esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### TRA Module
Similarly to the deraining filter, we first computed the dark channel map of the snowy image \(I_{3}(x,\lambda)\) and selected the brightest pixel. Then, by averaging the corresponding pixels in the snowy image \(I_{3}(x,\lambda)\), we could estimate the light reflected by snowflakes \(S(x)\). The final desnowing filter can be expressed as follows:  
\[H(x,\lambda)=I_{3}(x,\lambda)-\varepsilon_{3}S(x) \tag{6}\]  
By adjusting the parameter \(\varepsilon_{3}\), we could control the degree of desnowing and restore a clearer image. Through the above design, we could utilize the principles of dark channel prior and the atmospheric scattering model, combined with the learning parameter approach, to design defogging, deraining, and desnowing filters. These filters could mitigate the impact of weather on images to some extent, thereby improving image quality and clarity. The synthetic dataset consisted of power scene data captured by drones under normal weather conditions, upon which a dataset of images under complex weather conditions was constructed offline. By adjusting parameters to specific values, set to 0.6 and 0.1, foggy images were obtained. Rainy images were generated by setting the droplet diameter to 2 pixels and simulating random distribution to mimic raindrops. Gaussian blur with a standard deviation of 1 was applied to simulate environmental reflective light and replicate the lighting effect of raindrops to obtain rainy images. Snowy images were obtained by setting the snowflake diameter to 5 pixels, transparency to 0.8, and color to white. The TRA module was designed for image defogging, deraining, and desnowing, initially involving the physical modeling of fog, raindrops, and snowflakes in the images. This module primarily focused on parameter estimation and image degradation. Through joint learning, it utilized convolutional networks to estimate key operators for fog, rain, and snow, facilitating image restoration. In the parameter estimation module, five convolutional layers were employed to fuse multiscale information, effectively integrating coarse and fine-scale features through the concatenation of parallel convolutional layers (Concat layers). This process also involved estimating the parameters of the input image. The convolutional network learned the specific values of these parameters, which were integral components of the atmospheric scattering model and significantly impacted the results of image restoration. Notably, the design of the Concat layer incorporated hierarchical connections, with each Concat layer progressively connected to other convolutional layers. This design effectively compensated for any information loss during the convolution process, ensuring comprehensive feature detail acquisition. The output of the parameter estimation module included key parameters in the atmospheric scattering model, playing a critical role in the subsequent image restoration process.
## 3 Calculation Methods
The boundary and initial conditions took into consideration measurements of the fluid temperature at the minichannel inlet and outlet as well as the recorded temperature of the heated wall and the assumption of ideal thermal insulation on the respective edges. \[T_{F}(0,y,t)=T_{F,in}(t) \tag{3}\]  
\[T_{F}(L,y,t)=T_{F,out}(t) \tag{4}\]  
\[T_{H}(x,0,t)=T_{H,IR}(x_{i},t) \tag{5}\]  
\[T_{H}(x,y,0)=T_{H,IR}(x_{i},0) \tag{6}\]  
\[T_{F}(x,y,0)=T_{F,in}(0) \tag{7}\]  
\[\frac{\partial T_{H}}{\partial y}(0,t)=0 \tag{8}\]  
\[\frac{\partial T_{H}}{\partial y}(L,t)=0 \tag{9}\]  
where: \(T_{F,in}(t)\), \(T_{F,out}(t)\)--temperatures at inlet and outlet to the minichannel, respectively, and \(T_{H,IR}(x,t)\)--temperature on the outer side of the heated wall. All data were obtained from experimental measurements. The heat transfer problem in minichannels was solved by two numerical methods: FEM with Trefftz-type basis functions (FEMT) and ADINA software. The boundary and initial conditions listed above were used to solve the inverse heat transfer problem by FEMT. In the approach proposed by ADINA software, the direct heat transfer problem was solved with the application of additional boundary conditions formulated in Section 3.2.
