esa-sceva/satcom-chunk-collection huggingface dataset:
# Angular Trajectory Design for MR-SPS Using Bezier Shaping Approach
## 2 Model Description
### Attitude Dynamic Model
The torque balance equations of the central body and the \(j\)th (\(j\) = 1,2) solar array are listed as follows:  
\[\textbf{T}_{c}+\textbf{T}_{ao}+\sum\
olimits_{k=1}^{2}(\textbf{T}_{ak}+\textbf {r}_{ck}^{\times}\textbf{F}_{ak})=\textbf{I}_{a}\dot{\textbf{\omega}}_{a}+ \textbf{\omega}_{a}^{\times}\textbf{I}_{a}\textbf{\omega}_{a} \tag{11}\]  
\[\textbf{T}_{soj}+\textbf{T}_{sij}=\textbf{I}_{sj}(-\textbf{\omega}_{sj}^{ \times}\textbf{C}_{sj}\textbf{\omega}_{a}+\textbf{C}_{sj}\dot{\textbf{\omega}} _{a}+\textbf{\omega}_{sj})+(\textbf{C}_{sj}\textbf{\omega}_{a}+\textbf{\omega} _{sj})^{\times}\textbf{I}_{sj}(\textbf{C}_{sj}\textbf{\omega}_{a}+\textbf{ \omega}_{sj}) \tag{12}\]  
\[\textbf{T}_{sij}=-\textbf{C}_{sj}\textbf{T}_{aij} \tag{13}\]\[\tau_{sij}=D_{j}^{T}T_{sij} \tag{14}\]  
where \(\mathbf{I}_{a}=\text{diag}(I_{a1},I_{a2},I_{a3})\) denotes the moment of inertia of the central body relative to the center of mass, and \(\mathbf{I}_{sj}=\text{diag}(I_{sjr},I_{sjr},I_{sjr})\) denotes the moment of inertia of the \(j\)th solar array relative to the center of mass. \(\mathbf{T}_{c}\) and \(\tau_{scj}\) are the control torques of the central body and the \(j\)th solar array. \(\mathbf{T}_{aoj}\) and \(\mathbf{T}_{soj}\) are external disturbance torques acting on the central body and the \(j\)th solar array. \(\mathbf{T}_{aij}\) and \(\mathbf{T}_{sil}\) are the torques exerted on the central body by the \(j\)th solar array and on the \(j\)th solar array by the central body. \(\mathbf{\omega}_{sj}=\mathbf{D}_{j}\dot{\theta}_{j}\) denotes the angular velocity of the \(j\)th solar array respect to the central body frame expressed in \(j\)th solar array frame, where \(\mathbf{D}_{j}= \begin{bmatrix}0&1&0\end{bmatrix} ^{T}\) represents the unit vector of the direction of the rotation axis of the \(j\)th solar array and \(\theta_{j}\) represents the angle that the \(j\)th solar array rotates. \(\mathbf{C}_{sj}\) can be written as:  
\[\mathbf{C}_{sj}= \begin{bmatrix}\cos\theta_{j}&0&-\sin\theta_{j}\\ 0&1&0\\ \sin\theta_{j}&0&\cos\theta_{j}\end{bmatrix}  \tag{15}\]  
The attitude dynamics are expressed in a form convenient for the subsequent research on attitude control.
## 2 Theoretical Background
### Imputation Methods
#### 2.1.1 Single Imputation  
This imputation method replaces each missing value with a single appropriate value according to a specific rule [4,20,21,22]. Several methods can be used for imputing missing values. **Measures of central tendency**, such as the mean, median, and mode, can serve as effective statistical measures to represent the center or typical value of the data [10,23]. Another approach is **regression-based data imputation**, which involves estimating the relationships between a dependent variable and independent variables while minimizing the mean square error over a training data set. This method trains a regression model using complete cases and then predicts missing values based on the predictor variables [4,24]. The **Hotdeck imputation** groups cases with complete data according to their similarity to the cases with missing values and randomly selects one or more donor cases from this group to provide values for imputation [11,12]. The **K-Nearest Neighbors (KNN) method** is a non-parametric technique that computes the similarity between observations using metrics such as Euclidean distance. It selects the \(k\) nearest neighbors to impute missing values by taking the mean for numerical variables or the mode for categorical variables [13,25,26] In this study, we utilized the code from the KNNImputer documentation at [[https://scikit-learn.org/stable/modules/generated/sklearn.impute.KNNImputer.html](https://scikit-learn.org/stable/modules/generated/sklearn.impute.KNNImputer.html)]([https://scikit-learn.org/stable/modules/generated/sklearn.impute.KNNImputer.html](https://scikit-learn.org/stable/modules/generated/sklearn.impute.KNNImputer.html)), accessed on 11 February 2025, which was introduced in [25]. Finally, the **GenerativeAdversarial Imputation Network (GAIN)The code for GAIN is here [[https://github.com/dhanajitb/GAIN-Pytorch](https://github.com/dhanajitb/GAIN-Pytorch)]([https://github.com/dhanajitb/GAIN-Pytorch](https://github.com/dhanajitb/GAIN-Pytorch)), accessed on 11 February 2025 is a novel approach that combines a generative adversarial network with a discriminative network to assess imputation quality. The generator is trained to minimize reconstruction error by producing realistic imputed values that fool the discriminator, which discriminates between imputed and real data. Once trained, GAIN can impute missing values in new records, generating values for missing entries based on incomplete data [14; 27; 28].
