esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Methods
### Application of CNN-LSTM for Artifact Removal
#### 5.1.3 Generating Augmented EEG Recordings with Muscle Artifacts  
The method of generating augmented EEG recordings with added EMG artifacts enables the creation of controlled yet realistic conditions for evaluating and refining algorithms designed to remove muscle interference. To achieve this, the generated EEG and EMG signals are utilized. EMG artifacts undergo random amplitude scaling and random time shifting. During the scaling stage, the artifact is multiplied by a random factor (ranging from 0.5 to 1) to adjust its amplitude to the desired values. The positioning stage determines the point in the EEG signal at which the artifact will be introduced. This can be any location within the signal. Next, the generated EMG artifacts are added to the generated EEG recording, resulting in an EEG signal corrupted by muscle artifacts.  
The process of synthetic data generation involves all 6 signal channels--1 EEG channel and 5 EMG channels. Both the amplitudes of individual artifacts and the weights used to combine them were randomly selected for each generated sample. As a result, a high level of variability was achieved in terms of the type, intensity, and distribution of artifacts in the signals. This approach enables the creation of realistic and diverse data, which is crucial for effectively training the denoising model. A visual inspection of several dozen randomly selected augmented signals confirmed that they resemble real signals. Figure 6 presents sample EEG and EMG signals generated for training a CNN. Artifacts originating from the Fp1, HEOG, rape, cheek, and jaw electrodes are highlighted in blue. The EEG signal containing these artifacts is shown in magenta, while the artifact-free EEG signal is depicted in red. The generated dataset has been made available in the EEG/EMG database on the website: [[https://dx.doi.org/10.21227/c4k4-zd80](https://dx.doi.org/10.21227/c4k4-zd80)]([https://dx.doi.org/10.21227/c4k4-zd80](https://dx.doi.org/10.21227/c4k4-zd80)) (accessed on 25 April 2025).  
Through this approach, we have a dataset containing both clean EEG signals and EEG signals contaminated with muscle artifacts, allowing for a reliable assessment of artifact detection and filtering methods. It also provides a rich training resource for machine learning models. During the generation process, no attempt was made to reflect SSVEP.
# A Nighttime and Daytime Single-Image Dehazing Method
## 3 Proposed Dehazing Method
### Regularization and Dehazing  
With the purpose of improving the spatial correlation of image pixels to avoid problems such as haze residue or excessive dehazing, we used regularization to find the optimal transmission to improve the image quality. We used the L2-norm as the regularization term and as the data term to prevent over-fitting. We used the L1-norm, which is more robust to outliers, instead of the L2-norm so as to increase the depth of information sensitivity. We find the optimal transmission \(t_{R}\) by minimizing the following objective function:  
\[L=\sum_{x}(t_{F}(x)-t_{R}(x))^{2}+\lambda\sum_{x}\sum_{y\in N_{x}}w(x,y)|t_{F} (x)-t_{F}(y)| \tag{14}\]  
where the first part is the data term that measures the fidelity by calculating the Euclidean distance between the transmission \(t_{F}\) with the optimized transmission \(t_{R}\). The second part is the regularization term, where \(\lambda\) is the regularization parameter. \(N_{x}\) is the index set of the pixel \(x\), and \(w(x,y)\) is the weighting function. Here, we use the mixed function of color difference and brightness difference as seen in the following:  
\[w(x,y)=\alpha\cdot exp\left(\frac{-\|c(x)-c(y)\|^{2}}{2\sigma^{2}}\right)+(1+ \alpha)\Big{(}|l(x)-l(y)|^{\beta}+\delta\Big{)} \tag{15}\]  
where \(\alpha\) is the weight of the color difference and brightness difference, \(c(x)\) and \(c(y)\) are the color vectors, \(\sigma\) is the prescribed parameter with the value of 0.5, \(l(x)\) and \(l(y)\) are the  
Figure 3: Comparison of the high-light compensation effects.  
brightness vectors, \(\beta\) is the brightness sensitivity parameter (typically 0.3) and \(\delta\) is a small constant (typically 0.0001) to prevent this term from being 0.  
