esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling and Problem Formulation  
### Modeling of Mobile Manipulator  
A typical single-arm mobile manipulator consists of a mobile base and a multi-joint manipulator with an end-effector. The mobile manipulator in this paper is shown in Figure 1. The mobile base is an ordinary mobile robot, which is differentially driven with two drive wheels. It can rotate or move forward in a plane. The manipulator has seven DOFs and has a gripper as an end-effector.  
The kinematic model of the mobile base can be represented by three parameters: the position of the center and the heading angle of the mobile base. The kinematic model of the manipulator can be developed using the classical modified Denavit-Hartenberg (D-H)  
Figure 1: Model of a mobile manipulator.  
coordinate system. The overall kinematics of the mobile manipulator can be derived by transforming the manipulator coordinate to the mobile base coordinate. The transformation matrix from the end-effector to the world frame is  
\[{}^{W}T_{E}\left(q^{mm}\right)={}^{W}T_{MB}{}^{MB}T_{M}{}^{M}T_{E}, \tag{1}\]  
where \(q^{mm}\) is the configuration of the mobile manipulator, \({}^{W}T_{MB}\) is the transformation matrix from the mobile base to the world frame, \({}^{MB}T_{M}\) is the transformation matrix from the manipulator to the mobile base, and \({}^{M}T_{E}\) is the transformation matrix from the end-effector to the manipulator.
## 4 Implementation Scenario and Conceptual Framework
### Computing Resource Utilization of NTPA Using Different DL Models  
#### 5.3.1 Memory Usage  
We examined the memory usage (in MB) of DL models. As shown in Figure 13, we observe that the GRU-based NTPA has the highest and the CNN-based NTPA has the lowest memory usage compared to other models.
# Systematic Mapping Study on Remote Sensing in Agriculture
## 3 Results of the Systematic Mapping Study
### Mq1. What Publication Channels Are the Main Targets for RSA?  
This question refers both to the type of channel and the name of the publication. Figure 2 shows that almost all the selected papers were published in scientific journals, except for two conference papers and one book. The names of the journals with more than one publication are shown in Table 2. It is interesting to observe that all these journals are indexed in the Journal Citation Reports, being most of them in quartiles Q1 and Q2.
## 5 Numerical Results
### Simulation Configurations  
The distance between the two suspicious UAVs is \(D\), which various from 500 m to 2000 m, and the path length of \(UAV_{L}\) is \(\pi D/2\). The patrolling speed of \(UAV_{L}\) is set to 10 m/s. In fact, we do realize the policies for using UAVs in our country. It is allowed for flying UAVs freely under altitudes of 120 m. In our research, the distance variation (from 500 m to 2000 m) is mainly in the same altitude, which can be within the permission of policies. We use MATLAB to conduct the experiments instead of an actual simulator, however, the experiments can be legally carried if there are enough equipped UAVs. The detailed system-level simulation parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline \(K_{1}\) & 0.2 \\ \(K_{2}\) & 3 \\ \(\beta_{0}\) & 2.6 \\ \(\beta_{1}\) & 1 \\ \(\varphi\) & 60 \\ \(\varphi v\) & \([-10,10]\) \\ \(\theta\) & \([0,\pi]\) \\ \(f\) & 20 \\ \(l\) & 10 \\ \(\epsilon\) & 0.05 \\ \(N_{0}\) & \(3.98\times 10^{-12}\) W \\ \(b\) & 100 bytes \\ \(\delta\) & 3 \\ \(\lambda\) & 0.3 \\ \(n\) & 0.005377 \\ \(\alpha_{1}\) & 3 \\ \(\alpha_{2}\) & 2.5 \\ \(D\) & 500 m, 1000 m, 1500 m, 2000 m \\ \(p_{L}^{max}\) & \(8\times 10^{-6}\) W \\ \(\rho\) & 1, 2, 4, 8 \\ Constant & \(10^{-8}\) W \\ Jamming Power & \(10^{-8}\) W \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.  