# Robot Swarms Decide under Perception Errors in Best-of-N Problems
## 4 Simulations  
The simulated environment consists of a 2D arena divided into three regions: two sites (A and B) and a nest in the middle of the two sites. Figure 3 shows a snapshot of the arena in which the best-of-n decision problem is implemented. We add lights above the nest in the middle of the arena to enable robots to use phototaxis and antiphototaxis behaviors while navigating form and towards the nest.  
As mentioned in the robot's basic and extended behavioral models and illustrated in both Figures 1 and 2, all robots start in the exploration state and with no commitment to any of the two sites (options). They leave the nest to explore the arena by performing antiphototaxis behavior. Robots use random walks [25] to search for the different sites. Once a robot encounters a site, it starts inspecting its quality value \(\gamma_{i}(l)\) by taking \(\xi\) measurements. After that, the robot exploits phototaxis behavior to navigate back to the nest. In the nest, robots communicate the information they have collected in order to achieve a consensus optimally on the site with the highest quality value. Our experiments are divided in two main sets: (i) In the first set, we enable measurement errors to occur at Site A (red) only, and (ii) we enable measurement errors to occur at both sites A and B. This can be mapped to real scenarios where the sites are characterized with specific access conditions.  
We have performed a set of preliminary experiments to finetune the parameter values of the basic behavioral model. Table 1 holds the used values.  
As the accuracy of the classification algorithm does not change by varying the number of sites at which robots may obtain erroneous measurements, we examine the accuracy of the classification algorithms using the following set: (i)--errors in measurements can occur at site A only. Differently, investigating the accuracy of the emergent swarm's decision may depend on the number of sites where inaccurate measurements can occur. Therefore, we consider both sets (i) and (ii) of experiments when examining the accuracy of the collective decision under both conditions of using a classification algorithm vs. no classification. For this investigation, we select the classification algorithm that results in the best accuracy.  
Figure 3: Snapshot of the site selection arena in ARGoS environment.
# Developing a Serious Video Game to Engage the Upper Limb Post-Stroke Rehabilitation
## 2 Materials and Methods
### Game Implementation
#### 2.4.3 Avatar  
For this work, two avatars were selected (as shown in Figure 4), one female and one male, to replicate the patient's movements while using the application. The avatars were selected from Mixamo due to their seamless integration with Unity and their suitability for accurately replicating movements.  
Figure 3: Kinect V2’s skeleton of the user’s position.  
Following the completion of the initial configurations to integrate Mixamo avatars with Unity 3D, a method was required to transfer the points captured by the Kinect V2 sensor to the avatars, enabling them to replicate the movements of the user. The approach employed to accomplish this was Inverse Kinematics (IK). IK in Unity refers to the computational technique used to determine the necessary joint rotations and positions within a hierarchical skeletal structure to achieve a specific target position for an end effector, such as a hand or foot. Unlike forward kinematics, where joint angles are directly manipulated to compute the position of the end effector, IK works in reverse, calculating the required joint configurations to reach a predetermined position. In Unity, IK is utilized primarily in character animation to ensure that parts of a character's body, like limbs, can dynamically and naturally interact with the environment [26]. The Two Bone IK constraint enables the inversion of control within a simple hierarchy of two GameObjects, allowing the tip of a limb to reach a designated target position. An additional Hint GameObject can be used to define the desired orientation of the limb when it bends [27]. With all configurations applied, the avatar in the development scene environment will appear as shown in Figure 5.  
The avatar's movements are utilized at three distinct stages within the application. During gameplay, motion capture data from Kinect V2 are applied for generating brief  
Figure 4: Avatars used in the game: (**left**)—female avatar; (**right**)—male avatar.  
