esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup
### Evaluation Metrics  
The evaluation metrics used to measure the classification performance are Precision, Recall, and F1-score. We calculated these metrics for each class and utilized the weighted average based on the data distribution of each class. We calculated each evaluation metric as follows:  
\[Precision=\frac{\sum_{i=0}^{n}{precision_{i}}\cdot\text{ number of class}_{i}^{\prime}s\text{ issue reports}}{\sum_{i=0}^{n}{number\text{ of class}_{i}^{\prime}s\text{issue reports}}} \tag{1}\]  
\[Recall=\frac{\sum_{i=0}^{n}{recall_{i}}\cdot\text{ number of class}_{i}^{\prime}s\text{issue reports}}{\sum_{i=0}^{n}{number\text{ of class}_{i}^{\prime}s\text{issue reports}}} \tag{2}\]  
\[F1\text{-}score=\frac{2\cdot(Precision\cdot Recall)}{(Precision+Recall)} \tag{3}\]  
In the above equations, \(class_{i}\) represents the \(i\)-th class, which consists of two categories: 'bug' and 'feature'. \(precision_{i}\) is the ratio of issues predicted as \(class_{i}\) correctly out of all the issues predicted as \(class_{i}\). \(recall_{i}\) is the ratio of issues correctly predicted as \(class_{i}\) out of all the actual \(class_{i}\) issues. \(F1\)-\(score\) is the harmonic mean of \(Precision\) and \(Recall\).
# Influence of Spectral Filtration on Pulse Dynamics in Ring-Cavity Mamyshev Oscillator
###### Abstract  
Here we present a numerical study of pulsing build-up dynamics inside the fiber Mamyshev Oscillator (MO). The main scope of the investigation is to describe the influence of the spectral separation between the filters on self-starting MO dynamics and transition from multipulse to single-pulse generation regimes. It was found that Floquet stability analysis provides a straightforward way to determine whether the system will be self-starting or if it has to be excited by external source and predicts the repetition rate of the pulse train. We showed that spectrally overlapped bandpass filters provide reliable multi-pulse generation due to Faraday instability. Adiabatic increase in the spectral separation between the filters decreases the number of pulses down to single-pulse regime, therefore providing a flexible way to generate adjustable number of mode-locked pulses on demand.  
M +
Footnote †: journal: Applied Sciences  
**Keywords:** Mamyshev oscillator; spectral filtration; build-up dynamics; Faraday instability; Floquet stability analysis; numerical modeling
## 2 Decomposition of the Standard JRC Profile
### Decomposition Result of the Standard JRC Profile
#### 2.2.2 The EEMD Results  
The EEMD of ten standard roughness profiles is realized by Matlab software. Figure 3 shows the decomposition result of the \(\theta^{\#}\) standard JRC profile. The EEMD results of the other standard JRC profiles were similar to this one, so only the decomposition results of the \(\theta^{\#}\) standard JRC profile are shown here.  
The approximate component was characterized by large amplitude and low frequency, which reflected the macroscopic fluctuation of the joint surface, and the detail component showed small-scale fluctuation with small amplitude and high frequency when the  
Figure 3: The EEMD results of the \(6^{\#}\) standard JRC profile (**a**) Approximate component, (**b**) Detail component.  
Figure 2: Standard JRC profile (**a**) Before reconstruction, (**b**) After reconstruction.  
decomposition level was lower than 3 (as shown in Figure 3a). With the increase in the decomposition level, some features of the approximate components were extracted as detail components, so the approximate components were no longer able to reflect the macroscopic fluctuation of the joint surface, as shown in Figure 3b.
