esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
The MLP model was subsequently evaluated on unseen external test samples, achieving an accuracy of 1.8 \(\pm\) 8.9e\(-\)8 mm/year. Of the other models tested, internal full feature, internal RFE, and external validation accuracies were, respectively, as follows: for Random Forest (RF), 1.5 \(\pm\) 3.8e\(-\)4 mm/year (uncertainty: \(\pm\) 0.05 mm/year), 1.48 \(\pm\) 3.7e\(-\)4 mm/year (uncertainty: \(\pm\) 0.04 mm/year), and 1.95 \(\pm\) 6.9e\(-\)4 mm/year (uncertainty: \(\pm\) 0.06 mm/year); for eXtreme Gradient Boosting, 1.42 \(\pm\) 4.2e\(-\)4 mm/year (95% PI: [1.38, 1.46] mm/year), 1.40 \(\pm\) 4.0e\(-\)4 mm/year (95% PI: [1.36, 1.44] mm/year), and 1.97 \(\pm\) 7.1e\(-\)4 mm/year (95% PI: [1.93, 2.01] mm/year); and for Support Vector Machine (SVM), 1.45 \(\pm\) 4.1e\(-\)4 mm/year, 1.40 \(\pm\) 3.9e\(-\)4 mm/year, and 1.90 \(\pm\) 7.2e\(-\)4 mm/year. Overall, the results from the study demonstrate that the model was able to predict the growth rate of a AAA to an accuracy of between 1.35 and 1.8 mm/year, with minimal variation in prediction values across internal and external datasets. Using dropout during MLP model inference, uncertainty was quantified and its impact on the variation in growth rate prediction accuracy, for each unseen internal test sample is summarised by the violin plots shown in Figure 5 for models trained using the full and reduced feature sets. Model uncertainty was generally higher when trained using the reduced feature set (across all cross-validation experiments), relative to the full feature set. This is verified by the higher median values and interquartile ranges for the MAE, visible for all 29 internal and 55 external test samples when the full set of features were used to train the models. Additionally, the kernel density estimates for the MAE for each test sample show that predictive growth rate errors are most likely to occur in the range of 1.30-1.40 mm/year when the full feature set is used, and 1.40-1.50 mm/year when the reduced feature set is used.
###### Abstract  
Cloud detection is a crucial task in satellite remote sensing, influencing applications such as vegetation indices, land use analysis, and renewable energy estimation. This study evaluates the performance of cloud masks generated for KOMPSAT-3 and KOMPSAT-3A imagery using the DeepLabV3+ deep learning model with a ResNet-101 backbone. To overcome the limitations of digital number (DN) data, Top-of-Atmosphere (TOA) reflectance was computed and used for model training. Comparative analysis between the DN and TOA reflectance demonstrated significant improvements with the TOA correction applied. The TOA reflectance combined with the NDVI channel achieved the highest precision (69.33%) and F1-score (59.27%), along with a mean Intersection over Union (mIoU) of 46.5%, outperforming all the other configurations. In particular, this combination was highly effective in detecting dense clouds, achieving an mIoU of 48.12%, while the Near-Infrared, green, and red (NGR) combination performed best in identifying cloud shadows with an mIoU of 23.32%. These findings highlight the critical role of radiometric correction and optimal channel selection in enhancing deep learning-based cloud detection. This study demonstrates the crucial role of radiometric correction, optimal channel selection, and the integration of additional synthetic indices in enhancing deep learning-based cloud detection performance, providing a foundation for the development of more refined cloud masking techniques in the future.  
operational system; GK-2A satellite; deep learning; solar radiation; aerosol optical depth +
Footnote †: journal: Applied Sciences
## 2 Coordinate System
### Aircraft Body (B) and Camera (C) Coordinate System  
The aircraft body coordinate system is defined as follows: the \(Bx\)-axis points in the direction of the aircraft's forward flight, the \(By\)-axis points to the right wing of the aircraft, and the \(Bz\)-axis points directly downward. The navigation coordinate system, the aircraft body coordinate system, and the camera coordinate system share the same origin, located at the aircraft's center of mass.  
