esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, the problem of wavelength assignment in optical backbone networks has been revisited, with particular attention given to the question of survivability. The dependency of wavelength requirements on both spectral and non-spectral topological parameters was also examined, indicating that algebraic connectivity is the most influential parameter among those analyzed. The analysis was performed considering both real-word networks and random networks generated using a \(k\)-nearest neighbors model. The results showed that for 1 + 1 OCh protection, the wavelength increment factor ranges from 1.49 to 3.07, with a mean value of 2.26. Other important outcomes of this work include the derivation of formulas to estimate the wavelength requirements and the wavelength increment factor in terms of algebraic connectivity and the number of nodes. Notably, by applying the latter formula to a set of 27 real-world networks, a mean relative error of 12.7% was obtained, as well as a relative error smaller than 15% for about 70% of the instances analyzed.  
**Author Contributions:** Conceptualization, F.C. and J.P.; methodology, F.C. and J.P.; software, F.C.; validation, F.C.; formal analysis, F.C.; investigation, F.C. and J.P.; writing--original draft preparation, J.P.; writing--review and editing, J.P.; visualization, F.C.; supervision, J.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(N\) & **\textless{}\textless{}\textgreater{}** & **W/o Protection (ms)** & **W/Protection (ms)** \\ \hline \multirow{3}{*}{10} & 3 & 4.63 & 29.2 \\  & 4 & 4.11 & 32.0 \\  & 5 & 4.00 & 36.4 \\ \hline \multirow{3}{*}{20} & 3 & 67.9 & 313.7 \\  & 4 & 58.8 & 316.2 \\  & 5 & 54.1 & 342.7 \\ \hline \multirow{3}{*}{30} & 3 & 404.0 & 1682.0 \\  & 4 & 322.7 & 1400.0 \\  & 5 & 289.2 & 1384.0 \\ \hline \multirow{3}{*}{40} & 3 & 1450.3 & 5251.0 \\  & 4 & 1135.0 & 4312.0 \\  & 5 & 988.5 & 4018.6 \\ \hline \multirow{3}{*}{50} & 3 & 4086.9 & 15,093.2 \\  & 4 & 3145.6 & 11,124.2 \\ \cline{1-1}  & 5 & 2565.0 & 9815.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational time (in ms) for random networks of different sizes.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Methodology and Search Strategy  
This systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [72, 73]. Online databases such as PubMed, Google Scholar and Scopus were accessed to retrieve published literature using the MeSH terms \"dendritic cells\" AND \"cancer immunotherapy\" AND \"plants\" AND \"phytochemicals\" AND \"immunopharmacology\". Literature published in the last two decades was retrieved. For screening purposes, automatic search tools were used to remove some of the articles, on the other hand, others were screened manually. The articles which were published in languages other than English were removed during screening time. Reviews, book chapters, and conference papers were also excluded from this review. A total of 61 studies, including 45 in vitro studies, nine preclinical in vivo studies, and seven clinical trials, were retrieved and discussed in this study (Figure 1). The information obtained from research articles was systemized in the table.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Plant Name/Extracts** & **DCs-Based Clinical Study against Cancer** & **References** \\ \hline _Nicotiana benthamiana_ NbPVX-E7 pulsed-MDDCs/PBMC & Increase cytotoxic activity against HPV16 E7 & [23] \\ \hline Chinese herbal medicine Shenqi Fuzheng & DCs activation in breast cancer patients & [67] \\ \hline Lingdankang composite combined DCs and cytokines & Effective against leukemia patients & [68] \\ \hline Neef leaf glycoprotein (NLGP) & Dendritic cells attenuating hyperactive regulatory T-cells & [69] \\  & in cervical cancer (stage IIIB) patients & [69] \\ \hline DCs exposed to Amomi Semen extract & Activation of phenotypes of DCs and inhibits the growth & [70] \\ \hline Glycolipid mixture containing B-glucosidaseeramides & & \\ from herb & DCs expressed ICAM 1 in cancer patients & [50] \\ \hline Lipid-soluble extract of _Pintelli pedatiscta Schott_ & Stimulates intratumoral dendritic cell activation in & [71] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Plant extracts on DCs-based clinical study against cancer.  
