esa-sceva/satcom-chunk-collection huggingface dataset:
# An Aeromagnetic Compensation Algorithm Based on a Temporal Convolutional Network
## 3 Experimental Details  
### Data Preparation  
#### 3.1.1 Simulation Dataset  
Based on the ArduPilot simulation flight control software, we simulated the aeromagnetic compensation calibration flight process for the fixed-wing aircraft Cessna-208B. The simulated flight path and maneuvering angle changes are shown in Figure 5, where the start recording position is 112.729\({}^{\circ}\) E, 16.720\({}^{\circ}\) N, the stop recording position is 112.734\({}^{\circ}\) E, 16.779\({}^{\circ}\) N (excluding takeoff and landing), and the average flight altitude is 3000 m. The simulated flight consists of two maneuvering circles, each consisting of four orthogonal flight segments with a length of 10 km on one side. The aircraft performs pitch, yaw, and roll movements of 5\({}^{\circ}\) to 15\({}^{\circ}\) in each of the four orthogonal directions.  
We simulated the main magnetic interference components of the flight platform based on the magnetic inclination, magnetic declination, and geomagnetic field strength at an altitude of 3000 m in the South China Sea (112.7\({}^{\circ}\) E, 17.6\({}^{\circ}\) N). The intrinsic magnetic field was simulated through the magnetic dipoles of motors and engines located on the aircraft's flaps, elevators, and rudders as field sources, as shown in Figure 6a. The induced magnetic field was simulated through finite element models of the aircraft's aluminum fuselage, steel fuel tank, and engine compartment, as shown in Figure 6b. In order to simulate the eddy-current magnetic field, the aircraft skin was regarded as equivalent to multiple coils located on the wings, nose, and tail, as shown in Figure 6c. Changes in the background magnetic field will cause changes in the magnetic flux passing through the coils, thereby generating eddy currents. The current will continue to change to form the corresponding eddy-current magnetic field. An optical pump magnetic sensor and a three-axis fluxgate sensor are installed in the center of the cabin to calculate the three types of magnetic interference at the sensor location.  
Figure 5: The flight maneuver simulation results: (**a**) the longitude and latitude of the flight path; (**b**) flight attitude changes during the route.  
The magnetic signal on the route was collected at a frequency of 10 Hz based on the above simulation strategy, as shown in Figure 7.  
Figure 6: Illustration of flight platform interference simulation, where the gray part represents the aircraft model: (**a**) position of magnetic dipoles in intrinsic magnetic field simulation (indicated by red dots); (**b**) finite element division in induced magnetic field simulation (indicated by the purple square); (**c**) position of equivalent coils in eddy-current magnetic field simulation (indicated by red lines).  
Figure 7: Total magnetic signal collected on the simulated route.
## 3 Methodology
### Dataset  
This study uses a carefully curated dataset of 6043 English-Twi parallel sentence pairs, divided into 80% training data and 20% validation data for diverse linguistic representation. The target dataset \(D_{1}\) consists of data sampled from the low-resource distribution \(D\) and is pivotal for direct optimization for Twi translation tasks. The auxiliary datasets \(\{D_{i}\}_{i=2}^{n}\), derived from high-resource languages, such as English, to enable cross-lingual transfer learning. These auxiliary corpora facilitate knowledge transfer from well-represented languages to Twi, significantly improving translation quality.  
The English-Twi data were compiled from a mix of publicly available bilingual resources and manually curated translations. A major portion of the corpus was extracted from the Seventh-day Adventist Sabbath School quarterly books, which address a broad array of life-related topics. These books, published four times per year, served as a rich bilingual resource spanning 2019 to 2022, resulting in 12 editions. The sentence-level alignment was performed manually in collaboration with native Twi speakers and language experts, following structured curation guidelines:  
* Semantic alignment: Only sentence pairs with clear contextual equivalence were retained;
* Direct one-to-one mapping: Ensured consistent parallelism across the corpus;
* Noise reduction: Irrelevant commentary, redundancies, or non-alignable segments were excluded;  
Figure 2: Conceptual framework for low-resource machine translation.  
* Normalization: Variations in punctuation, casing, and formatting were standardized for consistency. This rigorous process ensures that the dataset captures both formal written structures and colloquial expressions, exposing the model to a wide range of syntactic and semantic patterns. Although modest in size, the dataset is highly task-specific and plays a critical role in advancing low-resource machine translation research and supporting inclusive NLP development for underrepresented languages like Twi.
