esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 DRFM Performance Results  
To understand how DRFM performance changed with the system architecture and the waveforms it encountered, a comparison of received and transmitted waveforms was conducted using a correlation. In radar terminology, the correlation was the matched filter response, specifically, the zero Doppler cut of the ambiguity function  
\[\chi(\tau,f)=\int_{-\infty}^{\infty}s(t)s^{*}(t-\tau)e^{j2\pi f}dt \tag{1}\]  
where \(s(t)\) is the waveform, \(f\) is the frequency, and \(\tau\) is a dummy variable [21]. The implementation used to measure the DRFM system performance is equivalent to  
\[\chi=\sum_{m=-\infty}^{\infty}x(n)h^{*}(n-m) \tag{2}\]  
where \(x(n)\) is the received waveform, and \(h(n)\) is the transmitted waveform.  
The output of the correlators was a 2-D plot showing the compressed waveform for the correlation of the transmitted waveform, along with various distortions for DRFM variants. However, the primary metrics of concern for the DRFM system performance were the signal processing loss compared to the ideal case (the autocorrelation), and the change in time delay of the peak of the compressed DRFM signal.  
Figure 8: The top level sub-system model of the radar receiver in Simulink, showing the correlators implemented for the three signals of interest: the original waveform, the amplitude DRFM, and the I-Q DRFM.  
To understand the various factors that might affect processing gain and time delay, the performance of the DRFM system was evaluated under a variety of different conditions, including the ADC resolution, DRFM receiver SNR, waveform time-bandwidth product, and the I-Q vs. amplitude DRFM models.
## 3 Application to Vibration Control
### Computer Simulations
The simulation results show very effective vibration control of the seat suspension system subjected to severe vibration with mixed disturbances. This is from the new modification of the Riccati-like equation with embedded parameters of the PID controller, and the combination of the sliding surface of sliding mode control with the fuzzy neural networks model to take account for the uncertainties and disturbances. Figure 3: Simulation result of power spectral density of displacements under random bump road excitation. Figure 2: Simulation results of displacement of seat under random bump road excitation: (**a,b**) general view; and (**c,d**) large view.
## 3 Results and Discussions
### Biodegradation
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Treatment**} \\ \cline{2-3}  & **Control** & **5\% Biochar** & **10\% Biochar** \\ \hline Remaining VS (Day 20, g) & 408.5 \(\pm\) 3.6 & 411.1 \(\pm\) 3.6 & 423.5 \(\pm\) 1.3 \\ Decomposed VS (g) \({}^{\ddagger}\) & 185.9 \(\pm\) 4.0 \({}^{\text{a}}\) & 211.8 \(\pm\) 5.5 \({}^{\text{b}}\) & 218.4 \(\pm\) 2.9 \({}^{\text{b}}\) \\ \hline \hline \end{tabular} Note: Different letters in the same row mean each group is significantly different (\(p<0.05\)). \({}^{\ddagger}\) Decomposed VS = Initial VS + Added VS-Sampling VS-Remaining VS.  
\end{table} 
Table 3: _Cont._The initial free air space (FAS) was 4.6% and 9.6% higher in the 5% and 10% biochar treatment groups, respectively, compared to the control, due to biochar addition (Table 4). Over the 21-day experiment, the control showed a 28.7% reduction in FAS, whereas the 5% biochar treatment exhibited a 30.9% reduction, approximately 1.07 times higher than the control, though not statistically significant. Meanwhile, the 10% biochar treatment showed a further reduction of 32.9%, approximately 1.14 times higher than the control, with statistical significance (\(p<0.05\)). This indicates active organic matter decomposition in the treatment groups. As microbial decomposition progresses, the particle size of the bedded pack tends to decrease. This reduction in particle size is associated with the physical breakdown of organic materials [28; 29]. Specifically, adding biochar to the bedded pack can facilitate microbial activity in some regions of the manure, manifesting as an increase in the O2 consumption rate. The O2 concentrations of the treatment groups exhibited a generally lower trend than those in the control over the 21-day experimental period (Figure 3). The tendency of lower O2 concentrations in the treatment groups, which had higher FAS compared to the control, indicates that active decomposition of organic matter by aerobic microorganisms occurred in the treatment groups. Biochar, exhibiting physical properties similar to bulking agents, supports forming a favorable environment for aerobic bacteria to proliferate stably in the bedded pack [30]. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multirow{2}{*}{**Control**} & \multicolumn{2}{c}{**Treatment**} & \multirow{2}{*}{**SEM**} & \multicolumn{2}{c}{\(p\)**-Value**} \\ \cline{3-6}  & & **5\% Biochar** & & **10\% Biochar** & & **5\% Biochar** & **10\% Biochar** \\ \hline Day 0 (\%) & 45.1 & 47.2 & 49.5 & 0.7 & 0.054 & 0.002 \\ Day 21 (\%) & 32.2 & 32.6 & 33.2 & 0.6 & 0.714 & 0.233 \\ Reduction rate of & 28.7 & 30.9 & 32.9 & 1.6 & 0.432 & 0.094 \\ free air space (\%) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of free air space changes. (\(n=3\)). Figure 3: Comparison of O2 concentrations in different experimental groups.