Figure 1: PRISMA 2020 flow diagram for the systematic review of dendritic cell (DC)-based cancer immunotherapy with medicinal plants and their phytochemicals.
# A Feature Selection Algorithm Performance Metric for Comparative Analysis
## 2 Background and Related Work
### Approaches to Evaluating Feature Selection Algorithm Performance  
When new feature selection algorithms are proposed, justification has to be provided in the form of a performance comparison with other common feature selection algorithms. A common approach is to measure the performance of the underlying classifier after the feature selection algorithm has been applied. Classifier accuracy, i.e., counting the number of correctly classified instances vs. the number of incorrectly classified instances, is a popular approach [8; 9; 10; 11]. Other accuracy measures have also been used to compare the performance of feature selection algorithms, such as the F-measure [12] and Cohen's kappa statistic [13]. A disadvantage to using classifier accuracy alone as a performance measure for feature selection algorithms is that it makes it impossible to compare feature selection algorithms across datasets. This is because the classifier may not be well suited for the variety of classification tasks. What is needed is a way of quantifying the relative improvement gained by the feature selector. For example, a feature selection algorithm that increases classification accuracy on one problem from 50% to 70% should arguably be regarded as more successful than an algorithm that increases accuracy from 90% to 92%.  
Attempts to measure the quality of the feature subset obtained by the application of feature selection algorithms have been made. Measures include feature redundancy [8] and the reduction in the number of features [12] amongst others.  
There is no clear correct performance model for evaluating the performance of feature selection algorithms for the purpose of automatic algorithm selection [1].
## 4 Implementation and Experimental Setup
### Evaluation Metrics  
The performance of our system is measured by using the following evaluation metrics.  
1. Accuracy: The accuracy of the model is calculated by using the following formula as shown in Equation (23)  
\[Accuracy=1-\frac{\|Y-\hat{Y}\|F}{\|Y\|F} \tag{23}\]  
where \(Y\) & \(\hat{Y}\) and represent the actual data and predicted data, respectively.  
2. Root mean square error (RMSE): The RMSE is calculated using Equation (24).  
\[RMSE=\sqrt{\frac{1}{MN}\ \sum_{j=1}^{M}\sum_{i=1}^{N}\left(y^{ij}-\hat{y}^{ij} \right)^{2}} \tag{24}\]  
where \(y^{ij}\) and \(\hat{y}^{ij}\) are subsets of \(Y\) & \(\hat{Y}\) and represent the actual data and predicted data at the \(j\)th time sample in the ith session, respectively. M is the total time samples, and N is the number of projects. RMSE is precisely used to evaluate the prediction error. The smaller the value of RMSE is, the better is prediction rate or score according to Equation (25).  
\[\textit{Prediction rate}\ \propto\ \frac{1}{\text{RMSE}} \tag{25}\]  
While accuracy is used to detect predictions' precision, it has an opposite effect than RMSE on the prediction rate, as shown in Equation (26). The higher the value of accuracy is, the better is the prediction rate.  
\[\textit{Prediction rate}\ \propto\ \textit{Accuracy} \tag{26}\]
## 2 Overview and Outline of Machine Learning Analysis
### Unsupervised Machine Learning  
The purpose of the dimensionality reduction of high-dimensional data such as neutron transmission spectra can be classified into two main categories. The first of these is data compression. This can reduce computational costs when using supervised ML, where datasets tend to be large. The other purpose is data visualization. This can help to convert high-dimensional data whose characteristics are difficult to recognize into a two- or three-dimensional diagram. For these purposes, unsupervised ML can be used as a method to perform the dimensionality reduction of high-dimensional data.  
