esa-sceva/satcom-chunk-collection huggingface dataset:
# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 2 Materials and Methods
### Epipolar Geometry-Based Extrinsic Parameters Determination
#### 2.3.4. Bundle Adjustment Based Extrinsic Parameters Refinement  
After decomposition of the essential matrix, an initial estimation of the camera pose can be obtained. The epipolar constraint in Equation (8) cannot be fully satisfied due to the presence of image noise and interference in the stereo correspondence and camera parameters estimation. Therefore, the bundle adjustment method is used to optimize the extrinsic parameters of the cameras. The Euclidean distance between the reprojection point and the original coordinate point is the reprojection error. By minimizing this reprojection error, the optimal solution of the extrinsic parameters of camera can be obtained. The solution equation for bundle adjustment method can be expressed as:  
\[\underset{\{\mathbf{R}^{j}\boldsymbol{\dot{\mathbf{v}}}\},\{\mathbf{x}^{i}\}} {\operatorname{argmin}}\sum_{i=1}^{N}\sum_{j=1}^{2}\|\mathbf{K}^{i}\Big{[} \mathbf{R}^{j}\Big{|}\boldsymbol{\dot{\mathbf{v}}}^{i}\Big{]}\mathbf{X}^{i}- \mathbf{x}^{i}_{j}\|_{2}^{2} \tag{12}\]  
where \(\mathbf{X}^{i}\) is the three-dimensional coordinate in the actual three-dimensional frame. The corresponding reprojected 2D points in the two image planes can be expressed as \(\mathbf{K}^{i}\Big{[}\mathbf{R}^{j}\Big{|}\boldsymbol{\dot{\mathbf{v}}}^{i} \Big{]}\mathbf{X}^{i}\). \(\mathbf{x}^{i}_{j}\) is original the pixel coordinates of the \(i\)th point in image \(j\). When \(j\) equals 1, it means left camera, and when \(j\) equals 2, it means right camera. \(\mathbf{K}\) is the intrinsic matrix of camera pairs, and \(\mathbf{R}\), and \(\mathbf{t}\) are the rotation matrix and translation vector with respect to the left camera, respectively. For the left camera, \(j=1\), we have \(\mathbf{R}^{j}=\mathbf{I}\) and \(\boldsymbol{\dot{\mathbf{v}}}^{i}=0\).  
The optimization method used here is the Levenberg-Marquardt algorithm. Through the bundle adjustment method, the optimal extrinsic parameters of each camera pair can be obtained. It should be noted that the translation vector calculated by the above method is the normalized values, where \(\|\mathbf{t}\|=1\). In Algorithm 2, the relevant algorithm for extracting extrinsic parameters is given step by step.  
```
Input: normalized image coordinate: \(\mathbf{p}\) and \(\mathbf{q}\). Output: extrinsic parameters: \(\mathbf{R}\) and \(\mathbf{t}\). Initially extrinsic parameters estimation
1. Estimate the essential matrix \(\mathbf{E}\) by using the five-point algorithm.
2. Based on the SVD of \(\mathbf{E}\), calculate \(\mathbf{R}\) and \(\mathbf{t}\) using Equations (10) and (11). Refine extrinsic parameters
3
```  
**Algorithm 2** Determining extrinsic parameters from matched points.
# An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis
## 2 Methods
### Feature Extraction Module  
The Feature Extraction Module aims to extract deep features from the Mel-spectrogram to calculate similarity. Compared to feature extraction based on image blocks or keypoints originally designed for other image tasks, using deep neural networks to extract features has a huge advantage, which is that the neural network automatically learns features suitable for copy-move forgery detection from training data.  