## 4 Results
### Experimental Data  
In this section, a benchmark dataset (FLShip) was constructed for ship target detection, tracking, and fused localization. Compared to existing open-source maritime datasets, FLShip introduces multidimensional innovations in its design. It not only includes continuous stereo and monocular image frames captured from a first-person perspective during navigation but also introduces high-frequency E-AIS information corresponding closely to the image data of both target and own MASSs for the first time. This feature significantly enhances the dataset's applicability in supporting multi-source information fusion research for MASSs, providing crucial data support for research in maritime target detection, multi-sensor data fusion, and autonomous navigation. The experiments utilized seven MASSs developed by Tianjin University as test subjects. The E-AIS data from the MASSs, including attitude and GPS information, are obtained by subscribing to the /mavros/imu/data topic for attitude data and the /mavros/global_position/global topic for positional data via the ROS system. These data are uploaded in real time to a server via 4G networks (which can be replaced by satellite communication as needed), ensuring efficient data transmission and storage. Figure 11 and Table 2 provide additional details about the MASSs used in this experiment, including their ID, length, breadth, equipped equipment, and levels of autonomy navigation. Among them, the edge computing devices equipped on the experimental ship include Raspberry Pi 4B (manufactured by Raspberry Pi Foundation, Cambridge, UK), Jetson Orin Nano Developer Kit, and Jetson Nano (manufactured by NVIDIA Corporation, Santa Clara, California, USA).  
Figure 10: Asynchronous ship trajectory association and joint data storage method.  
To validate the effectiveness of the proposed method, seven scenes with varying resolutions, noise levels, and visibility conditions were extracted from the dataset for ship target detection, tracking, and fused localization experiments, as illustrated in Figure 12. Table 3 presents additional details about the FLShip dataset, including the number of ships (NOS), the number of ships with E-AISs (NOE), the frequency of ship E-AISs (FOE), video quality, video frame rate, video resolution, and the average speed of the host ship.
# Polar Code Parameter Recognition Algorithm Based on Dual Space
## 5 Conclusions  
To reduce the complexity and improve the performance of a polar code parameter recognition algorithm, a traditional recognition scheme suitable for blind recognition of polar code parameters in AWGN channels is proposed in this paper. The hypothesis test of the empirical relationship is studied, the specific calculation process of statistics and decision thresholds is given, and the effectiveness of the proposed algorithm and the conditions of different code lengths, code rates, and number of received codewords are simulated. Simulation results show that the recognition performance of the algorithm proposed in this paper increases with the decreasing code length, increasing number of intercepted codewords, and decreasing code rate.  
Compared with the existing algorithms for AWGN channel simulation, the algorithm proposed in this paper not only has the lowest simulation complexity, but also has good recognition performance. More importantly, it is valid for unstructured polar codes, making it outperform other algorithms. The research results in this paper can be applied to simulation implementations in the fields of intelligent communication, communication reconnaissance, and network countermeasures. In the future, we will study the problem of polar code parameter recognition under other channels and consider adding machine-learning algorithms to our studies.  
Writing--original draft preparation, H.L.; writing--review and editing, Z.W. and W.Y.; project administration, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China grant number 91538201.  
The authors declare no conflict of interest.
## 4 The Model
### Proposed Model
The reconstruction loss is employed to quantify the discrepancy between the original input and the reconstruction generated by the decoder, utilising the mean square difference, with a particular emphasis on temporal dependencies. In contrast, the KL loss is employed to quantify the divergence between the latent distribution inferred by the model and a standard normal distribution. The total loss is a weighted combination of these two components and is employed to optimise the weights of the model. This approach enables the learning of a meaningful latent representation while ensuring that the reconstructed data are faithful to the original one. As previously stated, the robustness of the model has been evaluated by incorporating missing values into the dataset at varying percentage levels and examining the impact on the model's performance. To effectively address the presence of NaN values in the data, a model configuration has been implemented, capable of handling such data. Specifically, an additional layer is applied to the inputs entering the encoder, whereby NaN values are replaced with zeros to circumvent calculation errors, and a mask is generated that identifies the values that were originally NaN. This ensures a more robust training of the model without the distortions that would otherwise result from the presence of missing values. Indeed, the loss function of the VAE model was modified to incorporate the use of a mask, which identifies valid values, thus excluding the calculation of the reconstruction loss of any missing data. Alternative strategies for handling missing values will be considered in future work. For example, statistical imputation methods, such as mean and median imputation as well as \(k\)-nearest neighbour (KNN) imputation, provide an effective way to estimate missing values based on observed data trends. Additionally, time-series-specific smoothing techniques, such as Kalman filtering and spline interpolation, help maintain temporal consistency in the imputed data by leveraging the sequential nature of financial time series. Furthermore, ML-based approaches, including recurrent neural networks (RNNs) and Gaussian processes, will be investigated to estimate missing values based on historical trends.
