esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 4 Cislunar Dynamics (Constellation Scenario)  
The dynamics of being aligned for the proposed method are the most critical parts of making operations feasible and adaptable for the cislunar space. The Earth and the Moon both have distinct environments, and their orientations are uniquely defined. The majority of the concerns have a root cause in the way their dynamics have been designed and incorporated into the small satellites. As this class of satellites are resource-constrained, it is usually difficult to make a clear balance of the dynamics proposed and designed to the real-time conditions. Hence, this paper makes a sincere effort to have a better interface of the proposed methodology to real-time operations.  
Reference [26] is used as a defining input for the constellation building in the Cislunar with each small satellite as a three-body problem with reference to Earth and Moon and also with respect to the other satellites as chasers or followers with one reference body as Earth or Moon, respectively. The state vector is defined as:  
\[x=[x,y,z,\dot{x},\dot{y},\dot{z}]^{T} \tag{1}\]  
This vector for the satellite relative to the Earth and Moon center is organized in terms of rotating coordinates. The mass parameter is given as:  
\[\mu=\frac{ms_{1}}{ms_{1}+ms_{2}} \tag{2}\]  
where \(ms_{1}\) and \(ms_{2}\) are the masses of the primary bodies of the Earth and the Moon. The first-order non-dimensional equation of motion is presented as:  
\[\dot{x}=f(x) \tag{3}\]  
with vector fields,  
\[f(x)=[\dot{x},\dot{y},\dot{z},2n\dot{y}+U_{x},-2n\dot{x}+U_{y},U_{z}]^{T} \tag{4}\]  
where \(n\) is the non-dimensional mean motion of the primary system and \(U\) is the pseudo-potential function of a three-body scenario in the Cislunar space. This is mainly for the circular orbits, but the variations over other orbital configurations can be planned with respective alternations in defining the governing equations.  
The detailed derivations and the sequential alignment of the theories can be found in [26], respectively. This forms a fundamental background of the small satellite-based constellation design and simulation with the Earth-Moon Cislunar boundaries. New terminologies have been discussed in detail with the concept and the techniques proposed in this work.
## 3 Wireless Body Area Network (WBAN)
### EEG sensors  
Electroencephalography (EEG) sensors are devices used to detect and record the electrical activity of the brain. EEG is a non-invasive neuroimaging technique that measures the voltage fluctuations resulting from the ionic current flows within the neurons of the brain. These sensors have a wide range of applications, including diagnosing epilepsy, sleep disorders, brain injuries, and neurological conditions. They are also used in neurofeedback, brain-computer interfaces (BCI), and cognitive research [53].  
EEG sensors consist of multiple electrodes that are typically attached to the scalp. These electrodes are small metal discs or sensors that come into contact with the scalp to pick up the electrical signals produced by the brain. Electrodes are either wet or dry. Wet electrodes require a conductive gel to be applied between the electrode and the scalp to ensure good electrical contact. The gel helps to reduce impedance and interference. While dry electrodes do not require any additional gel and can be directly applied to the scalp [54]. They are more user-friendly and are commonly used in consumer-grade EEG devices. The number of electrodes used in an EEG system can vary, ranging from a few channels to several hundred, depending on the level of spatial resolution required. The positioning of EEG electrodes is essential to capture specific brain activity patterns accurately [55].  
Moreover, EEG sensors are connected to amplifiers that strengthen the weak electrical signals picked up by the electrodes. Amplifiers play a crucial role in maintaining the signal-to-noise ratio, which is critical in obtaining high-quality EEG data. The amplified analog signals are then converted to digital signals using analog-to-digital converters (ADCs). Digital signals are easier to process, store, and transmit for further analysis.  
The EEG system includes a signal processing unit that filters, amplifies, and digitizes the raw EEG data. Signal processing techniques are employed to remove artifacts and noise from the signals. Modern EEG systems often have data interfaces, e.g., Bluetooth, allowing real-time data streaming and integration with other devices or software applications [56].