## 3 Solar Radiation Prediction Time Horizons
### Time Horizon
#### 3.1.1 Nowcasting Solar Radiation  
The prediction horizon ranges from 15 min to a few hours, with no unanimity in its value [26]. In the short term, intra-hour forecasts are particularly useful for carrying out operations in the solar plant, balancing the grid, achieving automatic generation and trade control. Currently, it is very difficult to accurately predict solar radiation in the short term, as it involves knowing in advance how much energy solar plants produce instantly and this would be of great help in avoiding problems of supplying the line or avoiding surplus energy [27]. On this horizon, the statistical models that show the best performance are  
Figure 1: Prediction scale according to time horizon methods.  
those that use satellite images with greater accuracy in nowcasting predictions [15; 28] and statistical models for solar radiation time series, such as NWP [29; 30; 31]. Likewise, in recent years, several studies have been presented on the use of Machine Learning (ML) for solar radiation prediction using vector support machines (SVM) that show better performance in classification and regression analysis in time series [32; 33].
# Design and Analysis of a Novel Variable Stiffness Joint Based on Leaf Springs
## 2 Principle of the VSJ
### Design of Stiffness Adjustment Mechanism  
The working principle of the leaf spring effective length adjustment mechanism is shown in Figure 2. The position of the shift lever is determined by the intersection point of the straight rail on the drive disk and the arc-shaped rail on the stiffness varying disk. The adjustment of the effective working length of the leaf spring is achieved by changing the radial position of the shift lever across the entire joint diameter. The radial movement of the shift lever is driven by the rotational motion of the stiffness varying disk. The leaf spring effective working length is determined by the rotation angle of the adjustment plate, meaning it is necessary to establish the corresponding relationship between the radial displacement of the transmission pawl and the rotation angle of the adjustment plate. The symbols in Figure 2 are defined as listed in Table 1. Point A, S, and C are placed on the arc \(\widetilde{ASC}\) with the center point \(O_{1}\)  
The distance \(l_{2}\) from the slider to the output shaft center can be represented as follows:  
\[l_{2}=e_{3}+\sqrt{e_{3}^{2}-e_{4}} \tag{15}\]  
where \(e_{3}=x_{o1}\sin\alpha-y_{01}\cos\alpha\) and \(e_{4}=x_{01}^{2}+y_{01}^{2}-r_{s}^{2}\).  
The complexity of the arc groove curve determines the positioning accuracy, stability, and response speed of the adjustment process. In order to reduce the complexity of the VSM adjustment control, it is necessary to simplify Equation (13) and obtain the fitted function as follows [39]:  
\[l_{1}=p\alpha+q \tag{16}\]  
where \(p\) and \(q\) are the circular arc parameters of the adjustment disk.
## 3 Methodology
### Iterative Elastic Bin Refinement  
Accurately estimating depth from a monocular image is challenging due to the ambiguities and uncertainties inherent in the image-to-depth mapping process. One effective method for improving depth accuracy is iterative refinement, where depth predictions are progressively improved over multiple stages. In the DI-MDE framework, we utilize an iterative elastic bins (IEBins) refinement strategy to enhance the precision of depth predictions across both static and dynamic regions. This approach enables the depth estimation network to gradually narrow the search space for depth values. It focuses on areas with high uncertainty and refines the predictions over time. The key idea behind iterative elastic bin refinement is to treat depth prediction as a classification-regression problem, where the depth range is divided into discrete bins. Instead of predicting continuous depth values directly, the network classifies each pixel into one of these bins. The refinement process involves dynamically adjusting the bin widths based on depth uncertainty and iteratively refining the depth estimate in subsequent stages. The elastic bin refinement strategy builds on previous work by [7], where depth predictions are refined iteratively through a multi-stage process. The main innovation of our approach is the use of elastic bins. These bins have widths that are dynamically adjusted based on the uncertainty of the depth predictions for each pixel. This approach ensures that the refinement process focuses on the most uncertain regions, where precise depth estimation is most needed. In each iteration, the predicted depth for each pixel is refined by searching for the optimal depth value within a smaller, more precise range. The bin widths are adjusted dynamically based on the depth uncertainty, ensuring that the refinement process is both efficient and effective.
