esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Units** & \begin{tabular}{c} **Control** \\ **Group** \\ \end{tabular} & \begin{tabular}{c} **Case** \\ **Group** \\ \end{tabular} & \
\begin{tabular}{c} **\(p\)** \\ \end{tabular} &
\begin{tabular}{c} **MD \(\pm\) SD** \\ \end{tabular} \\ \hline \multicolumn{5}{c}{Touch Down} \\ Thorax Flexion & deg & 19.91 \(\pm\) 9.53 & 24.04 \(\pm\) 8.07 & 0.253 & \(-\)4.13 \(\pm\) 3.52 \\ Hip Flexion & deg & 16.94 \(\pm\) 16.65 & 12.04 \(\pm\) 13.02 & 0.354 & 4.90 \(\pm\) 5.18 \\ Knee Flexion & deg & 25.73 \(\pm\) 12.99 & 23.56 \(\pm\) 19.93 & 0.758 & 2.17 \(\pm\) 6.95 \\ \multicolumn{5}{c}{Deepest Position} \\ Max Hip Extension Torque & Nm/kg & 1.12 \(\pm\) 0.53 & 1.13 \(\pm\) 0.36 & 0.954 & \(-\)0.01 \(\pm\) 0.18 \\ Max Knee Extension Torque & Nm/kg & 1.49 \(\pm\) 0.83 & 1.39 \(\pm\) 0.38 & 0.697 & 0.10 \(\pm\) 0.25 \\ Max Ankle Extension Torque & Nm/kg & 1.20 \(\pm\) 0.71 & 0.86 \(\pm\) 0.33 & 0.124 & 0.34 \(\pm\) 0.21 \\ Max Knee Valgus Torque & Nm/kg & \(-\)0.06 \(\pm\) 0.27 & \(-\)0.79 \(\pm\) 2.83 & 0.402 & 0.74 \(\pm\) 0.86 \\ \multicolumn{5}{c}{General} \\ Jump Height & cm & 22.28 \(\pm\) 6.47 & 22.29 \(\pm\) 3.81 & 0.995 & \(-\)0.01 \(\pm\) 2.07 \\ Depth of countermovement & cm & 24.82 \(\pm\) 6.74 & 24.93 \(\pm\) 5.84 & 0.964 & \(-\)0.11 \(\pm\) 2.42 \\ Max Hip Flexion & deg & 54.10 \(\pm\) 25.02 & 66.92 \(\pm\) 13.98 & 0.117 & \(-\)12.82 \(\pm\) 7.88 \\ Max Knee Flexion & deg & 80.01 \(\pm\) 25.03 & 92.04 \(\pm\) 13.38 & 0.136 & \(-\)12.03 \(\pm\) 7.79 \\ Max Ankle Flexion & deg & 27.85 \(\pm\) 7.30 & 29.28 \(\pm\) 4.35 & 0.550 & \(-\)1.42 \(\pm\) 2.34 \\ Peak vGRF & N/kg & 20.11 \(\pm\) 6.29 & 15.44 \(\pm\) 4.97 & 0.049 \({}^{\star}\) & 4.67 \(\pm\) 2.25 \\ Hip Rotation at IC & deg & \(-\)4.54 \(\pm\) 3.79 & \(-\)5.94 \(\pm\) 3.11 & 0.321 & 1.40 \(\pm\) 1.38 \\ Knee Flexion at IC & deg & 25.73 \(\pm\) 13.99 & 23.56 \(\pm\) 19.93 & 0.758 & 2.17 \(\pm\) 6.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between control group and case group during bilateral drop vertical jump.
