esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Methods
### Linear Regression Applied to Artifact Elimination  
Linear regression was the second algorithm used by us to remove muscle artifacts from EEG signals. This approach models the relationship between reference signals, such as EMG, and the recorded EEG signal. EMG signals serve as indicators of interference, which are then subtracted from the EEG signal based on the calculated regression coefficients. According to [21], regression methods were frequently used in the 1990s to remove EOG artifacts due to their simplicity and low computational demands. They remain popular and widely used for artifact removal to this day [21]. The technique requires one or more electrodes, referred to as reference electrodes, dedicated to recording interference. These are often placed near the eyes or muscles to capture EOG and EMG artifacts for subsequent removal. Linear regression assumes that each EEG recording channel is the sum of an uncontaminated source signal (neuronal activity) and a linear combination of the reference signals [22]. Neuronal activity is considered independent of the reference electrodes. The goal of regression is to estimate the optimal propagation coefficient for each reference electrode using the least squares method. The correction of a contaminated EEG signal is performed by subtracting a weighted portion of the reference signal from the contaminated EEG, resulting in a cleaned signal.  
Regression for EEG signal cleaning has several significant advantages, making it a popular tool in neurophysiological data analysis. First, it is a straightforward, computationally efficient method that does not require advanced algorithms. Consequently, it is fast and easy to implement. Despite these strengths, regression also has certain limitations. One of the main assumptions is that artifacts are a linear combination of signals from the reference electrodes, which can be a constraint. In reality, artifacts may be more complex and nonlinear, making regression insufficient in such cases for effective signal cleaning.  
Using regression to remove artifacts requires the careful selection of reference electrodes, ensuring that these electrodes contain only artifacts rather than neuronal signal components. Regression remains an effective method for cleaning EEG signals and is still used today, particularly when fast, real-time calculations are needed (e.g., in BCI).  
In our study, all EMG/EOG electrodes (HEOG, neck, cheek, and jaw) were used as reference electrodes. Cleaning focused on signals from the electrodes most commonly used in BCI--O1, Oz, O2, and Cz. Each electrode was cleaned sequentially using the reference channels. Regression was performed in time windows rather than applied once to the entire dataset, meaning the regression model operated on smaller, shifting segments of data (windows), allowing local temporal relationships and variability to be taken into account. The window length was experimentally set to 1 s (256 samples). A wider window would contain multiple artifacts as well as artifact-free signal segments, while a narrower window might not capture individual artifacts.
## 8 Conclusions  
In this work, we formulated and studied the statistical problem of the propagation of a low-frequency sound signal in a homogeneous water column of a random shallow-water waveguide with 2D Gaussian sound speed fluctuations in the layer (and half-space) of liquid bottom sediments. Based on the performed statistical modeling, the following main results were obtained.  
1. For waveguides with an essential average penetrability of the bottom boundary, the fact of a significant slowing down of the decay of the average signal intensity (reduction of transmission loss) along the propagation path has been established. Compared to a deterministic waveguide with similar regular parameters, the reduction in transmission loss in the presence of fluctuations can reach several tens of decibels (in the average statistical sense). This reduction in signal transmission loss occurs already on rather short paths of 3-10 km.
2. Simultaneously with the decrease in transmission losses, there is an increase in the fluctuations of the signal intensity. We found that the scintillation index describing the development of such fluctuations grows rather rapidly with distance, exceeding unity level already at distances of several kilometers. Thus, the stochastization of a signal in the randomly inhomogeneous waveguide occurs rather quickly. An increase in the scintillation index is observed along the entire propagation path without a transition to the saturation regime, which is typical for media with energy losses.
3. The maximum effect of decreasing the average transmission loss was achieved at a large value of the vertical correlation radius of the sound velocity fluctuations \(L_{z}\) in bottom sediments. However, even with a small value of \(L_{z}\)-\(\lambda\), where \(\lambda\) is the sound wavelength, the result of reducing the average loss in the waveguide remains in effect.
4. Numerical simulations show that the results obtained are described fairly well by the adiabatic approximation, which neglects the coupling of modes. This fact, as well as random fluctuations of the speed of sound in the water layer, is explained by the overwhelming contribution to the intensity of most realizations of the first (least attenuated) mode, which manifests itself already at rather small distances from the source.  