Figure 5: Avatar structure: the blue skeleton represents the bone renderer; the red cubes indicate the targets; the red spheres mark the hints; and the green boxes in the hands are the box colliders.  
animations to demonstrate specific movements, such as illustrating the proper technique for performing a lateral arm raise, as depicted in Figure 6, and for the movement of specific body parts to determine the type of gameplay (for instance, changing the leg position to simulate a standing or sitting position).  
The data used by the code to modify the avatar's movements are provided in the form of three-dimensional spatial coordinates (x, y, z). This design enables the system to accept motion data from cameras other than Kinect V2, facilitating the game's operation. For integrating alternative input sources, it was enough to develop code that outputted three-dimensional spatial coordinates and supplied them as input to the avatar.
## 2 Descriptions of DUT and HPEMs
### HPEM Waves  
The main types of HPEM environment waves are described in this subsection, which contains HEMP, UWB and HPM. The HEMP environment has three stages, as early (E1), middle (E2) and late (E3). For small-scale metal structures like antenna or cables, the HEMP E1 wave is easier to induce coupling EM pulse energy impacting on the circuits of the victim system. Hence, it is considered in this paper. It takes a double exponential waveform as the standard wave, with rising time of 2.5 nanoseconds, a pulse width of 23 ns and peak value of 50 kV/m (Figure 3a). Most of 99% spectrum energy concentrates under 300 MHz, illustrated in Figure 3b. The HEMP formula is expressed by [19]  
\[e_{inc}(t)=\begin{cases}0&t\leq 0\\ E_{0}\cdot k_{0}\Big{(}e^{-at}-e^{-bt}\Big{)}&t>0\end{cases} \tag{1}\]  
where, \(E_{0}=50\) kV/m, \(a=4\times 10^{7}s^{-1}\), \(b=6\times 10^{8}s^{-1}\), \(k_{0}=1.3\).  
UWB is a fast-rising edge or a single-period pulse with an ultra-wide frequency spectrum range, the main waveform form of which is unipolar or bipolar. UWB has no standard time domain waveform parameters, while the researchers generally adopt differential Gaussian pulse to represent bipolar UWB. Its expression is  
\[E(t)=E_{0}k\frac{t-t_{0}}{\tau}\exp\left(-\frac{4\pi(t-t_{0})^{2}}{\tau^{2}}\right) \tag{2}\]  
where, \(E_{0}\) is peak value, \(t_{0}\) is the time of reach to the field peak, \(\tau\) is pulse width, and peak factor \(k\) is set as \(\sqrt{8\pi}\exp(0.5)\). Considering the center frequency of the RF channel used in this paper, the main parameters of UWB are sets as: \(E_{0}=5\times 10^{4}\), \(\tau=1\) ns, and \(t_{0}=1\) ns. The time and frequency domain wave of the UWB is illustrated in Figure 4.  
Figure 3: HEMP waves: (**a**) time domain; (**b**) frequency domain.  
Similar with UWB, there is no strict definition for HPM waves. Traditionally, the EMP with peak power larger than 100 MW and carrier frequency among 1 GHz-300 GHz is assumed as HPM. The simplified time domain form of HPM is expressed by  
\[E_{(t)}=\left\{\begin{matrix}E_{0}\frac{t}{t_{1}}\sin(2\pi f_{0}t)&0<t<t_{1}\\ E_{0}\sin(2\pi f_{0}t)&t_{1}\leq t<t_{1}+\tau\\ E_{0}(\frac{\tau+2t_{1}}{t_{1}}-\frac{t}{t_{1}})\sin(2\pi f_{0}t)\,t_{1}+\tau \leq t<2t_{1}+\tau\end{matrix}\right. \tag{3}\]  
where the center carrier frequency \(f_{0}\) is 1.5 GHz, pulse width \(\tau=80\), and \(t_{1}=10\) ns is its rising and falling time. Figure 5 shows the time and frequency domain waves.  
Figure 4: UWB waves: (**a**) time domain; (**b**) frequency domain.  
Figure 5: HPM waves: (**a**) time domain; (**b**) frequency domain.
# Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia
###### Abstract  
The new Sentinel-2 Multi Spectral Imager instrument has a set of bands with very similar spectral windows to the main bands of the Landsat Thematic Mapper family of instruments. While these should, in principle, give broadly comparable measurements, any differences are a function not only of the differences in the sensor responses, but also of the spectral characteristics of the target pixels. In order to test for and quantify differences between these sensors, a large set of coincident imagery was assembled for the Australian landscape. Comparisons were carried out in terms of surface reflectance, and also in terms of biophysical quantities estimated from the reflectances. Small but consistent differences were found, and suitable adjustment equations fitted to enable transformation of Sentinel-2A reflectance values to more closely match Landsat-7 or Landsat-8 values. This is useful if trying to take models and thresholds fitted from Landsat and use them with Sentinel-2. The fitted adjustment equations were also compared against those fitted globally for NASA's Harmonized Landsat-8 Sentinel-2 product, and found to be substantially different, raising the possibility that such adjustments need to be fitted on a regional basis.  
landsat; Sentinel-2; surface reflectance; comparison +
Footnote †: journal: Remote Sensing  
1
Footnote 1: institutetext: Department of Physics, University of Queensland, St. Lucia, QLD 4072, Australia; [EMAIL_ADDRESS]; Tel.: +61-7-3170-5677
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 8 Underwater SLAM Systems Evaluation and Comparison
### Mapping Evaluation  
Mapping evaluation assesses the quality and accuracy of the maps generated by SLAM systems. Key considerations include map consistency, ensuring that the map accurately represents the environment without distortions or inconsistencies. Standard metrics for mapping evaluation are the structural similarity index (SSIM), which measures the similarity between the generated map and a ground truth map, and the intersection over union (IoU), which evaluates the overlap between the mapped areas and the actual environment [154].  
To evaluate how accurately a SLAM system reconstructs the underwater environment, mapping metrics focus on comparing the generated map to a reference or ground truth representation.  
**Structural Similarity Index:** The SSIM quantifies the similarity between two images (in this case, the generated map and the ground truth map). It considers luminance, contrast, and structural information to produce a value in the range \([-1,1]\), where \(1\) indicates perfect similarity.  
A simplified form of the SSIM between two images \(X\) and \(Y\) is given by  
\[\text{SSIM}(X,Y)=\frac{(2\mu_{X}\mu_{Y}+C_{1})(2\sigma_{XY}+C_{2})}{(\mu_{X}^{2 }+\mu_{Y}^{2}+C_{1})(\sigma_{X}^{2}+\sigma_{Y}^{2}+C_{2})} \tag{1}\]  
where \(\mu_{X}\) and \(\mu_{Y}\) are the mean intensities of \(X\) and \(Y\), \(\sigma_{X}^{2}\) and \(\sigma_{Y}^{2}\) are their variances, \(\sigma_{XY}\) is the covariance, and \(C_{1}\), \(C_{2}\) are small constants to avoid division by zero.  
**Intersection over Union:** The IoU measures the overlap between the mapped regions and the ground truth areas. It is defined as the ratio of the intersecting region to the union of the predicted and ground truth sets of mapped points:  
\[\text{IoU}(A,B)=\frac{|A\cap B|}{|A\cup B|} \tag{2}\]  
where \(A\) is the set of points mapped by the SLAM system, and \(B\) is the set of ground truth points. An IoU closer to 1 indicates a better overlap between the generated and actual maps.  
These mapping metrics help determine if the SLAM-generated maps are free of distortions and inconsistencies, ensuring that the environment is accurately represented.
## 3 DOA Estimation Based on Cyclostationary Characteristics  
### Cyclostationary Properties of OFDM Signals  
Based on the signal model described in Section 2.1, the mathematical expectation and autocorrelation function of OFDM signals can be derived as  
\[E\{x_{0}(t)\}=0, \tag{7}\]\[\begin{array}{ll}\mathrm{R}_{x_{0}x_{0}}(t,\tau)&=E\{x_{0}(t+\tau){x_{0}}^{*}(t)\} \\ &=\frac{\sigma_{x}^{2}}{4}\left[\begin{array}{l}\sum\limits_{n}\sum\limits_{l =-N/2}^{N/2}q(t+\tau-nT_{0}-t_{0})q(t-nT_{0}-t_{0})e^{-j2\pi\pi(\left\{ \Delta f+f_{c}\right\}\right\}\\ \left.+\sum\limits_{n}\sum\limits_{l=-N/2}^{N/2}q(t+\tau-nT_{0}-t_{0})q(t-nT_ {0}-t_{0})e^{j2\pi(\left\{\Delta f+f_{c}\right\}\right\}\\ \end{array}\right].\end{array} \tag{8}\]  
According to the definition of the cyclic autocorrelation function, with the help of the LPTV model, the cyclic autocorrelation function of the OFDM signal can be derived as [28]:  
