esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Background
### Linear Feedback Shift Registers  
An LFSR defined over \(GF(q)\) is a collection of \(L\) memory cells \(b_{i}\), \(0\leq i\leq L-1\), whose contents belong to that field and are updated synchronously by a master clock, by the following equations:  
\[\begin{array}{l}\gamma=b_{L-1}c_{1}+b_{L-2}c_{2}+\cdots+b_{1}c_{L-1}+b_{0}c _{L},\\ b_{i}=b_{i+1},\quad 0\leq i\leq L-2,\\ b_{L-1}=\gamma,\end{array} \tag{3}\]  
giving rise to the sequence \(D=d_{0}d_{1}d_{2}\cdots\), which is completely determined by the initial state of the cells, named _seed_, and the feedback coefficients \(c_{i}\in GF(q)\), \(1\leq i\leq L\) according to the linear recurrence:  
\[d_{i}=d_{i-1}c_{1}+d_{i-2}c_{2}+\cdots+d_{1}c_{L-1}+d_{0}c_{L}, \tag{4}\]  
where \(d_{0},d_{1},\cdots,d_{L-1}\) correspond to the seed (see Figure 1). The length of the sequence \(D\) can be analyzed in terms of the connection polynomial \(p(x)\) composed with the feedback coefficients:  
\[p(x)=c_{0}+c_{1}x+c_{2}x^{2}+\cdots+c_{L-1}x^{L-1}+c_{L}x^{L}, \tag{5}\]  
in such a way that the maximal sequence length \(q^{L}-1\) is achieved when \(p(x)\) is primitive. In such a case, the sequence is called \(m\)-sequence and is independent from the chosen seed.  
Stream ciphers are mainly based on LFSRs defined over finite fields with \(q=2\)[1]. Hence, the cell content is one bit, and the addition and multiplication correspond to XOR and AND operations, respectively. However, for efficiency reasons, in the generation process, LFSRs defined in \(GF(2^{n})\) are also being used in current communication systems. When LFSR is defined in this extension field, the cells contain \(n\)-bit words, where \(n\) matches the processor's word length. Although the equations that govern the LFSR are the same,as described above, addition and multiplication are defined as polynomial addition and multiplication modulus \(h(x)\), the polynomial used to construct the \(GF(2^{n})\). From now on, we shall use the notation \(<L,p(x)>\) to refer to an LFSR with \(L\) cells in \(GF(2)\) and connection polynomial \(p(x)\in GF[2,x]\) of degree \(L\). The form \(<L,p(x),n>\) is for an LFSR of \(L\) cells in \(GF(2^{n})\) and connection polynomial \(p(x)\in GF[2^{n},x]\) of degree \(L\).
## 3 Results
### Seasonal Variations
However, according to the available data, the seasonal profiles of the GPS-TEC, GIM and IRI-2016 models at HUTB were very similar to the seasonal profiles of the GPS-TEC, GIM and IRI-2016 models at PPPC for the descending phase year of 2016, though with slightly lower values. During the descending phase year of 2017 and the minimum phase (2018-2019), in which GPS-TEC values were not available, it was possible to estimate GPS-TEC values from the averages of GIM and IRI-2016 following the consistencies in the data used in this research. At PPPC (Figure 11), seasonal variations in the GPS-TEC, GIM and IRI-2016 models also showed semi-annual variations, with higher values during the equinoxes than the solstices for the descending and minimum phases, except for GPS-TEC values, in which the December solstice values were a little higher than the September equinox values. The shapes of GPS-TEC, GIM and IRI-2016 followed similar patterns, showing higher values during pre-sunrise to noon-time than the rest of the hours of the day with respect to UT during both phases. Additionally, the peaks of GPS-TEC, GIM and IRI-2016 occurred during the pre-noon hours with respect to UT during both phases. However, IRI-2016 was observed to show two pre-noon peaks with respect to UT. The GIM values were observed to be higher during most seasons for both phases. The maximum GIM values (37-52 TECU) during the equinoxes for the descending phase were higher than the maximum GIM values (24-32 TECU) during the equinoxes for the minimum phase. This was followed by the maximum range of the GPS-TEC values (22-43 TECU) during the equinoxes for the descending phase, which was also higher than their maximum values for the minimum phase (19-28 TECU). Lastly, the maximum values of IRI-2016 during the equinoxes for the descending phase (28-35 TECU) were higher than their maximum values for the minimum phase (25-27 TECU). At PPPC, the GIM assessment of GPS-TEC can be assumed to be weaker than IRI-2016 model predictions, since there were observed over-estimations at most hours for all seasons, especially for the descending phase of the solar cycle.