In theory, the Feature Extraction Module can choose existing deep learning backbone networks with better performance, such as VGG, ResNet, Res2 Net [22], MPIF-Res2 Net [23], Vision Transformer [24], etc. But, these networks are all applied to traditional images, and Mel-spectrograms are different from traditional RGB images. For visualization purposes, the Mel-spectrogram colorizes a two-dimensional matrix based on element values, with colors mapped from amplitudes. The three channels of the Mel-spectrogram do not have physical, practical significance. Therefore, Section 3.2 designed experiments to evaluate the ability of each backbone network to extract features from Mel-spectrograms. The experiment uses different backbone networks to binary classify the tested audio to determine whether it has been forged. After experiments, the overall structure of the Feature Extraction Module was finally determined, as shown in Table 1: The Feature Extraction Module extracted features from the Mel-spectrogram of 3 \(\times\) 128 \(\times\) 256, resulting in a deep feature map of 1024 \(\times\) 8 \(\times\) 16.  
\begin{table}
\begin{tabular}{c c} \hline
**Network Architecture** & **Output Dimension** \\ \hline
7 \(\times\) 7 conv, 64, stride 1 & 64 \(\times\) 128 \(\times\) 256 \\
3 \(\times\) 3 conv, 64, stride 2 & 64 \(\times\) 64 \(\times\) 128 \\ MPIF-Res2 Net block \(\times\)3 & 256 \(\times\) 32 \(\times\) 64 \\ MPIF-Res2 Net block \(\times\)4 & 512 \(\times\) 16 \(\times\) 32 \\ MPIF-Res2 Net block \(\times\)6 & 1024 \(\times\) 8 \(\times\) 16 \\ \hline \end{tabular}
\end{table}
Table 1: The network structure of Feature Extraction Module.  
Figure 3: Method for localizing audio copy-move forged.  
The MPIF-Res2 Net block in Table 1 is shown in Figure 4. In MPIF (multi-perspective information fusion), \(i\) is an integer between 1 and 4, Avg is Adaptive AvgPool2d, and FC is a fully connected layer. The overall structure on the left is similar to Res2 Net, except that after the input feature map is convolved by \(1\times 1\) and divided into 4 parts along the channel dimension, MPIF replaces the 3 \(\times\) 3 convolutional layer in the Res2 Net structure. The MPIF first obtains the multi-scale feature \(\mathbf{P}\) by convolution kernels with different dilations and further obtains the weight \(\mathbf{Q}\) of each channel by convolution, Sigmoid, and Adaptive AvgPool2d. Finally, multiply the \(\mathbf{P}\) and \(\mathbf{Q}\) of each branch and sum them up.
###### Abstract  
The aim of the present study is to improve the thermal and hygric performance of magnesium oxychloride (MOC) cement composites by the incorporation of waste plastic-based aggregate and the use of the inner and surface hydrophobic agents. The crushed waste expanded polypropylene particles were used as a full replacement of natural silica sand. The aggregate properties were evaluated in terms of their physical and thermal parameters. The caustic calcined magnesite was studied by SEM, XRF, and XRD spectroscopy. The MOC cement composites were characterized by SEM/EDS, XRD, and FT-IR spectroscopy and measurement of their structural properties, strength parameters, thermal conductivity, and volumetric heat capacity. Assessment of water- and water vapor transport properties was also conducted. The results show significantly improved thermal parameters of MOC cement composite containing expanded polypropylene (EPP) as aggregate and indicate high efficiency of surface hydrophobic agent (impregnation) as a barrier against the transport of liquid and gaseous moisture. The resulting lightweight EPP-MOC cement composite with improved thermal insulation function and suitable mechanical properties can be used to produce thermal insulation floors, ceilings, or wall panels reducing the operational energy demand of buildings.  
Keywords:magnesium oxychloride cement; waste expanded polystyrene; mechanical properties; thermo-physical parameters; hygric properties; hydrophobic additives; water resistance +
Footnote †: journal: Applied Physics Letters
# Performance Evaluation of a Mesh-Topology LoRa Network
## 7 Results
### Power Consumption Comparison
The displayed power consumption of devices located between 1 and 2 km represent nodes which all transmit directly to the gateway, while nodes located between 3 and 4 km from the gateway represent nodes which need to relay packets in the multi-hop network. Lastly, nodes located between 5 and 6 km from the gateway represent nodes that are in the first hop of the multi-hop network, or nodes which use a higher \(SF\) (10-12 \(SF\)) in the single-hop network. In Figure 10, it is clear that the number of nodes in an evenly distributed network does not have a statistically significant impact on the average power consumption of the nodes in the network. An increase in nodes which need packets that need to be relayed is met with an increase in relay nodes. Furthermore, it is clear that although there is a significant increase (315%) in the average power consumption of nodes which need to relay packets in the multi-hop network, the nodes located 5-6 km sees a dramatic decrease (477%) in power consumption, as their ToA is reduced due to the lower transmission \(SF\).