_UAV\({}_{ST}\)_ communicates with _UAV\({}_{SR}\)_ in a TDMA fashion for suspicious collision-free transmission. Especially, we consider that a TDMA frame contains 6 time slots, and each of which is 10 s long. In one time slot, _UAV\({}_{ST}\)_ transmits its data to _UAV\({}_{SR}\)_, where _UAV\({}_{L}\)_avesdrops and decides to jam the suspicious communication according to the selection policy. In addition, the suspicious link, eavesdropping link, and jamming link are assumed to be block-fading, i.e., the channels remain unchanged during each transmission block, and may change from block to block.
## 4 Calculation Results
### Homotopy-Pade Approximation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(\epsilon=0\)} & \multicolumn{2}{c}{\(\epsilon=8/\pi^{2}\)} \\ \cline{2-6} \(\alpha\) & \multicolumn{1}{c}{**Exact Solution**} & \multicolumn{2}{c}{**(HAM [48])**} & \multicolumn{2}{c}{**(IHAM)**} \\  & & \(Pade[11,11]\) & & \(Pade[15,15]\) \\ \cline{2-6}  & \(\Theta_{b}\) & \(\Theta_{b}\) & \(\Delta\) & \(\Theta_{b}\) & \(\Delta\) \\ \hline
0 & 0 & 0 & 0 & 0 & 0 \\ \hline
0.5 & 0.15567 & 0.15583 & 0.0991\% & 0.15594 & 0.173\% \\ \hline
1 & 0.29371 & 0.29440 & 0.237\% & 0.29507 & 0.463\% \\ \hline
1.5 & 0.40714 & 0.40824 & 0.268\% & 0.40974 & 0.639\% \\ \hline
2 & 0.49768 & 0.49887 & 0.239\% & 0.50102 & 0.671\% \\ \hline
2.5 & 0.56977 & 0.57095 & 0.206\% & 0.57340 & 0.637\% \\ \hline
3 & 0.62772 & 0.62875 & 0.165\% & 0.63134 & 0.577\% \\ \hline
3.5 & 0.67489 & 0.67527 & 0.0565\% & 0.67834 & 0.511\% \\ \hline
4 & 0.71380 & 0.71244 & 0.192\% & 0.71700 & 0.448\% \\ \hline
4.5 & 0.74630 & 0.74176 & 0.608\% & 0.74920 & 0.389\% \\ \hline
5 & 0.77373 & 0.76472 & 1.16\% & 0.77639 & 0.344\% \\ \hline
5.5 & 0.79711 & 0.78279 & 1.80\% & 0.79971 & 0.326\% \\ \hline
6 & 0.81723 & 0.79728 & 2.44\% & 0.82010 & 0.351\% \\ \hline
6.5 & 0.83466 & 0.80932 & 3.04\% & 0.83834 & 0.441\% \\ \hline
7 & 0.84986 & 0.81979 & 3.53\% & 0.85507 & 0.613\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Thirtieth homotopy–Pade approximation solutions \(\Theta_{b}^{[P]}\) by IHAM and HAM. Figure 4 compares the 30 th-order iteration solution with the exact solution, from which the followings can be observed:  
* Discrepancy between the linear solution and the exact solution is very large;
* Thirtieth-order solution by IHAM with \(\varepsilon=8/\pi^{2}\) approaches the exact solution much closer than the 30 th-order solution does by HAM with \(\varepsilon=0\);
* The convergence region and rate have been substantially improved by homotopy-Pade approximation;
* And again the Pade approximation solution \(Pade[15,15]\) by IHAM is closer to the exact solution than is the Pade approximation solution \(Pade[11,11]\) by HAM.