# Recurrence Quantification Analysis as a Form of Postural Control Assessment: A Systematic Review
## 1 Introduction  
The phenomenon of postural control requires a discussion of two crucial elements. The first refers to defining balance while maintaining an upright standing position, and the second refers to evaluation methods. [PERSON] et al. [1] define standing balance as the capacity to stand with as little sway as possible. The general tendency for postural sway to increase with age, pathology, or when standing tasks come with a sensory challenge has been repeatedly highlighted and proven in the literature [2; 3; 4]. Such results appear to be influenced by many factors. Thus, the primary systems involved in postural and balance control are visual, vestibular, and somatosensory. The central nervous system (CNS) regulates sensory information from all these systems to produce adequate motor power to maintain a controlled, upright posture. However, how the nervous system handles this information describes postural control [3].  
Assessment of postural control involves the platform's data captured during various balance tasks. Linear and nonlinear parameters are calculated from time series describing the displacement of the foot's center of pressure separately for the mediolateral and anterior-posterior directions. The linear analysis describes the quality of motion, magnitude, and/or variance of CoP displacements (CoP path length, CoP velocity, sway range, and area of the ellipse). However, this is not enough information, given how complex the process hasto be described. Therefore, the classical linear methods have been recently increasingly supplemented by nonlinear methods. Nonlinear measures provide information about the temporal organization of the variation in CoP displacement regarding motor behavior over time. Nonlinear measures include the entropy family, the fractal dimension, the largest Lyapunov exponent, the Hurst index, and recurrence quantification analysis (RQA). In their review, [PERSON] and [PERSON] [3] showed the use of entropy, fractal dimension, and the Lyapunov exponent to evaluate postural control. It seems that the next step should be to demonstrate the application of RQA analysis to assess the complexity of this phenomenon.
## 2 Theoretical Background
### Classic Fault-Related Patterns Produced by the Occurrence of Faults in Bearings  
The identification of bearing faults through vibration-based analysis has been considered as a reliable diagnostic approach that has been widely accepted by the industrial sector. On the other hand, the analysis of vibrations by means of frequency-domain techniques, such as the FFT, has been widely implemented because this technique allows representing the discrete and periodic components of signals into a frequency plane, where the characteristic and most representative frequency components are projected [34]. Accordingly, dealing with the diagnosis of bearing faults, the vibration spectra are used for detecting the occurrence of faults in bearings. As it is known, the common failures in bearings are associated to defects in the outer race, inner race, rolling elements, and the cage. Thereby, the occurrence of any of these faults may produce a characteristic pattern that can be associated a specific frequency component. Hence, to assess the faults in the bearings, it is mandatory to know fault-related frequency components that are produced by the sudden occurrence of bearing faults [12]. In this regard, the identification of bearing defects can be performed by locating their fault-related frequencies over the vibration spectrum; in fact, high amplitudes on the fault-related frequency components are due to the fault appearance.  
Figure 1: Electrical erosion produced on the bearing surfaces by means of the current leakage phenomenon that consist of: (**a**) the crossing of the electric current through the rolling elements; (**b**) the electric arc welding occurs between the rolling elements and bearing surfaces; (**c**) the molten material solidifies and then separates; (**d**) the excessive material in the rolling elements wears away.  
Therefore, the mathematical equations to identify defects in the outer race, inner race, and balls of bearings are related to the fault-related components of Equations (1) and (2), respectively. With the calculation of the BPFO (Ball Pass Frequency of the Outer race) and BPFI (Ball Pass Frequency of the Inner race) components, it is possible to locate the fault-related frequency components in the frequency spectrum and determine the presence of bearing faults on the outer race and inner race. It should be noted that these fault-related frequency components are computed in terms of the number of rolling elements (balls), \(N_{b}\); the ball diameter, \(BD\); the pitch diameter, \(PD\); the contact angle, \(\theta\); and the rotational frequency, \(f_{rm}\), [41].  
