esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Proposed Fault Diagnosis Method  
The new fault diagnosis method, which combines EEMD, permutation entropy, and the GG clustering algorithm, has the following characteristics. It makes full use of the characteristics of EEMD, which can restrain mode confusion in the EMD decomposition process. Besides, permutation entropy has a low requirement for data length. Aiming at the new problem of high dimension and data visualization in the entropy eigenvectors obtained by this method, the linear discriminant analysis (LDA) is used to reduce the dimension of the eigenvectors. Finally, the main eigenvectors with low dimensions, high sensitivity, and low classification error rates are input into the GG clustering algorithm for cluster analysis.  
The algorithm steps designed for the above process are as follows.  
Step 1: Decompose the vibration signal by EEMD to obtain several IFMs;  
Step 2: Calculate the permutation entropy of the IFM components. Each IFM component will have a permutation entropy. Arranging the entropy of each component in order, we will obtain the high-dimensional permutation entropy eigenvector;  
Step 3: Linear discriminant analysis is used to reduce the dimension of the eigenvector of the entropy value.  
Step 4: The reduced dimension feature vectors are used as the input of the GG clustering algorithm for clustering analysis. Use the cluster evaluation index to evaluate the clustering effect.  
The data processing flow corresponding to the above algorithm is shown in Figure 1.
# M13 Bacteriophage-Assisted Synergistic Optical Enhancement of Perovskite Quantum Dots
## 1 Introduction
Among these biomolecules, the M13 bacteriophage has emerged as a particularly effective platform for perovskite quantum dot synthesis and functionalization [46]. The M13 bacteriophage (Figure 1a) is a nanofibrous biomaterial with a height of approximately 880 nm and a diameter of around 6.6 nm [49; 50]. The bottom and top tail ends of the M13 phage consist of p7, p9, p6, and p3 coat proteins, with 2700 copies of p8 protein on its body surface. Preliminary studies have already demonstrated the feasibility of utilizing this biomaterial to couple with perovskite quantum dots (PQDs) [46]. Furthermore, the use of the M13 bacteriophage has also enabled the formation of highly ordered structures of perovskite quantum dots, which can lead to an improved charge transport and a reduced non-radiative recombination [51]. In addition to coupling with biological molecules, another approach to improving the photoluminescence efficiency of perovskite quantum dots is using hybrid structures [47; 48]. In our previous studies, the M13 phage contributed to the growth of CsPbBr\({}_{3}\) quantum dots and formed hybrid structures, which exhibited excellent optical properties. However, the participation of biomaterials in the synthesis process may affect the stability of the synthesis and the optimization of process conditions. Here, we propose a simpler method for fabricating hybrid structures with the M13 phage that improves the photoluminescence efficiency of PQDs. When the synthesized quantum dots are mixed with the M13 phage, the quantum dots self-assemble around the M13 bacteriophage surface, forming a hybrid structure. The affinity of the M13 bacteriophage surface protein for perovskite materials is the principle behind achieving the hybrid structure in a simple way. CsPbI\({}_{3}\) PQDs are favored over CsPbBr\({}_{3}\) PQDs for their superior photoluminescence efficiency and wavelength tunability, primarily due to their larger exciton binding energy and narrower bandgap. The larger exciton binding energy in CsPbI\({}_{3}\) PQDs facilitates a more efficient radiative recombination of excitons, resulting in higher photoluminescence efficiency, while the narrower bandgap enables a wider tunability of the resonance wavelength, offering more flexibility in optical applications [15; 52]. Considering these advantages, we utilized the combination of the M13 phage and CsPbI\({}_{3}\) PQDs to investigate the optical properties. This approach has demonstrated promising results in enhancing photoluminescence efficiency, holding significant implications for optoelectronic applications.