## 3 Methods
### Sample Selection  
The aquaculture areas in the Pearl River Basin (Guangdong) hold regular spatial morphological characteristics in remotely sensed images (Figure 5). In this study, the aqua  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Detailed Description** \\ \hline Angular Second Moment & Number of repeated pairs \\ Contrast & Local contrast of within an image window \\ Correlation & Correlation between pairs of pixels \\ Variance & Dispersion of gray-level distribution \\ Inverse Difference Moment & Homogeneity within window \\ Entropy & Randomness of a gray-level distribution \\ Sum Average & Sum Average of pixels within window \\ \hline \hline \end{tabular}
\end{table}
Table 1: Textural measures based on GLCM method in this study.  
Figure 4: Direction measure of GLCM calculation in this study.  
culture areas were roughly divided into inland aquaculture ponds and marine aquaculture. Inland aquaculture ponds are mainly distributed in the junction zone between water body (river, lake and sea) and land. The inland aquaculture ponds are formed through the reclamation of coastal wetlands or inland lakes, and they are usually partitioned by embarkments and have regular and compact shapes (Figure 5b,c). Inland aquaculture ponds units are usually clustered together, showing regular texture structures.  
Marine aquaculture areas are often located in bays and inshore seawaters, and they include raft aquaculture (Figure 5d,e) and cage aquaculture (Figure 5f). The raft aquaculture facilities in meritic zone are composed of aquatic bamboo raft (for floating) and subaquatic thick rope (for fixing aquatic products). Correspondingly, the raft aquaculture areas are characterized by dull grey stripes (Figure 5d,e). The cage culture areas consist of aquatic plastic frames and suspended net cages (Figure 5f). Comparing with water bodies, cage aquaculture facilities appear as brighter colors in remotely sensed imagery, and their distributions are more concentrated, with regular rectangles.  
In this study, a total number of 8317 samples were selected for classifier training (\(n=7317\)) (Table 2) and accuracy assessment (\(n=1000\)).  
Figure 5: Typical aquaculture samples over the study area in high resolution Google Earth image (**a**): costal aquaculture pond (**b**,**c**), visible raft aquaculture (**d**,**e**), and concentrated-distributed cage culture area (**f**).
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 2 Grid Planning
### Load and Generation Forecasting in Planning
#### 2.1.3 Comprehensive Approaches to Forecasting  
Clustering-based techniques streamline load forecasting by grouping similar load patterns, which aids in managing the variability from different energy sources and consumer behaviors. Techniques like K-means, hierarchical clustering, combined locally linear embedding (LLE), principal component analysis (PCA), and multi-layer perceptrons (MLPs), enhance accuracy, integrate renewable energy sources (RESs), aid demand-side management (DSM), and bolster SG functions [15; 43; 44; 45; 80]. Time series load forecasting (TSLF) methods utilize historical data, enhanced by advanced algorithms such as ARIMA and neural networks, to predict future demand. These techniques are pivotal for incorporating renewable energy sources effectively within the grid, supporting demand-side management and facilitating SG functionalities [47; 48; 52; 81].  
By advancing both load and generation forecasting techniques, the energy sector can achieve higher efficiency and sustainability, ensuring a reliable and balanced power system capable of supporting diverse energy landscapes.
## 3 Proposed Method
### Two-View-Based Deep Model
#### 3.3.5 Fusion Layer  
Fusing features from both CC and MLO views enhances mammography classification in multi-label tasks. CC views offer a lateral perspective of breast tissue, while MLO views provide an oblique angle, capturing additional tissue. Merging these views gives classifiers a more comprehensive understanding of breast tissue, thus improving model performance.  
For feature fusion, methods like concatenation, average-wise, and element-wise operations were considered. Following an ablation study, we chose average-wise operations for fusing CC and MLO view features. This involves computing the global average pooling for channel attention feature maps from both the CC and MLO branches, each resulting in a 1D vector. We then average these vectors to form the final fused feature representation. This approach aggregates relevant information from both views, balancing their differences for a robust feature representation for classification.  
The dimension of the channel attention feature map from the backbone model's last layer is \(H\times W\times C\), with \(H\)_and_\(W=7\)_and_\(C=1536\). In this context, we denote the number of channels as \(D\) (\(D=1536\)) to differentiate from previous sections.  