## 4 CEL Modelling
### Simulation of Forced Replacement Method
Based on the instantaneous velocity vector plot, the range and magnitude of soil heave can be predicted practically and qualitatively. Figure 8: CEL model for the simulation of forced replacement method: (**a**) Dimensions and boundary conditions; (**b**) typical mesh. Figure 10 illustrates the results of simulating forced replacement method for \(H\) = 2.0 m. For the embankment height of 2.0 m, the replacement depth and replacement thickness are approximately 4.0-4.1 m and 1.7-2.1 m, respectively. When compared with the field investigations, the replacement depth of 4.0-4.1 m from CEL analyses is similar to the measured replacement depth of 4.0-5.0 m. In addition, the height and range of soil heave are about 1.1 m and 22.6 m. In addition, the replacement angle \(\alpha\) ranges from 27 to 57 degrees; hence, the replacement shape can be predicted with varying the embankment geometries, such as the height, width, and angle of embankment. Therefore, it can be extremely useful to estimate the reasonable quantity of soil for the replacement, based on the predicted replacement shape. Based on the comparison between results of measured in field test and CEL analyses, it is found that the results of CEL analyses quantitatively agree well with the results of the field tests, also ensuring the reasonable accuracy of CEL technique. Furthermore, this simulation shows the capabilities of CEL to simulate the forced replacement method reasonably. Additionally, the engineers can consider not only the constructability factors such as the replacement depth, height and range of soil heave but also the economic factor such as quantity of soil using CEL technique. For the embankment height of 2.0 m, the additional embankment should be needed to set ground level straightly, because the maximum settlement of 2.4 m occurred. Thus, the additional analysis for the embankment height of 3.0 m is conducted to review the feasibility of forced replacement method. Figure 11 illustrates the results of simulating forced replacement method for \(H\) = 3.0 m. In this case, all of the very soft soil is replaced by embankment, meaning that the embankment soil reaches the bottom of soft soil deposit. Since the soil profile comprised the very deep soft soil deposit of 16.0 m, the quantity of soil for replacement would be substantial and the costs for construction would be increased. Because of this, it is hard to apply the forced replacement method in this site. Figure 10: _Cont._  
Figure 9: Instantaneous velocity plots.
# UY-NET: A Two-Stage Network to Improve the Result of Detection in Colonoscopy Images
## 4 Results and Discussion
Regardless of their one-stage or two-stage detection design, both RetinaNet and Faster R-CNN do not achieve the same level of accuracy in polyp detection as UY-Net. This finding lends strong support to the second hypothesis:  
2. The two-stage network UY-Net would be more accurate in detecting colorectal polyps than the one-stage or two-stage detection algorithms. It also highlights the need to experiment with a segmentation architecture and a detection algorithm to design an innovative two-stage network. To illustrate, in the present study, we hypothesized that the more precisely a region with abnormalities could be segmented in advance, the more likely it was for these abnormalities to be accurately detected thereafter. Therefore, U-Net was trained first to precisely extract and obtain regions of interest (ROI) from images. Then, YOLOv4 underwent training, but it was not applied to analyze the well-segmented regions until its accuracy was elevated. The exceptional performance of UY-Net in polyp detection validates our hypothesis, implying that the procedural sequence should factor into the improved accuracy of object detection. The two algorithms of a two-stage model should be trained independently, with the segmentation algorithm being trained first, followed by the training of the detection algorithm and its application. To the best of our knowledge, this study may be the first attempt to create a two-stage network by combining U-Net and YOLOv4. As [PERSON] et al. ([34], p. 10) argued, \"the YOLO architecture has been the preferred go-to solution for real-time detection tasks as it can process 45 frames per second\". This feature makes it popular among researchers and one of the most used methods for polyp detection. [PERSON] and [PERSON] [38] also emphasized that U-Net is distinguished from other segmentation algorithms by its relatively simple structure with few parameters. This simplicity helps to avoid overfitting and improves the accuracy of image segmentation. U-Net is, therefore, one of the most preferred image segmentation methods in the medical domain, especially for small datasets such as the Kvasir-SEG dataset. Furthermore, the common adoption of YOLOv4, U-Net, and their revised versions has led to the availability of several open-source libraries for executing these algorithms [39]. Encouraged by the promising results of the present study and the availability of the source codes, some researchers in the medical field may choose to develop and evaluate their own two-stage network models using the recently improved versions of YOLO and U-Net. Other researchers may be motivated to incorporate different CNN-based segmentation and detection architectures to develop novel two-stage frameworks. Either way, these researchers can generate new models to improve the accuracy of detecting colorectal polyps from endoscopic images. In this respect, the present study significantly contributes to the medical research community by creating a new and promising pathway and protocol for advancing medical image research.
# Intrinsic Interference Cancellation Scheme for FBMC-OQAM Systems Based on Power Multiplexing
## 3 Description of the Proposed Scheme  
The CAP method described in Section 2 eliminates intrinsic interference by sacrificing data symbols around pilot symbols, resulting in a decrease in spectral efficiency, especially in the high pilot density case. In this section, a new scheme is proposed to eliminate intrinsic interference without sacrificing any data symbols by applying power multiplexing coding in the transmitter and successive interference cancellation in the receiver. Details of the proposed method are introduced in the following.