The aircraft's attitude is provided by a position and orientation system (POS), which integrates an inertial measurement unit (IMU). Given an initial yaw angle \(\psi_{0}\) of the aircraft, the navigation coordinate system undergoes a sequential clockwise rotation around the \(z\)-, \(y\)-, and \(x\)-axes by \(\psi_{pos}-\psi_{0}\) (yaw angle), \(\theta_{pos}\) (pitch angle), and \(\phi_{pos}\) (roll angle) to form the aircraft body coordinate system. Since the imaging system employs a two-axis gimbal structure, where the roll axis is the outer frame and the pitch axis is the inner frame, the aircraft body coordinate system is further transformed into the camera coordinate system. This transformation involves first rotating clockwise around the \(y\)-axis by the camera pitch angle \(\theta\), followed by a clockwise rotation around the \(x\)-axis by the camera roll angle \(\phi\). The real-time line-of-sight (LOS) direction of the imaging system aligns with the C\(z\)-axis, as illustrated in Figure 2.  
According to the definition of rotational relationships, the rotation matrix \(R_{N}^{B}\) from the navigation coordinate system to the aircraft body coordinate system is as follows [20]:  
\[\begin{array}{l}R_{N}^{B}=R_{Nx}(\phi_{pos})R_{Ny}(\theta_{pos})R_{Nz}(\psi_ {pos}-\psi_{0})\\ =\left[\begin{array}{ccc}1&0&0\\ 0&\cos\phi_{pos}&\sin\phi_{pos}\\ 0&-\sin\phi_{pos}&\cos\phi_{pos}\\ \end{array}\right]\left[\begin{array}{ccc}\cos\theta_{pos}&0&-\sin\theta_{ pos}\\ 0&1&0\\ \sin\theta_{pos}&0&\cos\theta_{pos}\\ \end{array}\right]\left[\begin{array}{ccc}\cos(\psi_{pos}-\psi_{0})&\sin( \psi_{pos}-\psi_{0})&0\\ -\sin(\psi_{pos}-\psi_{0})&\cos(\psi_{pos}-\psi_{0})&0\\ 0&0&1\\ \end{array}\right]\end{array} \tag{1}\]  
Figure 1: Ground coordinate system and navigation coordinate system.  
Similarly, the rotation matrix \(R_{B}^{C}\) from the aircraft body coordinate system to the camera coordinate system is as follows:  
\[R_{B}^{C}=R_{Cx}(\phi)R_{Cy}(\theta) \tag{2}\] \[=\left[\begin{array}{ccc}1&0&0\\ 0&\cos\phi&-\sin\phi\\ 0&\sin\phi&\cos\phi\end{array}\right]\left[\begin{array}{ccc}\cos\theta&0& \sin\theta\\ 0&1&0\\ -\sin\theta&0&\cos\theta\end{array}\right]=\left[\begin{array}{ccc}\cos \theta&0&\sin\theta\\ \sin\theta\sin\phi&\cos\phi&-\cos\theta\sin\phi\\ -\sin\theta\cos\phi&\sin\phi&\cos\theta\cos\phi\end{array}\right]\]
# Reliability and Maintenance Analysis of Unmanned Aerial Vehicles +
## 4 Reliability Assessment Hierarchy
### Ground Control System (GCS)  
The Ground Control System, also called GCS, is the part with the highest maintainability of the whole UAV system; this is because it is easily accessible at any time during the mission. It is mainly composed of COTS (Commercial Off-The-Shelf) components with large inventories. This does not mean that it is less safe; the fact that it is ground-based allows the introduction of a good percentage of redundant systems with hot and cold stand-by configurations, reducing the off-line time nearly to zero [53].  
