esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Virtual Constellation Development  
The proposed virtual constellation is a remote sensing modeling system that integrates three satellite sensors to produce a single NDVI image each month that is free from cloud contamination (Figure 4). The method was developed under two assumptions regarding its primary application, long-term analysis of coastal marsh vegetation. First, we assume that NDVI is a proxy for overall CW vegetation health [54], and a monthly NDVI time series will follow a typical seasonal pattern except when influenced by major external forces such as sea level rise (long-term) or hurricane storm surge (short term). Second, inequality of NDVI between different sensors has systematic [20] and random [67] components and their relationship can be modeled using long-term historical data [68].  
The first objective of the virtual constellation development was to align the NDVI value scales across the three sensors. Common observations from the three sensors during the coincident time-period (i.e., same calendar month) provided the opportunity for inter-sensor comparison. Relationships were analyzed at the pixel level between sensor pairs. The reason for developing the relationships between two sensors at a time is that each pair of sensors has unique inconsistencies from systematic and random error components [67]. The systematic inconsistency comes from climate conditions or geographical locations of the satellites [69] and the random inconsistency comes from multiple sources such as difference in overpass timing, sun angle, sensor mechanism and other sensor specific features. The amount of inconsistency varies in each sensor combination. Traditional (LR) would be the simplest method to establish NDVI relationships between sensors. However, LR has previously been shown to be ineffective in capturing complex relationships when synthesizing remote sensing imagery so the proposed virtual constellation implements an RF similar to previous research [54]. By using four years of monthly NDVI data adjusted to be compatible with L8 with sufficient temporal coincidence among the sensors, the RF was developed to predict NDVI values for L8 pixels obscured by clouds and shadows.
## Abstracts
### OSC-XR: A Toolkit for Extended Reality Immersive Music Interfaces  
[PERSON], [PERSON], and [PERSON]  
Currently, developing immersive music environments for extended reality (XR) can be a tedious process requiring designers to build 3D audio controllers from scratch. OSCXR is a toolkit for Unity intended to speed up this process through rapid prototyping, enabling research in this emerging field. Designed with multi-touch OSC controllers in mind, OSC-XR simplifies the process of designing immersive music environments by providing prebuilt OSC controllers and Unity scripts for designing custom ones. In this work, we describe the toolkit's infrastructure and perform an evaluation of the controllers to validate the generated control data. In addition to OSC-XR, we present UnityOscLib, a simplified OSC library for Unity utilized by OSC-XR. We implemented three use cases, using OSCXR, to inform its design and demonstrate its capabilities. The Sonic Playground is an immersive environment for controlling audio patches. Hyperemin is an XR hyper instrument environment in which we augment a physical theremin with OSC-XR controllers for real-time control of audio processing. Lastly, we add OSC-XR controllers to an immersive T-SNE visualization of music genre data for enhanced exploration and sonification of the data. Through these use cases, we explore and discuss the affordances of OSC-XR and immersive music interfaces.  
S3.5. No Strings Attached: Force and Vibrotactile Feedback in a Guitar Simulation  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]  
In this paper, we propose a multisensory simulation of plucking guitar strings in virtual reality. The auditory feedback is generated by a physics-based simulation of guitar strings, and haptic feedback is provided by a combination of high fidelity vibrotactile actuators and a Phantom Omni haptic device. Moreover, we present a user study (n = 29) exploring the perceived realism of the simulation and the relative importance of force and vibrotactile feedback for the creation of a realistic experience of plucking virtual strings. The study compares four conditions: No haptic feedback, vibrotactile feedback, force feedback, and a combination of force and vibrotactile feedback. The results indicate that the combination of vibrotactile and force feedback elicits the most realistic experience, and during this condition, the participants were less likely to inadvertently hit strings after the intended string had been plucked. Notably, no statistically significant differences were found between the conditions involving either vibrotactile or force feedback, which points towards an indication that haptic feedback is important but does not need to be of high fidelity in order to enhance the quality of the experience.
# Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images
## 3 Results and Analysis
### Assessment of the Winter Wheat Map  
The accuracy of winter wheat mapping was assessed using the validation samples. The confusion matrix calculation showed that the OA of the winter wheat map was 92.2% and the Kappa coefficient was 0.84 (Table 2). In addition, the F1-score for the winter wheat map was 0.96.  
In addition, we compared with some of the currently available datasets of winter wheat to more objectively evaluate the accuracy of winter wheat identification. They are the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multicolumn{4}{c}{**Validation Samples**} \\ \cline{2-5}  & **Winter Wheat** & **Others** & **Total** & **UA (\%)** \\ \hline Winter wheat & 134 & 14 & 148 & 0.91 \\ Others & 8 & 126 & 134 & 0.94 \\ Total & 142 & 140 & 282 & \\ PA (\%) & 0.94 & 0.90 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The confusion matrix of winter wheat map.  
Figure 5: Winter wheat map with 10 m resolution and close-up views of Shandong Province.  
30-m winter wheat maps in China (ChinaWheat30) [61] and the first 10-m resolution maps of winter wheat over China (ChinaWheat10) in 2020 [62]. It can be seen from Figure 6 that compared to the 30 m resolution ChinaWheat30, the 10 m resolution ChinaWheat10 and the winter wheat maps produced in this research showed more details, like rivers, roads, etc. From the comparison in the first row, it can be seen that the classification results of the three maps are roughly the same, and especially the ChinaWheat10 results are the most detailed and credible. However, we can still see that in the complex landcover conditions and in the hilly areas (the second and third rows), the Shandong winter wheat map gives better results, is more consistent with the reality and allows more complete wheat cropping plots to be obtained.
## 4 Results
### The Relationship between Population Density and Impervious Surface Area Fraction  
The accuracy assessment was conducted using the reference data and estimation results based on LSMA and object-based SVM algorithm. 100 random points of impervious surface were used as reference, and the reference data of ISA percentage value in the result validation were defined by the percentage imperviousness map based on the regression model of WorldPop (100 m resolution in 2010), Global Human Settlement Layer (GHSL: 250 m resolution in 2014) and Night-time light images (1 km resolution in 2009, 2010, 2011). Table 1 demonstrates that the R\({}^{2}\) between pixel-wise LSMA ISA values and GHSL values were 0.062, 0.312, and 0.194 respectively, lower than the correlations between object-based ISA values and GHSL values (0.319, 0.313, and 0.229 respectively). The R\({}^{2}\) between pixel-wise LSMA ISA fraction values and WorldPop values (0.168, 0.269, and 0.133 respectively) are also lower than the correlations between object-based ISA fraction values and WorldPop values (0.272, 0.234, and 0.202 respectively). The R\({}^{2}\) between pixel-wise LSMA ISA fraction values and Night-time light image values were 0.103, 0.268, and 0.302 respectively, while the correlations between object-based ISA values and Night-time light image values were 0.473, 0.489, and 0.156 respectively. The object-based ISA fraction demonstrated higher degree of correlation with the products of population density, night-time light images and global human settlement layers.  
At the same time, the overall estimates of RMSE of the LSMA algorithm for the three years were 0.506, 0.343, 0.357 respectively, with bias of \(-\)0.031, \(-\)0,070, \(-\)0.087. While the RMSE of SVM-LSMA  
\begin{table}
\begin{tabular}{l c c c} \hline  & **GHSL** & **WorldPop** & **Night-Time** \\ \hline Object-based ISA fraction in 2009 & 0.319 ** & 0.272 ** & 0.473 ** \\ Object-based ISA fraction in 2010 & 0.313 ** & 0.234 ** & 0.489 ** \\ Object-based ISA fraction in 2011 & 0.229 ** & 0.188 & 0.445 ** \\ Pixel-wise ISA fraction in 2009 & 0.062 & 0.168 & 0.103 \\ Pixel-wise ISA fraction in 2010 & 0.312 ** & 0.269 ** & 0.268 ** \\ Pixel-wise ISA fraction in 2011 & 0.194 & 0.133 & 0.302 ** \\ \hline \end{tabular} \({}^{*}\) Correlation is significant at the 0.05 level (2-tailed); ** Correlation is significant at the 0.01 level (2 tailed).  