Although TMDs are useful for mitigating the resonance, their mass, stiffness, and damping properties can influence their performance [14]. To optimize the TMD parameters, a non-linear control target and a new optimization procedure have been suggested in [14]. However, TMDs are not very appropriate for wind turbines because wind turbines possess a high number of eigenfrequencies below 355  Hz and TMDs are effective in a small range around their resonance frequency. A comparison between the optimal TMD and a suspended particle damper is investigated by [PERSON] et al. [15]. They have shown that a suspended particle damper when attached to a 1.5 MW wind turbine \begin{table}
\begin{tabular}{c c c} \hline
**Types of Areas** & **Day (6 a.m. to 10 p.m.) dB(A)** & **Night (10 a.m. to 6 p.m.) dB(A)** \\ \hline Industrial & 70 & 70 \\ Commercial & 65 & 50 \\ Purely residential & 50 & 35 \\ General residential and small residential estate & 55 & 40 \\ Core areas, village areas and mixed-use zones & 60 & 45 \\ Spa areas, for hospitals and nursing homes & 45 & 35 \\ \hline \end{tabular}
\end{table} 
Table 1: Wind turbine noise threshold limits in Germany [9]. tower performs better than that of TMD for the de-tuned case. Furthermore, it has been concluded that the suspended particle damper is significantly more robust in comparison to the optimal TMD. In the current contribution, the focus is on the application of a passive vibration and sound reduction concept of the wind turbine generator without influencing the performance of the wind turbine. This paper aims to use particle dampers to reduce low-frequency vibration amplitude from an onshore wind turbine generator. The damping characteristics of particle dampers are highly dependent on particle materials, particle size, as well as on the shape and texture of particles [16, 17, 18]. Furthermore, packing ratio is also one of the major parameters for analyzing the damping behavior of a particle damper [16, 17, 18]. This shows that the behavior of particle dampers is highly non-linear. The current article introduces a honeycomb damping plate (HCDP) concept to evaluate the transmission path damping of a wind turbine generator. The developed HCDP cells are partially filled with granular materials, which are then mounted at different positions on wind turbine stator (WTS) test specimen to study their influence in reducing the vibration amplitude of the structure. The decision for filling the HCDP partially and not the whole volume was based on a previous study [17]. For the experimental investigation of this concept at a lab scale, a laser scanning vibrometer (LSV) device has been used. The current work shows that the developed HCDP concept is suitable for reducing the vibration from a wind turbine generator. This paper is organized into six sections. In Section 2, an overview of particle damper applications is presented. The detailed design concept of the WTS test specimen and HCDP is discussed in Section 3. Section 4 is dedicated to the experimental setup followed by experimental results in Section 5. Finally, Section 6 contains the summary of this work.
## 2 Methodology  
The topology of the BDS-3 satellite links is a dynamic network. Based on a pre-designed timeslot scheduling, a satellite can establish a connection with another satellite in 3 s following a time division multiple access (TDMA) scheme. Since the round-trip measurement period is short, the dual one-way pseudoranges are usually normalized to the same reference time to derive geometry-free or clock-free observables. Once the derived observables are obtained, joint orbit determination can be performed with L-band code and phase measurements.
## 3 Application and Results  
This section presents the application of the ALMO framework introduced in Section 2 to six well-known benchmark problems: OSY [25], BNH [26], TNK [27], MW1, MW9, and MW12 [28]. The active learner in ALMO uses a Random Forest model as its estimator throughout these case studies. In Sections 3.1-3.6, we evaluate ALMO's performance on each benchmark problem to demonstrate two key aspects: (1) ALMO consistently  
Figure 5: Flowchart of the constrained optimization procedure using active learning.  
