esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Utilization of an underwater sensor network (USN) or autonomous underwater vehicles (AUVs), or both, for military, science, and industrial applications is receiving increasing attention [1, 2]. In order to advance underwater applications, underwater acoustic (UWA) communication is essential for establishing connectivity between devices, sensors, and AUVs.  
Energy constraints are critical in UWA communications. In the case of USN, replacing or charging the batteries of sensor nodes leads to huge costs and might not be practical in some networks. The lifetime of the battery heavily determines the range and duration of AUV operation. For energy-efficient communication, a UWA-OFDM receiver with low-power consumption is indispensable. To achieve energy-efficient communication, retransmission occurrences should also be minimized so that unnecessary power consumption via retransmission can be prevented.  
Recently, orthogonal frequency division multiplexing (OFDM) has been studied for UWA communication systems due to the advantages (e.g., high robustness to multi-paths). The UWA channel is known to be very difficult in communication media because it suffers from fast time-varying characteristics in time and frequency domains, resulting in multi-paths with long delays as well as a severe Doppler effect [3]. Under a harsh UWA channel, accurate channel estimation is crucial to fully enjoy the advantages of an OFDM-based modulation technique. Accurate channel estimation also leads to the successful reception of data and reduction of retransmission occurrences, thereby, achieving energy-efficient communication.  
For UWA-OFDM systems, channel estimators (CEs) based on the principle of compressed sensing (CS) [4, 5, 6] and sparse Bayesian learning (SBL) [7] have been proposed, both of which operate in iterative ways to achieve high channel estimation performances. For further accuracy, many variants based on the principle of SBL [8; 9; 10] including temporal multiple SBL (TMSBL) [7; 11], and partition-based clustered-sparse Bayesian learning (PB-CSBL) [12] have recently been proposed. Improved performances of SBL-based algorithms lead to successful decoding of transmitted data, resulting in no retransmission. However, this performance gain is obtained through iterative operations, which consume much more battery power compared to non-iterative methods.  
In this paper, a fast SBL (F-SBL) algorithm is proposed for channel estimation in the UWA-OFDM system. The proposed method uses the weighted learning rule (LR) for hyperparameters. It has been shown by numerical analysis that weighted LR enables the SBL estimator (to converge faster and achieve a more accurate channel estimation). Simulation results confirm that the proposed technique shows an improved performance compared to conventional SBL-based methods [5; 7; 13], with fewer iteration numbers.
## 3 Methodology  
### RGB-Based CNN Architecture for Classification in Nuclear Medical Imaging  
In this research study, we apply an efficient and robust CNN model, the RGB-CNN (proposed in a recent study in the domain of bone scintigraphy), to precisely categorize MPI images as normal or abnormal suffering from CAD. The developed CNN will demonstrate its capacity for high accuracy utilizing a fast yet straightforward architecture regarding MPI classification. A number of experiments were performed for different values of parameters, like pixels, epochs, drop rate, batch size, number of nodes and layers as described in [56; 57; 58]. Then, appropriate features are extracted and selected manually, following the most common classic feature extraction techniques. On the other hand, CNNs that resemble ANNs, achieve automatic feature extraction by applying multiple filters on the input images. Next, they proceed in selecting the most suitable for image classification through an advanced learning process.  
A deep-layer network is constructed within this framework, embodying five convolutional-pooling layers, two dense layers, a dropout layer, followed by a final two-node output layer (see Figure 3).  
The dimensions of the input images vary from 250 \(\times\) 250 pixels to 400 \(\times\) 400 pixels. According to the structure of the proposed CNN, the initial convolutional layer includes 3 \(\times\) 3 filters (kernels) followed by a 2 \(\times\) 2-sized max-pooling layer and a dropout layer entailing a dropout rate of 0.2. The first convolutional layer is formed by 16 filters, whereas each layer that follows includes a double number of filters compared with the previous one. The same form is followed by the max-pooling layers that come next. A flattening operation is then utilized to transform the 2-dimension matrices to 1-dimension arrays so that they are inserted into the hidden dense layer of 64 nodes. The role of the dropout layer that follows is to randomly drop the learned weights by 20% to avoid overfitting. The output two-node layer comes as last in the proposed CNN model architecture.  