After the optimized transmission is obtained by the least square method, the dehazing image can be obtained using (3). As shown in Figure 4, regularization can effectively improve the structure of image details.
## Appendix A  
Existing models have estimation bias for GPP under different cloud cover conditions, and cloud cover index can adjust this phenomenon (Figure A3). Under mostly cloudy conditions, it can be found that the overall distribution of PB in all models is biased to the negative side, which indicates that the model underestimates GPP under mostly cloudy conditions. Under partly cloudy conditions, the existing models PB is more evenly distributed around the unbiased line, yet systematically giving lower values of GPP than on clear days. Some models are more affected than others by this cloudiness-induced bias, with VPM, VI, and GR models being those most affected by GPP underestimation on cloudy days, whereas other models had less overall deviation. In order to improve model performances, the cloudiness correction factor (Equation (3)) dependent on cloud cover index (CI) was added and compared with the original model. We found that the cloudiness factor clearly improved GPP prediction for most models, except for GR, MODIS, and AVM models, which showed no obvious improvements. For example, the original MODIS model underestimates GPP under partly cloudy and mostly cloudy conditions, with no obvious deviation under sunny conditions. However, after adding CI, GPP underestimates appear under all three cloud cover conditions. Comparing the fitting performance of the models at different scales can reflect the ability of the model to reflect spatio-temporal changes (Table 2). As expected, the R\({}^{2}\) of the model was larger on the coarser spatio-temporal scales. On one hand, for the same time scale, the R\({}^{2}\) of the biome scale was higher than that of the site scale. Similarly, for a same spatial scale, the R\({}^{2}\) were increasingly larger from the daily, yearly, and to the scale of many years. This indicates that the model captures the spatial and temporal variation of GPP, but the capture capability is different. In addition to the biome_years scale, the R\({}^{2}\) of the LUE-NDWI model was 0.94 less than VPM model, and the R\({}^{2}\) of the LUE-EF and LUE-NDWI models were higher than that of other models. It can be seen that two new modes had advantages in capturing spatial and temporal changes. Second, on the scale of site_year, the slope of LUE-EF and LUE-NDWI models was 1 and extremely significant (_p_value < 0.01), R\({}^{2}\) was also higher, 0.85 and 0.75 (_p_value < 0.05), indicating that the LUE-EF and LUE-NDWI models explained 85% and 75%, respectively, of the EC-tower GPP. In addition, the slope of LUE-NDWI model in site_years was also 1 (_p_value < 0.01), indicating that the model had the optimal simulation effect on these scales, that is, the applicability of the model was the most reliable.
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 3 Reliability of Tunnel Lighting System
### Reliability Model Created in This Research  
In this study, the reliability model discussed stems from one tunnel lighting system with the same framework as [13], the reliability block diagram (RBD) in Figure 8 can be used to analyze its system reliability.  
For a reliability analysis of the whole system, this TLCS is subdivided into three series subsystems or components; the lighting environment sensing component (LESC) is the component used to sense outside lighting conditions and report this collected light data to the tunnel system manager (TSM) in which the intelligent lighting adjustment strategy resides. Each tunnel onsite manager (TOM) receives and parses lighting adjustment commands (LAC) from TSM, then executes the adjustment of lighting parameters (inside luminance and color temperature). The system reliability is denoted as Equations (9) and (10),  
\[R=R_{LS}\times R_{SM}\times R_{OM} \tag{9}\]  
\[R_{OM}=R_{OM1}\times R_{OM2}\times\ldots\ R_{OMn}. \tag{10}\]Here, \(R_{LS}\), \(R_{SM}\), \(R_{OM}\) and \(R_{Omi}\) (\(i\) = 1, 2,... \(n\)) denote the reliabilities of LESC, TSM, TOM superset and TOM units individually.  
For each component, its reliability is evaluated by the embedded hardware and software entities. For example, lighting environment sensing devices, LED lights, lighting parameters modulators, and communication cables (LAN/RS485/Serial) are all hardware entities, whose reliabilities are basically determined at the stage of installation, mainly by respective manufactures. The influence factors for these will not be given more explanation since this is not the core topic in this paper.  