## 5 System Evaluation  
### Prototype Development  
In order to evaluate the advantages of the proposed iAPR system, a prototype of the iAPR was implemented by adopting the above-mentioned system evaluation scheme. In particular, the iAPR server was developed by utilizing the Krpano 1.19 framework [49], which provides stable and open interfaces for VP applications. Krpano 1.19 is certified to adapt to the latest version of webvr engine in various web browsers. Moreover, the Krpano enables advanced web-app programming languages such as Hypertext Markup Language version 5 (HTML5), Cascading Style Sheets version 3 (CSS3), eXtensible Markup Language (XML), and JavaScript. On the other hand, mySQL server [50] is used for iAPR database management. The iAPR system was installed on a Raspberry Pi Model B [51] equipped with a 64 GB SD card. The Raspberry Pi server connects to a WiFi access point, which provides both local and Internet access for user devices; see Figure 4. In the user devices, the iAPR client consists of a built-in web browser for permanent 360 VP scenario access and an NGRAIN interface [52] for 3D anatomy popup window per user demands. The iAPR client is installed on Windows OS. Technical details of the iAPR system setup are summarized in Table 1. For VP data preparation, Samsung Gear 360 camera [53] is used to capture real scenes from construction sites while multimedia videos are uploaded to either an authorized YouTube channel or a local server to get their links.  
Figure 4: The iAPR system prototype implementation.
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 2 Background Research
### Vision Transformers for Object Detection  
ViTs have emerged as a powerful tool for capturing global context and long-range dependencies in visual data. Early implementations in object detection, such as Pyramid Vision Transformer (PVT) and E-PVT [21], demonstrated their potential to outperform traditional CNN-based methods. However, standard ViTs lack inherent mechanisms to address rotational invariance, a critical requirement for oriented object detection.  
To bridge this gap, hybrid architectures combining ViTs with convolutional layers have been explored. For example, POE [22] integrates convolutional inductive biases to enhance local feature extraction while preserving the global representation capabilities of ViTs. RASST advances this concept by introducing rotationally aware positional encodings and patch embeddings, enabling it to effectively handle objects at arbitrary orientations. Additionally, the inclusion of adaptive rotational convolutions allows RASST to capture both global and local patterns with high precision, making it particularly well-suited for oriented object detection tasks.
# Enhancing Weather Forecasting Integrating LSTM and GA
## 2 Related Work
[25] presented a transformer-based model with wavelet transform for wind speed and wind energy forecasting, showing superior performance compared to a baseline LSTM model. However, [PERSON] et al. [26] found that the performance of transformer-based models varied depending on the specific forecasting task and the data provided, with the LSTM model outperforming all others in predicting onshore wind and photovoltaic energy. Recurrent neural networks (RNNs) are widely used in weather forecasting due to their computational power, sophisticated algorithms, and storage capacity [27]. However, RNNs have a drawback when it comes to handling long-term correlations, as they suffer from gradient loss and explosion [28]. One solution to this problem is to use LSTMs, which are a type of RNN-based model. They excel at solving both short- and long-term problems by effectively predicting and storing temporal data [29; 30]. [PERSON] et al. [31] proposed an LSTM method to efficiently predict solar radiation 15 min ahead in certain areas with high intermittency. The proposed model outperformed other models such as auto-regressive or persistence by about 40%, improving the accuracy and efficiency of the model. Similarly, short-term wind energy forecasts are prone to accuracy problems due to the intermittent nature of wind [32]. Thus, an LSTM was proposed to deal with linear and non-linear components of time series of wind power generated by offshore turbines. The proposed model increased the prediction accuracy by 13.2% compared to a baseline model. Finally, in the agricultural sector, LSTM networks are also being considered for weather forecasting. For example, in [PERSON] et al. [33] an LSTM was used to accurately determine the amount of rainfall needed to increase crop yields and reduce agricultural costs. The model takes into account weather variables such as temperature, humidity, and wind speed to make predictions and reports an accuracy of 76% in predicting rainfall.