## 3 Application
### Erosion
The numerical analysis of mitigating elbow erosion with a rib may not capture the long-term effects of erosion and the performance of mitigation strategies over time. In addition, the complexity of multiphase flow erosion is that the change in the content of different phases can lead to changes in bulk properties. [PERSON] et al. studied the particle erosion caused by gas-liquid-solid three-phase flow in the process of flushing an oil tank with nitrogen [35]. In the process of oil loss, the gas-liquid-solid three-phase flow gradually changes into gas-solid two-phase flow, and the gas-solid erosion is more serious than the three-phase flow due to the high gas velocity. Not only solid particles but also fluids have an effect on the pipeline losses. Therefore, the erosion theory and prediction of more common multiphase flow need to be further developed. CFD-DPM has become a pivotal technique for analyzing erosion processes, offering a sophisticated and detailed understanding of particle-laden flow dynamics and the resulting wear on materials. Its strength lies in its ability to simulate complex multiphase interactions, providing insights that would be difficult to obtain through experimental methods alone. It allows for the prediction of erosion patterns under various conditions, which is crucial for the design and optimization of equipment in industries such as oil and gas. However, the technique also faces challenges, including high computational costs and the need for simplifications in modeling complex real-world scenarios. Despite these limitations, the current status of CFD-DPM sees it widely applied in the field, with continuous advancements being made to improve its accuracy and applicability. Future development is expected to focus on enhancing computational efficiency, integrating machine learning for model optimization  
Figure 3: The erosion ratio distribution in submerged slurry jet test as predicted by coupled DPM and DDPM [31]. and developing real-time monitoring capabilities to predict and mitigate erosion, thereby further increasing the reliability and longevity of industrial equipment.
# Origin and Composition of Exosomes as Crucial Factors in Designing Drug Delivery Systems
## 7 Targeted Delivery  
Another factor influencing exosomes targeted delivery is the cell origin and membrane-incorporated targeting moieties [91; 92]. Isolated exosomes are proposed for targeted therapy and drug delivery to the same type of cells the exosomes were derived from. It is due to the fact that different membrane moieties are differently expressed by various cells. Surface moieties can be either expressed via genetic engineering of the endosomal sorting mechanism or fixed at the surface of released exosomes with click chemistry (Table 3) [85; 92]. A similar approach is described for other DDS devices such as fibrin [93].  
\begin{table}
\begin{tabular}{c c c c} \hline
**miRNA Level** & **Source of** & **Disease** & **Source** \\
**Change** & **Exosomes** & & **Source** \\ \hline \(\uparrow\) miR-1910-3p & Serum & Breast cancer patients & [81] \\ \hline \(\uparrow\) miR-423-5p & Serum & Lymph node metastasis of gastric cancer patients & [82] \\ \hline \(\uparrow\) miR-106b & Serum & Lung cancer patients & [83] \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._Another problem in targeted delivery is the insufficient half-life time of exosomes administered in vivo. Studies have shown the rapid clearance of exosomes by macrophages limiting the half-life of unmodified exosomes to 2-30 min [103]. To overcome this problem, additional proteins (such as CD47, PD-L1, CD31, and CD24) prolonging circulation time are added to the surface, or exosomes are coated with PEG to avoid recognition of the immune system [92; 103]. Macrophage, monocyte, and MSC-derived exosomes were found to be especially immune to phagocytosis and fast body clearance [88; 104].  