F.Z., data curation, writing--original draft, funding acquisition; O.E.G., conceptualization, formal analysis, validation, writing--review and editing, methodology; I.O.Y., supervision, project administration, software, investigation. 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 no. 41406041). The research was also carried out as a part of the Russian State assignment on the topic \"Study of the fundamental basis of the origin, development, transformation, and interaction of hydroacoustic, hydrophysical, and geophysical fields of the World Ocean\" (state number registration: AAAA-A20-120021990003-3).  
Not applicable.  
This study did not report any data.  
The authors declare no conflict of interest.
# Design and Analysis of a Novel Variable Stiffness Joint Based on Leaf Springs
## 3 Overall Structural Design of VSJ
The diameter, height (axial), and part material of the VSJ are limited at the beginning of the design, based on past experiences. After the design is completed, the final dimensions and estimated weight are obtained via measuring of the CAD model. The maximum elastic torque is obtained through the use on Equation (7) and simulation analysis (as shown in Figure 7). Under the assumption of small deformation, combined with empirical values and safety factors, the maximum deformation \(\theta\) of the leaf spring can be limited at about \(9^{\circ}\), and the deflection angle range \(\varphi\) of the joint can be solved using Equation (2). Similarly, the stiffness adjustment range can also be obtained via the use of Equation (9) and simulation analysis (as shown in Figure 3). Referring to Figure 1, when the slider approaches the fixed end of the leaf spring, K also tends towards infinity, thus obtaining a large stiffness adjustment bandwidth. Other parameters are preset values and can be flexibly modified during the design process for different application scenarios. In order to compare and analyze the advantages of the proposed VSJ, several similar VSAs are selected for comparison, as shown in Table 3. The stiffness adjustment bandwidth of the VSJ is relatively large when compared to vsaUT-II [20], REGT-VSA [23] and CLSM-VSA [29]. According to the step response simulation analysis, the stiffness regulation time of the VSJ is about 0.34 s, which is relatively fast. This is mainly due to the simplicity of the stiffness adjustment mechanism, which adjusts the stiffness only via rotary motion, thus avoiding the response delay caused by the ball screw or rocker linkage mechanism when used to convert rotary motion into linear motion. It is worth noting that the proposed reconfigurable performance of the VSJ is the most competitive. With the help of this reconfigurable mechanism, the stiffness adjustment range and nominal elastic torque of the VSJ can be easily expanded for a specific requirement without requiring any disassembly and re-assembly of the actuators. Compared with RVSA [31] and CLSM-VSA [29], this design advantage avoids the tedious work of recalibration and debugging required after disassembling the actuator, and has high practical value. However, as it is currently in the conceptual design stage, the weight of the VSJ is only measured using CAD models, resulting in a relatively large theoretical weight. However, this can be reduced through the use of lightweight design methods, such as finite element simulations and structural topology optimization, before the prototype is built, if necessary.
## 4 Proposed Method
### Ensuring Traceability of Requirements and Component Architecture Elements  
#### 4.5.1 Tracing Between Structured Features and Requirements  
Software requirement specifications and hardware system models are necessary to generate the structured features. Figure 6 shows the relationship between requirements analysis artifacts and the attributes of the structured feature model.  
\begin{table}
\begin{tabular}{l l} \hline  
The software requirements specification and hardware system model together directly or indirectly compose the attributes of the structured features. Figure 6 shows the parts of the structured feature model composed of these elements. The requirements specification affects all attributes of the structured feature model, and contains not only functional requirements but also non-functional requirements, as well as foundational information such as the scope of the software being designed and developed. This information influences the entire development process, including design and implementation. To satisfy the requirements, structured features can represent this information either directly, such as in Req. ID attributes that specify the scope of the software, or indirectly, such as through input and output data and events. The hardware system model is explicitly represented by the input and output data and the events of the structured feature model. The existing information is expressed as attribute values within the structured feature model, creating a connection between the artifacts of the requirements analysis phase and the outputs of the structured feature analysis phase.  
A structured feature \(SF\) is defined as \(SF_{id}=(RID,A,ID,OD,IE,OE)\), as shown in Figure 2. The attributes of a structured feature can be classified into those related to requirements (RID), actions (A), data (ID and OD), and events (IE and OE).  