# A Study on the Properties of Recycled Aggregate Concrete and Its Production Facilities
## 3 Development of the Technology to Improve the Recycled Fine Aggregate Quality
### Principle of Technology
#### 3.3.5 Contents of Organic and Inorganic Impurities  
Waste concrete has a variety of impurities that are mixed in the recycled aggregate, which was produced by crushing them, resulting in a declined quality. Accordingly, the KS F 2527 \"Crushed Aggregate for Concrete\", defined the impurity contents (organic and inorganic impurity) of the recycled fine aggregate. This test was implemented by the KS F 2576 \"Method to Test the Foreign Substance Contents of Recycled Aggregates\", and as a result of the experiment (as shown in Figure 6), before and after penetration, the organic impurity experiment demonstrated a decrease in the organic impurity, for an average of approximately 0.59%, when penetrating in the first test of 1.21% \(\to\) 0.58%, the second test of 1.47% \(\to\) 0.82%, and the third test of 1.25% \(\to\) 0.77%, and as a result of the experiment, before and after penetration, in the inorganic impurity experiment, it demonstrated that a decrease in the contents of the inorganic impurity for an average of approximately 0.46%, in the first test, it showed a decrease from 0.95% \(\to\) 0.48%, in the second test, from 1.05% \(\to\) 0.55%, and in the third test, from 0.94% \(\to\) 0.54%.  
Figure 6: **(a)** Absolute dry density, **(b)** absorption rate, **(c)** percentage of absolute volume of the particle shape, **(d)** statistical volume of 0.08 mm, **(e)** contents of organic impurities, and **(f)** contents of inorganic impurities of recycled fine aggregate, before and after passing.
## 2 Preliminaries
_The subset_ \(C\) _of_ \(\mathbb{R}_{\mathbb{F}}\) _is level-equicontinuous in_ \(r_{0}\in[0,1]\) _if, for all_ \(r\in[0,1]\)_, we have_ \[\lim_{r\to r_{0}}d_{H}([\phi]_{r},[\phi]_{r_{0}})=0,\] _where_ \(\phi\in C\) _is a fuzzy-valued function._
3. _The continuous function_ \(\gamma:[a,b]\times\mathbb{R}_{\mathbb{F}}\to\mathbb{R}_{\mathbb{F}}\) _is compact if, for every_ \(a\leq c,b\leq d\) _and_ \(C\in\mathbb{R}_{\mathbb{F}}\) _(_\(C\) _is bounded), the set_ \(\gamma([c,d]\times C)\) _is relatively compact in_ \(\mathbb{R}_{\mathbb{F}}\)_._  
**Lemma 1** ([9]). : _Let \(C\) be a compact-supported subset of \(\mathbb{R}_{\mathbb{F}}\). Then, \(C\) is relatively compact if and only if \(C\) is level-equicontinuous on \([0,1]\)._  
Let us denote the space of all continuous fuzzy-valued functions by \(C([a,b],\mathbb{R}_{\mathbb{F}})\) and the space of all non-empty, closed, bounded, and convex subset of \(C([a,b],\mathbb{R}_{\mathbb{F}})\) by \(G\). **Theorem 1** ([9]). : _Let \(X\) be a non-empty, closed, convex, bounded subset of \(C([a,b],\mathbb{R}_{\mathbb{F}})\), and let \(T:X\to X\) be a continuous compact mapping. Then, there exists at least one point \(x^{*}\in X\) with \(T(x^{*})=x^{*}\)._  
**Definition 6**. : _Let \(\lambda>0\). On the space \(C([a,b],\mathbb{R}_{\mathbb{F}})\), we define the supremum metric \(H_{J}(\phi,\overline{\phi})=\sup_{\xi\in J}(d_{\infty}(\phi(\xi),\overline{ \phi}(\xi))\) and also define the weight metric_  
\[\hat{H}_{J}(\phi,\overline{\phi})=\sup_{\xi\in J}\Big{\{}e^{-\lambda(\xi-q)}d _{\infty}(\phi(\xi),\overline{\phi}(\xi))\Big{\}}.\]  