# Optimization of Multi-Mission CubeSat Constellations with a Multi-Objective Genetic Algorithm
## 3 Problem and Optimization Formulation  
### Problem Statement  
CubeSats are becoming capable of multi-objective missions due to advancements in their platforms. An optimized CubeSat constellation system can perform multiple missions, including global Earth observation and regional disaster monitoring. However, these missions have conflicting requirements that must be addressed by the system. The optimization problem aims to improve system performance by reducing ART for global and regional missions while maximizing total area coverage and minimizing the number of satellites, reducing cost and development time.  
Inclination and the number of satellites in terms of orbital planes and satellites per plane are chosen as optimization parameters. This multi-objective optimization problem can be solved using different techniques, with evolutionary algorithms, such as NSGA-II, commonly used for non-linear global optimization problems. NSGA-II algorithms have been modified to handle integer optimization variables, such as the number of orbital planes and satellites per plane.  
Figure 2: Regional data collection centers, evenly distributed over the target area.
# An ELECTRA-Based Model for Power Safety Named Entity Recognition
## 4 Discussion  
This paper combines RMSNorm and SwiGLU into the ELECTRA model to propose the RS-ELECTRA model. It is integrated with BiLSTM and CRF to build a model for Named Entity Recognition in power safety texts. Experiments on the power safety dataset showed that this model improved Precision by 2.2%, Recall by 2.1%, and F1-score by 3.3% compared to the BERT-BiLSTM-CRF model. Compared to the BiLSTM-CRF model, the proposed model improves Precision by 6.9%, Recall by 5.6%, and F1-score by 6.8%. This demonstrates that this method has made progress in NER for power safety texts.  
Figure 4: Performance comparison for each entity type of different models.  
This research has broad applications in the power industry. When processing fault reports, maintenance logs, safety rule documents, accident investigation reports, etc., NER can quickly extract important entities, providing data support for fault analysis, risk assessment, and accident prevention. Additionally, the application of NER can promote the construction of intelligent monitoring systems. By analyzing text data generated by the system in real time, it can promptly identify safety risks and automatically generate warning information, thereby reducing the likelihood of accidents.  
There are still many areas for improvement in this research. The power safety dataset is relatively small and only covers a part of the power safety field. In the future, larger and more diverse power safety data from various fields can be considered to improve the model's generalization ability and adaptability. CRF can establish global constraints between entities and relationships, but it also has some limitations, such as the inability to handle overlapping or nested entities or relationships, and difficulty in extending to multivariable or complex relationships. In the future, more flexible and general annotation systems, such as Span [32] or Joint [33], can be considered as sequence labelers to improve the model's adaptability and robustness in complex NER scenarios.  
Conceptualization, P.L. and Z.S.; methodology, Z.S.; software, Z.S. and B.Z.; validation, Z.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Inner Mongolia Electric Power Research Institute (Project Number: 0722-2023 FE7015 NMF).  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors declare no conflicts of interest.
## 6 Results and Discussion
### Comparative Analysis of FOPI-FOPI and PI-PI Controllers  
#### 6.2.1 Stability Analysis Using Bode Plots  
The stability of the system is evaluated through Bode plot analysis, focusing on key performance indicators such as phase margin and gain margin, which directly influence the dynamic performance of the DSTATCOM. The small dynamic model is presented in Appendix A[38].  
The Bode plot of the PI-PI controller, shown in Figure 11a, provides insights into the stability margins of the control loop. The PI-PI controller exhibits a phase margin of \(135.15^{\circ}\), which indicates moderate stability. The gain margin is approximately 61 dB, ensuring that the system can tolerate a certain level of gain increase without becoming unstable. The Bode plot analysis confirms that the PI-PI controller provides reasonable stability and performance for DSTATCOM control.  