The most common function utilized by CNNs is ReLU, which is applied to all convolutional and fully connected (dense) layers. In the output nodes, the categorical cross-entropy function is applied. The algorithm is tested through multiple runs by trying a different number of epochs varying from 200 to 700 to fully exploit the most valid number of epochs for CNN training. In this context, the ImageDataGenerator class from Keras is used, providing specific augmentation tasks over images, such as rotation, shifting, flipping and zoom. Finally, the categorical cross-entropy function is considered as a performance metric applied for the calculation of loss. It employs the ADAM optimizer, an adaptive learning rate optimization algorithm [36].  
Figure 3: RGB-CNN architecture for CAD classification using SPECT MPI scans.
## 5 Conclusions  
An algorithm was developed to identify, in a timely manner, the possibility of cascading failure processes and their development trajectory in EPSs with networks characterized by topologies of varying complexity. This algorithm also facilitated the subsequent implementation of effective measures, both topological and operational, to bolster the survivability of the EPSs.  
The modeling results for three test schemes of networks operating at voltage levels of 10, 35, 110, and 500 kV proved the effectiveness of the proposed algorithm in finding the boundaries between the regions of admissible and inadmissible post-contingency power flows. A systemic failure that took place in a real-world power system was used as an example to demonstrate the practical application of the developed algorithm for analyzing the causes of the cascading systemic failures and their development.  
The use of DG facilities of different capacities and various FACTS devices, alongside the redistribution of power flows in the network by changing the load of power plants with different costs of electricity generation, were considered tools for enhancing the survivability of the EPSs. The results of a simplified technical and economic analysis for the considered topology and operational measures were presented. The loading of existing power plants that generate \"expensive\" electricity in annual terms is five times cheaper than the installation of CSCs; however, given their long service life, their use can be economically viable. The cost of constructing a new 500 kV overhead PTL is 1.4 times higher than that of UPFCs, making it the most expensive solution. However, if an overhead PTL is necessary for the long-term expansion of an EPS, the choice of this solution can be justified.  
The implementation of the developed algorithm in the planning and management of power flows by the dispatching personnel of an EPS will prevent cascading systemic failures and their development causing large-scale disruptions in power supply for consumers and substantial damages.  
Conceptualization, P.I., B.G. and I.S.; methodology, I.S. and B.G.; software, B.G. and P.I.; validation, P.I. and K.S.; formal analysis, I.S. and K.S.; investigation, B.G. and I.S.; resources, P.I. and I.S.; data curation, P.I.; writing--original draft preparation, B.G. and P.I.; writing--review and editing, I.S. and K.S.; visualization, B.G. and P.I.; supervision, I.S.; project administration, P.I.; funding acquisition, P.I. and K.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Informed Consent Statement**: Not applicable.  
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
Conflicts of Interest: The funders had no role in the design of this study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Theory Background
### Teager Energy Operator  
The Teager energy operator is a nonlinear operator proposed by Teager and Kaiser. It has high temporal resolution and can adaptively detect an instantaneous change of signal [34; 35; 36]. As a result, it is suitable for extracting the impulse fault features of rolling bearings. Moreover, as the Teager energy signal contains the total energy of the signal, including the information of amplitude modulation (AM) and frequency modulation (FM) caused by impulse fault feature, the Teager energy operator can improve the signal to noise ratio (SNR) and make the impulse fault feature extracted from a rolling bearing more reliable [34]. It is also capable of increasing the signal-to-noise ratio of the spectrum, sharpening the spectral peaks that reveal the presence of faults.  
The Teager energy operator for the continued signal \(v(t)\) can be expressed as:  
\[\varphi_{c}[v(t)]=\left[\dot{v}(t)\right]^{2}-v(t)\tilde{v}(t) \tag{19}\]  
Moreover, the Teager energy operator for the discrete signal \(x(n)\) can be expressed as:  
\[\varphi[x(n)]=\left[x(n)\right]^{2}-x(n-1)x(n+1) \tag{20}\]  
Therefore, considering that the Teager energy operator has good adaptability to detect the transient components of a signal, it is employed to detect the fault features of rolling bearings in this paper.