There are also reports that certain particles, such as sphingolipids, can affect exosomes cell uptake [105].
## Abbreviations  
Bi-LSTM Bidirectional Long Short-Term Memory  
CNN Convolutional Neural Networks  
CRT Choice Reaction Time  
CSV Comma-Separated Values  
EAR eye aspect ratio  
ECG electrocardiography signals  
EEG electroencephalogram signals  
EDA electrodermal activity  
EM EXYE-MOUTH  
FIS Fuzzy Inference System  
FOM Frequency of Open Mouth  
HF high frequency  
HAVD Human Fatigue Assessment Based on Video Data  
HOG Histogram of Oriented Gradients  
HRV heart rate variability  
JADE Joint Approximate Diagonalization of Eigenmatrices  
KSS Karolinska Sleepiness Scale  
LF low frequency  
LSTM Long Short-Term Memory  
MAE Mean Absolute Error  
MFRNN Multimodal Fusion Recurrent Neural Network  
MLP Multi-Layer Perceptron  
MOR mouth opening ratio  
MTCNN Multitask Convolutional Neural Network  
NLR nose length ratio  
NRMSE normalized root mean square error  
PERCLOS Percentage of Eyelid Closure over the Pupil  
PSG polysomnography
# Digital-Twin-Based Monitoring System for Slab Production Process
## 3 The Proposed System
### Defect Detection Module
#### 3.2.2 Loss Function  
The occurrence of defects in slabs is often stochastic, leading to the possibility of encountering low-quality defect samples. Additionally, the presence of various factors may result in inadequate model generalization capabilities. Therefore, this paper introduces a dynamic non-monotonic focal mechanism, denoted as the Weighted Intersection over Union (WIoU), defined as presented in Formula (1) [43].  
\[\beta=\frac{L_{IoU}^{*}}{L_{IoU}}\in[0,=\infty) \tag{1}\]  
\[L_{WIoUv3}=rL_{WIoUv1},r=\frac{\beta}{\delta\alpha^{\beta-\delta}} \tag{2}\]  
\[L_{WIoUv1}=R_{WIoU}L_{IoU} \tag{3}\]  
Figure 4: Attention unit (AU).  
\[R_{WIoU}=exp\Bigg{(}\frac{\left(x-x_{i}\right)^{2}+\left(y-y_{i}\right)^{2}}{ \left(W_{i}^{2}+H_{i}^{2}\right)^{2}}\Bigg{)} \tag{4}\]  
In this context, \(L_{IoU}^{*}\) represents the monotonic focusing coefficient for \(L_{WIoU}\). \(L_{IoU}\) denotes the moving average with a momentum of m. r represents the non-monotonic focusing coefficient composed of \(\beta\). \(\alpha\) and \(\delta\) are hyperparameters. \(W_{i}\) and \(H_{i}\) indicate the size of the predicted box. \(R_{WIoU}\) signifies the amplification of regular-quality anchor boxes for \(L_{IoU}\). \(L_{IoU}\) is formulated to reduce \(R_{WIoU}\) for high-quality anchor boxes, significantly diminishing attention to the center-point distance when the anchor box and target box overlap well. A smaller \(\beta\) assigns a higher quality to the anchor box, and WIoU allocates to it a small gradient gain, guiding the bounding box regression toward a regular-quality anchor box. A larger \(\beta\), indicating lower anchor box quality, results in a smaller gradient gain from WIoU, reducing harmful gradients. Simultaneously, it focuses on regular-quality anchor boxes to enhance the model's generalization ability.
## 5 Results and Discussion
### Time-Domain Characteristics  
The UWB technology transmits and receives data using short pulses. The transmission channel and distance may be the cause of signal distortion and dispersion. As a result, compared to normal antennas, the UWB antenna requires additional parameter evaluation. The two identical antennas are positioned 100 mm apart in face-to-face and side-to-side configurations for the time-domain investigation. Significant time-domain characteristics that govern the phase linearity and form of the received pulses are the group delay (GD) and fidelity factor (FF). Equations (11)-(14) are used to determine the GD and FF [26]. Figures 20 and 21 show the intended design's GD, normalized input, and output signal. The configurations for the FF, which is for several ports, are shown  
Figure 19: The simulated and measured gain of the projected antenna.  