# High-Fidelity Steganography: A Covert Parity Bit Model-Based Approach
## 2 Related Works
These techniques typically use a coefficient replacement strategy in which insignificant DCT coefficients are replaced with scaled-down secret pixels. One of the earliest attempts to develop a model-based steganography scheme was the Model-Based (MB1) scheme reported in [42]. This scheme is based on modeling the LSB plane with the assistance of the Generalized Cauchy Distribution (GCD). However, because there is no ideal model that can perfectly obtain a copy of the original image, there are noticeable differences between the original cover signal and the model-generated signal. Therefore, a basic first-order statistical analysis is able to overcome the security of the MB1 scheme [43]. Furthermore, the MB1 scheme suffers from 'blockiness' artifacts between the original cover image and the stego image generated by the MB1 scheme. Therefore, an enhanced version with de-blocking features, named MB2, was proposed in [42]. Despite this, MB2 can be broken using a Markov Process (MP), as shown in [44]. The first DCT model-based steganography scheme was proposed in [21]. The proposed scheme is based on a basic modeling approach in which every high-frequency DCT column is modeled by inspecting every local maximum and minimum point of the DCT column, then performing a linear interpolation process between every maximum and minimum point. The main drawback of the scheme proposed in [21] is its high communication cost, as the sender needs to transmit all maximum and minimum points to the receiver to reconstruct the secret image. To address this issue, an improved model-based version was presented in [22] that reduces communication costs by employing polynomial modeling. In this enhanced approach, high-frequency DCT coefficients are first modeled using polynomial fitting, then the down-scaled secret pixels are added to the polynomial-fitted signal. This paper presents an improvement over the polynomial-based steganography scheme proposed in [22]. Instead of arithmetically adding the secret pixel using the polynomial-generated signal, the proposed technique tends to adaptively choose to either add or subtract the secret pixels to or from the modeled pixels in order to match the original DCT coefficients of the cover signal. This procedure maximizes the similarity between the stego signal and the original cover signal, resulting in enhanced stego quality.
## 3 Results  
### Studies' Selection and Characteristics  
The literature search, following PRISMA guidelines, for antimicrobial activity of _Humulus lupulus_ led to the retrieval of 2553 articles. After the application of eligibility criteria, we ended up with 18 articles that included 450 individual studies (Figure 1).  
The included studies were investigating the antimicrobial activity of various hop compounds (Figure 2) that include flavonoids, bitter acids, and three types of extracts, i.e., supercritical fluid extracts (CO\({}_{2}\)-based, SFE), hydroalcoholic, and hydroacetonic. From them, 122 report MIC values on flavonoids, 152 on bitter acids, 67 studies on CO\({}_{2}\)-extracts, 79 on hydralcoholic extracts, and 28 on hydroacetonic extracts. Strains, which are used to measure antimicrobial activity against them, are 55. From the 450 studies, 174 of them were investigating antimicrobial effects after treatment for 24 h, while 276 studies reported results for the 48 h time point (Table 1 and Supplementary Table S1). The characteristics of the included studies reporting MIC values, incubation time, and experimental repetitions were taken into consideration for the meta-analysis.  
Figure 1: PRISMA-compliant flow diagram illustrating the systematic review process used to identify studies included in the meta-analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Year** & **Strain** & **Type of Compounds/Extracts** & \begin{tabular}{c} **Number of** \\ **Experiments** \\ \end{tabular} & **Time (h)** & \
\begin{tabular}{c} **Temperature** \\ **(°C)** \\ \end{tabular} \\ \hline [PERSON] & 2015 &
\begin{tabular}{c} _Staphylococcus aureus/Listeria_ \\ _monocytogenes_/_Escherichia_ \\ _coli_/_Salmonella enterica_ \\ _Propinibacterium_ \\ _acuses_/**Staphylococcus aureus_ \\ _Bacteroides fragilis_/_Clostrium_ \\ _perfringers_/_Clostridium difficile_ \\ _Stphylococcus_ \\ _Sepatternidis_ \\ _Sep