Of all the software entities, the intelligent adjustment strategy module (IASM) is the main software entity, its major functions or customer requirements are as listed in Table 12.  
The quality and reliability of IASM impact the whole tunnel lighting quality most significantly and determine whether the whole system meets customer requirements with reasonable performance and high confidence, so these are the key factors for the whole control system and thus the core object of this study.  
As can be seen from Equations (5) and (6), this TLSC is a system formed in series by LESC (Level-1, \(R_{LS}\)), TSM (Level-2, \(R_{SM}\)) and TOM (Level-3, \(R_{OM}\)). \(R_{LS}\) and \(R_{OM}\) are basically determined by the production of relevant hardware manufacturers or integrators and the reliability of configuration software. As the integrator of the overall TLCS, our main efforts are focused on the second level (\(T_{SM}\), mainly IASM) to improve the reliability level of this software system as a whole.  
Figure 8: Reliability block diagram of TLCS.
# Design and Modeling of a Parent Big STAR Robot Platform That Carries a Child RSTAR
## 2 Design and Manufacturing
### Robotic Design
#### 2.1.1 The Big STAR (BSTAR--The Parent Robot)  
The BSTAR, which is presented for the first time in this manuscript, is a sprawled robot similar to the original lightweight STAR robot [9] and reconfigurable RSTAR [12] but larger than the two by a factor of 7:1 and 6:1 respectively. It is also fitted with a tail to carry, deploy and recover the RSTAR robot. Given the size difference between the new robot and the previous ones, and the fact that it possesses a tail and must be able to carry the smaller robots, it needed to be fully redesigned. Unlike the 3D printed STAR and RSTAR, most of the BSTAR's body and its transmission must be made of metal to support its larger mass. Further, again due to the size difference, the wheel transmission is now based on roller chains. Attempts to design a lighter version using medium-density fiberboard (MDF) material were unsuccessful.  
The BSTAR is designed to carry substantial payloads of batteries, cameras, communication equipment and other sensors for search and rescue missions. The sprawl angle of the Big STAR can be varied in the range of negative 7 degrees to positive 66 degrees. The positive sense of the sprawl angle is downwards and equals zero when the legs are parallel to the body. The BSTAR weighs 9.8 Kg (including batteries and controllers), and its characteristic length (rear wheel axle to front wheel axle) \(L_{B}\) is 82.5 cm (5.7 times longer than RSTAR).  
The skeleton of the robot, including the main body, two legs and a tail, is made out of \(2\times 2\) cm aluminum profiles (with a linear density of 3.5 g/cm). The main body, which houses the controller and batteries, is a \(22\times 30\) cm rectangle constructed of an aluminum profile and Plexiglass.  
The power transmission of the wheels is based on roller chains and all three wheels on each side are actuated by a single motor. The wheel radius is 15.5 cm. The dimensions of the links of BSTAR are: \(L_{B}\) = 82.5 cm, \(L_{ruB}\) = 21.1 cm, \(L_{ruithB}\) = 5.8 cm, \(L_{1B}\) = 5.1 cm, \(L_{2B}\) = 2.7 cm, \(L_{3B}\) = 42.2 cm, \(L_{4B}\) = 6.6 cm, \(L_{hB}\) = 9.1 cm and \(L_{tail}\) = 44 cm (see Figure 2).
## 10 Future Research Directions
### Memory Management and Security  
Little work has been done on memory management for WSN OSs. The primary reason behind this is that it has been assumed that only a single application runs on a WSN OS. In many contemporary OSs, like TinyOS and Contiki, the application program and kernel compiles together, thus sharing the same address space. This can be harmful, therefore an OS like LiteOS provides separate address spaces for different applications and the kernel. Such techniques need to mature further to support a multitude of (concurrent) applications on wireless sensor nodes.
