esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Results of Ensemble Learning with Four Feature Transformation Methods for 2D Data  
The experimental results of our study are presented in Table 8 and Figure 6. We evaluated the performance of two single DL models (2D CNN and 2D FCN) and an ensemble method (2D EnCF) that combines these two models using four different feature transformation techniques (PR, GAF, SWT, and RP). The RP preprocessing technique yielded the best results for all three models, with the 2D EnCF ensemble method achieving an accuracy of 0.9528, outperforming the single 2D CNN and 2D FCN models. The 2D EnCF ensemble method consistently outperformed or matched the performance of the single 2D CNN and 2D FCN models across all preprocessing techniques. This suggests that combining multiple deep learning models can lead to improved performance. The results demonstrate that the ensemble method (2D EnCF) is superior to the single DL models (2D CNN and 2D FCN) in terms of performance and robustness. The combination of multiple models can leverage their strengths and mitigate their weaknesses, leading to improved overall performance. Additionally, the choice of preprocessing technique substantially impacts the results of all three models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Feature**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1 Score**} \\  & \multicolumn{1}{c}{**Transformation**} & & & & \\ \hline
2D CNN & PR & 0.8208 & 0.8271 & 0.8208 & 0.8176 \\
2D FCN & PR & 0.8396 & 0.8410 & 0.8396 & 0.8384 \\
2D FCN & PR & 0.8396 & 0.8517 & 0.8396 & 0.8358 \\
2D CNN & GAF & 0.8679 & 0.8689 & 0.8679 & 0.8672 \\
2D FCN & GAF & 0.7925 & 0.8034 & 0.7925 & 0.7868 \\
2D CNN & RP & 0.9340 & 0.9340 & 0.9340 & 0.9339 \\
2D FCN & RP & 0.8774 & 0.8820 & 0.8774 & 0.8777 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of DL models on 2D data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Feature**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1 Score**} \\  & \multicolumn{1}{c}{**Transformation**} & & & & \\ \hline
2D CNN & PR & 0.8208 & 0.8271 & 0.8208 & 0.8176 \\
2D FCN & PR & 0.
# Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks
## 4 TPEG Framework
### Threshold Adjustment
#### 4.5.1 Node Level Adjustment  
At the node level, ordinary nodes send monitoring readings to RSUs so that an event-checking procedure would be triggered. Readings with a weight larger than \(\tau_{o}\) are sent to the RSU for further processing. Each node moving through the RSU coverage area would send a histogram-based sketch about its monitored readings to the RSU. A sketch \(sk\) is composed of tuples in the form:  
\[\{<(h_{1},h_{2}],n_{1}>,..,<(h_{i},h_{i+1}],n_{i}>,..,<(h_{k-1},h_{k}],n_{k}>\} \tag{12}\]  
where \((h_{i},h_{i+1}]\) is the range of weight and \(n_{i}\) is the number of readings whose weight is within the range. Sketches could be stored in any histogram like data structures. Suppose RSU expects to receive \(m\) readings for the event-checking decision, then the threshold \(\tau_{o}\) is set as follows:  
\[\tau_{0}=argmin_{x}f(x)=\{\sum_{sk\in\Phi}\eta(sk,x)\geq m\},\ \ x\in\{h_{0},h_{1},..,h_{k-1}\} \tag{13}\]  
where \(\eta(sk,x)\) is the number of readings in sketch \(sk\) whose weight is larger than \(x\), \(\Phi\) is the set of sketches received at the RSU during the average duration of a node moving through the RSU. The node receiving the update message from the RSU would just reset the threshold \(\tau_{0}\).
###### Abstract  
Investigating the mechanism of sudden water inrush and mudflow in mountain tunnels is crucial for implementing preventive measures. Tunnel excavation through a fault or fractured zone can easily trigger sudden water inrush or mudflow. In this paper, the coupled lattice Boltzmann method (LBM) and discrete element method (DEM) were employed to reproduce the process of water and mud inrush in mountain tunnels. The failure of tunnel mud burst and water inrush involves a fluid-solid coupling process. A two-dimensional Boltzmann method for fluids and DEM for particles were utilized, with the coupled LBM-DEM boundary adopting the immersed moving boundary method. For simulating the water inrush process, a numerical model was established to replicate the flow of water particles within karst pipelines, featuring dimensions of 7 cm length, 4 cm width, and consisting of 100 particles. Particles are transported through water flow to the outlet of karst pipelines under hydraulic gradient loading. When the hydraulic gradient exceeds 6, the Darcy velocity gradually tends to be constant. As for simulating the mud inrush process, a numerical model was developed with dimensions of 5 cm length and 4 cm height, incorporating 720 randomly generated particles. The results demonstrated the successful reproduction of the evolution process encompassing three consecutive stages of tunnel mud-burst failure: initiation, acceleration, and stabilization. The occurrence of mud inrush disasters is attributed to combined action involving disaster-causing geotechnical materials, groundwater pressure, and tunnel excavation.  