During the structured feature analysis phase, the designer refines the software requirement specifications into software requirement elements to enable the extraction of those elements required for the structured features. These software requirement elements are then assigned as values to each structured feature attribute. The hardware system model is refined into hardware elements and assigned as values to the action-, data-, and event-related attributes of the structured feature. Through this assignment, both the software requirements and the hardware system model are reflected in the resulting structured feature. This creates links between the attributes of the structured feature and the requirements, which can then be used to establish traceability between the requirement RID and structured feature, as the RID is assigned to the attributes of the structured feature.
# A Novel Expectation-Maximization-Based Blind Receiver for Low-Complexity Uplink STLC-NOMA Systems
## 4 Blind Energy Estimation (BEE)
```
1:Input:\(\widetilde{\mathbf{y}}\). 2:Output:\(\widehat{P_{1}\gamma_{1}}\), \(\widehat{P_{2}\gamma_{2}}\). 3:forall\(n\)\((\in\{1,2\})\)do
4:\(\mathbf{z}= \begin{cases}\Re[\widetilde{\mathbf{y}}],&\text{if $n=1$},\\ \Im[\widetilde{\mathbf{y}}],&\text{if $n=2$}.\end{cases} \)
5: Initialize the means \(\mu_{1},\ldots,\mu_{M}\) and variance \(\sigma^{2}\) by dividing \(\mathbf{z}\) into \(M\) clusters, \(\pi_{k}=1/M\), \(\forall k\in\{1,\ldots,M\}\), and \(F_{\text{old}}=0\)
6: Calculate log-likelihood \(F_{\text{new}}=\sum\limits_{l=1}^{L}\ln\left[\sum\limits_{k=1}^{M}\pi_{k} \mathcal{N}(z_{l}|\mu_{k},\sigma^{2})\right]\)
7:while\(|F_{\text{new}}-F_{\text{old}}|\geq\epsilon\)do\(\triangleright\)Expectation (E-Step)
8: Update responsibilities \(r_{k}^{(l)}=\pi_{k}\mathcal{N}(z_{l}|\mu_{k},\sigma^{2})\Big{/}\sum\limits_{k ^{\prime}=1}^{M}\pi_{k^{\prime}}\mathcal{N}\big{(}z_{l}|\mu_{k^{\prime}}, \sigma^{2}\big{)},\forall k,l\)\(\triangleright\)Maximization (M-Step)
9: Update means \(\mu_{k}=\sum\limits_{l=1}^{L}r_{k}^{(l)}z_{l}/\sum\limits_{l=1}^{L}r_{k}^{(l)}\), \(\forall k\)
10: Update variance \(\sigma^{2}=\sum\limits_{k=1}^{M}\left(\sum\limits_{l=1}^{L}r_{k}^{(l)}(z_{l} -\mu_{k})^{2}\Big{/}\sum\limits_{l=1}^{L}r_{k}^{(l)}\right)\Big{/}M\)
11: Update weights \(\pi_{k}=\sum\limits_{l=1}^{L}r_{k}^{(l)}\Big{/}\sum\limits_{k=1}^{L}\sum \limits_{l=1}^{L}r_{k}^{(l)}\Big{(}=\sum\limits_{l=1}^{L}r_{k}^{(l)}\Big{/}L \Big{)},\forall k\)
12: Update log-likelihood \(F_{\text{old}}\gets F_{\text{new}}\)
13: Calculate log-likelihood \(F_{\text{new}}=\sum\limits_{l=1}^{L}\ln\left[\sum\limits_{k=1}^{M}\pi_{k} \mathcal{N}(z_{l}|\mu_{k},\sigma^{2})\right]\)
14:endwhile
15: Estimate \(\sqrt{\widehat{P_{n}}\gamma_{n}}=\frac{\sqrt{M\sum\limits_{n=1}^{M}\left(2m-1 -M\right)^{2}}}{\sum\limits_{n=1}^{M}\left|2m-1-M\right|}\sum\limits_{k=1}^{ M}\pi_{k}|\mu_{k}|\)