Figure 1: Failure Rate vs. Flight hours for the F-16 and some common drones (figure extrapolated on the data of: Barnard Microsystems Inc. et al. [5]).  
Figure 2: The Hierarchy of the Reliability Assessment (every \(10^{3}\) system failures) for UAVs. The structure is subdivided into main systems (yellow), subsystems (blue), all together representing the UAV system (orange); (figure extrapolated on the data of: Barnard Microsystems Inc. et al. [5]).
## 5 Plasma Precursors
### Helium  
Helium is another noble gas that is used in plasma cleaning processes [1]. Helium atoms, having an atomic mass of 4 u, are lighter than argon atoms. It is important to note that mass is an essential characteristic for physical plasmas, since the kinetic energy of particles is dependent on the mass and the square of their velocity. Helium ions therefore do not have a very effective etching efficiency [113].  
The first ionization potential of helium is the highest known, at 24.59 eV. This means that it needs a very high energy level to lose one electron compared with other particles, such as argon or hydrogen presented above [36].  
Table 14 presents the grading of the parameters relevant to helium plasmas to outline helium's suitability for use in cleaning plasmas.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Summary** & **Parametric Score** \\ \hline Removal of Organic & Helium particles do not react chemically with organic \\ Contaminants Through & contaminants, especially at the low energy levels used in \\ Chemical Reduction & plasma cleaning systems. & \\ and Oxidation & & \\ \hline Removal of Oxides Through & Helium particles do not react chemically with metal oxides. & \\ Chemical Reduction & & \\ \hline \hline  & Helium is a non-reactive gas and therefore can be utilized for \\  & the creation of plasmas that conduct cleaning through \\  & physical bombardment. However, helium particles have a \\  & low mass of around 4 amu when compared with 40 amu for \\ Physical Bombardment & argon particles. While helium ions gain similar kinetic \\  & energy to heavier ions when accelerated by the plasma \\  & sheath, their low mass results in inefficient momentum \\  & transfer during collisions with heavier surface atoms. \\  & Consequently, helium ions are much less effective at \\  & dislodging (sputtering) surface particles compared with ions \\  & like argon [14; 114]. & \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline Plasma Reliability for Sensitive & Helium plasma processes are not aggressive and are \\ Devices (Minimizing Damage) & effectively useful for gentle surface activation without \\  & material removal. & \\ \hline \hline  & Helium gas is not toxic and not flammable. The fact that \\ Plasma Safety: Handling & helium is a light element precludes it from displacing \\ Precursors and & oxygen in a setup envisaged for the deployment of a plasma \\ Emissions Risks & system. The handling of helium in the manufacturing \\  & environment is therefore not difficult. Emissions from \\  & helium plasma processes are similarly not toxic. & \\ \hline \hline \end{tabular}
\end{table}
Table 14: Grading of parameters for helium plasmas.
## 6 Numerical Simulation, and Experiment
### Behavior under Correlated Sources  
This subsection investigates, under correlated sources, the level of robustness of all methods. The two sources are in the same position with the results given in Figure 3 using the wave correlation coefficient \(\alpha=0.7\) and 250 snapshots. Figure 6 shows the illustration of the RMSE of all the approaches against the SNR. It can be observed that using correlated sources, each subspace-dependent approaches degrades in performance when compared with the uncorrelated sources scenario; however, the SH-ANM-ESPRIT approach is robust with regard to correlated signals. Estimating the azimuth, the SH-ANM-ESPRIT approach  
Figure 4: Comparison of performance with different value of snapshots under uncorrelated sources at \((\theta,\varphi)=(30^{\circ},60^{\circ})\) and \((36^{\circ},138^{\circ})\), 0 dB SNR. (**a**) Azimuth. (**b**) Elevation.  
Figure 5: Resolution probability against angle of separation under uncorrelated sources at \((\theta,\varphi)=(40^{\circ},60^{\circ})\) and \((40^{\circ}+\theta,60^{\circ}+\theta)\), 3 dB SNR. (**a**) Azimuth. (**b**) Elevation.  
achieves the best accuracy level among all approaches, as the SNR varies from 10 dB to 20 dB. The performance of the SH-ANM-ESPRIT approach is satisfactory for elevation estimation.  