mountain tunnel; lattice Boltzmann method; discrete element method; water inrush; sudden mud inrush
# Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis
## 3 Methodology  
This study proposes a multi-task learning aspect-based sentiment analysis model that uses adaptive semantic relative distance to integrate local contextual information into the model.  
Given a text \(S=\{w_{1},w_{2},...,w_{n}\}\), where n is the length of the text. ABSA model is able to extract the aspect terms in the text, \(A=\{a_{1},a_{2},\ldots,a_{m}\}\), where \(m\) is the length of the aspect terms, and determine their sentiment polarity, \(y_{p}\in\{Positive,\;Negative,\;Neutral\}\).  
This section introduces the structure of the A-ABSA model and the methods involved in it. The ATE module and the ASC module are introduced in order from bottom to top according to the order of the network hierarchy. The overall structure of the ABSA model is shown in Figure 2.  
A-ABSA uses BERT as the word embedding layer, which can effectively obtain contextual information. The aspect sentiment classification model structure refers to that proposed by [PERSON] et al. [22] on which adaptive semantic relative distance is added to solve  
Figure 2: Overall structure of ABSA.  
the problem of different sizes of semantic relative distance, see Section 3.2.2 for details. Aspect term extraction model structure classical model is BiLSTM+CRF, based on which CNN and gate mechanism are introduced to further extract word vector information, see Section 3.1 for details.  
A-ABSA model workflow: (i) input text and convert it into model input format; (ii) aspect term extraction, consisting of word embedding layer, convolutional layer, gated unit layer, Bi-LSTM layer, and CRF layer; (iii) output aspect term; (iv) transform aspect sentiment classification input format; (v) aspect sentiment classification, consisting of word embedding layer, CDM layer, MHSA layer, and interactive learning layer; (vi) determine sentiment polarity.
## 2 Materials and Methods
### Characteristics of the IRIDE Constellation
\begin{table}
\begin{tabular}{c c c} \hline _Resolution_ & _Description_ & _Note_ \\ \hline \multirow{4}{*}{**Spatial**} & \multirow{2}{*}{\(\sim\)2 m} & \(\sim\)1–3 m according to the bands and device SARs, optical, TIR. None of the information is still available for the hyperspectral. The coverage will be global with a particular focus on Italy, Europe, and the Mediterranean areas. \\ \hline \multirow{2}{*}{**Temporal**} & \multirow{2}{*}{\(<\)1 day} & from daily to multi-daily (8–12 times a day only for Italy and some EU and Mediterranean areas) \\ \hline \multirow{6}{*}{**Spectral**} & \multirow{6}{*}{depending on sensor typology} & Depending on each sensor. Multispectral with panchromatic and hyperspectral will be compatible with Sentinel-2 ([[https://sentinels.copernicus.eu/web/sentinel/missions/sentin](https://sentinels.copernicus.eu/web/sentinel/missions/sentin)]([https://sentinels.copernicus.eu/web/sentinel/missions/sentin](https://sentinels.copernicus.eu/web/sentinel/missions/sentin)) e12-instrument-payload/resolution-and-swatu *) and PRISMA respectively \\  & & ([[https://www.asit.eu/en/earth-science/prisms/](https://www.asit.eu/en/earth-science/prisms/)]([https://www.asit.eu/en/earth-science/prisms/](https://www.asit.eu/en/earth-science/prisms/)) *). While thermal would be probably close to SaFU/ missions ([[https://www.satellitevcom/](https://www.satellitevcom/)]([https://www.satellitevcom/](https://www.satellitevcom/)) *) within the Platino-2 platform powered by Stial ([[https://www.stiael.com/space/references-and-customers/earth-observation/](https://www.stiael.com/space/references-and-customers/earth-observation/)]([https://www.stiael.com/space/references-and-customers/earth-observation/](https://www.stiael.com/space/references-and-customers/earth-observation/)) *) for ASI. The SAR will probably operate in band X as reported by the contractor company \\  & & MefSensing ([[https://tentensing.com/it/matensering-selected-to-provide-the-sar-ayload-sensor-for-the-nox-mission-in-talys-irde-constellation/](https://tentensing.com/it/matensering-selected-to-provide-the-sar-ayload-sensor-for-the-nox-mission-in-talys-irde-constellation/)]([https://tentensing.com/it/matensering-selected-to-provide-the-sar-ayload-sensor-for-the-nox-mission-in-talys-irde-constellation/](https://tentensing.com/it/matensering-selected-to-provide-the-sar-ayload-sensor-for-the-nox-mission-in-talys-irde-constellation/)) *) and for sure will be on Platino-1 platform powered by Stialeal and ION platform for ASI within the NOX \\  & & Project. The SAR mission will acquire data in Stripmap, Spotlight, ScanSAR and TopSAR \\  & & mode. (* last access 20 January 2024) \\ \hline \multicolumn{2}{c}{**Radiometric**} & \multicolumn{1}{c}{\(>\)16 bits} & 16 bits integers or upper \\ \hline \end{tabular}