# Polymeric and Crystalline Materials for Effective and Sustainable CO\({}_{2}\) Capture
## 1 Introduction  
Carbon dioxide (CO\({}_{2}\)) is the ultimate byproduct arising from the combustion of carbonaceous matter and is characterized by exceptional stability [1]. In 2021, the annual CO\({}_{2}\) emission rate was approximately 34.9 Gt CO\({}_{2}\), primarily originating from the power industry (combustion of fossil fuel) as well as the steel and cement industries, which account for the majority of the global emissions [2]. These activities stand as the primary cause of the greenhouse effect, consequently contributing to global warming and sparking environmental concerns regarding mitigation efforts. Recently, the scientific community has increased its focus on capturing, utilizing, and studying CO\({}_{2}\) chemistry [3].  
Foreseeing that carbon-based fossil fuels will maintain their significance as a key energy source for the next few decades, ongoing efforts are necessary to counter the escalating CO\({}_{2}\) emissions and their resulting impact on the greenhouse effect. Strategies like directly reducing emissions through the efficient use of energy, using carbon-free fuels, deploying CO\({}_{2}\) capture and storage, and converting CO\({}_{2}\) into components for platform chemicals and fuels are among a few of the important solutions proposed [4; 5; 6; 7; 8; 9].  
Herein, our attention will be focused mainly on polymeric materials as an effective means to capture CO\({}_{2}\). We have explored the latest studies (2014-2024) with an important emphasis on the preparation (synthesis) of these polymers and their CO\({}_{2}\) uptake capacity as one figure of merit. It is important to point out that several reviews have been published on the topic of CO\({}_{2}\) absorption/capture/uptake based on polymeric materials. Therefore, the reader is referred to the works of [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and others in which they reported an extensive list of polymer materials together with their corresponding performances [10; 11; 12; 13; 14; 15; 16].  
The present review explores the fundamentals of CO\({}_{2}\) capture and summarizes the leading materials used in the process, both inorganic and organic. It examines, particularly,how they are made, contrasting petroleum-based with bio-based approaches. A range of petroleum-derived polymers, such as amine-based, polymeric ionic liquids, ionic polymers, conjugated macro/micro-cyclic polymers, and porous organic polymers are examined and compared with their bio-based counterparts. A section also examines the latest crystalline materials (i.e., MOFs) used for CO\({}_{2}\) capture. The review assesses the performance and sustainability of these materials to provide a comprehensive overview of the current landscape to consider when developing innovative and performing organic-based materials for CO\({}_{2}\) capture [17]. In brief, this review aims to inform the reader about which polymer structures to consider when developing innovative and well-performing materials for CO\({}_{2}\) capture.
# Face Stability Analysis for Tunnels under Steady Unsaturated Seepage and Inhomogeneity Conditions
###### Abstract
In this study, cohesion is assumed to vary vertically while remaining constant horizontally. The impacts of steady unsaturated seepage and soil inhomogeneity are integrated into the stability analysis of the tunnel face in this study. To achieve this, the theory of generalized effective stress is employed to obtain the distribution of suction stress, while an extended Mohr-Coulomb failure criterion is utilized to determine the strength of unsaturated soils. In addition, an inhomogeneous cohesion model utilized by [PERSON] et al. (2008) [16] is employed to simulate the inhomogeneous condition of the soil. For the sake of characterizing the stability condition of the tunnel face, a 3D log-spiral failure mechanism is applied. An iterative method was used to determine the safety factor within the framework of strength reduction techniques to find zero supporting pressure on the tunnel face. The effects of different parameter variations are discussed. The findings can serve as a foundation for future tunnel excavation design.
# Dynamic Compensation of a Fluxgate Magnetometer Based on a Hybrid Optimizing Algorithm
## 4 Experiment and Discussion
### Experimental Equipment and System  
The designed strap-down geomagnetic vector measurement system is shown in Figure 16, and the installation details of the internal instruments are shown in Figure 16b.  
Figure 16: Strap-down geomagnetic dynamic measurement system: (**a**) overall system appearance; (**b**) internal system composition.  
Figure 15: Amplitude/phase–frequency response curves of the fluxgate magnetometer before/after dynamic compensation: (**a**) amplitude–frequency curves; (**b**) phase–frequency curves.  
