esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Preparation of Samples  
PCB was extracted from the marine cyanobacterium _Arthrospira maxima_ (formerly _Spirulina maxima_) (23), grown and provided by the Jeju Bio Research Center, Korea Institute of Ocean Science and Technology (Jeju, Republic of Korea). First, dried _A. maxima_ powder was suspended in 10 mL of 0.1 M sodium phosphate buffer (pH 7.0) at a 1:100 _w/v_ ratio. The suspension was homogenized for 15 min in a sonicator and subjected to three freeze-thaw cycles. The crude extract was collected by centrifugation at 2322\(\times\)\(g\) for 20 min. The phycocyanins were purified according to the method described by [PERSON] et al. (24). The crude extract was subjected to single-step precipitation with 65% (NH4)2 SO4 and kept overnight at 4 \({}^{\circ}\)C. The pellet was recovered by centrifugation of the extract at 4000\(\times\)\(g\) and 4 \({}^{\circ}\)C for 20 min and dissolved in 10 mL of the same extraction buffer. Next, 10 mL of the extract was dialyzed twice against 1000 mL of the extraction buffer in the dark at 4 \({}^{\circ}\)C for 24 h. The resulting extract was recovered from the dialysis membrane and filtered through a 0.45 \(\upmu\)m syringe filter. To extract PCBs from phycocyanins, 30 g of phycocyanin extract powder was suspended in 1.5 L of ethanol at a ratio of 1:50 (_w/v_). The extraction was performed at 70 \({}^{\circ}\)C for 15 h in the dark. The suspension was centrifuged at 2322\(\times\)\(g\) to separate the crude extract. The supernatant was then filtered through a 0.45 \(\upmu\)m filter. After filtration, the solvent was evaporated, and the extract was concentrated to obtain the final PCB powder (SP). SP was stored at \(-\)20 \({}^{\circ}\)C in a dark place before use. For preparing wheat bran water extract (WB), 20 g of wheat bran powder (Red Mill Natural Foods, Milwaukie, OR, USA) was extracted in 200 mL distilled water at 4 \({}^{\circ}\)C by shaking at 50 rpm for 8 h. Then, the extract was centrifuged at 2322\(\times\)\(g\) for 20 min at 4 \({}^{\circ}\)C. The supernatant was dialyzed using a 100 kD MW cut-off membrane for 24 h at 4 \({}^{\circ}\)C, concentrated using a rotary vacuum evaporator, and finally lyophilized into a powder. For preparing a mixture of SP and WB (SPWB), SP was dissolved in an aqueous solution of WB to the same concentration for further experiments.
## 2 Related Work
### Attention-Based Models  
Attention-based models have emerged as a powerful alternative to traditional sequential methods to better capture user intent and contextual nuances. These approaches assign dynamic weights to historical check-ins and contextual signals, thereby enhancing the interpretability and adaptability of location prediction. STAN [16] employs a bi-layer spatiotemporal attention mechanism to model temporal dependencies and geographical proximity while incorporating personalized item frequency to account for repetitive behavioral patterns. Extending this idea, GETNext [17] constructs a global trajectory flow map and uses graph convolutional layers to integrate spatial, categorical, and temporal properties, offering a holistic representation of user mobility dynamics. More advanced models like Diff-DGMN [18] fuse dual-graph learning with diffusion-based sampling to jointly model user-specific sequential transitions and global location prediction spatial distributions. Its directional attention and stochastic reverse process allow for effective preference generation even under cold-start conditions. Similarly, ImNext [19] addresses the challenges of unstructured spatiotemporal gaps and multitask learning by combining self-attention mechanisms with robust trajectory modeling, outperforming various baselines across diverse urban datasets. While attention-based models demonstrate strong capability in capturing salient features from complex mobility data, they often treat user behaviors as flat sequences or rely on pairwise relations, lacking an explicit mechanism to disentangle diverse interaction signals such as collaborative preferences, temporal transitions, and geographic proximity in a unified framework.
# Symbol-Level Selective Channel Estimation in Packet-Based OFDM Systems
## 3 Related Work
### DD-TT Channel Estimation Scheme  
The DD-TT scheme effectively removes the noise component in the initial channel estimate \(\hat{H}(m,k)\) using time-domain truncation, thereby improving the channel estimation performance.  
First, \(\hat{H}(m,k)\) is represented as a column vector.  
\[\hat{\mathbf{H}}_{m}=[\hat{H}(m,-26),\hat{H}(m,-25),\hat{H}(m,k) \tag{6}\] \[\cdots\ \hat{H}(m,26)]^{T},\qquad k\in\mathcal{K},\]  
where \(\mathcal{K}\) defines the index set of data and comb pilot subcarriers.  