**Definition 7**. : _Let \(\sigma\in(0,1]\) and let \(H_{\sigma}(\phi,\overline{\phi})=\max\Bigl{\{}H_{[0,\sigma]}(\phi,\overline{ \phi}),\hat{H}_{[\sigma,1]}(\phi,\overline{\phi})\Bigr{\}}\). Denote \(\mathbb{X}=(C([a,b],\mathbb{R}_{\mathbb{F}}),C([a,b],\mathbb{R}_{\mathbb{F}}))\) and let \(\tau:\mathbb{X}\times\mathbb{X}\to R_{+}^{2}\) be \(\tau((\phi,\psi),(\overline{\phi},\overline{\psi}))=(H_{\sigma}(\phi,\overline {\phi}),H_{\sigma}(\psi,\overline{\psi}))\)._  
**Proposition 2** ([14]). : _The metric spaces \((C(J,\mathbb{R}_{\mathbb{F}}),H_{J})\), \((C(J,\mathbb{R}_{\mathbb{F}}),\hat{H}_{J})\), \((C(J,\mathbb{R}_{\mathbb{F}}),H_{\sigma})\), and \((\mathbb{X},\rho)\) are complete._  
**Definition 8** ([11]). :
1. _Let_ \(l\in(0,1]\)_,_ \(\phi\in C(J,\mathbb{R}_{\mathbb{F}})\) _and_ \([\phi(\xi)]_{r}=[\underline{\phi}_{r}(\xi),\overline{\phi}_{r}(\xi)]\)_,_ \(r\in[0,1]\)_. The Riemann-Liouville fuzzy fractional integral is defined as_ \[\stackrel{{ RL}}{{{}_{\mathbb{F}}}}l^{l}_{0+}\phi(\xi)=\frac{1}{ \Gamma(l)}\odot\int_{0}^{\xi}(\xi-z)^{l-1}\odot\phi(z)\mathrm{d}z,\ \xi\in[a,b],\] _and for the r-level of_ \(\stackrel{{ RL}}{{{}_{\mathbb{F}}}}l^{l}_{0+}\phi(\xi)\)_, we have_ \[[\stackrel{{ RL}}{{{}_{\mathbb{F}}}}l^{l}_{0+}\phi(\xi)]_{r}=[ \stackrel{{ RL}}{{{}_{\mathbb{F}}}}l^{l}_{0+}\underline{\phi}_{r}( \xi),\stackrel{{ RL}}{{{}_{\mathbb{F}}}}l^{l}_{0+}\overline{\phi} _{r}(\xi)],\ \xi\in J,\] _where_ \([\phi]_{r}=[\underline{\phi}_{r},\overline{\phi}_{r}]\)_._
2. _Let_ \(l\in[0,1)\) _and_ \(\phi\in C^{1}([a,b],\mathbb{R}_{\mathbb{F}})\)_.
## 4 Highly Effective Analysis Method Based on Residual Neural Networks
### Network  
In the design of network modules, taking into account the one-dimensional nature of side-channel signals, we selected the residual network structure as our training model. We utilized the optimal structure proposed by the authors in reference [39], which was introduced as a deep convolutional neural network architecture known for effectively training ultra-deep neural networks in reference [40]. In this paper, the residual block, as shown in Figure 2, consists of batch normalization, convolutional layers, pooling layers, and activation functions. In this network structure, BN (Batch Normalization) and ReLU are applied before the weights. This pre-activation approach makes it easier to optimize the model function and helps reduce overfitting.  
The key innovation of the ResNet structure lies in the introduction of residual structures or skip connections. These structures address the issue of gradient vanishing during the data flow process as the network becomes deeper, avoiding suboptimal training results. There are several advantages of using deep residual network models to analyze and process our electromagnetic signals:  
1. The electromagnetic signals have a larger number of effective points compared to individual image pixels. While each image typically has a few hundred numerical values, electromagnetic signals usually consist of tens of thousands of effective values. Increasing the depth of the network would require dealing with a large number of parameters. However, the residual network keeps each hidden layer relatively thin, allowing for control over the parameter count and saving computational space.  
2. Electromagnetic signals differ from pixel-level differences in images. The differences in electromagnetic signals are reflected in small amplitude statistical characteristics, requiring deep neural networks to extract deeper-level features. The hierarchical structure of the residual network ensures a progressive increase in the number of features at each layer, thereby maintaining the expressive power of the output features.  
Figure 2: The detail network structure with residual blocks.  
3. The residual structure of the identity mapping in the ResNet architecture inherently avoids the problem of gradient vanishing, which can hamper network training.  
Accuracy serves as the evaluation metric in our experiments, allowing for a comprehensive quantification of the results. The specific formula for calculating accuracy is as follows:  
\[A_{CC}=\frac{\sum TP}{TP+TN+FP+FN} \tag{4}\]  
where \(A_{CC}\) represents the overall accuracy of the evaluation. \(TP\), \(TN\), \(FP\), and \(TN\) represent the number of true positives, false negatives, false positives, and true negatives.