## 3 Results
### Meat Composition and Quality  
Results of the \(LT\) muscle chemical composition indicated a significant difference under different restricted grazing time and indoor supplementary feeding systems. Table 4 showed that the contents of crude protein and IMF in \(LT\) muscle were affected by different feeding systems (\(p<0.05\)). The \(LT\) muscle of lambs in the G0 group had lower crude protein content than the other four groups (\(p<0.05\)), among which there were no significant differences (\(p>0.05\)). Higher IMF contents were detected in the muscle of G8, G2, and G0 groups than that of G12 group (\(p<0.05\)), but there were no obvious differences among the G8, G4, G2, and G0 groups (\(p>0.05\)). Additionally, the moisture and ash contests of the \(LT\) samples did not change significantly with the variation of grazing time and concentrate (\(p>0.05\)). The effects of restricted grazing time and indoor supplementary feeding systems on pH, color parameters, and cooking loss of \(LT\) muscle from Tan lambs are shown in Table 5. The pH\({}_{49\mathrm{min}}\) of \(LT\) muscle showed a nonsignificant decreasing trend as restricted grazing time increased (\(p>0.05\)), and no significant differences were observed in the pH\({}_{24\mathrm{h}}\) of \(LT\) muscle (\(p>0.05\)). The L* value of the \(LT\) muscle was the lowest in animals of the G12 group, which was significantly lower than those in the groups of G4, G2, and G0 (\(p<0.05\)). \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Items**} & \multicolumn{4}{c}{**Treatments**} \\ \cline{2-5}  & **G12** & **G8** & **G4** & **G2** & **G0** \\ \hline Moisture (\%) & 76.17 \(\pm\) 0.16 & 75.08 \(\pm\) 0.43 & 75.17 \(\pm\) 0.44 & 75.45 \(\pm\) 0.20 & 75.90 \(\pm\) 0.46 \\ Protein (\%) & 19.75 \(\pm\) 0.14 a  & 20.05 \(\pm\) 0.15 a  & 19.51 \(\pm\) 0.19 a  & 19.74 \(\pm\) 0.33 a  & 18.88 \(\pm\) 0.21 b  \\ IMF (\%) [19] & 2.00 \(\pm\) 0.18 b  & 3.93 \(\pm\) 0.63 a  & 2.97 \(\pm\) 0.43 ab  & 3.81 \(\pm\) 0.33 a  & 3.62 \(\pm\) 0.52 a  \\ Ash (\%) & 1.12 \(\pm\) 0.06 & 1.03 \(\pm\) 0.04 & 1.13 \(\pm\) 0.03 & 1.19 \(\pm\) 0.06 & 1.21 \(\pm\) 0.05 \\ \hline \hline \end{tabular} Different superscripts (a and b) mean significant differences (\(p<0.05\)). IMF, intramuscular fat. The values are the means \(\pm\) SE (\(n=10\) per group). G12, G8, G4, and G2 mean that the lamb grazing time was 12 h/d, 8 h/d, 4 h/d, and 2 h/d, respectively. G0 means indoor supplementary feeding.  
\end{table} 
Table 4: Chemical composition of \(LT\) muscle in Tan lambs under different restricted grazing time and indoor supplementary feeding systems.
## 2 Materials and Methods
### Electrophysiological Processing
#### 2.6.1 ERP Analyses  
In accordance with established recommendations for attentional N200 and P300 studies [38], EEG recordings were filtered 0.1-45 Hz with a 60 Hz notch (Butterworth zero-phase infinite impulse response filter; 48 dB/octave). Filtered recordings were downsampled from 300 Hz to 150 Hz and segmented into 1 s test epochs ranging from \(-\)200 to +800 ms, relative to the onset of each letter stimulus (\(t\) = 0 ms). Test epochs were then baseline corrected using the 200 ms prior to stimulus onset (\(-\)200 to 0 ms), separated according to target class, and averaged within each condition. In order to estimate ERP amplitudes, a semi-automatic peak detection tool labelled weighted local maxima (0.50 weight strength) for N200 (200-350 ms) and P300 (300-550 ms) in the averaged target letter epochs. Labels were visually inspected and adjusted as necessary to correct cases of high-frequency peak capture or other general inaccuracies. Finally, N200 and P300 amplitudes for target and non-target averages were estimated as the mean signed voltage (in microvolts; uV) \(\pm\) 4 sampled points from the labelled N200 and P300 peak latencies of the respective target responses. These estimates allowed us to analyze not only the ERP responses to target and non-target letter stimuli, but also the target attention effect, which we defined as the difference between measured target and non-target amplitudes.  
To approximate signal variance, we measured the standard deviation of each participant's grand-averaged test epochs. As well, we generated a second variance metric, which took an average of the standard deviation measured across all available epochs at each sampled point within the window of 200-600 ms post-stimulus onset. The logic behind using these two measures was that the standard deviation of the grand average would offer a general sense of noise, while the average standard deviation of the smaller window would offer a picture of variability within the window of the attentional ERPs.