\end{table}
Table 1: The differences between pixel-wise impervious surface fraction and object-based support vector machine-improvious surface fraction results related to GHSL, WorldPop, and Night-time light images.  
Figure 3: The estimation of impervious surface fraction based on two methods. (**a**) Pixel-wise linear spectral mixture analysis (**b**) Object-based support vector machine-linear spectral mixture analysis.  
for three years were 0.386, 0.392, 0.468, respectively, with the bias of 0.066, 0.042, 0.326. It is difficult to compare the differences of LSMA algorithm and SVM-LSMA analysis only based on statistical analyses.
###### Abstract  
Cables or slip-rings are often used to power loads on a rotating unit in the rotation modulated inertial navigation system (RMINS). However, these power supply methods have the disadvantages of cable winding and slip ring friction and wear, which reduces the reliability and life of the RMINS. Therefore, this paper applies magnetic coupling resonant wireless power transfer (MCRWPT) technology to the RMINS to avoid the shortcomings of the above power supply methods. Furthermore, according to the structure and working characteristics of the RMINS, a simple design method of the MCRWPT system without any feedback control is proposed. Based on the ANSYS simulation, the magnetic shielding structure is designed to reduce magnetic leakage, and the efficiency of the MCRWPT system is optimized by designing the excitation frequency. Experiments verify the effectiveness of the proposed method. The experimental results show that the designed MCRWPT system can achieve an efficiency of 74.6% with an output power of 10 W and has been successfully applied to the uniaxial rotation module inertial navigation system. Finally, the design method of the MCRWPT system is simple, and it has guiding significance for the design of the wireless power transfer system in the RMINS.  
wireless power transfer; magnetic coupling resonant; excitation frequency; rotation modulated; inertial navigation system +
Footnote †: journal: Applied  
Acticle Analysis and Design of Wireless Power Transfer System for Rotational Inertial Navigation Application  
[PERSON], [PERSON], X., Ma, H., Zhu, Z., Huang, T., Song, [PERSON] Analysis and Design of Wireless Power Transfer System for Rotational Inertial Navigation Application
# Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches
## 3 Materials and Methods
### Machine Learning Techniques  
Machine learning (ML) encompasses various algorithms used to learn from data and make predictions, forming the basis of many AI applications. Key classical algorithms used in our project for fake detection include the following:  
* Logistic Regression: A classification algorithm for binary problems, Logistic Regression predicts the probability of an observation belonging to one of two categories. It uses the logistic function to convert a linear combination of predictors into probabilities, applying a threshold (usually 0.5) to classify the observation [9].  
Figure 2: Datasets after processing: (**a**) FakeRom; (**b**) NEW.  
Support Vector Machine (SVM): SVMs are classification algorithms that separate data using an optimal hyperplane. The goal is to maximize the margin between the closest data points from different classes. For nonlinearly separable data, SVMs use kernel functions to project data into higher-dimensional spaces where a hyperplane can separate the classes [9].
* Naive Bayes: A classification algorithm based on [PERSON] theorem, assuming feature independence. Despite this unrealistic assumption, Naive Bayes performs well in many real-world applications, especially text classification. It calculates probabilities for each class and selects the class with the highest likelihood [9].  
Logistic Regression, Naive Bayes, and Support Vector Machine (SVM) are classical models that have been used for detecting fake news based on linguistic features such as word frequency, sentiment analysis, or part-of-speech tagging. While these models are computationally efficient and interpretable, their performance often lags behind deep learning models. Ref. [27] is a comprehensive survey that discusses the use of classical machine learning models, such as Logistic Regression, SVM, and Naive Bayes, highlighting their advantages and limitations in fake news detection. The LIAR dataset is introduced in [28]; it evaluates various machine learning models, including Logistic Regression and SVM, showing their performance on fake news detection tasks.