Figure 11: The system frequency response: (**a**) with PI-PI controller; (**b**) with FOPI-FOPI controller.  
The open-loop Bode plot of the DSTATCOM system, when controlled by the fractional-order cascaded FOPI-FOPI controller, is presented in Figure 11b. The FOPI-FOPI controller achieves a phase margin of 155.88\({}^{\circ}\), which indicates a more stable and robust system compared to the PI-PI controller. A higher phase margin suggests better performance during transient conditions, with less oscillatory behavior.  
The gain margin for the FOPI-FOPI controller is found to be 64 dB, which provides a greater tolerance for system gain variation without risking instability. The results from the Bode plot demonstrate that the FOPI-FOPI controller offers superior stability margins and overall better performance compared to the traditional PI-PI controller.
# Automatic Medical Image Segmentation with Vision Transformer
## 3 Method  
Given an input image \(x\in\mathbb{R}^{H\times W\times C}\) with spatial dimensions \(H\times W\times C\) channels, our objective was to predict the corresponding \(H\times W\) label map through pixel-wise segmentation. The common approach trains a CNN, such as U-Net, to directly encode the input image into high-level feature representations and then decode these representations back to full spatial resolution.  
In contrast to the current approaches, our framework incorporated self-attention mechanisms via transformer encoders. We first explain how transformers can directly encode features from decomposed image patches (Section 3.1). Unlike CNN encoders, our transformer encoder incorporated self-attention to model global contexts and long-rangedependencies in the image. This provides complementary information to aid medical image segmentation. Then, we elaborate on the overall framework.  
Rather than completely replacing CNNs, we selectively applied transformers to leverage both local precision from convolutions and global reasoning from self-attention. This hybrid architecture balances strengths by dividing responsibilities--the transformer focuses solely on encoding semantics, while the CNN decodes for spatial acuity.  
In summary, our key insight is to combine complementary components in a tailored encoder-decoder design for medical images. This moves beyond the existing methods by fusing advancements from both computer vision and NLP to best suit the problem domain of medical image segmentation.
## 3 Results and Discussion  
### Rheological Characterization  
Rheological measurements were performed to define the viscoelastic properties of the composite NaAlg/MC/TMC hydrogels containing different compositions and amounts of silica-based glasses. The mechanical spectra of the hydrogels are depicted in Figure 1a showing that the storage modulus (G\({}^{\prime}\)) was higher than the loss modulus (G\({}^{\prime}\)) for all hydrogels. This indicates the dominance of the elastic solid-like behavior of the samples over the frequency range tested. The addition of pure silica glass at 5% _w/w_ did not affect the stiffness of the Alg/MC/TMC hydrogel. However, when pure silica glass was partially substituted by the bioactive glass at the same final concentration (5% _w/w_), a significant increase in the G\({}^{\prime}\) values was observed. This could be attributed to the presence of calcium, which ionically crosslinks sodium alginate, therefore resulting in stiffer hydrogels. Similar G\({}^{\prime}\) values were also obtained for the composite hydrogels containing 10% Si, whereas the highest stiffness was observed for the hydrogel containing the glass 70 Si30 CaO at 10% _w/w_.  
The flow curves of the composite hydrogels are shown in Figure 1b demonstrating their shear-thinning behavior, as viscosity decreases with increasing shear rate. This is highly desirable for extrusion-based printing applications, in order to enable both smooth hydrogel extrusion without clogging, and structural recovery and integrity post-printing in self-standing constructs [26]. In agreement with the findings of the frequency sweep test, similar viscosity values were observed for the plain Alg/MC/TMC hydrogel and the Alg/MC/TMC hydrogel containing 5% pure silica glass, whereas higher viscosity values were obtained for the composite hydrogels containing a higher concentration of pure silica glass or the bioactive glasses.
## 2 Background
### Benefit of Using Network Coding  
We will introduce the principles and advantages of using network coding in this subsection. Figure 2 presents a simple example of communication networks, which is represented as a directed graph [1].  
Each directed edge represents a pathway for information transfer. Node \(S\) represents source and the nodes \(D\) and \(E\) are destinations. The other nodes are intermediaries, routing information to the destination nodes. If we assume that each link is limited in bandwidth one bit per unit time, in a traditional routing protocol, we are incapable of attaining higher throughput than the given limit. However, using network coding, we can achieve better throughput in excess of this limit.  