# Parallel Mechanism of Spectral Feature-Enhanced Maps in EEG-Based Cognitive Workload Classification
## 2 EEG Dataset and Physiological Underpinnings  
In order to compare the effects of the parallel mechanism of the spectral feature-enhanced (PMSFE) maps with spectral maps, the public database provided by [PERSON] on GitHub was used in our experiments (Web: [[https://github.com/pbashivan/EEGLearn](https://github.com/pbashivan/EEGLearn)]([https://github.com/pbashivan/EEGLearn](https://github.com/pbashivan/EEGLearn))). The dataset records 2670 trails of cognitive workload records from 13 subjects whose age ranged from 24 to 33, obtained with 64 electrodes placed over the scalp at standard 10-10 locations. The method of testing subjects' cognitive workload was similar to [PERSON]'s memory experiment [27; 28]. At the beginning, the participants see a number of English letters that are circularly distributed at the same angle in the center of the screen. The letters on the screen are continuously displayed for 0.5 s and participants need to remember these words in 0.5 s. After the letter disappears, participants need to judge whether a random letter appeared after three seconds' margin. If participants judge correctly, it will be recorded as a valid trail. A total of 2670 trails are recorded in the database and divided into four load levels according to the number of memory letters 2, 4, 6 and 8, respectively. The higher the number of memories, the higher the corresponding load level.  
Physiological studies show that prefrontal cortex is the mostly involved during cognitive and memory tasks [29], which is consistent with the features shown in Figure 1, which illustrates the mean topological spectral maps calculated for the \(\theta\), \(\alpha\) and \(\beta\) frequency bands of different load levels. The color in that prefrontal cortex in level 1 bias blue in all the \(\theta\), \(\alpha\) and \(\beta\) frequency bands. As the difficulty of memory tasks increases, this area is getting brighter and brighter. What's more, higher load levels enhance the power in higher frequency bands [30]. The spectral maps at level 2, level 3 and level 4 generally conform to physiological theory except level 1. This is because participants can manage simple memory tasks without paying too much attention. They may didn't plunge themselves in this task and many ideas which can be considered as noise in daily life may by going on in their mind. At level 2 and higher levels, they have to focus on the task or when they can't manage the task, then they reduce unnecessary thoughts and the spectral map is helped according to physiological theory.
# Hybrid Window Decoding for Joint Source Channel Anytime Coding System
## 3 Hybrid Window Decoding for JSCAC System
### Density Evolution Analysis for JHWD Algorithm  
In order to analyze the anytime properties and decoding complexity for the JHWD algorithm, we revised the density evolution algorithm for JSCAC with PJEWD scheme in [18]. In the following density evolution analyses, the specific formulas for message updates are omitted, which are the same as (6) to (15) in [18], and are referred to as \"_Message updates_\" for simplicity of description.  
**Step 1**: Initialize the parameters of \(x_{cp}^{0}(i_{c},t)=x_{cp}^{0}(j_{c},t)=\mu_{0}=\frac{2}{\sigma_{h}^{2}}\), \(0<i_{c},j_{c}\leq t\), and \(x_{cp}^{0}(i_{s},t)=x_{sp}^{0}(j_{s},t)=v_{0}=\log\frac{1-p}{p}\), \(0<i_{s},j_{s}\leq t\), where \(x_{cp}^{l}(i_{c},t)\) and \(x_{cp}^{l}(i_{c},t)\) are messages exchanged from channel IN\({}_{i_{c}}\) and channel PN\({}_{j_{c}}\) to connected channel CNs at the \(l\)-th iteration and time \(t\); \(x_{sp}^{l}(i_{s},t)\) (\(x_{sp}^{l}(j_{s},t)\)) are messages exchanged from source IN\({}_{i_{c}}\) (source PN\({}_{j_{c}}\)) to connected source SNs at the \(l\)-th iteration and time \(t\). Initialize the number of message updates as \(Num_{mc}=0\), and the number of iterations as \(l=0\).  
**Step 2**: Initial expanding window decoding (\(1<t\leq 3\gamma-2\))(1) Implement _\"Message updates\"_ at the time slot \(t\) within the expanding window of \([1:t]\) with \(I_{\max 1}\).  