Figure 18: The 2D radiation pattern of the projected design (**a**) 4 GHz, (**b**) 6 GHz, (**c**) 10.9 GHz, (**d**) photo of the measurement.  
in Table 6. Except for the notch bands, the projected antenna demonstrates a GD of less than 1 ns and an FF of more than 0.96 in all configurations.  
\[GD\big{(}\tau_{\mathrm{g}}(\omega)\big{)}=-\frac{d\varphi(\omega)}{d\omega} \tag{11}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Orientation** & **Ports** & **FF** \\ \hline Face to Face & (1, 3) & 0.98 \\ Face to Face & (1, 4) & 0.97 \\ Side to Side & (1, 3) & 0.96 \\ (1, 4) & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The FF values at different ports and orientations.  
Figure 21: The normalized amplitude curve of the input and output signal (**a**) face to face (**b**) side to side.  
Figure 20: The group delay of the projected antenna in different configurations.  
The phase difference between the ports is characterized by \(\varphi\).  
\[T_{s}^{n}=\frac{T_{s}(t)}{\sqrt{\int_{-\infty}^{\infty}\left|T_{s}(t)\right|^{2}dt}} \tag{12}\]  
\[R_{s}^{n}=\frac{R_{s}(t)}{\sqrt{\int_{-\infty}^{\infty}\left|R_{s}(t)\right|^{2} dt}} \tag{13}\]  
\[FF=\max\int\limits_{-\infty}^{\infty}T_{s}^{n}(t)\ R_{s}^{n}(t+\tau)dt \tag{14}\]
# A Deep Learning Method for the Prediction of Pollutant Emissions from Internal Combustion Engines
## 5 Conclusions  
This study proposed a deep learning methodology utilizing back propagation artificial neural networks to predict pollutant emissions in a single-cylinder spark-ignition engine across various operating conditions. The network architecture was refined by optimizing the number of hidden layers, neurons, and activation functions to ensure optimal performance. The model demonstrated robustness and reliability, achieving accurate predictions, with RMSE values below 6%, alongside strong regression accuracy, with R\({}^{2}\) values exceeding 0.93 in most instances.  
The back propagation artificial neural network's capacity to generalize across different dynamic cycles, encompassing variations in torque and engine speed, underscores its potential as an effective tool for real-time emission monitoring. By integrating this model into engine control units, it may be possible to conduct on-board emission estimation, thereby facilitating compliance with environmental regulations and reducing emissions in real-world driving scenarios. The implementation of this predictive architecture could significantly streamline emission monitoring, eliminating the need for cumbersome portable devices during certification cycles while supporting proactive maintenance strategies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Dynamic Cycle** \\ **Number** \\ **[-]** \\ \end{tabular} & \begin{tabular}{c} **Duration** \\ **[s]** \\ \end{tabular} & \begin{tabular}{c} **RMSE** \\ **NOx** \\ **[\%]** \\ \end{tabular} & \begin{tabular}{c} **RMSE** \\ **CO** \\ **[\%]** \\ \end{tabular} & \
\begin{tabular}{c} **RMSE** \\ **CO2** \\ **[\%]** \\ \end{tabular} &
\begin{tabular}{c} **RMSE** \\ **HC** \\ **[\%]** \\ \end{tabular} \\ \hline
1 & 100 & 4.29 & 5.65 & 5.27 & 1.57 \\ \hline
2 & 200 & 5.00 & 5.39 & 5.11 & 3.65 \\ \hline
3 & 300 & 4.22 & 5.46 & 5.01 & 1.97  
Future research should focus on experimentally validating and deploying trained back propagation artificial neural network within engine control units, addressing challenges such as compatibility with open and closed engine control unit architectures and fostering collaborations with stakeholders to ensure the seamless integration of the neural network into existing hardware systems.  
