esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Seismic Retrofitting Method Using Welded Concrete-Filled Steel Tubes  
Concrete-filled steel tubes (CFSTs) are structural members consisting of circular or square steel tubes charged with concrete (the concrete is confined to the interior of the tubes). Such members exhibit suitable stiffness, resistance, and deformation characteristics. Since the 2000s, several studies have been conducted on the seismic performance of CFSTs and their corresponding structural performance. The results of such research have confirmed the effectiveness of CFSTs in improving seismic performance [15; 16]. However, studies focused on utilizing CFST structures in the seismic retrofitting of existing buildings that are not seismic-resistant have been limited. As shown in Figure 1, the WCFST system proposed in this paper can reduce lateral deformation and increase seismic resistance via the attachment of concrete-filled steel tubes to the surface of an existing frame structure consisting of beams and columns. The seismic retrofitting procedure using the WCFST system is displayed in Figure 2. Firstly, the finishing material on the retrofitting surface is removed and a base plate is installed, as shown in Figure 2a. The base plate is then fixed using chemical anchors and reinforcement is placed, as illustrated in Figure 2b. After fixing the base plate to the existing frame, the CFST frame is installed on the columns and then the beams, as shown in Figure 2c; the base plate and CFST frame are connected by welding. High strength mortar is subsequently charged inside of the steel tubes. The WCFST system does not require additional formwork and is not affected by internal spatial conditions, as it is a method of construction on the outside of the structure. In addition, the strategy is advantageous in that it can minimize both damage to the structural members and changes in elevation by eliminating the need to remove existing structural members.
# Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems
## 4 Proposed Algorithm
### Approximate Log-Likelihood Ratio Calculation  
The low-complexity MMSE signal detection algorithm described in [13; 14; 15; 16] directly estimates the transmitted signal vector \(\mathbf{\hat{s}}\) without calculating \(W^{-1}\). The exact calculation of the LLR for the channel decoder input is described in Section 1 (i.e., using the exact \(W^{-1}\) matrix inversion information), which is not difficult to find with Equation (7). When the LLR of the first bit transmitted by the \(i\)**th** user is \(L_{i,\mathbf{b}}\), the inverse \(W^{-1}\) of the MMSE detector filter matrix \(\mathbf{W}\) needs to be used again to calculate the SINR of the \(i\)**th** user. Consider using the \(\mathbf{W}\) approximation of the diagonal property to replace \(W^{-1}\) and \(\mathbf{D^{-1}}\), that is, \(\widetilde{W}^{-1}\approx D^{-1}\), and then convert it to the complex domain to get \(\widetilde{W}_{\mathbf{c}}^{-1}\), in order to obtain the approximate channel gain and NPI variance, expressed as:  
\[\widetilde{\mathbf{\mu}}_{i}=\widetilde{\mathbf{U}}_{ii} \tag{20}\] \[\widetilde{v}_{i}^{2}=\sum_{j\
eq i}^{K}\left|\widetilde{\mathbf{U}}_{ji} \;\right|^{2}+\widetilde{E}_{ii}\sigma^{2} \tag{21}\]  
where \(\widetilde{\mathbf{U}}\approx\widetilde{W}_{\mathbf{c}}^{-1}\mathbf{G}_{\mathbf{c}}=\mathbf{D}_{ \mathbf{c}}^{-1}\mathbf{G}_{\mathbf{c}}\), and \(\widetilde{\mathbf{\mathrm{E}}}\approx\widetilde{W}_{\mathbf{c}}^{-1}\mathbf{G}_{\mathbf{c}} \widetilde{W}_{\mathbf{c}}^{-1}=\widetilde{\mathbf{U}}\widetilde{W}_{\mathbf{c}}^{-1}= \widetilde{\mathbf{U}}D_{\mathbf{c}}^{-1}\), so we can calculate \(Y_{i}=\widetilde{\mathbf{\mu}}_{i}^{2}/\widetilde{v}_{i}^{2}\).