# Robustness of Double-Layer Group-Dependent Combat Network with Cascading Failure
## 4 Simulation Experiments on Robustness
### Experiment Results and Analysis
#### 4.2.3 Robustness to Different Parameter \(\kappa\)  
Changing only the initial load parameters and keeping other parameters stable, we discuss the influence of parameter \(\kappa\) on the robustness of the DGCN of CSOS. Let the load parameter \(\kappa_{\text{G}}\) of the functional net vary from 0.2 to 1.2 at an interval of 0.1, and the parameter \(\kappa_{\text{W}}\) of the communication net also changes according to this rule. Simulation experiments are carried out with different parameters for two different hierarchical networks, and the results are shown in Figure 10. It can be seen from the figure that for the functional subnet, the robustness of the system improves with the increase of \(\kappa_{\text{G}}\), but the performance improvement is smaller and smaller. When the initial failure ratio is small, the robustness decline curves almost overlap. As the failure ratio increases, the difference becomes obvious. This is because the parameter \(\gamma\) of the node capacity is greater than 1, and the node capacity increases faster as the initial load increases, which appends the remaining capacity. Therefore, the overload cascading failure with a small initial failure ratio is alleviated to a certain extent. However, the robustness curve distinction is not obvious for the combat network with ER-net as the model network. Because the random connection makes the degree distribution more uniform, the node load and capacity are correspondingly more uniform and the difference of attack effect is not obvious. With regard to the communication net, the robustness curves of the DGCN of CSOS almost completely coincide with the  
Figure 8: Robustness with different attack modes.  
Figure 9: Robustness with tolerance parameter \(\tau\).  
increase of \(\kappa_{\text{W}}\). This is because the communication net plays the role of intermediary transmission. We found that when R reaches 0 for the first time, the maximal component of the communication net is still larger than the half of initial scale. For these homogeneous nodes of the communication net, as long as the nodes depending on the functional net are still in the maximal component of the communication net, the robustness of the combat network will not change. On the contrary, node failure of the functional net will have a great impact on the robustness.
## 4 Experimental Results and Discussions
### Experimental Setup
#### 4.1.1 Datasets  
All the synthetic datasets are 2-dimensional, as illustrated in Figures 5-8. The first data stream is Circle data of the Scikit-Learn, modified as shown in Figure 5b. Initially, 5000 samples were generated from the Circle dataset with the scale factor set to 0.5 and noise level 0.05. Then, only the samples with \(y\) values less than 0.5 and 0.2 for the outer and inner ring are used, respectively. Figure 5a represents the order that the samples arrive in the algorithm, and this data stream is named Horseshoe. Finally, this dataset has 3744 samples and values between \((-1.11,0.12)\) and \((1.13,2.07)\).  
The second dataset, named T, is illustrated in Figure 6b. This dataset starts with 100 samples of the tip numbered as one and posteriorly as two, taking 100 samples of each class repeatedly. It has 3744 samples belonging to only a cluster in its final configuration, as shown in Table 2.  
The third dataset is named Moon in Scikit-Learn [36]. In total, there are 5000 samples generated as described in Table 2. The noise level is set at 0.05, and the scale factor is 0.5. Furthermore, for the data to be a data stream, the samples' order has been modified. The algorithm first receives the first 100 samples from the place numbered with number one in Figure 7a. After this, it starts number two with the first 100 samples, and so on.  
Figure 5: The Horseshoe synthetic dataset. (**a**) The first 1870 samples from the Horseshoe dataset. (**b**)The Horseshoe dataset.  
Figure 6: The T synthetic dataset. (**a**) The first 1800 samples from the T dataset. (**b**) The T dataset.  
Finally, the Three Spirals dataset [37] had 312 samples initially, but this dataset was modified to be denser by averaging old samples, resulting in 621 samples. Each spiral of Figure 8b represents a class, for a total of three classes. Figure 8a shows the order of the data stream. Initially, the algorithm receives the class 1 samples in the direction outside to the interior of the spiral. This is also repeated for classes 2 and 3, respectively.