finds the near-optimal solutions referenced in the literature and (2) it significantly reduces the number of constraint evaluations compared to traditional constrained evolutionary algorithms, such as those discussed in the benchmark papers cited. The constraint and objective function plots of the benchmark problems used in this study are found in their respective original papers. These plots have been extensively studied and documented in the literature, so they are not reproduced here. The optimization is implemented in Python using a Python-based library: _pymoo_ Multi-objective Optimization in Python [22]. In all the case studies, a population size of 300 was considered, and the NSGA-II algorithm was used to conduct optimization for 600 generations. Initially, NSGA-II is applied to all the problems without any active learning integration to ensure the correctness of the setup and to establish a baseline for calculating the IGD. We then implement the ALMO framework to present the benefits as compared to the baseline found using NSGA-II. Later, we investigate the impact of varying \(\phi\) and \(\beta\) on the percentage of infeasible solutions present in the final Pareto front. Specifically, \(\phi\) is tested at values of 50, 100, 150, and 200, while \(\beta\) ranges from 0.6 to 0.9. The reported infeasibility rates reflect how effectively the ALMO framework manages constraints during optimization. Lower infeasibility rates indicate that the active learner successfully identifies feasible regions in the search space. A higher infeasibility rate suggests that the problem's constraints are more challenging for the learner to predict accurately. However, the consistently low infeasibility rates across most configurations demonstrate that ALMO maintains feasibility effectively while minimizing constraint violations, showcasing its robustness in handling complex multi-objective problems. Finally, we analyze how the savings in constraint evaluations evolve throughout the optimization process.  
MW1, MW9, and MW12 are selected to represent a range of problem complexities. MW1 is relatively easier, allowing us to demonstrate ALMO's effectiveness when constraints are manageable, while MW9 and MW12 are more challenging, highlighting the framework's performance under more complex conditions. This selection provides a balanced view of ALMO's capabilities across varying problem difficulties without a redundant analysis of the entire MW suite. By focusing on these specific problems, we show how ALMO handles both simpler and more difficult optimization challenges.
## 4 Conclusions  
This paper presents a dual-layer CP patch antenna with a circular polarizer that contributes to the polarization transformation from an LP wave to a CP one. The circular polarizer mainly consists of DSSRs with opposite-offset bottom splits. By simply reversing the offset orientation of the dual bottom splits of the DSSRs, the RHCPA and the LHCPA can be realized with the same \(\mid\)S\({}_{11}\mid\) and AR frequency response, only differing in radiation pattern, namely corresponding to the RHCP and LHCP radiation patterns, respectively. The measured 3-dB AR bandwidth of the proposed LHCP antennas is 65 MHz (fractional bandwidth 2.7%), covering 2.345-2.41 GHz, and its peak gain is as high as 9 dBi. The frequency shift between simulation and measurement is mainly owing to the practical dielectric loss leading to a rising effective dielectric constant. Dual-sensing CP characteristics are attributed to the structural symmetry for flexibly changing the orthogonal modes of the DSSRs that arise from the capacitive coupling resonance. The proposed antennas are kept with an overall size of 1.04\(\lambda_{0}\)\(\times\) 1.04\(\lambda_{0}\)\(\times\) 0.063\(\lambda_{0}\), and their performance characteristics could be suitable for wireless target detection applications.  
[PERSON] and [PERSON] conceived and designed the framework; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
**Funding:** This work was supported in part by the Graduate Scientific Research and Innovation Foundation of Chongqing, China contract number CYB16047, in part by the National Natural Science Foundation of China contract numbers 61471072 and 61701052, in part by the Funding of the Innovative Leading Talents in Science and Technology of Chongqing contract number CSTCCXLJRC201705, in part by the Funding of the leading research talent cultivation plan of Chongqing University contract number cqu2017 hbrc1A08, and in part by Funding of the Young Backbone Teachers in Colleges and Universities of Chongqing contract number 0307001104102.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Results
### Balanced Up-Downwind Semi-Discretized Scheme
#### 2.1.1 Case for \(\rho\in[-1,0]\)  
For smoothness of the nonuniform grids [18], we require that  
\[-\rho k_{\max}\leq\sigma h_{\min}\leq\sigma h_{\max}\leq-\frac{1}{\rho}k_{\min}. \tag{23}\]  
We propose that  
\[u_{xy}(x_{m},y_{n},\tau)=\frac{1}{2}(\Delta_{x,+}\Delta_{y,-}+\Delta_{x,-} \Delta_{y,+})u_{m,n}(\tau)+\mathcal{O}(h_{\max}+k_{\max}). \tag{24}\]  
Substitute all spatial derivative approximations into (9), and let \(w\) denote the approximate solution to \(u\). We acquire the following linear system,  
\[w^{\prime}(\tau)=Aw(\tau)+f(\tau), \tag{25}\]  
where \(w,f\in\mathbb{R}^{MN}\) and \(A\in\mathbb{R}^{MN\times MN}\) is block tridiagonal of the form  
Figure 1: Computational stencil for (17) and (20) [frame 1]; (18) [frame 2]; (19) and (22) [frame 3]; and (21) [frame 4].  