Let us assume that we have generated data bits \(a\) and \(b\) from source node \(S\), and that we wish to route the data to destination nodes \(D\) and \(E\). Data bit \(a\) is transported via path \(S\)-\(A\)-\(C\), \(S\)-\(A\)-\(D\) and data bit \(b\) via \(S\)-\(B\)-\(C\), \(S\)-\(B\)-\(E\).  
Figure 1: The block diagram of the Cell BE architecture.  
At the edge spanning nodes \(C\) and \(Z\), constrained by our bandwidth limitation, we can only transport one of either \(a\) or \(b\), per unit time. Suppose that we send \(a\) along the edge between nodes \(C\) and \(Z\). In this case, node \(D\) could not receive \(b\) and would only be capable of receiving \(a\) twice, from \(A\) and \(Z\). In addition, if we send \(b\) at the same time, node \(E\) would face the same problem. As it is not possible to transfer data bits \(a\) and \(b\) to both nodes \(D\) and \(E\) simultaneously, routing is inadequate.  
When using network coding, we are able to generate new data by first encoding \(a\) and \(b\), and then routing the encoded data through the directed linkage between nodes \(C\) and \(Z\). As a simple example, we use a bitwise \(xor\) to encode data bits \(a\) and \(b\). The new data is thus encoded as '\(a\)\(xor\)\(b\)' and is sent along paths \(C\)-\(Z\)-\(D\) and \(C\)-\(Z\)-\(E\), simultaneously. Node \(D\) would therefore receive data bits \(a\) and (\(a\)\(xor\)\(b\)) from nodes \(A\) and \(Z\), respectively. Further, node \(E\) would receive both data \(b\) and (\(a\)\(xor\)\(b\)) from nodes \(B\) and \(Z\). Therefore, both nodes \(D\) and \(E\) can collect data bits \(a\) and \(b\) using the \(xor\) operation. In conclusion, using a network coding technique allows us to achieve an enhanced multicast throughput of two bits to both nodes, subject to the same base network capacity of _one bit per unit time_.
## 4 The Proposed Algorithm
### Markov Decision Process Modeling
#### 4.1.2 Action Space  
In the multi-UAV MEC system, continuous actions are related to mobile device selection, UAV mobility, UAV CPU frequency adjustment, and task allocation. Based on the observed environmental state, the action vector for all UAVs at time slot \(t\) is defined as  
\[A_{t}=(a_{1},\ldots,a_{u},\ldots,a_{M}), \tag{37}\]  
\[a_{u}=(l_{u}(t),\theta_{u}(t),v_{u}(t),\alpha_{l,u}(t),k_{u}(t)), \tag{38}\]  
where \(a_{1},\ldots,a_{u},\ldots,a_{M}\) represent the actions of UAVs \(1\) to \(M\). For UAV \(u\), the action \(a_{u}\) includes the selected mobile device index \(l_{u}(t)\in[0,N]\), the flight angle \(\theta_{u}(t)\in[0,2\pi]\), the flight speed \(v_{u}(t)\in[0,v_{max}]\), and the task offloading ratio \(\alpha_{l,u}(t)\in[0,1]\). Similar to the single-UAV MEC system, the action space consists of continuous values, while the selected mobile device index \(l_{u}(t)\) is discrete. Specifically, if \(l_{u}(t)=0\), then \(l_{u}=1\); if \(l_{u}(t)\
eq 0\), then \(l_{u}=[l_{u}(t)N]\). Additionally, if mobile device \(l_{u}\) is selected, the service indicator is defined as \(\eta_{l,u}(t)=1\). The factor \(k_{u}(t)\) adjusts the CPU frequency of UAV \(u\).
## 6 Experiment and Result Analysis
### Analysis of Diffusion Model Effectiveness
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Deepfakes** & **Face2 Face** & **FaceSwap** & **NeuralTextures** & **Average** \\ \hline PSNR(db) & 35.71 & 36.44 & 38.36 & 38.93 & 37.31 \\ LPIPS & 0.2218 & 0.2203 & 0.2175 & 0.2154 & 0.2154 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results from the diffusion model across four different subsets of the FaceForensics++ dataset. Figure 16: The qualitative results of motion blur denoising (**left**) and Gaussian blur denoising (**right**) using the diffusion model on samples from the FaceForensic++ dataset. Based on the experimental results, it is obvious that our diffusion model performs well in denoising tasks. Table 5 displays the PSNR evaluation results of our method on four different deepfake face datasets, with PSNR values consistently above 35 for each dataset and LPIPS values around 0.2. Particularly notable are the highest PSNR values achieved on the FaceSwap and NeuralTextures datasets, indicating excellent generalization capabilities of the diffusion model on these datasets. According to the quality definitions of these two metrics, such results typically indicate a very close visual resemblance between pairs of images, highlighting the effectiveness of our method in reducing noise levels. This further underscores the significant potential of the diffusion model in handling real-world noise and artifacts.