16:endfor
```  
**Algorithm 1** Blind energy estimation (BEE) algorithm.
# Relay Selection Scheme for Multi-Hop Transmission of MU-MIMO System
## 5 Simulation Results
As shown in Figure 8, the random relay has lowest BER performance, and the proposed relay selection scheme has BER performance similar to the BHM scheme. Therefore, the proposed relay selection scheme can select the appropriate relay using only 3D channel information between the BS and relays. Figure 5: The BER performance of the proposed and conventional schemes in 2D channel environment \((N_{t}=2,N_{r}=2,N_{UE}=1)\). Figure 6: The BER performance of the proposed and conventional schemes in 2D channel environment \((N_{t}=4,N_{r}=4,N_{UE}=2)\). As shown in Figures 7 and 8, the proposed scheme has similar BER performance with the optimal relay selection scheme (BHM scheme) and has better BER performance than the random relay selection scheme in 3D channel environment. The proposed relay selection scheme has higher performance than the random relay selection scheme without considering any factors, since the proposed relay selection scheme selects an appropriate relay using the singular values of the channel matrix between the BS and relays. Additionally, in the BHM scheme (optimal relay selection scheme), the BS must know both the channel information between the BS and relays, and between the relays and UE. Therefore, the BHM scheme is an optimal relay selection scheme among the various relay selection schemes but it is very complicated. The proposed relay selection scheme selects one relay by using only the singular values of the channel matrix between the BS and relays. In other words, in the proposed relay selection scheme, even if the BS only knows the channel information between the BS and relays, an appropriate relay can be selected. As a result, the proposed relay selection scheme has similar BER performance of the BHM scheme, and can select an appropriate relay more easily than the BHM scheme. The proposed relay selection scheme provides the method for selecting the appropriate relay by using only the channel information between the BS and relays, unlike the conventional schemes. Therefore, the proposed scheme can select the appropriate relay more simply than the conventional schemes. In addition, the proposed scheme can select the appropriate relay in the 2D channel and 3D channel environment. As a result, the proposed relay selection scheme can improve the reliability of the system by selecting the appropriate relay and mitigating the error propagation problem of the multi-hop transmission for MU-MIMO system. Figure 7: The BER performance of the proposed and conventional schemes in 3D channel environment (Linear antenna).
## 3 Materials and Methods
### DiSDM with Random Dither Generation Unit  
The main contribution of this work is to develop a space vector-dithered sigma delta modulation (SV-DiSDM) scheme for reducing the effects of harmonics based on the digitalized controlling strategy. It is a kind of pulse density modulation scheme [33] and is mainly used for controlling the switching activities that result in the increased harmonic content. The block representation of the proposed SV-DiSDM ADC design for EMI reduction is shown in Figure 5, where the 60\({}^{\circ}\) coordinate system was utilized to represent the space vector model. Here, the mapping of the reference space vector is performed with two DiSDMs, in which one is used for the resolved vector space RV\({}_{5}\) at 60\({}^{\circ}\). Then, the error value E\({}_{V}\) is obtained by estimating the difference between the quantizer vector output QV\({}_{O}\) and reference vector RS\({}_{V}\). Consequently, the vector quantization methodology has been utilized to quantize the error value for attaining the switching vector of the inverter IS\({}_{V}\). The dithered signals, such as Di\({}_{x}\) and Di\({}_{y}\) are appended with the space vector, and these signals can be controlled by both the input of the space vector and output vector. Moreover, the feedback signals FB\({}_{1}\) and FB\({}_{2}\) are obtained with the switching signals of SW\({}_{1}\), SW\({}_{2}\), and SW\({}_{3}\) by using the quantized vector, and these signals are converted into 60\({}^{\circ}\) for creating the error signals of Er\({}_{1}\) and Er\({}_{2}\). The block representation of the random dither generation unit is presented in Figures 6 and 7.  
The random dither generation unit [34] comprises two stages such as the magnitude adjustment and signal discrimination, where the random number is selected with the sequence of 0 and 1 at each time instant. Consequently, its bias has been diminished with the value of 0.5 in the magnitude adjustment unit. Then, the main functionality of the sign discriminator is to determine whether the dither can be utilized or not. If the sign of both the input and output of DiSDM are not equal during the sampling instant, it cannot be used, which helps to suppress the loss of switching frequency. Typically, the error value obtained from the DiSDM is a random value, which can be mapped into the vector plane by using the vector quantization model. Here, the switching vectors are determined based on the amplitude of the reference space vector. Due to the random operations of SVPWM, it reduces the complex arithmetic operations and computations. The key benefits of using this scheme are reduced cost, low memory space, and increased speed.