The system enclosure is made of nonmagnetic material (aluminum alloy). The internal system consists of a high-precision fluxgate magnetometer, a high-precision inertial navigation system, and a data acquisition terminal, which are respectively used to provide geomagnetic three-component measurement values, a high-precision strap-down system attitude angle and data information fusion acquisition, and packaged output.  
During the experiment, the designed system slides back and forth on a designed truss, which is shown in Figure 17. The truss is 3 m \(\times\) 1.5 m and is made of nonmagnetic material as well. The 1 mm eccentric wheel of the trolley on the truss is designed to make the dynamic characteristic of the measurement signal more significant.
## 3 Dielectric-Based Passivation Layers
### Other Characterization Techniques for Further Characterizations  
In order to examine the effect of the passivation layer, photoluminescence (PL) and external quantum efficiency (EQE) measurements were done besides solar simulator measurements.  
PL is often used to reach a qualitative conclusion of the effect of the passivation layer, especially the influence of both radiative and non-radiative defects [9]. Under the influence of non-radiative recombination, the PL yield decreases [5]. CIGS solar cells that have a passivation layer have a narrower PL curve and/or higher PL intensity than reference/un-passivated solar cells. In [9], according to the authors, the smaller peak width of the PL response from the Al\({}_{2}\)O\({}_{3}\) passivated solar cells can be used as proof that the Al\({}_{2}\)O\({}_{3}\) layer was passivating the rear surface of CIGS solar cells.  
Rear surface passivation leads to an increase in \(V_{\infty}\), as explained earlier, and \(I_{sc}\) due to an increase in rear internal reflection (Rb). EQE measurement is another commonly used technique to prove the improvements in the carrier collection of the passivated cells compared with reference/un-passivated cells [14]. The effect of the passivation layer on EQE is more significant for ultra-thin CIGS solar cells due to the unabsorbed photons that get a second chance to be absorbed after being reflected back into the absorber layer. For thicker samples, where all photons are absorbed after one pass in the absorber layer, the increase in EQE can be explained by a longer minority carrier diffusion length and/or a large space charge region, which makes it possible to collect the additional generated carriers in back of the thick samples [17].
## 2 Materials and Methods
### Waves Spectrum  
The full spectrum of regular and trochoidal waves was already studied by authors in previous research works [27; 28] where the influence of the waves' full spectrum on the ship's behaviour was analysed. The present paper is not intended to study the influence of the waves' full spectrum on the ship's rolling motion. However, it is necessary to establish the characteristics of the mathematical models used for the studied cases of certain waves. For practical purposes, and according to other relevant research works, the waves are considered regular, where the analysed parameters are the following: wave period (Tw), translation velocity (Vw), and wavelength (Lw).  
With the help of empirical observations [29] and considering these three parameters, 21 sea state conditions were registered; their characteristics are shown in Table 1. The following polynomial regression equations were obtained:  
\[\text{Lw (m) }=\,1.5838\cdot\text{Tw}^{2}\,-\,0.5558\cdot\text{Tw }\,+\,1.4737, \tag{29}\]  
\[\text{Vw (knots) }=-\,0.0006\cdot\text{Tw}^{2}\,+\,1.5196\cdot\text{Tw}\,+\,0.1165, \tag{30}\]  
\[\text{Vw (m\!\cdot\!s^{-1})}=\,0.0009\cdot\text{Tw}^{2}\,+\,0.7632\cdot\text{Tw }\,+\,0.3669, \tag{31}\]  
The graphs shown in Figure 1 corresponding to Equations (29)-(31) reveal a very high precision for the determining factor.  