###### Abstract  
In soft soil areas, to compare the load-bearing characteristics of bag grouting piles and cement mixing piles and study the load-bearing mechanism of bag grouting piles, field tests are conducted in this study, including the comparative compressive test of bag grouting piles and cement mixing piles, and the analysis of pile axial force, pile side friction resistance, and pile end resistance. Moreover, a numerical simulation is developed using ABAQUS 2020 (finite element analysis software) for three-dimensional modeling. The numerical simulation results are compared with the field test results to verify the reliability of the numerical simulation. Furthermore, the influences of five factors are studied; namely, pile length, pile diameter, pile spacing, the thickness of the bedding layer, and grouting pressure are studied for their effects on the compressive bearing characteristics of the bag grouting pile. The results show the following: (1) For composite foundations, bag grouting piles are more effective than cement mixing piles in soft soil areas, and the former provide an 8.8% increase in the bearing characteristics. (2) With an increase in the load, the bag grouting pile experiences greater compression in the middle of the pile body, and the pile side friction resistance is increased; therefore, the pile side friction resistance can be fully developed, and the bag grouting piles have the ability to transfer the load from the top of the pile to the soil at the bottom of the pile. (3) When the external load is maximized, the sharing ratio of pile side friction resistance reaches 96.3%, which shows the excellent frictional performance of bag grouting piles. (4) Among the five factors mentioned above, the most important one is the pile diameter, followed by the pile length and pile spacing, the thickness of the bedding layer, and finally the grouting pressure. The optimal combination in this paper is a pile length of 18 m, pile diameter of 0.4 m, pile spacing of 1.0 m, bedding thickness of 0.3 m, and grouting pressure of 0.6 MPa. Therefore, changing the pile diameter can be given priority during the construction design. The findings in this paper can provide valuable insights and practical experience for the design of similar engineering projects.  
Bag grouting pile; field test; ABAQUS; bearing characteristics; pile side friction resistance; orthogonal test +
Footnote †: journal: Applied Sciences
## 4 Discussion
More generally, if we intend to fully exploit the multi-frequency capabilities of the MIPS system, we have to consider acquisition campaigns consisting of a greater number of flight missions. In this case, the corresponding time loss could be managed by separately processing the data acquired in the different flight missions of the same campaign. However, to effectively apply such a strategy, it is necessary to reduce the currently required time for data download and transfer, which, in the considered case study, was on the order of 5 h. Finally, the time required to execute the overall data processing chain must be considered, particularly the AC step, which is performed in the time-domain to guarantee the high accuracy of the focused SLCs. As remarked above, to reduce the processing time, the overall data processing chain of the IREA-CNR infrastructure is based on a parallel programming strategy to take advantage of the multi-core capability of the IT platform of the infrastructure. Exploiting these features for the considered case study allowed us to process about 130 GByte of radar data in 12 h of computing, achieving 16 SLCs with 75 cm \(\times\) 27 cm (range \(\times\) azimuth) resolution covering the entire Stromboli island and a final DEM of the island with 2 m \(\times\) 2 m (range \(\times\) azimuth) resolution and a height error with a standard deviation on the order of 1.5 m.
## 2 Materials and Methods
### Data Sources and Preprocessing  
The data utilized in this work primarily comprise remote sensing image data, digital elevation model (DEM) data, vector data, and socio-economic data. Remote sensing image data encompass multiple sources of data, such as radar and optical image data. Table 1 displays the fundamental details of the data. The data utilized in this study are the C-band Synthetic Aperture Radar (SAR) data from Sentinel-1, a satellite operated by the European Space Agency (ESA) accessible at [[https://www.esa.int/](https://www.esa.int/)]([https://www.esa.int/](https://www.esa.int/)), accessed on 10 October 2022. The duration of the revisit is 12 days. Single Look Complex (SLC) data stripping was performed at a resolution of 5 m \(\times\) 5 m. The Interferometric Wide Swath (IW) has a resolution of 5 m by 20 m. A total of 152 Sentinel-1 photos captured between 1 January 2017 and 30 December 2021 (with a time frame of 1824 days) were selected to extract surface subsidence in the study area. The European Space Agency (ESA) acquired optical remote sensing picture data from satellites such as Sentinel-2 and GF-1, namely the Sentinel-2A and Sentinel-2B, throughout the months of July and August in the years 2017, 2018, 2019, and 2021. Due to the unavailability of Sentinel-2 images on the ESA platform for the research region between July and August 2020, the decision was made to use domestic GF-1 multi-spectral data from July 2020 instead with the resolution being similar to that of Sentinel-2. The data were acquired via the China Centre for Resources Satellite Data and Application, accessible at [[https://www.cresda.com/](https://www.cresda.com/)]([https://www.cresda.com/](https://www.cresda.com/)), accessed on 10 October 2022. The DEM data, acquired from the United States Geological Survey (USGS) at a resolution of 30 \(\times\) 30 m, serves as supplemental data for the aim of eliminating the terrain phase in the interferogram.  