Conceptualization, F.R. and F.M.; methodology, F.R. and M.A.; software, F.R. and M.A.; validation, F.M.; formal analysis, F.R. and M.A.; investigation, F.R. and M.A.; resources, F.R. and F.M.; data curation, F.R. and M.A.; writing--original draft preparation, F.R. and M.A.; writing--review and editing, F.R., M.A. and F.M.; visualization, F.R. and M.A.; supervision, F.M.; project administration, F.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Research on Environmental Vibration Induced by High-Speed Maglev Transportation
## 4 Environmental Ground Surface Vibration  
In previous studies on the environmental vibration induced by urban railway transportation, researchers usually assume that ground vibration magnitude is only related to the transversal distance from the central railway line, regardless of the longitudinal distance from the column of the railway structure. This assumption ignores the superposition and interference of waves propagated from different columns. When the span is long enough and the train speed is slow, the vibration has enough decaying time, and the error caused by this assumption is acceptable. However, for high-seed maglev transportation with a speed of over 100 m/s and a span of less than 30 m, the ground vibration level in the whole ground plane should be considered.  
In this part of the research, the propagation laws in different directions (0, 30, 45, 60, and 90 degrees from the central line) were measured and analyzed so that the environmental vibration propagation and attenuation laws for the whole ground plane could be revealed.
## 2 Results
### Gene Ontology Analysis
To initially verify candidate phosphoproteins in patients with DN, immunodetection was employed to validate the distinct phosphorylated proteins, including p-AQP2 and p-GSK3\(\beta\). Given the considerable variation in uEVs numbers among clinical samples, which can significantly affect the expression of phosphorylated proteins, the normalization of uEVs in all samples is necessary. Alix, a marker protein of uEVs, was used for normalization [22; 23]. By normalizing uEVs with phosphorylated protein/Alix, phosphorylated protein expression in the same number of uEVs could be compared, avoiding differential phosphorylation events due to differences in the number of uEVs. Western blot analyses revealed that p-AQP2 in uEVs was significantly downregulated in patients with DN compared to diabetic patients, while p-GSK3\(\beta\) in uEVs was significantly upregulated in patients with DN compared to diabetic patients (Figure 6). These results are consistent with previous findings and indicate that phosphorylated proteins are more prevalent in uEVs than in urine. A large-scale proteomic assessment of urinary extracellular vesicles has recently demonstrated their reliability in reflecting changes in kidney protein levels [8; 24]. To confirm the hypothesis that phosphoproteins in uEVs can also represent phosphorylation events in kidney tissues, IHC was performed on kidney tissues from mouse models and patients with DN. The well-established animal models for type 2 diabetes and DN, db/db mice, exhibit insulin resistance and hyperglycemia in the first 8 w of life and are characterized by progressive albuminuria and significant matrix accumulation in the glomeruli from 28 w of age. By contrast, db/db mice at 8 w of age and control wild-type mice (db/m) exhibited no renal disease. Based on the phosphoprotein analysis and immunodetection results, pAQP2(S256) and GSK3\(\beta\)(Y216) were investigated in db/m and db/db mice at 8 and 28 w of age. The renal expression of p-GSK3\(\beta\)(Y216) in db/db mice at 28 w of age was higher than that in db/db mice at 8 w of age, whereas that of p-AQP2(S256) decreased. Both showed similar changes in the kidney tissue of DN patients compared to those of DM patients with NDKD (Figure 7). Figure 6: Phos-tag western blotting comparing p-AQP2(S256) and p-GSK3\(\beta\)(Y216) in uEVs from patients with DN, DM, and NC subjects for confirmation. The Histograms represent the normalized phosphoproteins p-AQP2(S256)/Alix and p-GSK3\(\beta\)(Y216)/Alix.