After applying GAP across \(\mathrm{H}\times W\), we obtain the vector with dimension 1x1536. The output of the fusion model is given below:  
\[x_{fused}=g(\ g_{GAP}(z_{cc}),\ g_{GAP}(z_{mlo})) \tag{5}\]  
where \(x_{fused}eR^{D},z_{cc},z_{mlo}eR^{W\times H\times D},g_{GAP}(z_{cc})=\)\(=\)\([\alpha_{1},\alpha_{2},\ \ldots,\alpha_{d}]^{T},g_{GAP}(z_{MLO})=\beta=[\beta_{1}, \beta_{2},\ \ldots,\beta_{d}]^{T}and\ g(\alpha,\beta)=\left[\frac{\alpha_{1}+\beta_{1}}{2 },\ \ldots,\ \frac{\alpha_{d}+\beta_{d}}{2}\right]\).  
The function is \(g_{GAP}\), and \(g\) represents the global average pooling and point-wise average operation.  
The diagram in Figure 4 shows the details of the fusion layer.  
Figure 4: In the fusion block, GAP is calculated separately for the CC and MLO feature maps produced by attention block, followed by average CC and MLO feature correspondence to the same position.
## 4 Case Studies
### Discussion
#### 4.3.2 Comparison of Different Methods  
In order to reflect the advantages of the proposed MISOCP model in this paper, the intelligent method (PSO algorithm) is used to solve the original MINLP problem and the results of the two methods are shown in Table 6 (using the prediction power of the load, WT and PV in Figure 3 as the test scenario).  
It can be seen that the proposed method in this paper is superior to the PSO algorithm in terms of solving quality and the computational time is much less than that of the PSO algorithm. This is mainly because the original MINLP problem contains a number of integer variables and the PSO algorithm easily gets stuck at a locally optimal value. Thus, it may take multiple iterations for the PSO to jump out of partial optimization, giving it a low computational efficiency. However, the difficulty of the original MINLP problem is significantly reduced by conic relaxation, thus the proposed method performs better than the PSO algorithm.
## 4 Experimental Verification  
### Experimental Platform  
The configuration of the experimental platform is given in Figure 14, and the practical prototype is shown in Figure 15. The system parameters are shown in Table 2. IRFP4668 PBF-type MOSFETs were used as power switches and diodes. The upper arm of the half bridge was kept off, while the lower arm was controlled by a PWM signal. Then, the boost converter was designed in such a manner. For simplicity, all the current loops share the same control parameters.  
To accomplish dual-loop control, the feedback signal of inductor current was sent to its own DSP, while the sensing signal of \(u_{\rm{dc}}\) was addressed by DSP#1 (master) to conduct voltage regulation. The resulting reference current was also calculated there and subsequently shared through a controller area network (CAN). Detailed communication variables are presented in Table 4. Considering that the balancing time is quite long, the overall process was recorded using LabVIEW 2018 software. In the RS232 style, the laptop receives the timing data package from DSP#1 and depicts waveforms in LabVIEW.  
Figure 13: Bode diagram of open-loop system: (**a**) current loop and (**b**) voltage loop.
## 2 Materials and Methods
### Study Design  
This study was performed retrospectively using CT data acquired from the University Hospital Sharjah, UAE, investigating the use of CT and the factors affecting radiation dose. This study was approved by the Ethics and Research Committee of the University Hospital Sharjah on 19 June 2017 (Ref. UHS-HERC-030-07062017). From all of the CT exams collected, the radiology team specified the images by the exam type and the patient gender. Each CT exam contains a set of information regarding the CT modality technical parameters, patient-related parameters and dose estimation parameters. From all of them, we were interested in extracting the parameters of CTDI\({}_{\text{vol}}\), DLP, ED, SSDE, BMI, weight and age. These parameters comprise the dataset used. Patients with missing data or outliers were excluded, which is a necessary step to make future results more reliable and robust. After that, Pearson correlation and linear regression were implemented to investigate the relationships between patient-related parameters and the different CT dose indices, aiming to answer the following research questions:  
Q1. How much does each patient-related parameter impact each of the dose indices?  