\[BPFO = \frac{N_{b}}{2}f_{rm}\bigg{(}1-\frac{BD}{PD}\cos\theta\bigg{)} \tag{1}\] \[BPFI = \frac{N_{b}}{2}f_{rm}\bigg{(}1+\frac{BD}{PD}\cos\theta\bigg{)} \tag{2}\]
## 3 Field Testing
### Results of Deflection-Based LTE (DLTE)  
Table 1 shows the measurement results of deflection-based LTE (DLTE) in the two tests. Figure 7 compares the DLTE measured on different dates and joints. It is found that the DLTE of J4 was 0.814 and 0.688 in July 2020 and November 2020 respectively. Both the DLTEs were positive but the decrease was about by 18.31%. Similarly, the DLTE of J8, J9, and J11 decreased by about 15.57 to 32.25% in the second test, but the other joints' DLTE vary more slightly (decreased by 9.28% or increased by 2.47% in maximum). Considering these joints are mostly dummy joints and the second test was conducted in winter, the decrement of their DLTE should be due to the slab movements caused by temperature, because the width of dummy joints increases at a lower temperature, as a result of slab movements, which results in significant deterioration of DLTE.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Test** & **No.** & & & & & & & & & & & \\
**Series** & **Joint** & & & & & & & & & & & \\ \hline \multirow{9}{*}{1\#} & J1 & 0.900 & 0.930 & 0.936 & 0.937 & 0.959 & 0.940 & 0.945 & 0.929 & 0.931 & 0.934 & 0.015 \\  & J2 & 0.882 & 0.879 & 0.915 & 0.897 & 0.896 & 0.881 & 0.931 & 0.933 & 0.936 & 0.906 & 0.022 \\  & J3 & 0.828 & 0.838 & 0.823 & 0.838 & 0.834 & 0.814 & 0.852 & 0.840 & 0.838 & 0.834 & 0.010 \\  & J4 & 0.762 & 0.721 & 0.866 & 0.848 & 0.882 & 0.835 & 0.778 & 0.803 & 0.835 & 0.814 & 0.050 \\  & J5 & 0.987 & 0.965 & 0.985 & 0.915 & 0.954 & 0.990 & 0.954 & 0.963 & 1.018 & 0.970 & 0.028 \\  & J6 & 0.953 & 0.918 & 0.909 & 0.961 & 0.987 & 1.014 & 0.946 Also, there are some deviations among the nine measurements at the same joint, which are between 0.002-0.05. These deviations are due to the measurement error from FWD (whose accuracy is \(\pm\)2 \(\upmu\)m according to its specification). Since the DLTE is a ratio of measured deflections, the influence of measurement error becomes more significant after the division between two measurements.
## 4 IETF 6 LoWPAN Working Group (IPv6)  
The IPv6 protocol has a high overhead and restrictions that make it unsuitable for LLNs such as IEEE 802.15.4 networks. For instance, considering the limited space available for the MAC payload in an 802.15.4 MPDU, the use of a 40-byte IPv6 header would be too excessive. Therefore, the IETF 6 LoWPAN (IPv6 for Low-power Wireless Personal Area Network) Working Group was formed to work on the IPv6 protocol extensions required for such networks where the nodes are interconnected by IEEE 802.15.4 radios [14]. Meanwhile the working group has produced several proposed standards and informational documents regarding these required extensions.  
Starting from a well-defined set of assumptions and a problem statement, as defined in the informational RFC 4919 [1], a solution for transmitting IPv6 packets over IEEE 802.15.4 networks was defined, resulting in RFC 4944 [15]: _Transmission of IPv6 Packets over IEEE 802.15.4 Networks_. This document describes the frame format, the methods of link-local address formation and autoconfigured addresses, simple header compression and mesh-under routing for multi-hop IEEE 802.15.4 networks. Subsequent RFCs of the 6 LoWPAN working group, _Compression Format for IPv6 Datagrams over IEEE 802.15.4-Based Networks_ (RFC 6282) [16] and _Neighbor Discovery Optimization for IPv6 over Low-Power Wireless Personal Area Networks (6 LoWPANs)_ (RFC 6775) [17], which respectively cover advanced header compression and Neighbor Discovery optimization, have updated RFC 4944. In addition to this, the working group has produced 2 other informational RFCs addressing use case descriptions (RFC 6568) [18] and routing requirements (RFC 6606) [19] and is in the process of finalizing an internet draft on the transmission of IPv6 packets over Bluetooth (draft-ietf-6 lowpan-btle-11) [20].  
Further, the working group is also expected to collaborate with other organizations (such as IEEE and ISA SP100) and other IETF working groups (such as ROLL) on common interest issues and is mandated with providing implementation and interoperability guides [21].