# Using a Machine Learning Method to Predict the Penetration Depth of a Gravity Core
## 6 Conclusions  
In this study, a machine learning model using an MLP neural network was applied to predict the penetration depth of a gravity core. A database of 75 gravity core penetration depths from both real sampling at sea and physical model test data was used to generate the datasets for modeling, considering six penetration depth factors. The models were validated using the _MSE_, _MAE_, _EVS_, and R-square methods. The results show that the proposed machine learning model achieved great accuracy in predicting the gravity core penetration depth (test set accuracy: _EVS_ = 0.95, _MAE_ = 0.26, _MSE_ = 0.38, _R_\({}^{2}\) = 0.94). Furthermore, in this study, we used three analytical solution models of gravity core penetration depth to predict the same cases. The results show that the machine learning model is superior to the traditional analytical solution models in terms of both the accuracy of the prediction results and the scientific nature of the computational process. Thus, it can be reasonably concluded that the proposed machine learning model can be used to achieve better penetration depth prediction. However, many factors affect the accuracy of the machine learning model: problem type, selection of influential factors, quality and quantity of data, and selection of machine learning models. Therefore, it is better to collect more high-quality data and select a moderate machine learning model and influential factors to obtain a more accurate machine learning model.  
Conceptualization, Y.S. ([PERSON]); methodology, X.D.; program, X.D.; validation, Y.S. ([PERSON]) and Z.X.; resources, Q.Z.; data curation, Q.Z.; writing--original draft preparation, X.D.; writing--review and editing, Q.Z.; visualization, X.D.; supervision, Y.S. ([PERSON]); project administration, X.D and Y.S. ([PERSON]); funding acquisition, X.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China under contract No. 42102326, the Shandong Provincial Natural Science Foundation, China, under contract No. ZR2020 QD073, and the National Natural Science Foundation of China under contract No. 41876066.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data that support the findings of this study are available from the corresponding author upon reasonable request.  
**Acknowledgments:** Thanks to the developers of scikit-learn for their contributions to the use and promotion of machine learning models.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Prestress Loss Tests
### Modal Analysis Results  
The OMA method was adopted to investigate prestress loss conditions. Using the Frequency Domain Decomposition (FDD) method ([39; 40]), the singular values of the power spectral density were calculated to reveal the energy magnitude of the dominant frequencies with the peak picking (PP) method. This approach allows us to determine the natural frequencies corresponding to the predominant mode shapes. To facilitate this process in the OMA analysis, a Python (2022) module called PyOMA ([41]) was applied.  
Figure 16 presents the first three modal frequencies and their corresponding mode shapes for each test. The differences in Mode 1 and Mode 3 are evident. The lower prestress is associated with lower modal frequency. However, the mode shape curves are spline interpolations designed to fit the bending motion of the bridge. Consequently, changes in  
Figure 14: Correlation between velocity variation of vibration signals and bridge temperature in continuous monitoring.  
Figure 15: The prestressing force setup in short-term measurements.  
the amplitude of the mode shapes are not reliable for identifying prestressing levels. This phenomenon will be validated through velocity studies in the subsequent section of the paper, which will demonstrate the impact of material changes.  
As shown in Figure 17, lower prestress results in a significant reduction in frequency, with test #1 serving as the reference for comparison. However, changes in the first mode do not consistently decrease or increase in the same proportion with adjustments in post-tensioning forces. Notably, there is a drop between tests #5 (approximately \(-3\%\)) and #6 (approximately \(-5\%\)) in the first mode, while significant drops occur between tests #4 (approximately \(-4\%\)) and #6 (approximately \(-10\%\)) in the second and third modes. These observations suggest that cracks began to open when the force dropped below 300 kN, highlighting the combined effects of reduced prestress and existing cracks. Subsequently, the cracks tended to close as the force increased in tests #7 to #11. However, the modal information is insufficient to identify the temperature effects and damage location.  
Figure 16: The first three modes obtained by using the OMA analysis.
