quantified differences between manual and automatic segmentations across both test sets. All segmentations were scored by radiotherapy technicians for clinical acceptability using a 4-point Likert scale. Results: The nnU-Net significantly outperformed the SynthSeg model on the internal test dataset in terms of median [range] DSC, 0.93 [0.86-0.95] vs. 0.85 [0.67-0.91], HD95, 0.9 [0.7-2.5] mm vs. 2.2 [1.7-4.8] mm, surface DSC, 0.97 [0.90-0.98] vs. 0.84 [0.70-0.89], and APL, 876 [407-1298] mm vs. 2809 [2311-3622] mm, all with \(p<0.001\). No significant differences in these metrics were found in the external test set. However clinical ratings favored nnU-Net segmentations on the internal and external test sets. In addition, the nnU-Net had higher clinical ratings than the GT delineation on the internal and external test set. Conclusions: The nnU-Net model outperformed the SynthSeg model on the internal dataset in both segmentation metrics and clinician ratings. While segmentation metrics showed no significant differences between the models on the external set, clinician ratings favored nnU-Net, suggesting enhanced clinical acceptability. This suggests that nnU-Net could contribute to more time-efficient and streamlined radiotherapy planning workflows.  
deep learning; nnUNet; autocontouring; ventricles; periventricular space; neuro-oncology
###### Abstract  
To enhance the safety of grid operations, this paper proposes a high-precision short-term photovoltaic (PV) power forecasting method that integrates information from surrounding PV stations and deep learning prediction models. The proposed method utilizes numerical weather prediction (NWP) data of the target PV station and highly correlated features from nearby stations as inputs. This study first analyzes the correlation between irradiance and power sequences and calculates a comprehensive similarity index based on distance factors. Stations with high-similarity indices are selected as data sources. Subsequently, Bayesian optimization is employed to determine the optimal data fusion ratio. The selected data are then used to model power predictions through the convolutional long short-term memory with attention (Conv-LSTM-ATT) deep neural network. Experimental results show that the proposed model significantly outperforms three classical models in terms of forecasting accuracy. The data fusion strategy determined by Bayesian optimization reduces the root mean square error (RMSE) of the test set by 20.04%, 28.24%, and 30.94% under sunny, cloudy, and rainy conditions, respectively.  
photovoltaic power forecasting; deep learning prediction models; surrounding PV stations; Conv-LSTM-ATT neural network Article