## 3 Results and Discussion
### Characterization of L1 and L2
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**\%wt**} & \multicolumn{3}{c}{**Atomic Ratios**} & \multicolumn{3}{c}{**Ln Absorbed**} \\  & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{**(ugg-1)**} & \multicolumn{3}{c}{**Proposed Formula**} \\ \cline{2-10}  & C & N & H & Zn/Al & C/N & H/N & La & Eu & \\ \hline L1 & 0.06 & 4.26 & 2.38 & 2.10 & 0.00 & 7.82 & 15.9 & 18.4 & [Zn\({}_{2}\)Al (OH)\({}_{8}\)]NO\({}_{3}\) \\ L2 & 13.7 & 3.35 & 3.60 & 1.67 & 0.31 & 15.1 & 87.6 & 103 & [Zn\({}_{2}\)Al(OH)\({}_{6}\)]\({}_{2}\)Cl\({}_{6}\)H\({}_{4}\)N\({}_{2}\)O\({}_{8}\)] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Chemical analysis results of L1 and L2. Figure 3: SEM images of (a) L1, (b) L2, (c) L2-La, (d) L2-Eu and (e) mapping image of L2-Eu. nm and 1.47 nm, respectively. These values were calculated by the Bragg equation. The interlayer distance was obtained by subtracting from the basal spacing to the layer width (0.48 nm) [39]. The interlayer distance of L2 and L2-Eu indicates that EDTA coexists and that EDTA is intercalated into L1 successfully. The adsorption process also increases the interlayer distance because it is close to the dimensions of EDTA complexes (0.9-1 nm) generally found for by single-crystal XRD of M-EDTA (M: Metallic ions) compound [40]. The very sharp peak at 1380 cm\({}^{-1}\) in Figure 5a can be attributed to the NO\({}_{3}\)\({}^{-}\) stretching vibration, while the corresponding peak is absent in Figure 5b,c. The absorption bands around at 1624 and 1394 cm\({}^{-1}\) are characteristics of the symmetrical and asymmetrical vibrations of COO-groups [39]. The peaks around 2950 cm\({}^{-1}\) (-CH) and 1111 cm\({}^{-1}\) (-CN) are typical peaks of EDTA as well. A relatively sharp band at 776 cm\({}^{-1}\) is assigned to C-H out-of-plane bending, and a broad band at about 609 cm\({}^{-1}\) is due either to a vibrational mode of hydroxyl groups and water or to a metal-oxygen vibration \(\
u\)(M-O). Two peaks at about 562 and 430 cm\({}^{-1}\) are attributed to \(\
u\)(M-O) and \(\delta\)(M-O-M) (M: Zn, Al or Eu), respectively [41; 42]. No significant changes were observed in this region upon the incorporation of the Europium ions.
## 2 Mobility Models in FANETs
### Group Mobility Models
#### 2.2.1 Column Mobility Model  
Each node in the Column (CLMN) mobility model [47] revolves around forward-moving a reference point on a specified line. Specifically, every mobile node in a CLMN model rotates around the point of reference at random speed and direction. Figure 8 shows an example of two UAV trajectories following CLMN. These models' spatial constraints can ensure that UAVs in each group stay connected while also preventing collisions. The CLMN mobility model can be applied for agricultural management or scanning application scenarios, where UAVs start flying in a specific area to scan for a specific target. Whenever one of the UAVs spot the target, it starts to transmit data to a base station through other relaying UAVs [48; 49].  
Figure 7: The trajectory of FANETs using MG models.
# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 3 Results
### Preprocessing
#### 3.2.2 Data Augmentation  
ECG data often has biased distributions of abnormal categories much less than normal categories, as the abnormal signals are more difficult to obtain. DL models trained with the imbalanced ECG dataset will, in nature, put more attention to majority categories and overlook the minority categories leading to biased learning. In this survey, we focus on the data argumentation technologies [39], which take effect during the data preprocessing step to gain more training samples. From the selected studies, 102 (28%) studies explicitly claim the use of data augmentation techniques in their work. The augmentation techniques can be categorized into two types, i.e., perturbation-based methods (64%, 65 out of 102) and synthetic-based methods (36%). Specifically, for perturbation-based methods, extra data samples can be added to ECG dataset by adjusting or perturbating the original samples from the same dataset, such as scaling and shifting ECG waveforms [40] or adding artificial noise to existing ECG signals [41]. The perturbation of data samples is essentially acquiring new data samples from the neighborhood of corresponding original data samples in the feature space. Hence, the new data samples could be highly correlated to the original samples based on which the new data is perturbated. On the other hand, synthetic-based methods generate synthetic ECG data either based on the linear combination of real data samples or the construction of ECG signals by imitating real ECG features. The synthetic minority oversampling technique (SMOTE) and its variants, such as SMOTENN [42], Borderline SMOTE [43], and SVM-SMOTE [42; 43; 44; 45], are often used to extend the minority categories. Just recently, DL techniques have also been used for synthetic data generation, e.g., the convolutional neural style transfer network [46], the generative adversarial network (GAN) [47], and the ACGAN consists of variational auto-encoder model [14].  
Figure 3b shows frequency counts of the two types of augmentation methods each year. One can see that the synthetic-based strategies have drawn more attentions in recent years as the number of works considering this type of data augmentation method increased from 1 in 2017 to 17 in 2022.