(2) Update the following messages as follows:  
\[x_{cv}^{t}(i_{c},t) =(x_{cv}^{t-1}(i_{c},t)+x_{cv}^{t}(i_{c},t))/2,\] \[x_{cp}^{t}(j_{c},t) =(x_{cp}^{t-1}(j_{c},t)+x_{cp}^{t}(j_{c},t))/2,\] \[x_{so}^{t}(i_{s},t) =(x_{so}^{t-1}(i_{s},t)+x_{so}^{t}(i_{s},t))/2,\] \[x_{sp}^{t}(j_{s},t) =(x_{sp}^{t-1}(j_{s},t)+x_{sp}^{t}(j_{s},t))/2. \tag{7}\]  
(3) Update the number of message updates \(Num_{mc}\).  
**Step 3**: Sliding window decoding (\(3\gamma-2<t\leq T\))  
(1) Normal sliding window decoding stage: implement _\"Message updates\"_ at the time slot \(t\) within the sliding window of \([t-3\gamma+3:t]\) with \(I_{\max 1}\); then update messages with (7), and update \(Num_{me}\).  
(2) Adaptive local expanding window decoding stage: The syndrome-check-based detection is adopted by checking \(x_{so}^{t}(i_{s},t)\), as below:  
\[S_{check1}=\begin{cases}1&\text{ if }\quad|x_{so}^{t}(i_{s},t)-x_{so}^{t-1}(i_{ s},t)|<\epsilon,\\ 0&else.\end{cases} \tag{8}\]  
where \(\epsilon\) is a threshold with a very small value. If \(S_{check1}=0\), implement _\"Message updates\"_ at the time slot \(t\) within the local expanding window of \([t-3\gamma+3:t-\gamma+k]\) (\(k=2:\gamma\)) with \(I_{\max 2}\). If \(S_{check1}=1\), which means that \(x_{so}^{t}(i_{s},t)\) could not update anymore compared with \(x_{so}^{t-1}(i_{s},t)\), update messages with (7) and \(Num_{me}\).  
**Step 4**: Calculate the error probability with (17) in [18].
# Usage of V2X Applications in Road Tunnels
## 4 Results
It is possible to observe that the update of the position corresponded to the layout of the map and that the position was located on the road. It is also possible to see that the first message received from the stationary vehicle was exactly at the entrance to the tunnel. Compared to ITS-G5 communication, V2X communication over the LTE network (see Figure 10) has great benefits, such as the availability of information about the stationary vehicle in the tunnel and its delivery with considerable advanced notice. From the driver's perspective, it is possible to react to the situation, and it is also possible to change the path or stop the vehicle in a case where the tunnel should be evacuated, for example, due to a traffic accident. It may seem that the reception of C-ITS messages outside the tunnel was more frequent compared to the reception when the vehicle arrived in the tunnel. Unfortunately, this is caused by the position update. The frequency of the received packets was almost the same throughout the test, but the visualization on the map did not take into account the number of messages captured in the same position. This fact slightly distorts the result, but at the same time, it points out that the position updating could be problematic. Figure 10: Captured V2X communication over the LTE network. Figure 9: Captured V2X communication over ITS-G5.
## 3 Results
### Evaluations of the Corrected BNU-1 Images
Due to the uniform characteristic in the snow surface, the scale effect is not significant on the surface with high reflectance. Therefore, the underestimations of radiances of the BUN-1 and OLI images are not detected. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stn NO.** & **Acquisition Time** & **Land Cover** & **The Amount for Data** \\ \hline P1 & 3 December 2019 & Rock & 12 spectral signatures \\ P2 & 5 December 2019 & Snow & 10 spectral signatures \\ P3 & 9 December 2019 & Rock & 10 spectral signatures \\ P4 & 9 December 2019 & Lichen & 8 spectral signatures \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The information of four ground locations near Zhongshan Station, East Antarctica. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stn NO. ** & **Band** & **Original** & **Corrected** & **Landsat** & **Ground** \\ \hline \multirow{4}{*}{P1} & P & 145.36 & 145.36 & 111.88 & 78.38 \\  & Blue & 136.89 & 177.16 & 116.89 & 51.18 \\  & Green & 155.07 & 169.57 & 114.11 & 75.99 \\  & Red & 134.49 & 148.17 & 113.32 & 81 \\  
Figure 12 shows the comparisons of radiances from the original, corrected BNU-1 image and the Landsat8 image. The BNU-1 and Landsat8 images were acquired in 12-13 December 2019, in East Antarctica. Figure 11 shows the BNU-1 and Landsat8 images. The blue scatters and line represent the linear relationship between the radiance of the original BNU-1 image and the radiance of the Landsat8 image, while the red scatters and line represent the linear relationship between the radiance of the corrected BNU-1 image and the radiance of the Landsat 8 image. As shown in Figure 12, the red scatters distribute closer to 1:1 line than blue scatters, which indicates that the radiances from the corrected BNU-1 image have small errors than the radiances from the uncorrected image. The RMSE decreases from 80.30 to 32.54 W/m\({}^{2}\)/m/sr. All these results indicate that the radiances from the corrected BUN-1 image is closer to the radiances from the Landsat 8 image than the original BNU-1 image.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 5 Outlook on Key Technologies for Lunar Constellation Navigation and Orbit Determination
### Libration Point Constellation Design  
With the development of lunar exploration missions, lunar probes will likely operate in various lunar orbits and various lunar surface positions. The lunar constellation navigation system must be able to cover the entire lunar orbit and the entire lunar surface, and cooperate with ground measurement and control as well as application systems. It can realize continuous and uninterrupted navigation services for various lunar probes. The future lunar constellation navigation and orbit determination system will develop a constellation system composed of multiple orbiting satellites through phased and step-by-step construction, and ultimately form a full-lunar surface and full-time coverage capability.  