\end{table} 
Table 1: IRIDE program and resolution missions: a general overview. missions globally, enabling Earth observation in the thermal infrared from an orbit less than 400 km, thereby significantly enhancing the resolution of the acquired images.
## 5 Experimental Outcomes
### Best Model SHAP Interpretation  
The attained best-performing model has been subjected to analysis through the use of Shapley additive explanations (SHAP) [51] to determine feature impacts on model decisions. The feature reduction CNN models as well as the best constructed XGBoost model have been interpreted using the kernel and tree explainer techniques. SHAP utilized a game-theory-based approach to determine the impact each feature poses on model decisions. The analysis outcomes are graphically presented in Figure 8 for the CNN model and in Figure 9 for the XGBoost model.  
From the feature importance analysis of the CNN feature reduction model, it can be deduced that the tcp.time_delta presents the highest influence on model decisions, closely followed by mqtt.conack.flags.reserved. The third-highest importance is shown by the mqtt.msgtype feature. The remaining features that have a significant influence are mqtt.kalive and mqtt.retain. Following these features, a significant reduction in importance can be observed. Accordingly, a set of six features have been maintained as outputs for the CNN and inputs for the XGBoost model. The importance of these synthetic features is shown in Figure 9.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Problem/\(p\)-Values** & **RSA** & **GA** & **PSO** & **FA** & **SSA** & **WOA** & **COISHADE** \\ \hline XG-Binary & 0.042 & 0.003 & 0.018 & 0.037 & 0.04 & 0.027 & 0.031 \\ XG-Multi & 0.042 & 0.003 & 0.018 & 0.037 & 0.04 & 0.027 & 0.031 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Wilcoxon signed-rank test values exhibiting \(p\)-values for all three experiments (GIRSA vs. others.)  
Figure 7: Objective function KDE plots for binary and multiclass classification.  
Since the synthetic features have no direct interpretation of the real-world dataset, they are simply assigned a number. Synthetic feature 3 has the highest impact on the model decision, followed by features 1 and 5. Finally, a small impact can be observed in feature 0.  
Figure 8: SHAP analysis outcomes for best CNN model.  
Figure 9: SHAP analysis outcomes for best XGBoost model.  
Features 4 and 2 do not seem to notably influence model decisions, suggesting that further feature reduction may be performed while maintaining computational complexity.
###### Abstract  
Ineffective healing and treatment of foot ulcers can lead to an infection and gangrene of the wound area that ultimately results in the loss of the limb. The incidence of foot ulcers is higher in patients with diabetes, peripheral vascular disease and kidney disease. Accordingly, this study was undertaken to assess the ability of foot bathing in CO\({}_{2}\)-enriched water to heal foot ulcers. The design was a double-blinded, randomized, placebo-controlled study. Patients with at least one foot ulcer were randomized to receive either a treatment with bath therapy at 37 \(\pm\) 0.5 \({}^{\circ}\)C containing either 1000-1200 ppm CO\({}_{2}\)-enriched tap water (the intervention) or non-carbonated tap water at 37 \(\pm\) 0.5 \({}^{\circ}\)C (the control group). Treatment was conducted three times/week for 15 min per session for up to 16 weeks for a total of 48 treatment sessions. Before and at the end of every treatment month, wound size, wound area oxygenation and the ankle brachial index were measured. In addition, the McGill pain questionnaire was conducted. Blood was also collected at these time points (for a total of five collections) for the measurement of different biomarkers. While no significant differences (\(p<0.05\)) in the group/time interaction effect were observed, a clear separation within the wound area reduction/wound area/oxygenated Hb outcomes was seen between placebo (control) and treatment (CO\({}_{2}\)) group. This pilot study is suggestive that bathing in CO\({}_{2}\)-enriched water may accelerate the healing of foot ulcers.  