## 2 Materials and Methods
### Climatic Data
#### 2.1.2 Future Scenarios  
To obtain high spatial and temporal resolution of future climatic data, the regional climate change simulations provided by the EURO-CORDEX initiative are used [29]. Within the EURO-CORDEX, the General Circulation Models (GCMs) from the Coupled Model Intercomparison Project 5 (CMIP5) are dynamically downscaled for the European domain using different regional climate models (RCMs). Since there are 17 GCM-RCM combinations available, the model ensemble is clustered using PAM (partitioning around medoids) through analysing their variations in future temperature and precipitation predictions, as suggested in [30; 31]. Four GCM-RCM combinations (abbreviated as M1, M2, M3, M4) are selected, which represent the possible range of future changes as presented in Table 2. Representative Concentration Pathway (RCP) 8.5 is adopted in this study as the business-as-usual scenario with GHG emissions continuing to rise in the 21 st century.  
Systematic biases stem from the imperfect representation of climate physics, a too-coarse resolution, and need to be assessed and minimised. Therefore, the raw outputs from the four future projections are bias-corrected and downscaled for the climate zones, employing quantile delta mapping (QDM) and N-dimensional probability density function transform (N-pdf). Compared to routinely used quantile mapping (QM), QDM could better retain the future changes in RCMs, especially for precipitation [32]. N-pdf is adopted to preserve the correlations between multiple climate variables, which has shown improved simulation results, e.g., in hydrology [33]. Each projection offers multi-year climate data of a near-future scenario (F1: 2041-2050) and far-future scenario (F2: 2091-2100) to reflect the long-term climate conditions in simulations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acronym** & **GCMs** & **RCMs** & **RCP** \\ \hline M1 & ICHEC-EC-EARTH & DMI-HIRHAM 5 & 8.5 \\ M2 & ICHEC-EC-EARTH & SMHI-RCA 4 & 8.5 \\ M3 & IPSL-IPSL-CSA- & SMHI-RCA 4 & 8.5 \\ M4 & MPI-M-MPI-ESM-LR & CLMcom-CCLM 4 & 8.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: GCM and RCM combination, and RCP of the selected climate models.  
Figure 1: Climate zones of South Tyrol defined in this study.
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 5 Empirical Results
### Multi-Session Mapping Results  
GEOSLAM processed two laps/sessions of data from each of the four campaigns conducted on 4 and 5 June, fusing the visual data from the captured images with the double-differenced pseudorange-based position estimates of the primary antenna. Figure 16 summarizes the result from GEOSLAM's multi-session GNSS-aided-visual SLAM. The black data points denote the difference between the ground truth trajectory of the primary GNSS antenna and GEOSLAM's estimate of the same in local east, north, and up directions for all eight sessions. The gray data points denote the difference between the ground truth trajectory of the primary GNSS antenna and the coincident double-differenced pseudorange-based estimate of the same for all eight sessions.  
As one might expect, the error in GEOSLAM's estimate of the antenna position was approximately the same as the average double-differenced pseudorange-based error over eight sessions. Furthermore, due to the approximately zero-mean nature of the double-differenced pseudorange-based estimates, the GEOSLAM estimate of the trajectory was within 50 cm of the truth trajectory in the horizontal plane. Note that the error in GEOSLAM's position estimate was highly repeatable over eight different sessions, so much so that it appears there is a single black trace in Figure 16, while in truth eight independent traces were plotted. This indicates that (i) the localization of the vehicle within the visual map was highly precise: GEOSLAM made the same errors with respect to ground truth over eight different sessions; and (ii) the visual map was merged across eight sessions from four different campaigns: if the maps from any two campaigns were not merged through visual matching of features, then the GNSS position estimate for a  
Figure 16: Errors in GEOSLAM’s estimate of the primary antenna position (in black) with respect to ground truth in the east, north, and up directions for eight mapping sessions from four different data collection campaigns. The errors in double-differenced pseudorange-based primary antenna position estimates for each of the eight sessions, fed as measurements to GEOSLAM, are plotted in gray for reference. Dashed reference lines are drawn at \(\pm\)50 cm.  
keyframe from one campaign would not affect another keyframe from a different campaign since they would not be covisible, and thus the eight black traces in Figure 16 would not overlap.