Figure 7 shows the RMSE performance of the proposed method, SH-ESPRIT, TSDA, and \(l_{1}-norm\) against snapshots, where it is assumed two correlated sources situated at \((\theta,\varphi)=(30^{\circ},60^{\circ})\) and \((36^{\circ},138^{\circ})\) with \(\alpha=0.8\) and 5 dB SNR. The results shows SH-ANM-ESPRIT gives a high level of robustness in relation to highly correlated sources.
# Model-Based Virtual Components in Event-Based Controls: Linking the FMI and IEC 61499
## 2 Related Work
### Distributed Control Systems Using IEC 61499  
The concept of events to overcome a strictly periodic operation is also found in industrial control applications [15; 16; 17]. The IEC standard 61499 defines a system-level design language for distributed event-based control systems and has been successfully deployed in several industrial applications [18; 19]. The scope of IEC 61499 is well beyond mere scheduling of control algorithms [15; 16; 17].  
The standard defines several architectural entities, such as devices, potentially distributed applications, and Function Blocks (FBs). In general, control applications are composed of connected FBsthat implement a certain operation and expose their functionality via dedicated in- and out-puts. An FB may provide event in- and out-puts that can be associated with data in- and out-puts, respectively. As soon as an event is encountered in one of the event inputs, the operation of the FB is triggered, data outputs may be updated, and output events can be fired. Some FB may additionally trigger output events without prior input events, e.g., in case some hardware inputs change or a timeout is reached.  
[PERSON] et al. demonstrated that IEC 61499 is not limited to asynchronous control strategies, but can also be successfully used in synchronous closed-loop controls [19]. As such, the concept of event-based virtual components is also applicable in synchronous systems.  
In the current IEC 61499 standard, it is not possible to express real-time semantics such as execution-time constraints [15; 20]. To overcome these limitations, [PERSON] et al. proposed backwards compatible real-time semantics and demonstrated its use in a response-time and CPU-time analysis [20]. Hence, it is possible to analyze the real-time behavior of IEC 61499-based controllers that access virtual components, but further information beyond the standard is needed.
# Cicada Species Recognition Based on Acoustic Signals
## 6 Conclusions  
The proposed improved Harma algorithm has demonstrated promising results for cicada species recognition. An appealing accuracy of up to 100% can be achieved using the proposed method. The proposed cicada species recognition system is important in automating the process of monitoring and classifying periodical cicada species to save time and effort. Although various insect detection and classification systems have been proposed in numerous papers by different researchers, researchers are constantly experimenting the topic to improve the existing solutions. The opportunity to contribute to this research topic is indeed inspiring and valuable.  
For future work, with more time and resources, more audio recordings will be collected to increase the size of the cicada species audio dataset. Besides, different cicada species can be investigated so that more variations of the sound of cicada species can be captured to generalize the model and the classification results can be improved further. Since this study only uses a deep learning model for training and classification, a potential direction of research can be by using different deep learning models or machine learning models for cicada species recognition. By this, a comparison among the performances of the models can be performed to find the most suitable model for the cicada species recognition task.  