\[A = \begin{bmatrix}D_{1}&Q_{1}&\cdots&\cdots&\cdots&0\\ P_{2}&D_{2}&Q_{2}&\cdots&\cdots&0\\ \vdots&\ddots&\ddots&\ddots&\cdots&\vdots\\ \vdots&\cdots&P_{M-2}&D_{M-2}&Q_{M-2}&0\\ \cdots&\cdots&\cdots&P_{M-1}&D_{M-1}&Q_{M-1}\\ 0&\cdots&\cdots&\cdots&P_{M}&D_{M}\end{bmatrix},\]  
where \(P_{i},D_{j},Q_{k}\in\mathbb{R}^{N\times N},\ i=2,3,\ldots,M;\ j=1,2,\ldots,M;\ k=1,2,\ldots,M-1\). Nontrivial entries of the matrices \(P_{m},D_{m},\) and \(Q_{m}\), for their respective ranges of \(m\), are as follows:  
\[p_{n,n}^{(m)} = \begin{cases}\dfrac{y_{n}}{h_{m}(h_{m}+h_{m+1})}+\dfrac{\rho cy_ {n}}{2h_{m}k_{  
It is observed that, in the event \(\rho=-1\), we have the following, due to (23):  
\[h_{\min}=h_{\max}=h,\ k_{\min}=k_{\max}=k,\ k=\sigma h,\]  
which indicates that uniform spatial grids must be employed. Thus, (25) reduces to  
\[w^{\prime}(\tau)=A_{s}w(\tau)+f_{s}(\tau).\]  
Nontrivial entries of \(A_{s}\) are readily obtained, based on the above discussion.
## 3 Immune Game-Based Abnormal Node Detection Algorithm  
### Basic Idea  
As we know, the node security of a mobile network ultimately reflects the system's survivability, i.e., its ability to perform critical tasks in a timely manner when certain nodes fail or are attacked [22].  
Edge nodes in mobile networks have the characteristics of dynamism, randomness, multiple sources, and correlation. The traditional method of judging an abnormal node often lacks real time judgement and effectiveness. An intrusion detection system can isolate abnormal nodes, so that subsequent routes no longer pass through them. At the same time, the use of a data security detection system needs more computing resources. Compared with the current situation of limited node resources, the detection system is always in the open state, which will occupy too many resources.  
Based on the theory of artificial immunity, we propose an algorithm for the detection of abnormal nodes with an adaptive function. The algorithm uses the hazard trigger threshold to identify a hazard source, generates an antibody library using an immune algorithm, classifies an abnormality according to the small habitat strategy, and updates the antibody library online according to the abnormal data. The immune game model is established, and the optimal node recovery strategy is determined under the premise of known abnormal nodes.  
The research idea of this paper is as follows: the monitoring region is divided in the mobile multimedia network. The node senses the information in the monitoring area. First, the node is self-immunized. In the process of immune detection, when the abnormal threshold value is exceeded, it is preliminary determined that the node occurs abnormally. At the same time, the node sends the abnormal information to the spatially related neighbor nodes for further determination. When the two diagnoses are consistent, it is judged to be an abnormal node. If no pings are received from the neighbor node, the relay node enters the abnormal diagnosis phase. Finally, it matches results with the data database to determine whether the abnormality has occurred. If it has occurred, the treatment of the immune stage is carried out. If the abnormality is determined to be unknown, the abnormal data will be processed and features will be extracted. A new abnormal detection library is generated through the processing of the immune detection algorithm. A further update of the existing abnormal detector is executed and secondary immunity carried out when such an abnormality occurs again.  
When an abnormal node is detected, the system builds a game model and treats all abnormal nodes as game participants. Each participant in the game is driven by their own interests and always wants to use the optimal strategy. At the same time, there is always inevitable competition between them. Competition and game play make these nodes closely connected, and they contest each other as well as depend on each other. The final result is to determine the recovery strategy of each node, so as to obtain real-time and effective security protection.