# Susceptibility and Remanent Magnetization Estimates from Orientation Tools in Borehole Imaging Logs
###### Abstract  
Orientation tools in borehole imaging logs acquire magnetic information that is currently used for spatial and geographical orientation of the images. We propose to use this magnetic field information to estimate both magnetic susceptibility and remanent magnetization of rocks inside wells. Measurements of these magnetic parameters are not often available in hydrocarbon exploration to support forward modeling of magnetic data, an interpretation tool that has played important role in the exploration risk reduction in the Pre-Salt prospects of Campos Basin, Brazil. The acquired magnetic data requires corrections for tool rotation and diurnal variation of the Earth's magnetic field before calculation. Then, using a set of simple equations and reasonable assumptions we were able to estimate the magnetic susceptibility of carbonates and basalts, as well as the remanent magnetization of the basalts, from a Pre-Salt well in Campos Basin. When compared to susceptibility values measured in laboratory for the same rock interval, our results show a significant match. This promising result shows the importance of our methodology in providing reliable information that can minimize uncertainties in forward modeling of magnetic data, which contributes to reduction of hydrocarbon exploration risks. Given that direct susceptibility and remanence measurements require oriented samples, a complex and expensive operation in wells, our results offer this rock information without any extra costs since imaging logs are commonly acquired in exploration wells. Besides its use in hydrocarbon exploration, our methodology can be applied to mineral exploration where magnetic susceptibility is an important property for rock identification.  
magnetic susceptibility; borehole imaging logs; hydrocarbon exploration Article
## 4 Experimental Results and Analysis
### Results and Analysis  
#### 4.3.1 Data Collection  
The point cloud data collection presented in this study was conducted using the FARO FOCUS 150 3D laser scanner. Six scanning stations were deployed, with a resolution of 11.0 million points, and color scanning was employed, as depicted in Figure 12.  
While preserving the geometric features of the point cloud model, uniform grid-based downsampling was employed. The resulting downsampled point cloud data for the conference room are shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Point Transformer (k = 64) (\%)** & **Feature-Enhanced Point Transformer (\%)** \\ \hline mIoU & 70.8 & 71.3 \\ \hline Ceiling & 95 & 95.3 \\ Floor & 98.5 & 98.6 \\ Wall & 88.3 & 89.1 \\ Beam & 0 & 0.1 \\ Column & 38 & 37.8 \\ Window & 64.4 & 63.5 \\ Door & 74.3 & 74.1 \\ Table & 89.1 & 89.3 \\ Chair & 84.4 & 85.6 \\ Sofa & 74.3 & 76.1 \\ Bookcase & 80.2 & 81.3 \\ Board & 76 & 77.1 \\ Clutter & 58.3 & 58.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific class intersection over union (IoU).  
Figure 11: Semantic segmentation visualization: (**a**) real data; (**b**) transformer; (**c**) feature-enhanced transformer; (**d**) label.
# Comparing Sentinel-2 and Landsat 8 for Burn Severity Mapping in Western North America
## 3 Results
### High-Severity Patch Interior and Severity Class Estimates between Fires
Comparing the same Sentinel categorical severity maps for a fire at 10 m resolution to a reduced resolution version at 30 m resulted in small but consistent shifts in the percent of a fire mapped at a given severity class. The higher resolution maps decreased the amount of moderate severity by an average of 1.0% across fires, increasing the amount predicted as low- and high-severity (all differences significant, \(p<0.001\), Figure 2C). This effect is illustrated in Figure 2D, which shows the frequency histogram of the continuous RdNBR values for the 2016 Hayes Two fire when mapped at 10 and 30 m spatial resolutions. Compared to the 30 m distribution, the 10 m imagery has fewer pixels in the moderate-severity range, and instead has more pixels in the high- and low-severity range. Across all 26 fires, the mean reduction in area mapped at moderate-severity corresponds to 2443 ha (0.7% of forested area). Finally, high-severity patch interior estimates were significantly higher for the 30 m Sentinel severity maps than those mapped at 10 m (mean 4.2%, \(p<0.001\), Figure 2C).
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 6 Potential Usage and Applications  
The outcomes of this research paper laid the foundation for improving the responsiveness of the future mobile internet. The potential benefits, usage, and applications can be observed from the following three different perspectives.