Subsequently, the frequency-domain channel vector is converted into a time-domain channel vector as follows:  
\[\hat{\mathbf{h}}_{m}=\frac{1}{K}\mathbf{W}^{H}\hat{\mathbf{H}}_{m}, \tag{7}\]  
where \(\mathbf{W}\) is a matrix obtained by converting the number of columns of the FFT matrix of size \(K\times K\) into \(L\), i.e., \(\mathbf{W}\) is a \(K\times L\) matrix. \(\mathbf{W}\) is expressed such that  
\[\mathbf{W}=\sqrt{K}\mathbf{F}_{K}(:,1:L), \tag{8}\]  
where \(L\) represents the channel order. In general, the delay spread in vehicular environments is smaller than the guard interval \(G\). Hence, the channel impulse response can be limited to a specific number \(L\). In [18], \(L\) is set to 8 considering the V2V channel model of [23].  
The time-domain channel vector is the value of the channel in which the noise components after the \(L\)-th tap are removed and indicated, as follows:  
\[\hat{h}(m,n)=\left\{\begin{array}{ll}h(m,n)+z(m,n),&n=1,2,3,\cdots,L\\ 0,&\text{otherwise},\end{array}\right. \tag{9}\]  
where \(n\) denotes the time sample, and \(h(m,n)\) and \(z(m,n)\) represent the channel and noise components in the time domain, respectively.  
The final channel estimate vector in the frequency domain can be obtained via time-domain truncation such that  
\[\mathbf{H}_{m}=\mathbf{W}\hat{\mathbf{h}}_{m}(1:L). \tag{10}\]The DD-TT improves performance by eliminating unnecessary noise components after the \(L\)-th tap in the time domain. At low SNRs, the effect on noise is larger, so the performance improvement caused by the noise component removal is outstanding. However, as the SNR increases, the effect of noise is reduced. This leads to the removal of necessary channel information and thus generates an error floor.
## 4 Experimental Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial Number** & \begin{tabular}{c} **Transmission** \\ **Information Class** \\ \end{tabular} & \begin{tabular}{c} **Encryption** \\ **Time (ms)** \\ \end{tabular} & \
\begin{tabular}{c} **Transmission** \\ **Delay (ms)** \\ \end{tabular} &
\begin{tabular}{c} **Transmission Result** \\ \end{tabular} \\ \hline
1 & \
\begin{tabular}{c} Real-time power \\ data \\ \end{tabular} & 12.5 & 8.2 &
\begin{tabular}{c} The data was transmitted successfully, the encryption \\ effect was good, and the data could not be analyzed \\ directly. \\ \end{tabular} \\
2 &
\begin{tabular}{c} Voltage monitoring \\ data \\ \end{tabular} & 10.7 & 7.5 & The data is transmitted safely and no leakage occurs. \\
3 & \
\begin{tabular}{c} Device remote \\ control instruction \\ \end{tabular} & 9.8 & 6.8 &
\begin{tabular}{c} Instructions were successfully executed without being \\ detected or tampered with during the transmission. \\ \end{tabular} \\
4 & \
\begin{tabular}{c} Energy \\ consumption \\ comparison report \\ \end{tabular} & 15.2 & 9.1 &
\begin{tabular}{c} The report is successfully transmitted, and the \\ confidentiality of the content is guaranteed. \\ \end{tabular} \\
5 & \
\begin{tabular}{c} Safety warning \\ information \\ \end{tabular} & 8.9 & 6.2 &
\begin{tabular}{c} Early warning information was communicated in a \\ timely manner, and relevant personnel responded \\ quickly. \\ \end{tabular} \\
6 & \
\begin{tabular}{c} Equipment \\ maintenance \\ reminder \\ \end{tabular} & 11.3 & 7.9 &
\begin{tabular}{c} A reminder message was successfully sent, and the \\ maintenance plan was successfully executed. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Secure transmission results of high-speed power line carrier communication. this method is 0.010, and the minimum bit error rate obtained by two-dimensional logistic mapping is 0.020. It shows that generalized two-dimensional polynomial chaotic mapping is more suitable for secure information transmission than two-dimensional logistic mapping, using reference [6]'s method and reference [7]'s method, which can effectively resist the interference of power line noise and signal attenuation and improve the confidentiality and stability of communication.
# EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer
## 4 Proposed Method
### Phase II: Preprocessing  
Each channel of the recorded EEG dataset initially used two standard filters. First, a bandpass filter (\(BPF\)) with frequencies between (8 and 30 Hz) was used to confine the band of the recorded EEG data, and second, a Butterworth (\(BW\)) notch filter at (50 Hz) was used to reduce the noise from power line interference. (\(ICs\)) \(s(t)=[s_{1}(t),\ldots,s_{n}(t)]\), utilizing the \(FastICA\) algorithm proposed by [55]. The set \(s(t)\) of \(n\) unknown components that were linearly mixed within matrix A and the set \(x(t)\) of \(n\) observations where \(x(t)=[x_{1}(t),\ldots,x_{n}(t)]\)[56; 57; 58] represent the EEGs and are related to \(s(t),t\), which is the time; hence, the ICA equation is  
\[x(t)=As(t) \tag{10}\]  
Then, three metrics were used to evaluate the artifactual components, _ICs_: kurtosis (\(Kurt\)), _skewness_ (Skw), and sample entropy (\(SampEn\)). If these parameters surpassed the \(pm1.2\) threshold for each \(IC\), the \(IC\) was marked as critical and denoised using WT. The practical value of the threshold was determined through trial and error and previous research [59; 60; 61]. The threshold value of \(\pm 1.2\) is not a drawback of the AICA-WT technique, as the artifactual _ICs_ were not rejected but were denoised using the WT technique. As a result, WT denoised the marked _ICs_ before returning the enhanced components to the original EEG dataset [22; 23].
## 5 Performance Evaluation and Analysis
### Network Performance Measure  
With Ant System; \(\mathbf{N}=10,\ \mathbf{E}=0.2\), the average of \(\mathbf{p}\) and \(\mathbf{q}\) was measured to be about 2 and 2, respectively. \(\mathbf{C}\) (Cycle) value is 50 ms, 100 ms, 200 ms \(\cdots\cdots\) 2000 ms, etc., the upper bound value of the overhead is given by (19):  
\[\mathrm{O*R}=960 \tag{19}\]  
Based on the experimental data, a regression curve was obtained by the regression analysis as (20):  
\[\mathrm{O*R}\approx 120 \tag{20}\]  
The curve of Equations (1) and (2) are depicted in Figure 19. The \(X\)-axis is the Recovery Time in sec. The \(Y\)-axis is expressed as overhead in Mbps and expressed in log-scale. Figure 19 shows that the upper bound and actual values are quite different because the packet size is calculated assuming MTU (1500 bytes) when deriving the theoretical value. As a result of the actual measurement, it is analyzed that the packet size is about 10% of the MTU and 20% of the MTU when the packet is transmitted. Also, it shows that if _Recovery Time_ decreases, then _Link Overhead_ increases exponentially. Therefore, there is a limitation in reducing the recovery time in real networks.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Description** \\ \hline
**M** & Number of messages per second (Times/s) \\
**C** & Pheromone evaporation cycle (s/cycle) \\
**N** & Number of ant per each cycle (Times/cycle) \\
**T** & Size of the packet (Byte) \\
**O** & Link overhead (bps or Mbps) \\
**R** & Recovery time (s) \\
**T\({}_{e}\)** & Link-error detection time (s) \\
**T\({}_{s}\)** & Shortest Path Search time (s) \\ \hline \hline \end{tabular}
\end{table}
Table 7: ACO algorithm variable and network-related variable.
# Localization of LHD Machines in Underground Conditions Using IMU Sensors and DTW Algorithm
## 1 Introduction
Thanks to this, we can identify the location of the machine with accuracy with respect to the excavation in which the drive is carried out. The informational potential of such a solution would therefore be comparable to the RFID gates usually used in this regard. The advantage of our approach is that the only equipment needed to apply this method is a three-axis accelerometer and information necessary for the operation of the auxiliary algorithm for the road quality classification (primarily vehicle speed). It is also necessary to collect an extensive database of this type of data from machine travels across various excavations. In practice, this information is usually either a part of the onboard machine's monitoring system or requires a relatively low-cost expansion of this system. Ground recognition also has its roots extensively in robotics, particularly in perception. In the literature, this problem is widely recognized and is used in adapting the robot's motor skills to specific conditions, protection against falls, or mitigation of energy consumption [31; 32; 33; 34; 35; 36]. In the future, our solution may also be adapted to autonomous LHDs. The challenges of robotization of haulage using LHD have been broadly described in [4; 6; 37].