The data obtained above underwent preprocessing. The vector data coordinate system was uniformly converted to the WGS-84 coordinate system. Baseline estimation and data cropping preprocessing were performed on Sentinel-1A. The open-source plug-in Sen2 Cor and SNAP software (v.10.0.0) were utilized to preprocess the Sentinel-2 remote sensing image data for atmospheric, terrain, cirrus corrections, and re-sampling. Additionally, the China Satellites, a homemade remote sensing image plug-in in the ENVI software (v.5.0.3), was employed for the radiometric calibration and atmospheric correction of GF-1.
# Novel ZIF-67@Bentonite (ZIF-67@BNT) Nanocomposite for Aqueous Methyl Orange Removal
###### Abstract  
The indiscriminate discharge of synthetic dyes into wastewater streams poses a severe threat to the environment as well as to human well-being. Among all these dyes, methyl orange (MO) attracts attention due to its widespread use and persistence in industrial effluents. This study investigated the use of \"zeolitic imidazolate\" framework and bentonite (ZIF-67@BNT) nanocomposite material for the removal of MO from the aqueous phase. Various characterization techniques were employed such as FTIR, XRD, and TGA to verify the successful synthesis of the ZIF-67@BNT adsorbent, which was subsequently utilized to investigate the adsorption of MO. Batch adsorption studies demonstrated a high MO adsorption capacity of 187 mg/g. A response surface methodology (RSM)-based modeling exercise was used to optimize the adsorption process. While assessing the impact of various operational factors, the initial MO concentration followed by ZIF-67@BNT dose were noted to be important. Adsorption kinetics and isotherm studies were also completed. The ZIF-67@BNT nanocomposite after adsorption analysis indicated multiple mechanisms facilitating MO uptake. Additionally, various machine learning (ML) models such ANN, SVR, RF, and GPR were also utilized to predict MO adsorption onto ZIF-67@BNT nanocomposite under a varying set of conditions.  
ZIF-67@Bentonite; methyl orange; adsorption; RSM and ML modeling 2020
# A New Modeling Method of Angle Measurement for Intelligent Ball Joint Based on BP-RBF Algorithm
## 1 Introduction
Under the basic coordinate system \(O\)-\(XYZ\), the magnetic induction intensity values measured by Hall sensors at the position of S1 are the composite values of the magnetic induction intensity in the direction of \(X\)-axis and \(Y\)-axis, which can be expressed as follows:  
\[B_{S_{1}}=B_{x}\cos\delta+B_{y}\sin\delta \tag{2}\]  
where the angle between the sensor axis and the \(X\)-axis is \(\delta\) and the value of \(\delta_{1}\) at \(S_{1}\) is 90\({}^{\circ}\). The relationship between the field values measured by \(S_{1}\) and the rotation angles \(\alpha\) and \(\beta\) can be obtained using the Equations (1) and (2) simultaneously, which are expressed by \(B_{S1}=f_{S1}(\alpha\), \(\beta\)). Similarly, \(B_{S2}\) and \(B_{S3}\) can be obtained. Theoretically, setting two Hall sensors is sufficient for constructing equations and obtaining the angles \(\alpha\) and \(\beta\). Here, three Hall sensors were employed redundantly, which is helpful to improve the accuracy and stability of the measurements. Through the experiment comparison and a test, the measuring error of the developed prototype based on the EMCM model was determined; the average errors were 16 min within a 10\({}^{\circ}\) measuring range, and 29\({}^{\prime\prime}\) within \(\pm\)20\({}^{\circ}\)[3; 4]. The theoretical and experimental results show that the mathematical model established by the EMCM has a higher accuracy for a small-angle range. However, the theoretical model requires strict structural parameters of the PM and assembly accuracy of the Hall sensor, while the calculation accuracy decreases with increasing measurement range. In addition, according to the mathematical model established by the previous research of the project team, the calculation takes a lot of time to iterate as the model contains multiple integrals. This is not leading to the real-time monitoring of the rotation angle of the ball joint. Therefore, we hope to find and update the model methods [5]. An artificial neural network (ANN) is a numerical modeling method by training and learning, which completely abandons the analytical modeling used in sensors. Its calculation time is very short  
Figure 2: Schematic diagram of measurement model. due to its simple structure. In the meantime, its robustness and adaptability make it insensitive to the mechanical structure parameter error of the ball joint, the residual magnetic deviation of the PM, and the manufacturing and assembly error. We expect to replace the complex mathematical model with the ANN algorithm [6].