# A Novel Method for Ground-Based Cloud Image Classification Using Transformer
## 3 Datasets and Experimental Settings
### Dataset Description
#### 3.1.2 Cirrus Cumulus Stratus Nimbus (CCSN)  
This dataset is an open-source dataset collected by the Nanjing University of Information Engineering, and it is categorized according to the World Meteorological Organization's genus-based classification proposal for the separation of cloud images into types, including  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Class** & **Training** & **Testing** & **Total** \\ \hline
1 & Ac & 2640 & 300 & 2940 \\
2 & Cb & 2740 & 300 & 3040 \\
3 & Ci & 2280 & 300 & 2580 \\
4 & Cl & 2850 & 300 & 3150 \\
5 & Cu & 2360 & 300 & 2660 \\
6 & Mi & 2410 & 300 & 2710 \\
7 & Sc & 2620 & 300 & 2920 \\  & Total & 17,900 & 2100 & 20,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training and testing samples for the ASGC dataset.  
Figure 8: Example images from the ASGC dataset: (**a**) altocumulus (Ac); (**b**) cumulonimbus (Cb); (**c**) cirrus (Ci); (**d**) clear (Cl); (**e**) cumulus (Cu); (**f**) mixed (Mi); (**g**) stratocumulus (Sc).  
altocumulus (Ac), altostratus (As), cumulonimbus (Cb), circumulus (Cc), cirrus (Ci), cirrostratus (Cs), contrail (Ct), cumulus (Cu), nimbostratus (Ns), stratocumulus (Sc), and stratus (St). Due to a large number of cloud types, the cloud images in this dataset have large light variations and intra-class variations; more details are provided in [27].  
All images are in a color JPEG format with a resolution of 256 \(\times\) 256 pixels. To meet the input size requirements of the model, the images were uniformly resized to 448 \(\times\) 448 pixels using bilinear interpolation [55]. The number of cloud images in each class varies from 140 to 340. In order to avoid the occurrence of overfitting, data augmentation is also used to expand the dataset. Figure 9 shows example images from the dataset, and the number of samples used for training and testing is described in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No** & **Class** & **Training** & **Testing** & **Total** \\ \hline
1 & Ac & 2550 & 300 & 2850 \\
2 & As & 2320 & 300 & 2620 \\
3 & Cb & 2440 & 300 & 2740 \\
4 & Cc & 2430 & 300 & 2730 \\
5 & Ci & 2660 & 300 & 2960 \\
6 & Cs & 2310 & 300 & 2610 \\
7 & Ct & 2280 & 300 & 2580 \\
8 & Cu & 2350 & 300 & 2650 \\
9 & Ns & 2240 & 300 & 2540 \\
10 & Sc & 2420 & 300 & 2720 \\
11 & St & 2700 & 300 & 3000 \\  & Total & 26,700 & 3300 & 30,000 \\ \hline \end{tabular}
\end{table}
Table 3: Training and testing samples for the CCSN dataset.  
Figure 9: Example images from the CCSN dataset: (**a**) altocumulus (Ac); (**b**) altostratus (As); (**c**) cumulonimbus (Cb); (**d**) circumulus (Cc); (**e**) cirrus (Ci); (**f**) cirrostratus (Cs); (**g**) contrail (Ct); (**h**) cumulus (Cu); (**i**) nimbostratus (Ns); (**j**) stratocumulus (Sc); (**k**) stratus (St).
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 2 Methods
### EEG Preprocessing and Generation
#### 2.1.3 Data Matrix Generation  
Given the single-channel time-series characteristics of epileptic EEG signals, we retain the time-series EEG signals as input for the micro-capsule network. Additionally, there is no need for channel selection, which usually reduces the amount of computation in the subsequent classification process. To better extract discriminative EEG features and adapt to the micro-capsule network's architecture, a dimensionality expansion technique transforms the EEG signals into a matrix. The data vectors are reshaped into a three-dimensional structure, with dimensions corresponding to length, height, and the number of EEG feature channels. The EEG data form the matrix elements in each trial, structured as follows:  
\[A_{m\times n}=\left[\begin{array}{cccc}a_{11}&a_{12}&\cdots&a_{1n}\\ a_{21}&a_{22}&\cdots&a_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ a_{m1}&a_{m2}&\cdots&a_{mn}\\ \end{array}\right] \tag{1}\]  
where \(A_{m\times n}\) is a three-dimensional matrix composed of one piece of sample data, \(m\) is the sampling point number of fragments analyzed in a sample, and \(n\) is the number of analysis segments in a sample.