applied sciences Article  
Short-Term Bus Passenger Flow Prediction Based on Graph Diffusion Convolutional Recurrent Neural Network [PERSON] and [PERSON]\({}^{\ast}\)\({}^{\copyright}\)  
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China  
\({}^{\ast}\) Correspondence: [EMAIL_ADDRESS]  
**Featured Application: This study integrates diffusion convolution in a graph into a recurrent neural network to capture the spatiotemporal dependencies of different bus lines in a bus network for better passenger flow prediction. The proposed method is implemented in the bus network of Jiading, Shanghai, and achieves better modeling performance than that of the classic recurrent neural network models.**  
The short-term bus passenger flow prediction of each bus line in a transit network is the basis of real-time cross-line bus dispatching, which ensures the efficient utilization of bus vehicle resources. As bus passengers transfer between different lines, to increase the accuracy of prediction, we integrate graph features into the recurrent neural network (RNN) to capture the spatiotemporal dependencies in the bus network. The diffusion convolution recurrent neural network (DCRNN) architecture is adopted to forecast the future number of passengers on each bus line. The demand evolution in the bus network of Jiading, Shanghai, is investigated to demonstrate the effectiveness of the DCRNN model. Compared with classic RNN models, our proposed method has an advantage of about 5% in mean average percentage error (MAPE). The incorporation of diffusion convolution shows that the travel demand in a bus line tends to be similar to that in the closely related lines. In addition, the improvement in MAPE shows that this model outputs more accurate prediction values for low-demand bus lines. It ensures that, for real-time cross-line bus dispatching with limited vehicle resources, the low-demand bus lines are less likely to be affected to maintain a decent level of service of the whole bus network.  
bus transit; passenger flow prediction; spatiotemporal dependencies; graph convolution; recurrent neural network
# Effects of Nano-TiO\({}_{2}\) Mediated Photocatalysis on _Microystis aeruginosa_ Cells
## 1 Introduction  
In recent decades, algal blooms have occurred increasingly frequently in lakes and reservoirs around the world, and seriously threaten the supply of clean drinking water [1; 2]. _Microystis aeruginosa_ is a common species that causes algal blooms and affects water quality [3; 4]. It can also release microcystins, which are a class of toxins that endanger the health of animals and humans [5; 6].  
Advanced oxidation processes (AOPs) can efficiently degrade various pollutants. The AOPs produce highly reactive free radicals, mainly the hydroxyl radical (HO-), which can break down organic molecules. The hydroxyl radical can convert organic molecules into CO\({}_{2}\) and H\({}_{2}\)O [7; 8]. Free radicals can also damage algae cells, inhibit the growth of algae, and thus can be used to treat algal bloom. Among the AOPs, photocatalysis by nano-TiO\({}_{2}\) has attracted much attention, because the catalyst is inexpensive and highly stable. In photocatalytic oxidation, photons with greater energy than the band-gap energy excite the valence band electrons and expedite the breakdown of organic pollutants [9]. In addition, illuminating the catalyst active surface with sufficient energy can help create a positive hole (h\({}^{*}\)) in the valence band and produce an electron (e\({}^{-}\)) into the conduction band. The positive hole can then oxidize either the organic pollutant or H\({}_{2}\)O and generate the hydroxyl radicals [10].  
Coagulation is one of the most common methods to treat algal bloom. The coagulants can turn the algal bloom suspended in water into a gel and then form a clot. _M. aeruginosa_ is known for its negatively charged surface, and can thus be aggregated through charge neutralization withcationic metallic coagulants (e.g., inorganic metallic substances such as aluminum and iron salts) [11]. The mechanism of coagulation involves activation, adhesion, and aggregation of the algal bloom [12]. It must be noted that the cell surface plays an important role in the protecting cell. In this work, to demonstrate the biological effect of photocatalysis on _M. aeruginosa_ with regard to metabolism, morphological change, and surface zeta potential, _M. aeruginosa_ was treated with nano-TiO\({}_{2}\) upon UV irradiation. Specifically, metabolic heat was measured to evaluate the effect of AOPs on _M. aeruginosa_. The morphology of _M. aeruginosa_ cells was observed to analyze the surface feature. Zeta potential and metallic adsorption were determined after nano-TiO\({}_{2}\) treatment to evaluate the role of metallic coagulant in damaging _M. aeruginosa_.