## 4 Results and Discussion
The results from the building orientation simulation indicate that building position is a major factor with an effect on energy performance. The south is the best principal orientation for buildings located in the Cold and Severe Cold regions. Energy consumption increases gradually when the main facade of the building moves to the west or the east. Figures 9 and 10 present the heating and cooling load of the RC, CLT, and straw buildings located with different orientations. These figures demonstrate that a south-facing facade has approximately 5%-7% less energy demand than one facing east or west. These results are useful to assess the influence of orientation on energy consumption when designing a building in these climate regions. Figure 9: Annual heating load for building in the five study cities with different orientation. The simulation results for energy consumption by building floor indicate that with an increase in building height, the energy consumption decreases gradually (Table 20). The energy consumption on the top and bottom floors of the building are apparently higher than on the other floors. Although CLT and straw are ideal materials for external walls and roof, they are not suitable for the floors. The energy consumption on the ground floor is approximately 50%-60% above the average for all buildings. The results also demonstrate that the middle floors of the building are the most energy-efficient areas.
## 5 Results and Discussion
### Discussion  
To verify the KA-DDPG based torque distribution strategy, we conducted the desired value tracking performance evaluation in two different scenarios, by comparing with the controller used for assisted learning. The contribution of the assisted learning methods was, also, verified by comparing the learning process of different cases of the KA-DDPG algorithm.  
For the desired value tracking performance evaluation, quantitative evaluations in different scenarios show that the KA-DDPG method had smaller desired value tracking errors than the baseline. In other words, the KA-DDPG method had a better desired value tracking performance. The great improvements in the tracking performance come from the fact that the KA-DDPG algorithm not only learned from the knowledge-assisted learning methods but also explored the better distribution strategy, through the exploration ability of RL.  
For the contribution of knowledge-assisted learning methods evaluation, we trained the KA-DDPG algorithm, with different configurations on three cases, namely the KA-DDPG algorithm with two assisted learning methods and the KA-DDPG algorithm with only one of the assisted learning methods. The result is the KA-DDPG with two assisted learning methods converged with less learning time than the other cases. The great improvements in the learning process come from the knowledge-assisted learning methods. The guiding reward method accelerates the learning process, by sharpening the updating reward with the assistance of the evaluation method, whereas the criteria action method achieves this by providing the learning direction through criteria actions, reducing the randomly searched actions.  
From all the simulation results, we conclude that the KA-DDPG algorithm, which combines the knowledge-assisted learning methods with the DDPG algorithm, can be successfully applied to the torque distribution of skid steering vehicles. This work lays a foundation for the use of RL technologies to directly distribute the torque of skid-steering  
Figure 15: The learning process of KA-DDPG without the guiding reward method: (**a**) total rewards for each learning process and (**b**) the average value of the total rewards.  
vehicles, in future practical applications. The knowledge-assisted RL framework proposed in this work provides a powerful weapon for applying RL technologies in overdrive systems, such as skid-steering vehicles. However, the verification part of this paper was carried out in a simulation environment, so challenges do still exist to transfer the proposed method to a real application. For future research, we will consider reducing the reality gap in a real application.