## 2 System Architecture  
### Powertrain System Configuration  
The SHEV has the simplest structure among all the hybrid powertrain systems, and Figure 1 shows the powertrain structure of the target vehicle, where the black lines and red lines denote the mechanical and electric connections, respectively. The unique characteristic of the SHEV powertrain system is that the engine is mechanically decoupled from the driveline, and it is only used to generate electricity through a generator connected to the engine. In this case, the engine's operating speed is not restricted by the vehicle's speed, and there are opportunities for it to always run at the most efficient points. Since the engine is connected to the generator with a mechanical coupler, the optimal engine speed is controlled by the generator. In this system, the peak power and torque of the generator are designed to be larger than the engine to avoid engine runaway.  
Figure 1: Powertrain structure of the SHEV.  
Similar to electric vehicles, a traction motor is the only component that is connected to the driveline, and it provides propulsion in SHEVs. In the target vehicle, the motor is connected to the final drive directly, without the transmission gear box.  
Besides the engine-generator set, the battery is the other power source providing electric energy to the motor. For a plug-in SHEV, the battery pack has a relatively large capacity, so that the vehicle has a certain electric driving range, and the battery can act as a \"power buffer\" to balance the difference between engine-generator output power and the total demanded power. Therefore, the engine has more opportunities to operate within the high efficiency range.
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 3 Technology Path of Image-Based 3D Reconstruction
### Image Collection and 3D Point Cloud Generation
#### 3.2.2 Data Type
to the point cloud generated by laser scanners, the 3D point cloud model of the observed object can be generated directly from the depth images. The range camera has attracted the attention of many scholars because of its low cost and portability. However, due to the limited shooting range, it is only suitable for indoor shooting, not for large-scale image acquisition [38; 39].
## Nomenclature  
\begin{tabular}{l l} \(A\) & Swept area of rotor \\ \(A\)_(r)_ & Blade sectional planform area \\ \(a\) & Axial induction factor \\ \(a^{\prime}\) & Angular or rotational induction factor \\ \(a_{\delta}\) & Induction factor due to solidity \\ \(a^{\prime}\)_(r)_ & Local angular or rotational induction factor \\ \(a_{w}\)_(r)_ & Local axial induction factor due to wake \\ \(Cp\) & Power coefficient \\ \(dC_{p}\) & Local power coefficient \\ \(c(r)\) & Local chord length \\ \(\delta\)_(r)_ & Local induction coefficient at Trefftz plane \\ \(\lambda\) & Tip-speed ratio of rotor blade \\ \(\lambda\)_(r)_ & Local tip-speed ratio \\ \(\varepsilon\) & Wind shear exponent \\ \(p_{1}\) & Pressure at WT inlet \\ \(p_{2}\) & Pressure at WT exit \\ \(U_{0}\) & Free-stream speed \\ \(U_{R}\) & Wind speed at rotor \\ \(U_{lh}\) & Average wind speed at hub height \\ \(U_{0}\)_(r)_ & Local Wind speed \\ \(U_{R}\)(r) & Local wind speed at rotor \\ \(\overline{V}\) & Wind velocity \\ \(\Gamma\) & Circulation \\ \(I_{R}\) & Moment of inertia of rotor \\ \(I_{aR}\) & Moment of inertia of air at rotor \\ \(I_{anv}\) & Moment of Inertia of air in the wake \\ \(r\) & Local radius \\ \(r_{3}\) & Local wake radius \\ \(\sigma^{\prime}\)_(r)_ & Local solidity \\ \(\omega\) & Angular speed of rotor \\ \(\omega_{3}\) & Angular speed of air at Trefftz plane \\ \(\omega_{un}\) & Angular speed of air in the wake \\ \(\theta\)_(r)_ & Local angle of inclination of local resultant to local tangential force \\ \end{tabular}