## 1 Introduction
During the optimization, the shared information is limited to expected exchange power, which protects the privacy of MGs and ESP. 4. An optimal framework is proposed, which is capable of taking into account the local objectives of MGs and the objective of ESP with regard to their interaction. The rest of the paper is organized as follows. In Section 2, the studied system structure with coalitions of MGs is illustrated, and the energy sharing structure of MGs in the coalition is described. In Section 3, the RESs and BESS in MGs are modeled. In addition, the power loss model and basic optimal dispatching model of MGs and ESP are introduced in this section. Section 4 describes the distributed optimal scheduling model and a two-level iterative algorithm based on ADMM for the MGs and ESP in coalition. In Section 5, numerical results are given for a coalition consisting of 3 MGs and an ESP, and the effectiveness of proposed model and algorithm in different scenarios is verified. Finally, the conclusion is drawn in Section 6.
## 4 Experimental Results  
### Training Data  
In our experiments, AVSs, as a novel sensing approach, offer substantial performance advantages over traditional scalar sound pressure sensors by enabling the synchronous and co-located measurement of sound pressure and acoustic velocity vector information along three orthogonal directions. Consequently, they have garnered significant attention in the fields of acoustic detection and signal processing. When employed as array elements  
Figure 6: Overall framework diagram of the model.  
in ULAs to receive acoustic signals from diverse directions, acoustic vector sensors provide a richer and more accurate array of signal characteristics by capturing both sound pressure and acoustic velocity vector data, ultimately enhancing the precision of DOA estimation.  
We adopt the ULA model introduced in Section 2 for acquiring the training dataset. The signal type is a monoharmonic acoustic signal and the noise type is set to red noise. The speed of the sound wave \(v\) is set to 1500 m/s and the frequency \(f\) is set to 1500 Hz; then, according to the description in Section 2, the wavelength \(\lambda\) is 1 m. The array configuration consists of 10 sensors spaced at \(\frac{3}{2}\). In this scenario, two sources impinge a ULA with a \(1^{\circ}\) angular separation, spanning a DOA range of \([-90^{\circ},90^{\circ}]\). Then, 200 samples are created for each angle, resulting in a comprehensive dataset of D = 36,000 entries. Half of this dataset is randomly designated as the training subset, while the remaining half serves as the testing subset. To enhance data randomness, we vary the number of snapshots across 50, 80, 100, 200, and 500, and adjust the signal-to-noise ratio (SNR) from \(-20\) dB to 20 dB in increments of 5 dB. For model parameter updates, we select the Adam optimizer with an initial learning rate of 0.0001. The batch size is configured to 180, and the training process spans 800 epochs. The program is developed using PyTorch and executed on a hardware platform featuring an Intel (R) Core (TM) i9-14900K CPU @ 3.20 GHz, RTX 4090 GPU.
###### Abstract  
In order to evaluate the improvement effect of fiber on the brittle failure of cement-treated subgrade soil, a series of triaxial unconsolidated undrained (UU) tests were carried out on samples of polypropylene fiber-cement-treated subgrade soil (PCS) with polypropylene fiber mass content of 0%o, 2%o, 4%o, 6%o, and 10%o. The results showed that, (1) the deviatoric stress-axial strain curve of PCS samples were all strain-softening curves. (2) For the same fiber mass content, the peak stress, residual stress, and strain at peak stress of PCS samples gradually increases with the increase in the confining pressure, while their brittleness index gradually decreases. (3) With the increase in confining pressure, compared with that of the 0%o PCS sample, the increase in peak stress, residual stress, and strain at peak stress of 6%o PCS sample were in the ranges of 24%-29%, 87%-110%, and 85%-120%, respectively. The decrease in the brittleness index and failure angle was 52%-79% and 16%, while the cohesion and internal friction angle increased by 25.9% and 7.4%, respectively. The results of this study indicate that it is feasible to modify cement subgrade soil with an appropriate amount of polypropylene fiber to mitigate its brittle failure.  