Furthermore, according to Equation (10), it is established that there is a direct relationship between the wave height (Hw), the wavelength (Lw), and the sinus of wave maximum slope (\(\theta_{\text{MW}}\)). Moreover, this \(\theta_{\text{MW}}\) is constant and equal to 0.1047 rad (\(6^{\circ}\)), regardless of sea state condition.  
\[\sin(\theta_{\text{MW}})\,=\,\pi\cdot\frac{\text{Hw}}{\text{Lw}}, \tag{32}\]  
Using Equation (32) and the wavelength calculated as per Equation (29), Hw and wave amplitude (Aw), i.e., half of Hw, are obtained. Therefore, the full spectrum of the regular waves studied in the present work is based on the results included in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Tw (s)** & **Lw (m)** & **Vw (m\!\cdot\!s^{-1})** & **Vw (Knots)** & **Hw (m)** & **Aw (m)** \\ \hline  
\end{tabular}
\end{table}
Table 1: Parameters of regular waves’ full spectrum.
# A Deep Learning-Based Approach for Extraction of Positioning Feature Points in Lifting Holes
## 4 Analysis and Discussion of Experimental Results
### Analysis of Fitting Results
#### 4.3.2 Error Analysis of Feature Points  
A total of 60 experiments were designed to quantify the improvement effects of the proposed method, using GT as the baseline. The experimental subjects included both normal and abnormal segmented result images, and the comparative result is shown in Figure 15.  
Figure 13: Comparison of line fitting results. (**a**) Fitting CA by LS. (**b**) Fitting CX by LS. (**c**) Fitting CA by VLFGM. (**d**) Fitting CX by VLFGM.  
From Figure 14, it can be observed that the LS method is highly sensitive to abnormal images. Moreover, the fitting performance of this method heavily relies on the results of Gaussian mixture clustering, as depicted in Figure 13a,b. Due to these reasons, the LS did not perform well in the experiments, even though some points had fitting errors as low as 0 pixel, the overall error still reached 7.70 pixels, as shown in Figure 15. However, the VLFGM method demonstrates stronger anti-interference capability, as long as the principal components of the point clusters are normal, the fitting results will not vary significantly. Of course, when the segmentation results are extremely poor, the improved method may still exhibit larger error points, but compared to the LS method within the same set of experiments, it is relatively smaller. Overall, the VLFGM method concentrates the vertex  
Figure 14: Comparison of line fitting results. (**a**,**b**) Normal shadow; (**c**-**e**) special cases.  
Figure 15: Comparison of vertex errors. (**a**) Line chart of errors; (**b**) box plot of errors.  
errors, and even when some image segmentation results are unsatisfactory, the average error in vertex fitting can still reach 5.39 pixels.  
In order to prove from another perspective that the proposed method has better fitting accuracy, centroid errors were compared with other methods, and the experimental result is shown in Table 6.  
In theory, there is no direct relationship between vertex error and centroid error. Due to the presence of numerous internal points in the image, there can be error compensation phenomenon, even resulting in smaller centroid errors, as shown in Table 6. MBR exhibits larger centroid error, reaching 4.05 pixels, due to factors such as target pose during center extraction. EC relies solely on edge extraction for centroid estimation, which can lead to significant errors in cases of misclassification within the target region, as shown in Figure 12d. Therefore, EC performs slightly better than MBR. LS, by fitting quadrilaterals close to the original results, yields similar errors to CD. However, since the original connected regions contain more points, CD achieves a better average error performance, surpassing LS by 0.15 pixels. On the other hand, VLFGM is capable of fitting connected regions closer to the ground truth, resulting in a centroid error of only 1.72 pixels for the proposed method. This demonstrates its superior fitting performance
# A Novel Deep Learning Framework for Intrusion Detection Systems in Wireless Network
## 5 Applying the HRC Strategy to an IDS  
### Framework Testing  
We test the IDS framework that applied our HRC strategy with the testing dataset to evaluate its performance. The results are shown in the confusion matrix in Figure 18 and the classification report in Table 2:  
Figure 17: The confusion matrices demonstrate the classifier’s prediction results when including the normal category (left graph) and excluding the normal category (right graph).  
Figure 18: The confusion matrix and normalized confusion matrix of the framework’s predictions from the test dataset.  