Specifically, in the future, there will be navigation demands for the exploration of invisible areas and large-scale activities on the lunar surface. Therefore, it may be necessary to deploy multiple satellites on multiple libration point orbits and phases to form a certain configuration and form a moving point constellation which can meet the particular requirements, so it is necessary to study the optimal design of libration point constellations under various constraints. Secondly, it is necessary to design the lunar surface positioning and navigation based on the libration point constellation. On the one hand, the Earth-Moon libration point constellation is limited by the amount of satellites, and the coverage of the lunar surface is relatively sparse. Therefore, the positioning conditions on the Moon are far worse than the GNSS constellation method. At the same time, due to the unique motion characteristics, the Earth-Moon libration point constellation cannot directly used the GNSS method to evaluate the lunar surface positioning accuracy and constellation positioning performance. On the other hand, due to limitations such as the mass and power of spaceborne equipment, the performance and accuracy of the communication and navigation equipment of Earth-Moon libration point satellites will be lower than that of ground teleoperation equipment, and the abnormality of their communication and navigation signals may also be higher than ground equipment. Therefore, it is necessary to study corresponding libration point navigation technologies and methods.
# Research on Computational Method of Supersonic Inlet/Isolator Internal Flow
## 3 DES/LES Computation Method
### DES Computation  
In the field of turbulence simulation, an obvious development trend is toward the hybrid RANS/LES model. In the near-wall area, the RANS method is used to solve the turbulent boundary layer flow state, and the LES method is used in the far-wall area to analyze large-scale vortices. This hybrid approach is an easy-to-implement and representative method [28].  
Since the transition zone between the RANS/LES computational domain is prone to exhibiting erroneous behaviors, [PERSON] proposed the delayed detached eddy simulation (DDES) [29], but both DES and DDES mainly aim to solve complex flow problems with large-scale separation; however, for small separation flow conditions, especially for turbulent flow near the wall, log-layer mismatch always occurs. An improved method is achieved by combining DDES and Wall-Modeled Large Eddy Simulation (WMLES) to propose an improved delayed detached eddy simulation (IDDES) [30], which gives a new definition of the subgrid-length-scale:  
\[\triangle=min\{max[C_{w}d_{w},C_{w}l_{max},h_{wn}],h_{max}\}, \tag{19}\]  
where \(C_{w}\) is an empirical constant that is always set equal to 0.15, \(d_{w}\) is the distance to the wall, \(h_{max}\) is the maximum local grid spacing, and \(h_{wn}\) is the grid step in the wall-normal direction. Then the length scale of the IDDES model can be constructed:  
\[l_{IDDE}=\bar{f}_{d}(1+f_{e})l_{RANS}+(1-\bar{f}_{d})l_{LES}, \tag{20}\]  
where \(l_{RANS}\) and \(l_{LES}\) are the length scales for RANS or LES model, respectively, (\(l_{LES}\) depends on \(\triangle\)). \(\bar{f}_{d}\) and \(f_{e}\) are both blending functions.  
The IDDES model is selected for DES simulation in this research, which is only available for 3D computation. If a 2D flow solution is required, the physical model has to be properly stretched along the thickness direction.