Q2. How do the relationships found in Q1 differ for different CT exams?
## 5 Proposed Methods Based on FTSP
### Advanced Adjusted Temperature FTSP (A2T-FTSP)  
The second approach is called _Advanced Adjusted Temperature FTSP_ (A2T-FTSP). If the temperature in the root more is known, a more precise _clock skew_ in the local mote can be obtained. The only requirement is to have temperature sensors, both in the root and the local motes.  
By using Equation (1), the _clock skew_ of the root mote and the one of the local mote can be compared. Thus, the _clock skew_ value of the local mote can be improved compared to AT-FTSP and FTSP approaches.  
\[Skew_{\text{A2T}}=\frac{f_{n}}{f_{r}} \tag{5}\]  
Equations (6) and (7) show the values of the variables of Equation (5).  
\[f_{n}=f_{0}^{n}(1+\beta_{n}(T_{n}-T0)^{2}) \tag{6}\]  
\[f_{r}=f_{0}^{r}(1+\beta_{r}(T_{r}-T0)^{2}) \tag{7}\]  
Thus, the _clock skew_ value due to temperature effects is determined by Equation (8).  
\[Skew_{\text{A2T}}=\frac{f_{0}^{n}(1+\beta_{n}(T_{n}-T0)^{2})}{f_{0}^{r}(1+ \beta_{r}(T_{r}-T0)^{2})} \tag{8}\]  
As the first approximation and in order to reduce the complexity of the formulation, it is possible to assume that \(\beta_{n}\simeq\beta_{r}\). Using the clock skew of Equation (8), it is possible to determine a correction factor based on temperature. Using this approach, the average time synchronization error can be reduced.  
\[Skew_{\text{A2T}}=\frac{1+\beta_{n}\Delta T_{n}^{2}}{1+\beta_{n}\Delta T_{r} ^{2}} \tag{9}\]  
\[Skew=Skew_{FTSP}\cdot Skew_{\text{A2T}} \tag{10}\]  
Before providing accuracy results, it is possible to analyze both proposed methods from a computational point of view. It can be concluded that AT-FTSP is a less complex method than A2T-FTSP, and therefore, it is more suitable for low computational power nodes. Besides, A2T-FTSP requires one to send the temperature of the reference node in each synchronization packet, reducing the bandwidth of the communication channel. However, experimental results shown in Section 7 permit one to evaluate both methods with analytical data, taking accuracy into account.
# Explainable Liver Segmentation and Volume Assessment Using Parallel Cropping
## 3 Methodology
### Post-Processing  
We apply a post-processing step after the initial prediction to improve the quality of U-Net-based liver segmentation. This step filters out small, spurious regions by retaining only the largest connected component in the segmentation mask. The rationale is that the liver typically forms the most voluminous continuous structure in the field of view.  
The post-processing procedure is presented in Algorithm 3.  
```
1:Bei: Binary segmentation mask SegmentationMask from U-Net
2:Eintex: Filtered mask CleanedMask
3: Label all connected components in SegmentationMask
4:for each component do
5: Compute voxel count (volume)
6:endfor
7: Identify the largest component \(L_{\text{max}}\)
8: CleanedMask\(\leftarrow\) binary mask containing only \(L_{\text{max}}\)
9:return CleanedMask
```  
**Algorithm 3**Post-processing after U-Net Segmentation  
This step is particularly effective in removing false positives that result from U-Net's tendency to segment outside the target region when encountering low-contrast structures or noise. We observed that applying this step leads to a marked improvement in Dice scores and contour accuracy.  