Generally, the proposed IDS framework's strategy shows a promising result of the overall accuracy reaching over 90%. According to the confusion matrices in Figure 18, the prediction accuracy for each category is no less than 85%; thus, the model satisfactorily predicted four types of attack and normal IP packets. Having a detailed look at Table 2, all the categories' recall scores are higher than 85%, showing that the model correctly identifies the right category for each new piece of data. In the aspect of the precision score, Botnet and Evil Twin, respectively, despite showing a low recall of 70% and 72%, have very high recall scores of 0.99 and 0.98, implying that the system effectively identifies these two attacks but may also occasionally misclassify some normal packets with them. Those precisions and recalls resulted in the overall false alarm rate for the IDS using this strategy being relatively low. Both categories have good F1 scores, showing a balance in their ability to identify relevant categories with their accuracy in avoiding misclassification. The weighted average F1 score of 0.92 indicates that the model performs very well overall. This proved the effectiveness of the proposed strategy in reducing the effect of imbalanced data for the IDS frameworks.  
We created an interface for convenient visualization of the results, which is shown in Figure 19. The interface receives the input testing files of IP traffic flows and displays the detection results (the detector's output before the threshold is applied) as a graph and the entire framework's final output (threshold applied).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type of Traffic** & **Precision** & **Recall** & **F1 Score** & **Support** \\ \hline Normal & 0.98 & 0.90 & 0.94 & 60,732 \\ \hline Website Spoofing & 0.84 & 0.85 & 0.85 & 1872 \\ \hline Evil Twin & 0.72 & 0.98 & 0.83 & 8149 \\ \hline Botnet & 0.70 & 0.99 & 0.82 & 684 \\ \hline Malware & 0.80 & 0.92 & 0.86 & 9823 \\ \hline
**Accuracy** & – & – & 0.91 & 81,260 \\ \hline
**Macro average** & 0.81 & 0.93 & 0.86 & 81,260 \\ \hline
**Weighted average** & 0.93 & 0.91 & 0.91 & 81,260 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification report.  
Figure 19: Visualization interface of the framework.
# Characterization and Optimization of PLA Stereocomplexed Hydrogels for Local Gene Delivery Systems
## 3 Results and Discussion
### Gelation Mechanism and Sol-to-gel Phase Transition
The phase diagram shows a linear decrease in sol-gel temperature with increasing _inner:outer_ ratio, indicating the incorporation of blank L-micelle contributes with the overall network formation (crosslinking). The 3 LM-1/20 solution has the highest L-micelle population, and the sol-to-gel transition occurred at the lowest temperature that is comparable to the 3 LM-blank mixture (50/50% L- and D-micelles). The chain exchange rate of PLA blocks between all micelles depends on the polymer and micelle compositions. The faster exchanges between simple L- and D-micelles for the systems with larger _inner:outer_ ratios in 3 LM would make the network structure even at the low temperature. Furthermore, the relatively large size of 3 LM may not be suitable for the close micelle packing needed for robust hydrogel formation. The blank PLLA-PEG-PLLA micelles could be able to bridge the gap and allow for more crosslinking points. As a result, the hydrogels are readily formed at lower temperatures compared to the system without blank PLLA-PEG-PLLA micelle (i.e., 1:1 _inner:outer_). Overall, sol-to-gel transition temperature is precisely tunable with micelle concentration and composition (Figure 5). Figure 4: Sol-to-Gel phase diagram: variation of: (**a**) total polymer concentrations with constant 3 LM _inner:outer_ = 1/1; and (**b**) 3 LM composition of _inner:outer_ ratios with constant polymer concentration at 20 wt % (open square: solution state, closed triangle: gel state by tilt method). Figure 3: WAXD profiles of hydrogels prepared mixing the following micelles with PDLA-PEG-PDLA micelles: (**a**) 3 LM using PDLLA-PEG-PDLLA (racemic polymer) outer layer; (**b**) typical 3 LM from PLLA-PEG-PLLA outer layer; and (**c**) simple PLLA-PEG-PLLA micelles.