**Author Contributions:** Conceptualization, T.C. and K.Y.C.; methodology, T.C., K.Y.C. and W.T.T.; validation, W.T.T., T.C., K.Y.C. and M.K.O.G.; formal analysis, W.T.T., T.C., K.Y.C. and M.K.O.G.; data curation, W.T.T.; writing--original draft preparation, W.T.; writing--review and editing, W.T.T., T.C., K.Y.C. and M.K.O.G.; supervision, T.C. and K.Y.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available at [[https://github.com/tconnie/cicada.git](https://github.com/tconnie/cicada.git)]([https://github.com/tconnie/cicada.git](https://github.com/tconnie/cicada.git)) (accessed on 14 July 2022).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Description** & **Input** & **Accuracy** \\ \hline Benchmark Test & Butterworth filter + Spectrogram + CNN & Full-Length Dataset & 77.78\% \\ Harma algorithm & Butterworth filter + Harma algorithm + Spectrogram + CNN & Full-Length Dataset & 77.78\% \\ Harma algorithm + Thresholding & Butterworth filter + Harma algorithm + Thresholding + & Cut Dataset & 92.70\% \\  & Spectrogram + CNN & Cut Dataset & 77.78\% \\ Improved Harma algorithm & Butterworth filter + Improved Harma algorithm + & Full-Length Dataset & 91.97\% \\  & Spectrogram + CNN & Cut Dataset & **100\%** \\ \hline \hline \end{tabular}
\end{table}
Table 5: A comparison among the methods.
## 3 System Design and Implementation
### Client Server Model
#### 3.4.4 Graphical User Interface  
In a post-disaster management scenario, the control room is occupied by strictly professional individuals who have the ability and authority to make critical decisions in a real-time environment that can cause a sizable impact on the overall operating results. In order to do so, a visual image of everything that is taking place in and around the disaster-struck area is needed. Therefore, visualizing the data being received from OBUs mounted on vehicles or hand-held devices in possession of volunteers and rescue staff, in a meaningful way, is one important aspect of this project. For this purpose, we used the simplest and most straight-forward Python module for the graphical representation of data, called Tkinter.  
GUI is divided into three blocks, as can be seen in Figure 5, each with a very specific responsibility.  
* The upper part of the Left Block is responsible for showing the map of the disaster area, while the lower part has the list of all the relevant departments that takes part in the rescue-and-response efforts;  
Figure 5: Graphical User Interface (GUI) using Python’s Tkinter library.  
* The top part of the Central Block presents the statistics of the damage caused by the calamity, such as the collapsed area, blocked roads, or broken bridges, while the bottom part shows the current availability of the resources at disposal;
* The Right Block is the activity log where all the alert messages are displayed.  
The map present on the GUI was captured from an open-source Application Programming Interface API provided by Google [33].
# A Correlation-Based Sensing Scheme for Outlier Detection in Cognitive Radio Networks
## 3 Proposed Scheme
#### 3.1.2 Step Two: SUs' Correlation  
The FC measures the difference value of each SU with the rest of the SUs as in Equation (8). Once the difference between the SUs was determined, we utilized the correlation tool defined in Equation (6) for all SUs, which is measured as  
\[rr_{\Delta d_{ij}\Delta d_{k}}=r_{\Delta d_{k}\Delta d_{j}=}\frac{\sum_{i=1}^{ N}\left(\Delta d_{ij}-\overline{\Delta d_{j}}\right)\left(\Delta d_{ik}- \overline{\Delta d_{k}}\right)}{\sqrt{\sum_{i=1}^{N}\left(\Delta d_{ij}- \overline{\Delta d_{j}}\right)^{2}\sum_{i=1}^{N}\left(\Delta d_{ik}-\overline {\Delta d_{k}}\right)^{2}}}\, \tag{9}\]  
where \(\Delta d_{ij}\)and\(\Delta d_{ik}\) are elements of the \(j\)th and \(k\)th user sample in the \(i\)thsensing interval, and \(\overline{\Delta d_{j}}\)and \(\overline{\Delta d_{k}}\) are the mean values calculated for the \(j\)thand \(k\)th SU samples.  
\[C=\left[\begin{array}{cccc}SU_{1}&\ldots&SU_{M}\\ SU_{1}&r_{\Delta d_{1}\Delta d_{1}}&\ldots&r_{\Delta d_{1}\Delta d_{M}}\\ \ldots&\ldots&\ldots&\ldots\\ SU_{M}&r_{\Delta d_{M}\Delta d_{1}}&\ldots&r_{\Delta d_{M}\Delta d_{2}}\end{array} \right]. \tag{10}\]  
The data of the normal SUs are separated from those of the outliers or MUs by adding all the correlation differences in (10) for each SU as follows.  