Despite U-Net's high accuracy on the full dataset, it occasionally segments structures outside the liver boundary, particularly in low-contrast regions. This often results in unwanted elements (false positives), as shown in U-Net, Figures 5-12 where the model erroneously includes adjacent organs. To correct this, a post-processing step was introducedto retain only the largest connected component in the output mask (U-Net Figures 13-16, effectively isolating the liver and improving segmentation fidelity.  
Finally, it is necessary to resize cropped inputs back to the fixed input shape required by U-Net.
# LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments
## 4 Multi-Sensor Data Fusion Framework
### Manifold-Based Unscented Kalman Filter
#### 4.3.2 System Updating  
In this approach, the results of point cloud matching serve as the observations. These results are obtained based on Equation (13), which takes input from both the point cloud map \(S_{pt}\) and real-time 3D lidar point cloud data \(H_{pt}\), satisfying the fundamental model described in Equation (15).  
\[Y_{input}=\left(\begin{array}{cc}R&P\\ 0&1\end{array}\right)\in G \tag{21}\]  
Due to the incorporation of observations, the system state in the Lie algebra representation temporarily expands from \(\mathbb{R}^{15}\) to \(\mathbb{R}^{15+6}\).  
By approximating the augmented portion with sigma points and applying the results to the original system state, we obtain a new set of sigma points \(sp_{n}\) after incorporating observations. For the Lie algebra-based point set \(sp_{n}\), the mean of the observation point set is calculated as shown below.  
\[\overline{sp}=\sum_{j=1}^{2d+1}W_{c}^{j}sp_{j} \tag{22}\]  
The observation covariance \(P_{yy}\) and cross-covariance \(P_{sp}\) are jointly obtained based on the mean of the observation point set \(\overline{sp}\) and the mean of the observations \(\tilde{y}\):  
\[\begin{split} P_{yy}=\sum_{j=1}^{2d+1}W_{c}^{j}(y_{j}-\tilde{y}) (y_{j}-\tilde{y})^{\text{T}}\\ P_{sp}=\sum_{j=1}^{2d+1}W_{c}^{j}(sp_{j}-\overline{sp})(y_{j}-\tilde{y} )^{\text{T}}\end{split} \tag{23}\]  
where \(y_{j}\) represents the approximation in Lie algebra form of the observation \(Y\). However, in accordance with Equation (15), the general observation model can be transformed into the new form shown below.  
\[\begin{split} y_{j}=\log(XV)=\log(\exp(\xi)V)\\ y_{j}=\xi+v+o(\xi)\approx\xi\end{split} \tag{24}\]  
By applying the BCH (Baker-Campbell-Hausdorff) formula, it can be verified that transforming the sigma points into the Lie algebra for computation ensures the normality of the probability distribution and prevents the occurrence of outliers in the algorithm's posterior results. The updating of the system's state and covariance is similar to the standard Unscented Kalman Filter.  
\[\begin{split}\bar{X}^{+}&=\bar{X}\exp(\sum_{j=1}^{2d+1 }W_{c}^{j}(y_{j}-g))\\ P^{+}&=P-P_{sp}(P_{sp}P_{yy}^{-1})^{\mathrm{T}}\end{split} \tag{25}\]
## 3 Emerging and State-of-the-Art Technologies
### On Digital Twins and Communications
Existing full-wave electromagnetic simulators, based on analytical and numerical methods, can be seen as early precursors of DT technology. Future DT engines will integrate these simulators with machine learning algorithms, creating multiphysics, multi-fidelity models capable of predicting device performance and optimizing new designs [236]. This simulation-hardware co-design approach will significantly reduce costs and enhance device maintenance. DTs will play a transformative role in next-generation communication systems, from LEO satellite networks and autonomous vehicles to drone-based delivery services and system-level design. It is these authors' opinion that by continuously collecting and analyzing real-world data, DTs will enable unparalleled optimization, decision making, and security, ultimately shaping the future of wireless communication systems and not only. Table 3 summarizes the application areas and anticipated key benefits of DTs as discussed in this section.