\[\begin{array}{ll}\mathrm{R}_{x_{0}x_{0}}^{a}(\tau)&=\frac{1}{T_{0}}\int_{-T_{ 0}/2}^{T_{0}/2}\mathrm{R}_{x_{0}x_{0}}(t,\tau)e^{-j2\pi\pi dt}\\ &=\left\{\begin{array}{l}\frac{\sigma_{x}^{2}}{4T_{0}}\sum\limits_{l=-N/2}^{ N/2}\left(e^{-j2\pi(\left\{\Delta f+f_{c}\right\}\right\}+e^{j2\pi(\left\{ \Delta f+f_{c}\right\}\right\})\int_{-T_{0}/2}^{T_{0}/2}\sum\limits_{n}q(t+\tau -nT_{0}-t_{0})q(t-nT_{0}-t_{0})e^{-j2\pi\pi dt}dt,\alpha=\frac{z}{T_{0}},\\ 0,\alpha\
eq\frac{z}{T_{0}}.\end{array}\right.\end{array} \tag{9}\]  
where \(z\) represents an integer and \(\alpha\) represents the cyclic frequency. Equation (9) indicates that the cyclic autocorrelation function of the OFDM signal exhibits discrete spectral lines specific to the cyclic frequency, appearing at \(\alpha=z/T_{0}\). When \(z=0\), the cyclic autocorrelation function reaches its maximum value, which is equivalent to the standard autocorrelation function. When \(z=1\), \(\alpha\) is referred to as the base cyclic frequency at which point the cyclic autocorrelation function attains its second-largest value.
## 6 Discussion on Clinical Relationship Extraction
### Supervised Learning Methods
#### 6.3.1 Traditional Machine Learning and Deep Learning-Based Methods
The various features used for RE as an input to the random forest classifier such as entity types, number of words in the entity, and an average of the entity word embeddings resulted in a micro-averaged F1-score of 0.88 (precision: 0.82; recall: 0.94). [PERSON] et al. (2018) [72] used node2 vec to learn the features from texts in networks in order to extract relations for biological pathways, which outshone the previous methods to detect relationships in the type 2 diabetes pathway. [PERSON] et al. (2018) [96] compared the performance of an SVM model with a deep learning-based LSTM model to extract relations towards drug surveillance. SVM showed better performance (89.1% F1-score) on the test data compared with that of LSTM. [PERSON] et al. (2019) [97] introduced a novel approach for RE in clinical texts by using neural networks to model the shortest dependency path between the target entities along with the sentence sequence. This approach used on the 2010 i2b2 relation extraction dataset improved the performance to an F1-score of 74.34%. The multi-class SVM model on this dataset, introduced by [PERSON] et al. (2019) [81] achieved an F1-score of 0.70, which is lower than the previous models. [PERSON] et al. (2020) [79] proposed an ensemble deep learning method to extract the adverse drug events and medications relations, which achieved a micro-averaged F1-score of 0.9472 and 0.8765 for RE and end-to-end RE, respectively. [PERSON] et al. (2020) [82] compared the performance of different deep learning methods such as CNN, GCN, GCN-CDT, ResNet, and BiLSTM on various combinations of features, as mentioned in the previous subsection for clinical RE. BiLSTM achieved the highest 9 class F1-score of 0.8808 in that dataset. Both CNN models used by [PERSON] et al. (2021) ([PERSON] et al., 2021), segment-CNN and the sentence-CNN, failed to surpass the performance of the rule-based model proposed for this dataset. Research has shown that the traditional ML methods have outperformed deep learning methods in many clinical RE tasks where the dataset has limited data instances, whereas in some cases where more data is present, deep learning methods given better performance. Additionally, the level of performance depends on the complexity of the data. Currently, it is not possible to generalize whether traditional ML methods or deep learning methods perform the best for clinical RE as the performance is very data-dependent. Some clinical RE tasks using traditional machine learning and deep learning-based methods are tabulated in Table A4.
###### Abstract
To achieve better measurement results, additional instruments (e.g., demodulation devices) are necessary; however, these auxiliary devices are costly and hinder large-scale application, while the quality of the transmitted signal is poor, making signal loss and other issues more likely. With the in-depth exploration of magnetic materials and advancements in material processing technology, permanent magnetic materials are being applied more widely. Permanent magnets offer advantages such as high magnetic field strength, favorable magnetic field distribution, and compact size, making them suitable as magnetic field sources across various fields [25; 26; 27; 28]. Building on this, this study aimed to integrate the magnetic field distribution characteristics of permanent magnets with anchor cable tensile force monitoring to develop a novel anchor cable force monitoring technology. This technology is characterized by strong and stable magnetic properties, high measurement accuracy, and low cost, making it a promising and reliable approach for anchor cable force monitoring.