\[C_{ij}=\ \left[\sum_{i=1}^{M}r_{\Delta d_{i}\Delta d_{1}}\ \sum_{i=1}^{M}r_{ \Delta d_{i}\Delta d_{2}}\ \sum_{i=1}^{M}r_{\Delta d_{i}\Delta d_{3}}\ \sum_{i=1}^{M}r_{\Delta d_{i}\Delta d_{4}}\ \ldots\ \sum_{i=1}^{M}r_{\Delta d_{i}\Delta d_{M}}\right]. \tag{11}\]  
Based on the results of Equation (10), the OMU had more negative values, followed by the ROMU, when compared with normal SUs. From Equation (10), the behaviors of all three categories of SUs, i.e., normal SUs, OMUs, and ROMUs, were identified. These behaviors identify the outliers in the network. These outlier values were further identified and classified in step three of the proposed scheme.
# Structure Preserving Convolutional Attention for Image Captioning
## 5 Conclusions  
In this paper, we propose a structure preserving convolutional attention (SPCA) module for image captioning. There are two submodules: convolutional spatial attention and cross channel attention, which can adaptively determine 'where' and 'what' should be attended to, respectively. Different from the grid-based attention, our SPCA preserves spatial structure and fuses channel information at each step when calculating the attention. Thus, we can get more accurate attention and achieve better performance than popular benchmarks.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Flixr-30K**} & \multicolumn{3}{c}{**MS-COCO**} \\  & **B4** & **MT** & **RG** & **CD** & **B4** & **MT** & **RG** & **CD** \\ \hline Google NIC [6] & - & - & - & - & 24.6 & - & - & - \\ Soft-Attention [5] & 19.1 & 18.5 & - & - & 24.3 & 23.9 & - & - \\ Soft-Attention* & 22.2 & 19.7 & 45.0 & 47.0 & 33.4 & 26.1 & 54.2 & 101.1 \\ Hard-Attention [5] & 19.9 & 18.5 & - & - & 25 & 23 & - & - \\ SCA-CNN [13] & 22.3 & 19.5 & - & - & 31.1 & 25 & 52.4 & 91.2 \\ ATT2 in [27] & - & - & - & - & 31.3 & 26.0 & 54.3 & 101.3 \\
2D-latent state [19] & 22 & - & 44.4 & 42.7 & 31.9 & - & 53.8 & 99.4 \\
2D-latent state* & 22.6 & 19.6 & 44.5 & 42.2 & 30.1 & 24.9 & 52.8 & 94.8 \\ \hline
1D-LSTM + SPCA & **23.4** & **20.2** & **46.2** & **50.0** & **33.9** & **26.4** & **55.1** & **106.3** \\
2D-latent-LSTM + SPCA & **23.7** & **20.3** & **45.6** & **49.9** & **32.1** & **25.8** & **53.9** & **99.3** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performances compared with state-of-art in Flickr_30K and MS-COCO dataset.  
Figure 4: Qualitative results for our method performance. From left to right: original image, soft-attention map (red), our attention map (green) corresponding the underline word.  
**Author Contributions:** Conceptualization, S.L., R.H. and J.L.; Data curation, S.L., L.G. and F.Z.; Formal analysis, S.L., R.H., J.L., L.G. and F.Z.; Investigation, S.L., L.G. and F.Z.; Methodology, S.L., R.H., J.L., L.G. and F.Z.; Resources, R.H. and J.L.; Software, S.L. and L.G.; Supervision, R.H. and J.L.; Writing--original draft, S.L.; Writing--review and editing, R.H., J.L., L.G. and F.Z.  
**Funding:** This work is supported by the National Nature Science Foundation of China (No. U1736206).  
**Conflicts of Interest:** The authors declare no conflict of interest.