## 6 Conclusions and Future Direction  
The objective of this research is to calculate overall sentiment from six classes of an image file. We have applied a very effective method for visual sentiment analysis that integrates a Convolutional Neural Network (CNN) with a Belief Rule-Based Expert System (BRBES). The CNN model is used to calculate the class-wise prediction probability, while the BRB triggers some particular rules for estimating the overall sentiment of the image. This integrated framework can be used to analyze the user's sentiment of social media platforms. In addition, this model can help to treat neurologically disorder patients.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline SVM & 0.54 & 0.53 & 0.54 & 0.53 \\ SVM (Hog) & 0.53 & 0.54 & 0.53 & 0.53 \\ Decision Tree & 0.67 & 0.67 & 0.67 & 0.67 \\ Classifier & 0.22 & 0.19 & 0.27 & 0.23 \\ VGG16 & 0.80 & 0.81 & 0.82 & 0.81 \\ VGG19 & 0.82 & 0.82 & 0.83 & 0.83 \\ ResNet50 & 0.81 & 0.82 & 0.82 & 0.82 \\ BRB-DL & 0.87 & 0.86 & 0.86 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparative evaluation of our proposed model against other models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fold** & **Training Accuracy** & **Validation Accuracy** & **Testing Accuracy** \\ \hline Fold-1 & 0.87 & 0.82 & 0.85 \\ Fold-2 & 0.92 & 0.79 & 0.82 \\ Fold-3 & 0.92 & 0.89 & 0.87 \\ Fold-4 & 0.95 & 0.87 & 0.86 \\ Fold-5 & 0.83 & 0.81 & 0.88 \\ Highest & 0.95 & 0.89 & 0.87 \\ Average & 0.89 & 0.84 & 0.85 \\ Standard Deviation & 0.047 & 0.042 & 0.023 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Cross validation.  
Although the prediction accuracy of each class in our experiment is promising, the model can be improved by increasing the accuracy of love and surprise classes.  
In the future, we have a plan to promote our system by adopting the BRBES-based adaptive Differential Evolution (BRBaDE) approach [39]. This can improve the prediction accuracy by using parameter and structure optimization.  
Conceptualization, S.N.Z., E.C., M.S.H., R.U.I., and K.A.; methodology, S.N.Z., E.C., M.S.H., and R.U.I.; software, S.N.Z. and E.C.; validation, M.S.H. and R.U.I.; formal analysis, S.N.Z. and E.C.; investigation, M.S.H., R.U.I., and K.A.; resources, S.N.Z., E.C., and M.S.H.; data curation, S.N.Z. and E.C.; writing--original draft preparation, S.N.Z., E.C., and M.S.H.; writing--review and editing, M.S.H. and K.A.; visualization, S.N.Z.; supervision, M.S.H. and K.A.; project administration, S.N.Z. and E.C. All authors have read and agreed to the published version of the manuscript  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results and Discussion  
### Germination Rate  
_Beta vulgaris_ germination results for control, NaOCl and PAW (using 5, 10 and 20 min of GAD treatment of water as PAW5', PAW10' and PAW 20') are summarized in Figure 2. Compared to the control, which had a rather high variability within samples, PAW treatment allowed for an increase in the average value of both \(G_{EN}\) and \(G_{C}\). The difference in germination energy was evident for PAW after 5 min of plasma treatment, for which the fraction of germinated seeds increased by 16%, performing better than sodium hypochlorite treatment. Similar \(G_{EN}\) results were obtained for a time of 10 min plasma treatment of water; however, this treatment time also allowed for an increase in \(G_{C}\), as opposed to PAW5', which had a shorter treatment time that accelerated initial growth followed by no germination of the remaining seeds. The best results were obtained for PAW with a treatment time of 20 min, for which all seeds from all samples germinated after 7 days. In the case of beetroot seeds, the highest ratio was obtained with the application of water treated with plasma for 20 min; this was also the case for carrot seeds in combination with sodium hypochlorite.  
The germination results obtained for _Daucus carota_ are shown in Figure 3.  
For the tested seeds, the germination of the control samples was low and did not exceed 10% even after 14 days. As the plasma treatment time increased, an increase in \(G_{EN}\) was evident, which more than doubled for a time of 20 min. However, the best results at the end of the test (14 days) were obtained for PAW, which undergone plasma treatment for shorter times (5 and 10 min), where the average \(G_{\mathrm{C}}\) value was almost three times higher than the control sample. For the tested seeds, the best results were obtained for the sodium hypochlorite treatment, although the results obtained from the different samples varied quite significantly.