# Action Recognition via Adaptive Semi-Supervised Feature Analysis
## 3 Related Work
### Embedded Subspace Representation  
Previous studies have shown that manifold subspace learning can mine geometric structure information by considering the space of probabilities as a manifold [31; 32; 33]. Recent research focuses on graph-embedded subspace or distance metric learning to measure activity similarity [34; 35; 36; 37; 38].  
[PERSON] et al. [39] build neighborhood graphs with geodesic distance instead of Euclidean distance, and project high-dimensional action to low-dimensional space by kernelized Grassmann manifold learning. [PERSON] et al. [40] propose an action-matching network to recognize open-set actions, construct an action dictionary, and classify an action via the distance metric. [PERSON] et al. [41] alleviate the over-smoothing issue of graph representation when multiple GCN layers are stacked by the flexible graph deconvolution technique.  
The two aforementioned studies [9; 10] are similar to ours. They assume that the visual words in different actions share a common structure in a specific subspace. A transformation matrix is introduced to characterize the shared structures. They solve the constrained non-convex optimization problem through an ALS-like iterative approach and matrix derivation. Nevertheless, the deduced inverse matrix is poorly scaled during optimization or close to singular, which may lead to inaccurate results.  
To address these problems, we hypothesize that manifold mapping can preserve the local geometry and maximize discriminatory power, as shown in Figure 2. However, we did not aim to mine shared structures. Therefore, we ignored shared-structure regularization and modeled the manifold by creating two graphs. As the optimization solution in [9; 10] may be mathematically imprecise, Karush-Kuhn-Tucker (KKT) conditions and PBB are introduced to improve algorithm convergence and avoid matrix inversion.  
Different from another related research [12], this work makes two major modifications as follows: multiple-feature analysis with combined Grassmannian kernels, and non-monotone line-search strategy with adaptive step sizes.
## 2 Methodology
### Guidance
#### 2.3.2 Path Tracking  
In order to evaluate the specified climb paths, a non-linear path-tracking guidance method was developed and used to guide the aircraft model in the h-M space. This was inspired by the Carrot Chasing algorithm [22], adapted to match the particular characteristics of the examined guidance problem.  
From the derivation of the previous sub-section, it was shown that, subject to some limitations, it is possible to fly in a particular direction in the h-M plane by properly adjusting the aircraft's flight path angle. Hence, a transition from an initial state (0) to new state (1) can be realized by setting the flight path angle to a value so that \(dh/dM=\tan\theta\), \(\theta\) being the angle formed between the states' relative position vector and the M axis (Figure 4). Consequently, instead of controlling the rate of rotation of the vehicle's velocity vector, as in typical guidance applications, direct control over the direction of displacement in the h-M plane is available. Based on this feature and the Carrot Chasing guidance scheme, a methodology for path tracking in the h-M plane was developed. This is presented schematically in Figure 5.  
Let C represent an arbitrary curve in the h-M plane, \(\overrightarrow{x}_{0}\) the vehicle's current position and \(\overrightarrow{x}_{1}\) the projection of \(\overrightarrow{x}_{0}\) on C. A reference point \(\overrightarrow{x}_{1}^{\prime}\) is generated on C, at a distance \(d_{11^{\prime}}\) downstream of \(\overrightarrow{x}_{1}\). The direction of the vehicle's displacement vector \(\overrightarrow{V}^{\prime}\) is defined as:  
\[\overrightarrow{V}//\left(\overrightarrow{x}_{1^{\prime}}^{\prime}- \overrightarrow{x}_{0}\right) \tag{17}\]  
Figure 4: Condition to achieve a transition from state (0) to state (1) in the h-M plane  
Figure 3: Schematic representation of feasible transitions in the h-M plane by means of flight path angle control (shaded area). Limits (a) and (b) correspond to a vertical climb and a vertical descent, respectively.  
Point \(\overrightarrow{x}_{1}^{\prime}\) is equivalent to the Virtual Target Point used in the Carrot Chasing path tracking algorithm and is generated by means of numerical integration over curve C so as to appear at a fixed curve length downstream of \(\overrightarrow{x}_{0}\).  
The path tracking methodology hereby presented was evaluated over a wide variety of flight paths, displaying very good overall performance in following the specified trajectories (Figure 6).