## 2 System and Theoretical Model
### Amplitude Fluctuations Variance Model
In the weak fluctuation region, the logarithmic amplitude fluctuations propagated by the coronal solar wind turbulence can be obtained according to the weak electromagnetic scintillation theory so that the Rytov theory applies [29]. As a consequence, we have that  
\[\left\langle\chi^{2}\right\rangle=16\pi t_{e}^{2}k_{n}^{-2}\int_{0}^{\infty} xdk\int_{0}^{\infty}dz\text{sin}^{2}\bigg{(}\frac{z\kappa^{2}}{2k_{n}}\bigg{)} \Phi_{N}(\kappa,z), \tag{5}\]  
where \(\left\langle\cdot\right\rangle\) represents the ensemble average of realizations, \(k_{n}=2\pi/\lambda\) is wavenumber, and \(\lambda\) denotes the wavelength of the signal. Substituting (2) into (5), the final simplified amplitude fluctuation variance model is acquired after some numerical transformations [7]:  
\[\left\langle\chi^{2}\right\rangle=-\frac{(p-3)\Gamma\left(\frac{p}{2}\right)r _{e}^{2}(2\pi)^{p-1/2}\pi}{8\Gamma\left(\frac{p-1}{2}\right)\Gamma\left(1+ \frac{p}{2}\right)}\times\eta^{2}N_{e}{}^{2}l_{o}{}^{3-p}L_{link}{}^{\frac{p} {2}}k_{n}{}^{-\frac{p}{2}-1}\sec\Big{(}\frac{\pi}{4}p\Big{)}, \tag{6}\]  
where \(r_{e}\) is the classical electron radius (=\(2.8\times 10^{-15}\) m). In addition, scintillation index \(m^{2}\) is another parameter for estimating amplitude fluctuation, which is defined as the ratio of the root mean square value of signal strength fluctuations to the mean signal intensity:  
\[m^{2}=\frac{\left\langle\left(I-\left\langle I\right\rangle\right)^{2}\right\rangle }{\left\langle I\right\rangle^{2}},\ m\leq 1, \tag{7}\]  
where \(I\) is the received intensity. Additionally, many studies have proved that \(m^{2}=4\left\langle\chi^{2}\right\rangle\)[29]. According to the magnitude of scintillation index, we can divide it into weak scintillation and strong scintillation. It is generally accepted that a scintillation index less than 0.3 generates a weak scintillation effect, an index between 0.3 and saturation indicates the transition region, and strong scintillation is determined by an index of 1. Since the derived scintillation index model is based on [PERSON] theory, that is, under the premise of weak scintillation, we have that \(m^{2}<1\).
# A Review of Foot-Terrain Interaction Mechanics for Heavy-Duty Legged Robots
## 3 Dynamics Analysis of Robot  
The robot foot-terrain interaction mechanics is a branch of contact mechanics. Studying the interaction process between the machinery and working terrain is of great significance when studying the design of foot mechanisms and the kinematic simulation of robot bodies. An accurate foot-terrain mechanics model is the basic condition for designing anti-sinkage, high-traction, and lightweight feet. At the same time, it can also optimize the path planning and motion control of the robots, thereby improving the terrain adaptability of the heavy-duty legged robots.  
The current research on the terrain mechanics of wheeled robots has been very extensive. There is relatively little research on the terrain mechanics of legged robots [76; 77]. The current research on the foot-terrain mechanical interactions of the legged robots will draw on the existing wheel-terrain mechanical models of the wheeled robots. [PERSON] [78] believes that legged robots can flexibly walk on rough and uneven surfaces, but due to the presence of multiple driving joints, the power consumption of their mobile systems is often high. Therefore, studying the foot-terrain interaction mechanics of heavy-duty legged robots can also provide a reference for low-power research on robots.  
The legged robots have the characteristics of discontinuous motion, large foot impact, and multiple degrees of control freedom. Each walk of the robot is equivalent to a collision between feet and terrain. The foot-terrain interaction models are used to analyze the relationship between terrain characteristic parameters and foot parameters of normal and tangential forces. In addition to the force from the normal direction, it is also subjected to tangential force including the horizontal direction. The normal force acting on a heavy-duty legged robot during movement is much greater than the tangential force, [PERSON] [79] conducted a static analysis of the feet of an electrically driven heavy-duty hexapod robot under a tripod gait. A trend chart of the changes in the normal force of the feet is obtained. The walking method that can achieve the most average distribution of each foot force is determined.  
During the walking process of the legged robot, the contact between the foot and the terrain not only exhibits normal relative motion but also tangential relative motion. It generates tangential relative motion and tangential friction. Taking into account both normal relative motion and tangential relative motion, analyzing the foot-terrain interactions generated by these two aspects is beneficial for further precise motion control and stable gait planning of the legged robots.