# Single Image Reflection Removal Based on Residual Attention Mechanism
## 4 Experiments  
In the experiments, we used the 64-bit Ubuntu16.04 operating system and the deep learning framework PyTorch.2.0. The GPU-accelerated training was performed on a GeForce GTX 1080 Ti with a graphics card memory of 32G, and the CUDA version was 10.2. To minimize the training loss, we employed the ADAM optimizer [36] to train our model for 90 epochs, where \(\beta_{1}\) and \(\beta_{2}\) in ADAM were both set to 0.5, the batch size was set to 2, and the learning rate for the whole network training was set to 0.0002. Too many training epochs do not provide better experimental results, and we chose to train 90 epochs based on our experiments.  
Deep neural network training needs a significant amount of data; however, the available reflection datasets are limited. Therefore, we also collected more real reflection images. We utilized a stand to hold the camera and a piece of movable glass in front of the camera for taking photos. Images with reflections were obtained by setting the glass between the camera and the objects to be photographed. Images without reflections were also obtained by quickly removing the glass. In Figure 5, the device for taking pictures and two pictures with and without reflection are illustrated. We published the dataset we collected in the Data Availability Statement section of our paper.  
As for the real data, we used 460 pairs of real images, which consisted of 250 pairs of real data from ERRNet [28], 110 pairs of real data from Zhang [1], and 100 pairs from the SCAU-RID dataset we collected. As for the synthetic data, we used the pictures dataset from [25] for the synthetic data, with 8000 pairs of synthetic images available for comparison (size: 512 \(\times\) 512).
# Path Tracking Control Strategy Based on Adaptive MPC for Intelligent Vehicles
## 2 MPC Path Tracking Controller  
### Vehicle Dynamics Modeling  
The three-degree-of-freedom vehicle dynamics model is capable of precisely capturing the lateral, longitudinal and yaw dynamic characteristics exhibited during the path tracking process of an intelligent vehicle. In order to investigate the path tracking control of front wheel steering vehicles under varying road conditions, a simplified three-degree-of-freedom vehicle dynamics model is established in this section to serve as the MPC prediction model, as illustrated in Figure 1. In the diagram, \(OXY\) is the geodetic inertial coordinate system; \(oxy\) is the vehicle coordinate system; \(a\), \(b\) is the distance from the front and rear axles of the vehicle to the center of mass; \(F_{x,t}\), \(F_{x,t}\) denote the forces acting along the x-axis on the front and rear wheels; \(F_{y,t}\), \(F_{x,t}\) denote the forces acting along the y-axis on the front and rear wheels; \(F_{l,t}\), \(F_{l,t}\) denote the longitudinal forces experienced by the front and rear wheels; \(F_{c,t}\), \(F_{c,t}\) denote the lateral forces acting on the front and rear wheels; \(\delta_{\rm f}\) is the steering angle of the front wheel; \(\dot{x}\), \(\dot{y}\) are the longitudinal and transverse speeds of the vehicle, respectively; \(\dot{\varphi}\) is the yaw rate of the vehicle.  
According to the literature [32], the expression of a three-degree-of-freedom vehicle dynamics non-linear model based on a linear tire model can be expressed as  
\[\left\{\begin{array}{l}\ddot{y}=\frac{2}{m}\left[C_{c,t}\frac{b\dot{y}-\dot{ y}}{x}+C_{l,t}S_{t}\delta_{\rm f}+C_{c,t}\left(\delta_{t}-\frac{\dot{y}+\dot{a} \dot{y}}{x}\right)\right]-\dot{x}\dot{\varphi}\\ \ddot{x}=\frac{2}{m}\left[C_{l,t}S_{\rm r}+C_{l,t}S_{\rm f}-C_{c,t}\left(\delta _{t}-\frac{\dot{y}+\dot{a}\dot{y}}{x}\right)\delta_{\rm f}\right]+\dot{y}\dot{ \varphi}\\ \ddot{\varphi}=\frac{2a}{t_{z}}\left[C_{l,t}S_{\rm f}\delta_{\rm f}+C_{c,t} \left(\delta_{\rm f}-\frac{\dot{y}+\dot{a}\dot{\varphi}}{x}\right)\right]- \frac{2b}{t_{z}}C_{c,t}\frac{b\dot{\varphi}-\dot{y}}{x}\\ \dot{y}=\dot{x}\sin\varphi+\dot{y}\cos\varphi\ \ \dot{X}=\dot{x}\cos\varphi-\dot{y}\sin \varphi\end{array}\right. \tag{1}\]  
where \(m\) is the vehicle mass; \(\ddot{x}\), \(\dot{y}\) are the longitudinal and transverse acceleration of the vehicle, respectively; \(\dot{X}\), \(\dot{Y}\) are, respectively, the longitudinal and lateral speeds of the vehicle in the global coordinate system; \(\varphi\) is the yaw angle of the vehicle; \(\ddot{\varphi}\) is the yaw rate acceleration; \(I_{z}\) is the moment of inertia of the vehicle around the \(z\)-axis; \(C_{l,t}\), \(C_{l,t}\) are the longitudinal stiffness of front and rear wheels; \(C_{c,t}\), \(C_{c,t}\) are the lateral stiffness of front and rear wheels; \(S_{\rm f}\), \(S_{\rm r}\) are the slip ratio of front and rear wheels. The test vehicle parameters are shown in Table 1.  