# Method to Remove Tilt-to-Length Coupling Caused by Interference of Flat-Top Beam and Gaussian Beam
## 3 Theoretical Analysis  
In the current theoretical analysis, the complex amplitude is normally used to calculate the phase information [8,9]:  
\[C=\int_{S}E_{meas}E_{ref}^{*}dr^{2} \tag{5}\]  
Because the QPD has four segments as shown in Figure 1, it gives researchers some degrees of freedom to define signals with different pathlengths. The phase definition selected by LISA Pathfinder is LISA Pathfinder (LPF) signal, which first sums the complex amplitudes of each quadrant of QPD and then uses the following argument [10]:  
\[\phi_{LPF}=\arg\Big{(}\sum_{n=1}^{4}C_{n}\Big{)} \tag{6}\]  
We focus here on the change in the optical path difference between the two beams. Because the path difference does not change over time, we can set t = 0 in the theoretical analysis. In addition, to simplify the calculations and obtain intuitive analytical solutions that clearly illustrate the parameter interdependency, we remove some of the terms that are unrelated to \(\alpha\) from the calculation.
# Optimization of Stop Plan for Skip-Stop Operation on Suburban Railway Line
## 2 Description of the Problem and Assumptions  
A suburban railway line on which both express trains, which skip some stations, and local trains, which stop at every station, are operated concurrently is examined in this study. Stop plans for E/L mode that can accommodate the specific passenger flow pattern on this route are calculated from the inter-station distances, the design speed, the acceleration and deceleration rates of the trains, safe headway, and the passenger origin-destination (OD) flow distribution.  
The following basic assumptions are made:  
(1) Regardless of their type, all trains depart from the originating station at equal time intervals.  
(2) Trains run on a double-track railway line on this route. The inter-station segments of this route are not equipped with facilities that allow one train to overtake another. Overtaking occurs only at stations equipped with the requisite facilities.  
(3) Passengers entering each station during the period examined in this study follow a uniform distribution.  
(4) A train accelerates when departing from a station, then travels at a constant speed (i.e., the maximum design operating speed), and finally decelerates when entering the next station it stops at.  
(5) The departure frequency of local trains is an integer multiple of that of express trains (this assumption facilitates the operational scheduling for trains).  
(6) A passenger can only be delayed once and can only transfer once.  
(7) Each passenger waiting at a station boards the train when it arrives at the station.  
(8) Express trains overtake local trains at overtaking stations without stopping.
## 2 Materials and Methods
### HS-SPME-GC-MS Analysis
#### 2.2.2 HS-SPME-GC-MS Analysis of Volatile Compounds  
Volatile compounds were measured by an HS-SPME-GC-MS system (8890/7000D, Agilent Technologies, Santa Clara, CA, USA) with an Agilent 5975C mass selective detector. An Agilent HP-5 MS column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) was used for the separation of volatile compounds. The heating procedure of the GC oven was as follows: 35 \({}^{\circ}\)C for 3 min, heated to 65 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, heated to 180 \({}^{\circ}\)C at 8 \({}^{\circ}\)C/min, then heated to 200 \({}^{\circ}\)C at 15 \({}^{\circ}\)C/min and finally heated to 260 \({}^{\circ}\)C at 20 \({}^{\circ}\)C/min for 5 min. Helium was used as a carrier gas at a flow rate of 1.0 mL/min. Mass selective detection was performed in scan mode (m/z 45-550, EI (70 eV), ion source temperature 230 \({}^{\circ}\)C, quadrupole temperature 150 \({}^{\circ}\)C, transmission line temperature 280 \({}^{\circ}\)C) [36].  
The results of the experimental mass spectra were compared with the mass spectra library from NIST11s, and the identification of volatile compounds was completed via comparing the retention indices (RI), which were calculated by analyzing n-alkanes (C7-C30) under the same chromatographic conditions and comparing with the NIST Standard Reference Database Number 69. Finally, the relative concentrations of the volatile compounds (semi-quantitative) were calculated by comparing the compounds with the IS (expressed as \(\upmu\)g/kg).