foot ulcer; CO\({}_{2}\)-enriched water; foot-bathing therapy +
Footnote †: journal: Applied Sciences  
**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Communication-Efficient Vertical Federated Learning
## 4 Experimental Setup
### Fitting and Selecting Components in PCA  
The local data of each of the clients is split into train (70%) and test set (30%). For the implementation of PCA the _sklearn.decomposition.PCA_ class has been used. The _fit()_ function is applied on the train set at each client end to compute the eigen vectors. PCA components are selected in such a way that 90% cumulative data variance is captured. Later, the _transform()_ function is used on both the train and test set to apply the projections of the eigen vectors obtained. The _transform()_ function on the train and test reduces the dimension of the original data while also explaining 90% variance.
# Mechanical Performance Characterization of Lignin-Modified Asphal Mixture
## 4 Results and Discussion
### Rutting Resistance  
The wheel tracking test results are shown in Figure 5. Different rutting depths as a function of time were observed. Figure 5 shows that both lignin products significantly reduced the rutting depths of asphalt mixture compared to the control one. SMA-LF had the smallest rutting depth after the loading. It can be also seen from Figure 5 that the rutting depth increased rapidly at the early stage, which is primarily because of the condensation of loose asphalt mixtures under the compressive loading pressure. Afterward, the increase rate of the rutting depth gradually dropped with time, which was primarily induced by the shear flow of asphalt mixture. Two stages of rutting development were observed. The slope of the linear line in the second stage reflected the increasing rate of rutting depth and dynamic stability. A higher slope value indicates lower dynamic stability of the material. The dynamic stability of the different mixtures calculated by Equation (2) is compared in Figure 6. Results show that SMA-LF had the highest dynamic stability followed by SMA-LP, indicating the highest rutting resistance. The reduction in rutting depth of SMA-LF can be attributed to two reasons: (1) Fibers can stabilize and keep the viscous asphalts together to resist the asphalt flowing at elevated temperatures and (2) fibers can form a three-dimensional network in the mortar, reinforcing the skeleton structure to resist the shear force [12; 13]. For SMA-LP, lignin powders with the asphalt mixture could  
Figure 4: The Cantabro loss of different asphalt mixtures.  
not form the three-dimensional network but did increase the high-temperature performance grade of asphalt binders, which also contributed to the increase of dynamic stability of asphalt mixture.
## 6 Quality Analysis of the Reconstructed Image
### Review and Analysis of Objective Measurement Results
#### 6.2.3 Objective Quality of the Test Image Mask  
The reconstructed images for each compression method and ratio are shown in Figure 16. At the lowest compression ratio (top row) because of the low detail of this image, it is quite difficult to distinguish between images at 0.8 and 0.4 bpp. At 0.2 bpp, JPEG starts to degrade and artifacts appear, especially at 0.1 bpp. JPEG 2000 and EEDC-BSSP are quite similar, except that JPEG 2000 emphasizes the artifacts that degrade the image more.  