## 1 Introduction
The respective optical signals are injected into the upper and lower slave DFB lasers by two optical circulators. Due to the influence of wavelength-selective amplification under the optically injected DFB lasers, the optical sidebands under the locking area are effectively amplified. Then, the dual-passband MPF is obtained by beating the optical carrier and amplified optical sidebands in a balanced photodetector (BPD). The center frequency of the MPF can then be tuned by adjusting the injection ratio and the detuning frequency. Furthermore, link noise can be reduced by the BPD's inherent nature of common-mode noise cancellation and the improvement of signal gain. The experimental results on the proposed MPF show that the center frequency of the dual-passband MPF can be tuned independently from 19 to 37 GHz. Meanwhile, the tunable range of the center frequency of the link is limited by the bandwidths of the modulator and photodetector. The MPF can work for a higher frequency by using a DOMZM and BPD with larger bandwidths. Furthermore, the insertion loss is about 15 dB without optical or electrical amplification, which is much lower than that for MPFs realized by other structures [8; 9; 10; 11].
## 4 Numerical Simulations and Discussions
### Communication Process without Noise  
Here, the case where \(A=0\) is discussed, which represents an ideal noiseless situation. When \(D=10+10j\), we obtain the projection spaces of \(L_{1}\) and \(L_{2}\) shown in Figure 3 and the state variable shown in Figure 4, where the blue line represents \(L_{1}\) and the dotted line represents \(L_{2}\). In fact, the receiver end \(L_{2}\) is reconstructed by the controller based on CMPDFS. The CMPDFS process with \(D=10+10j\) is demonstrated in Figure 5. It can be observed that the error \(\dot{H}^{i}\) is larger than that of \(\dot{H}^{r}\), because the amplitude of \(H^{i}\) is much smaller than that of \((Dy_{1})^{i}\). As \(\dot{e}^{i}(t)=x_{1}^{i}(t)-H^{i}(t)-(Dy_{1})^{i}(t)\approx x_{1}^{i}(t)-(Dy_{ 1})^{i}(t)\), \(\mathbf{H}^{i}(t)\) can be ignored and cannot be recovered. In fact, the CMPDFS of imaginary part is achieved.  
Next, \(D=0.1+0.1j\) is changed, and the CMPDFS process is shown in Figure 6. When the amplitude of the chaotic signal \((Dy_{1})^{r}\) is smaller than that of the message \(H^{r}\), the transmitted signal \(s^{r}\) cannot cover the message \(H^{r}\). The above figures highlight the effect that \(D\) has on \(s^{\prime}\). Therefore, we can choose the appropriate \(D\) to obtain the optional CMPDFS process. This will improve the limitation imposed by the amplitude problem.  
Figure 4: The diagram of state variables \(\mathbf{x}_{1}\) and \(\mathbf{y}_{1}\) without noise.  
Figure 5: The CMPDFS process without noise when \(D=10+10j\).  
Figure 3: The projection space of chaotic systems \(L1\) and \(L2\) without noise.  
Moreover, from a large number of experiments, when the amplitude of the chaotic signal \(Dy_{1}\) is approximately equal to that of the message signal \(H\), the sum of the error square is minimized. This conclusion can also be drawn from the bold fonts in Table 2, where \(max(Dy_{1})^{r}\) and \(max(Dy_{1})^{i}\) are the maximum amplitudes of the real part and imaginary part of the chaotic signal, respectively. The terms \(10,000*E_{H^{r}}\) and \(10,000*E_{H^{i}}\) represent the sum of error square of recovered signal as the simulation program runs 10,000 times. According to (11), the maximum amplitude of \(H^{r}\) is 10, while that of \(H^{i}\) is 1. When \(D=1+j\), \(max(Dy_{1})^{r}\) is 11.9294, which is the closest to \(H_{r}\), and the sum of error is the minimum 0.1927 in the fourth column; when \(D=0.01+0.02j\), \(max(Dy_{1})^{i}\) is 0.8632, which is the closest to \(H_{i}\), and the sum of error is the minimum 0.6959 in the fifth column.