Figure 1: Vehicle dynamics model.
## 4 Discussion
### Test Parameters
#### 4.2.3 Span Length  
According to [PERSON] et al. [19], the comparison of results from three-point bending tests is limited due to the variability in testing setups, particularly the distance between supports, which has a pronounced influence on measured outcomes [30]. In this review, a wide range of span lengths was observed, with 24 mm and 25 mm being the most commonly used. Only five studies adhered to established testing standards, i.e., ISO 20795 [22], ASTM D790 [19; 20; 33], and ANSI/ADA Specification 32 [32], all of which recommend span lengths up to 32 mm. These standards were originally developed for stiffer orthodontic materials with more uniform stress distribution, unlike thermoplastic aligner materials, which experience greater stress concentration in localized areas during clinical use [30]. Simulating these stresses during testing would require shorter span lengths and lower deflection values. [PERSON] et al. [30] found that a span length of 8 mm resulted in more realistic stress localization and reported an average force reduction of 86% when span length was increased from 8 mm to 16 mm. However, this finding is not universally supported; for example, [PERSON] et al. [24] reported no statistically significant differences in force reduction when comparing span lengths of 8 mm, 16 mm, and 24 mm. These discrepancies highlight the need for further research to determine the most clinically relevant span length for testing aligner materials.
# Algorithms for Calculating Generalized Trigonometric Functions
## 2 State of the Art  
The concept of _Ateb_-functions is intricately associated with asymptotic techniques in engineering [13], as evidenced in various studies [8; 9]. Following the influential publication of [3], significant advancements in _Ateb_-functions throughout the 20 th century were primarily made at the University of Novi Sad in Serbia and Lviv Polytechnic National University in Ukraine. More than a hundred scholarly articles by Serbian researchers have enriched this area of study. Although this paper does not aim to provide an exhaustive review of these contributions, it is worth highlighting a few significant works, such as the recent research presented in [7], which offers analytical solutions for a model that illustrates oscillatory motion with two degrees of freedom and Van der Pol coupling. Furthermore, book [6] delves into various aspects of modeling oscillatory motion.  
The theory of _Ateb_-functions began to take shape in Ukraine through the work published in [14; 15]. The integration and differentiation formulas for these functions are presented in [16]. Their use in generating noise signals is thoroughly examined in [9], highlighting the benefit of being able to modify the characteristics of the noise signal by choosing suitable parameters for the _Ateb_-functions. Furthermore, periodic _Ateb_-functions are utilized for modeling traffic in computer network as discussed in [17]. Reference [18] also investigates how analytical solutions using _Ateb_-functions can be employed to analyze the effects of oscillation amplitude and the elastic properties of board materials on the oscillation frequency of machine control components.  
In the 21 st century, there has been a notable increase in research surrounding _Ateb_-functions across various regions. Reference [19] discusses these functions using alternative terminology, labeling them as generalized trigonometric functions without specifically mentioning _Ateb_-functions. In [20], the authors derive analytical solutions for nonlinear oscillators that extend an isotonic potential. The relationship between _Ateb_-functions and other forms of generalized trigonometric functions is introduced in [21] and further examined in [22]. Additionally, fractional calculus related to trigonometric and other functions, along with their characteristics, is elaborated upon in [23; 24; 25; 26]. Despite their specific properties, _Ateb_-functions find extensive applications in various areas of mathematical modeling. Expanding their framework within fractional calculus is anticipated to greatly enhance their utility for different applications.