## 3 Results
### Quantitative Evaluation of Fog Detection
#### 3.1.4 Fog Detection Performance at Visibility Meter Stations  
Figure 6 presents the seasonal distribution characteristics of POD, FAR, and Bias for GK2A_FDA at 176 individual visibility meter stations by the nearest pixel approach. As indicated by the seasonal frequencies for each site shown in the figure, stations with high POD values (top 25%, POD \(\geq\) 0.78) are most frequently located over inland regions during winter, followed by coastal regions in spring and inland regions in autumn, while they are least frequent in summer (Figure 6a). Stations with low POD values (bottom 25%) show an opposite tendency, predominantly concentrated over inland areas in summer and distributed along the east and south coasts during autumn (Figure 6b). Figure 6c,d show the top and bottom 25% distributions of FAR. Stations with low FAR values (top 25%) are concentrated mainly in autumn, whereas those with high FAR values (bottom 25%) appear most frequently in summer, followed by winter, autumn, and spring. The spatial pattern of bias closely resembles that of FAR. Stations with low bias values (top 25%) are primarily concentrated over inland areas in autumn, while stations with high bias values (bottom 25%) occur most frequently in summer (Figure 6e,f).  
Figure 6: _Cont._
## 2 Related Works
### Overview of RPL  
RPL is considered as the de facto routing protocol for IoT. RPL maintains a distributed data structure, named Destination Oriented Directed Acyclic Graph (DODAG). The process starts with the root node transmitting a DODAG Information Object (DIO) that embeds the necessary information to construct a route towards the root (rank 0 for root node). Upon receiving the DIO packet, each node selects its preferred parent (based on some objective function) and schedules relaying a DIO packet with its non-decreasing rank to further advertise the network. Upward traffic can be routed after DIO packet transmission, but for downward routing, the root node (or parent in storing mode) gets notified about the high-rank nodes only after transmission of Destination Advertisement Object (DAO) packets.  
RPL allows two modes of operation--storing and non-storing--for downstream traffic. In non-storing mode, it is only the root that maintains the downward routes. This mode scales better, since the memory footprint at intermediate nodes does not increase with the size of the network. In RPL, it is more challenging to support mobility for downstream traffic since a mobile node must notify the root (rather than only updating its parent for upstream traffic). SDMob uses the non-storing mode of RPL so that the controller can manipulate the source-routed downward packets.  
In the original RPL, the most common Objective Functions (OFs) are (1) the Minimum Rank Hysteresis Objective Function (MRHOF), and (2) Objective Function Zero (OF0). Objective functions define how RPL nodes minimize the given routing metric: hop count, expected transmission count (ETX), or latency. MRHOF is designed to minimize the path cost while avoiding excessive churns in the network. The nodes using MRHOF only switch the best parent if the minimum is improved by a threshold (this is also known as the hysteresis mechanism) [14]. OF0 is faster in terms of updating the routes in a dynamic environment as it changes the best parent even after slight improvements in the routing metric [15]. Our proposed mobility solution (SDMob) does not enforce any specific requirement on the objective function or the routing metric. Rather, SDMob allows the baseline version of RPL to converge with arbitrary metrics and then injects higher priority routes from the controller.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 5 LiDAR in Space Missions
### Spaceborne LiDARs for Terrestrial Applications
#### 5.1.1 LTE--LiDAR In-Space Technology Experiment  
The LiDAR In-Space Technology Experiment (LITE), deployed aboard the Space Shuttle Discovery during the STS-64 mission (9-20 September 1994), was the first spaceborne elastic backscatter LiDAR designed to observe multilayer atmospheric structures, particularly clouds and aerosols, from orbit. The mission validated critical technologies for space-based LiDAR operations, including laser system performance, thermal management, optical alignment, and system [104; 142]. This was accomplished by investigating molecular atmosphere, aerosols, clouds, ground properties, atmospheric temperature, and density in the 25 to 40 km altitude range [104; 143]. LITE operated for approximately 53 h, collecting around 40 GB of data, significantly contributing to subsequent spaceborne LiDAR developments [7].  
The initial raw data immediately revealed the enormous promise of using LiDAR technology in space. Observations included layers of desert dust, biomass burning, continent-level pollution, volcanic aerosols in the stratosphere, and various storm systems [7]. The detailed description of the LITE instrument can be found in [16].