Tables 15-17 show the results that follow the quality of the reconstructed images on Figure 16.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**PSNR**} & & \multicolumn{2}{c}{**SSIM**} & \multicolumn{2}{c}{**MS-SSIM**} \\
**bpp** & **JPEG** & **EEDC** & **JPEG** & **JPEG** & **EEDC** & **JPEG** & **JPEG** & **EEDC** \\
0.4 & 31.98 & 34.55 & 32.83 & 0.89 & 0.93 & 0.91 & 0.98 & 0.99 & 0.98 \\
0.2 & 26.96 & 31.01 & 30.48 & 0.76 & 0.88 & 0.87 & 0.91 & 0.97 & 0.97 \\
0.1 & 23.81 & 26.66 & 27.05 & 0.68 & 0.79 & 0.80 & 0.85 & 0.92 & 0.93 \\
0.05 & / & 22.28 & 23.35 & / & 0.66 & 0.73 & / & 0.78 & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Results of the PSNR, SSIM, and MS-SSIM metrics for image _beauty_.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**SAE**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**SNR**} \\
**bpp** & **JPEG** & **EEDC** & **JPEG** & **JPEG** & **EEDC** & **JPEG** & **JPEG** & **EEDC** \\
0.8 & 2.53 & 2.08 & 3.01 & 15.53 & 8.23 & 17.16 & 25.05
## 3 Distributed Drive Vehicles Based on MCSRCKF
### Square-Root Cubature Kalman Filter
#### 3.1.2 Predict  
The error covariance matrix is decomposed by Cholesky to give:  
\[P_{k-1|k-1}=S_{k-1|k-1}S_{k-1|k-1}^{T} \tag{23}\]  
Calculation the state cubature point set:  
\[\begin{array}{c}X_{k-1|k-1}^{(i)}=S_{k-1|k-1}\hat{x}_{i}+\hat{x}_{k-1|k-1}\\ \hat{x}_{i}=\sqrt{n}\left[\begin{pmatrix}1\\ 0\\ \vdots\\ 0\end{pmatrix}\quad\ldots\quad\begin{pmatrix}0\\ 0\\ \vdots\\ 1\end{pmatrix}\quad\begin{pmatrix}-1\\ 0\\ \vdots\\ 0\end{pmatrix}\quad\ldots\quad\begin{pmatrix}0\\ 0\\ \vdots\\ -1\end{pmatrix}\right]_{i}\end{array} \tag{24}\]  
where \(i=1,2,\ldots,2n\), \(n\) is the state equation's dimension of the seven-degree-of-freedom vehicle model, i.e., n = 7.  
Propagate the state cubature point by the state function \(f(x_{k},u_{k})\):  
\[X_{k|k-1}^{*(i)}=f\left(X_{k-1|k-1}^{(i)},u_{k-1}\right) \tag{25}\]Set the state prediction:  
\[\hat{x}_{k|k-1}=\frac{1}{2n}\sum_{i=1}^{2n}X_{k|k-1}^{*(i)} \tag{26}\]  
Decomposing the state prediction values of the error covariance matrix, QR decomposition is introduced in CKF to avoid operations such as squaring and inverting, which increase the matrix's stability. The square root factor is obtained as follows:  
\[S_{k|k-1}=Tria\big{[}\Lambda\;S_{Q,k}\big{]} \tag{27}\]  
where \(Tria(\cdot)\) represents the QR decomposition of the matrix, \(S\) denotes the decomposed upper triangular matrix, _and_ the weighted center distance \(\Lambda\) is defined as  
\[\Lambda=\frac{1}{\sqrt{2n}}\Big{[}X_{k|k-1}^{*(1)}-\hat{x}_{k|k-1},X_{k|k-1}^{ *(2)}-\hat{x}_{k|k-1},\ldots,X_{k|k-1}^{*(2n)}-\hat{x}_{k|k-1}\Big{]} \tag{28}\]  
where \(Q_{k}\) denotes the process noise covariance matrix, \(S_{Q,k}\) represents the square root factor, and \(Q_{k}=S_{Q,k}S_{Q,k}^{T}\).
# Research on the Positioning Performance of GNSS with a Low-Cost Choke Ring Antenna
## 2 Materials and Methods
### Indicators of the Quality of the Satellite Constellation  
The DOP (Dilutions of Precision) geometric factors allow the evaluation of the accuracy of the positioning in the GNSS receiver. Their value depends on the geometry of the current satellite configuration. The following DOP can be distinguished:  
* PDOP (Positional Dilution of Precision),
* HDOP (Horizontal Dilution of Precision),
* VDOP (Vertical Dilution of Precision) [1,2,4,39,40,41].  
PDOP is the spatial factor of geometric accuracy referring to a three-dimensional position in the Cartesian system or geodetic coordinates. Another factor is HDOP, which is the plane (horizontal) coefficient of geometric accuracy referring to a two-dimensional position in a Cartesian or geodetic system. The last mentioned factor is VDOP. It describes the vertical factor of geometric accuracy related to the one-dimensional height measurement.