fiber-cement-treated subgrade soil; mechanical properties; triaxial test; brittleness index; failure angle +
Footnote †: journal: Applied Sciences
# Non-Destructive Testing Applications for Steel Bridges
## 5 Complementary to NDT Methods
### Implementation of Robotic Technology
#### 5.1.1 Aerial Robots  
In the past decade, the use of aerial robotics, specifically Unmanned Aerial Vehicles (UAVs), or drones, have been emphasized in various fields, including mapping, surveillance, traffic control, inspections, and the health monitoring of bridges. They can reach areas inaccessible to humans or other ground vehicles and take off vertically, eliminating a need for a predetermined path of flight [112]. However, the inspection and health monitoring with the implementation of UAVs is still in the development stage, with more NDT methods being added gradually [113; 114; 115; 116; 117]. An example of aerial drone inspection can be seen in Figure 34.  
Figure 34: (**a**) Implementing aerial robot (drone) for bridge inspection [118; 119]; (**b**) detected crack on a steel bridge component by the implementation of the visual inspection via aerial robot [115].  
Figure 33: Dangerous bridge inspections [108; 109; 110].  
There are also some limitations to this method, including the inability to remove any path obstructions, difficulty to fly during sustained gusts of winds and precipitation, battery life, network instability due to lack of GPS signal under the bridge, line of sight constraints, and the need for being close for crack detection [115; 120]. The development of techniques such as image processing and machine learning can improve the limitations mentioned above [121]. Its advantages include a lower cost due to a lower number of personnel, not requiring lane closures, reduced inspection time, increased safety, and the ability to reach wider areas of the bridge [116; 122].
## 4 Simulation Results and Discussion
### KPI Evaluation and Optimization Using Brute Force  
Aerial-HetNet performance when the 30, 20, and 10 number of UABSs are deployed on fixed hexagonal locations and at a fixed height is plotted in Figures 8 and 9. Subsequently, Figure 10 provides the performance comparison when GUEs are distributed using MCP. Similarly, when GUEs are distributed using TCP performance is plotted in Figures 11-13 provide the performance comparison. In particular, Figures 9 and 12 show the impact of positive-bias CRE at UABSs (along the \(x\)-axis) on the coverage probability (along the \(y\)-axis), whereas the performance of 5 pSE (along the \(y\)-axis) and positive-bias CRE at UABSs (along the \(x\)-axis) can be seen in Figures 8 and 11 shows 5 pSE. In an initial inspection of performance plots shown in Figures 8, 9, 11, and 12, we can intuitively conclude that FeICIC performs better when compared to eICIC and without any ICIC techniques when MBSs are 50% and 97.5% destroyed.
# Leveraging IoT Devices for Atrial Fibrillation Detection: A Comprehensive Study of AI Techniques
## 2 Materials and Methods
### Classification
#### 2.3.2 Deep Learning  
We explore two distinct approaches. First, the time series data (PPG) is directly input into a custom convolutional neural network (CNN). Second, we assess a transfer learning approach, utilizing spectrogram images derived from the PPG data as input.  
_Signal time series and custom CNN_  
A CNN was specifically designed to classify PPG time series data. The CNN architecture was tailored for feature extraction from PPG signals, employing a series of convolution, pooling, and normalization layers, as illustrated in Figure 4. The model begins with a convolutional layer containing 256 filters with a kernel size of 3, followed by batch normalization and max pooling operations. Subsequent convolutional layers, featuring 128 filters of varying sizes, are added to capture more complex patterns in the data. Each of these layers is also followed by batch normalization and max pooling to enhance stability and reduce dimensionality.  
The output from the final convolutional layer is flattened and passed through densely connected layers for final classification. To mitigate overfitting, dropout regularization is applied throughout the dense layers, which decrease in size progressively toward the output layer. The output layer consists of two units utilizing a Softmax activation function, enabling the classification of PPG signals into two distinct classes: atrial fibrillation (AF) and non-AF.  
After the initial training, transfer learning was employed to enhance model performance. The existing model, which already included a suitable classifier for the task, underwent further customization to optimize classification accuracy. New layers were integrated into the pre-trained model, consisting of an additional dense layer with 64 units and ReLU activation to capture complex patterns, a dropout layer to mitigate overfitting,and a final classification layer with 2 units using Softmax activation. Finally, fine-tuning was applied to adapt the model further, enabling improvements in classification accuracy.  