# Enhancing Weather Forecasting Integrating LSTM and GA
## 4 Results and Discussion
### Case I  
In this case, each variable was forecast for 3 to 15 days, using an LSTM with specific hyperparameters characterized in Table 3. Each LSTM network had a ReLU activation function, a dense layer with 1 neuron, to obtain the output value, a dropout layer equal to 0.2, with 10 neurons, and they were optimized using Adam optimization with a learning rate of 0.001 in order to minimize the MSE.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variable** & **Method** & \multicolumn{5}{c}{**Coefficients**} & \multicolumn{1}{c}{**Intercept**} \\  & & \(T_{MED1}\) & \(DD_{MED1}\) & \(FF_{MED1}\) & \(FF_{MAX1}\) & \(RG_{TOT1}\) & **Intercept** \\ \hline \(T_{MED}\) & Linear & 1.0190 & 0 & 0 & 0 & \(-0.0032\) & \(-0.1887\) \\ \(DD_{MED}\) & LASSO & 0 & 0 & 24.9277 & 0 & 0 & 96.7312 \\ \(FF_{MED}\) & Ridge & 0 & 0.0124 & 7.7472 & 0 & 0 & \(-7.2055\) \\ \(FF_{MAX}\) & LASSO & 0 & 0 & 7.2770 & 1.6848 & 0 & \(-5.3412\) \\ \(RG_{TOT}\) & Linear & 1.1333 & 0.0957 & 0 & 0 & 0.4822 & \(-22.3257\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coefficients for the calculation of dependent variables.  
Figures 6-8 show the results for the 3-, 7-, and 15-day forecasts, respectively. For each variable, the last 15 days of the test set and the predicted values for that period are also shown. In general, the predicted variables showed a similar behavior for all time periods, without significant fluctuations compared to the testing data.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline Number of LSTM layers & 1 \\ Number of LSTM units & 50 \\ Batch size & 32 \\ Number of epochs & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: LSTM’s parameters.  
Figure 5: Train (blue), validation (green), and testing (yellow) sets: (**a**) daily average temperature, (**b**) daily wind direction, (**c**) daily average wind speed, (**d**) daily instantaneous wind speed, and (**e**) daily solar radiation.  
Figure 6: Last 15 days of the testing set (blue) and the predicted data (red) for 3 days ahead in the future for (**a**) daily average temperature, (**b**) daily average wind direction, (**c**) daily average wind speed, (**d**) daily instant wind speed, and (**e**) daily solar radiation predicted values (red), using an LSTM.  
Figure 7: Last 15 days of the testing set (blue) and the predicted data (red) for 7 days ahead in the future for (**a**) daily average temperature, (**b**) daily average wind direction, (**c**) daily average wind speed, (**d**) daily instant wind speed, and (**e**) daily solar radiation predicted values (red), using an LSTM.  
Figure 8: Last 15 days of the testing set (blue) and the predicted data (red) for 15 days ahead in the future for (**a**) daily average temperature, (**b**) daily average wind direction, (**c**) daily average wind speed, (**d**) daily instant wind speed, and (**e**) daily solar radiation predicted values (red), using an LSTM.
## 4 Model Establishment and Evaluation
### Model Establishment
#### 4.1.3 Hybrid Models Based on Data Similarity  
As shown in Figure 7, we trained the autoencoder (AE) model, machine learning model, and empirical model with the test set data. The AE-ED was used to measure the data similarity, and the machine learning model and empirical model were used for ROP prediction. We calculated the AE-ED value through the autoencoder (AE) model to measure the similarity between each newly input piece of test set data and training set data. According to the value of the AE-ED, we judged and selected the corresponding model to form the hybrid model.  
Figure 7: Flowchart of hybrid model establishment.  
Combined with the AE-ED value in Figure 6, for newly input data with AE-ED values less than the threshold value (that is, below the threshold line), the machine learning model with higher prediction accuracy is adopted; and for newly input data with AE-ED values greater than the threshold value (that is, above the threshold line), the empirical model with better robustness is adopted. According to the automatic switching of different prediction models under the condition of the AE-ED threshold judgment, a new hybrid model based on data similarity is formed.  
In this paper, the main function of the empirical model is to make up for the loss of accuracy of the machine learning model in the case of low data similarity, and to improve the overall robustness and generalization ability. We just need to select a better empirical model. Therefore, we chose the selected model as a representative of empirical models, and we combined it with three machine learning models (SVM, ANN, RF) to establish three hybrid models.