Spectrograms and Transfer Learning  
We employed the DenseNet121 model [29] for the classification of spectrogram images derived from PPG data. To enhance variability, data augmentation techniques were applied to the images prior to training. The transfer learning process involved replacing the original classifier of the network with a custom multi-layer perceptron (MLP) tailored for our specific task. This custom classifier comprised two fully connected layers, each utilizing ReLU activation (refer to Figure 5 for the network structure). Additionally, the majority of the model parameters were frozen, while the remaining parameters of the custom classifier were adjusted to optimize performance for spectrogram classification.  
This change makes it clear that only certain parts of the model (likely the DenseNet backbone) were frozen, while other parts (like the newly added MLP classifier) were fine-tuned.
## 3 Theoretical Derivations
### Bearing Capacity of the Primary Support Derivation
Expand the above equation to obtain:  
\[q=\frac{2Q_{s}(1+\sin\varphi_{b})(L\tan\alpha-D_{b})}{D_{i}D_{a}(1-\sin\varphi _{b})[(2r_{0}+L)\tan\alpha-D_{b}]}+\frac{2T(1+\sin\varphi_{b})(L\tan\alpha-D_ {b})}{\pi r_{0}{}^{2}(1-\sin\varphi_{b})[(2r_{0}+L)\tan\alpha-D_{b}]} \tag{59}\]  
Simplify Equation (59):  
\[q=\lambda_{s}Q_{s}+\lambda_{T}T \tag{60}\]  
where  
\[\lambda_{s}=\frac{2(1+\sin\varphi_{b})(L\tan\alpha-D_{b})}{D_{i}D_{a}(1-\sin \varphi_{b})[(r_{0}+L)\tan\alpha-D_{b}]} \tag{61}\]  
\[\lambda_{T}=\frac{2(1+\sin\varphi_{b})(L\tan\alpha-D_{b})}{\pi r_{0}{}^{2}(1- \sin\varphi_{b})[(r_{0}+L)\tan\alpha-D_{b}]} \tag{62}\]  
Substitute the \(Q_{s}\) and \(T\) obtained in Sections 3.1.2 and 3.2.1, respectively, into Equation (60) to obtain the bearing capacity of the primary support. The specific process for calculating the displacement of the tunnel wall and the bearing capacity of the primary support is shown in Figure 5.  
Figure 5: Flow chart for calculating the bearing capacity of the primary tunnel support.
## 2 Materials and Methods
We used tools like LabelImg to label objects of interest (e.g., rockfalls or flooding signs) in training datasets. These annotations create bounding boxes that specify the spatial extent of each object, which are crucial for model accuracy. Image Resizing: For efficient model performance, images were resized, and annotations were formatted to meet YOLO's input requirements. YOLO Model Training and Inference:  
Training Phase: YOLO uses convolutional neural networks to learn patterns from annotated images. The model divides each input image into grids and predicts bounding boxes and class probabilities, making it highly efficient for large datasets such as satellite or UAV imagery. Inference Phase: Upon deployment, the YOLO model processes new images, detecting objects and providing outputs in the form of bounding boxes and class labels. These outputs are converted into georeferenced data for integration with GIS. Mapping YOLO Outputs to GIS Layers:  
Python Script Integration: We developed Python scripts to bridge YOLO outputs with ArcGIS's geoprocessing tools. These scripts translate the model's bounding box coordinates into GIS layers, making them suitable for spatial analysis. Postprocessing in ArcGIS: YOLO's georeferenced outputs are imported into ArcGIS, where further spatial analysis is conducted. This step enables the visualization of detected hazards, the overlaying of risk maps, and the assessment of potential impacts on road and railway infrastructure. Use Cases in Hazard Mapping:  
Figure 3: ESA SNAP software workflow image samples for rockfall detection from Sentinel-1 SLC product. Infrastructure Impact Assessments: The integration allows for the identification and mapping of risk-prone areas. This spatial analysis is crucial for understanding how natural hazards may affect transportation networks and for developing mitigation strategies ([PERSON] and [PERSON] 2021 [10]; [PERSON] et al., 2021 [11]).