There are various methods for the dimensionality reduction of data using unsupervised ML, including principal component analysis (PCA), latent semantic analysis (LSA), nonnegative matrix factorization (NMF), latent Dirichlet allocation (LDA), etc. Among them, PCA is one of the most typical dimensionality reduction methods; it has a long history and is widely used in various situations [22; 23]. There are two main methods used for the dimensionality reduction: selecting only important variables and not using the remaining variables, and building new variables from the variables of the original data. PCA applies to the latter. That is, original data can be explained by lower-dimensional variables converting high-dimensional data by PCA. In this study, we adopted PCA as a dimensionality reduction method for neutron transmission spectra.  
In PCA, the following procedure is used to obtain the principal components.  
1. Calculate the variance-covariance matrix.
2. Solve the eigenvalue problem for the variance-covariance matrix to find the eigenvectors and eigenvalues.
3. Represent data in the direction of each principal component.  
Consider a variance-covariance matrix \(A\) for the given data:  
\[Ax=\lambda x \tag{1}\]  
where \(A\) is an \(n\)th-order square matrix and \(x\) is an \(n\)-dimensional vector. Finding \(x\) and the eigenvalue \(\lambda\) that satisfy (1) is mathematically equivalent to the problem of finding orthogonal axes that maximize the variance. In PCA, when the eigenvalue problem is solved for the variance-covariance matrix \(A\), multiple combinations of eigenvalues and eigenvectors are calculated. The eigenvector with the largest eigenvalue is called the first principal component, while the eigenvector with the second largest eigenvalue is called the second principal component. After the third principal component, the eigenvectorswith the largest eigenvalues are expressed in order. Further, by dividing the eigenvalues calculated for each principal component by the sum of the eigenvalues, the importance of the principal components can be expressed as a ratio. This is called the contribution ratio, and it indicates how much each principal component expresses the data. The sum of the contribution rates in order from the first principal component is called the cumulative contribution rate, and this can be used to measure the amount of information lost through dimensionality reduction. The cumulative contribution rate is discussed in Section 3.2.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 4 Proposed System
### Computer Vision Subsystem  
Given a set of images taken from a UAV flying over the ocean with a color camera, the problem consists in detecting unauthorized boats in the area. This process must be executed in real-time  
Figure 7: Hardware specification of the proposed multi-UAV system. (a) XBee module embedded in UAV Phantom 3 Standard; (b) XBee module connected to the power bank; (c) BS equipped with a XBee module.  
conditions and aboard the aircraft. After detecting a boat, the system must send an alert signal to the operator along with an image of the boat.  
[PERSON] et al. [48] proposed an algorithm based on a segmentation technique similar to P-Tile as presented in Reference [49], which is associated with morphological operations and machine learning. After acquiring a colored frame candidate, the image is split into three component channels and regions of each channel are selected based on their color when compared to the background. In order to remove the noise caused by small spume regions, morphological operations are applied. The resulting regions are classified as boat candidates. By using this process some spume regions may be wrongly classified as boats. If the boats are not white, it is possible to distinguish them from spume based on the color. However, since most boats are actually white, it is not possible to classify a candidate based only on color.  
In order to differentiate boat candidates from spume, a feature descriptor was used. Specifically, a feature descriptor capable of taking into account the shape and texture of the candidate is employed. The histogram of oriented gradients (HOG) feature descriptor described in Reference [50] fits well for this purpose.  
Once the HOG feature vector is calculated for each boat candidate, it is possible to use classification techniques to distinguish between a boat and a spume. Supervised machine learning techniques were adopted for this purpose, that is, Support Vector Machine (SVM), \(K\) nearest neighbor (KNN) and decision trees, which were trained with a set of labeled images for classifying new images. The complete schematic of the proposed system is shown in Figure 8.
###### Abstract
According to the original signal characteristics in the time scale, finite Intrinsic Mode Functions (IMF) can be obtained by decomposing complex signals through Empirical Mode Decomposition (EMD) [21; 22], and each IMF contains different local characteristics in the time scale. EMD perform well in processing non-stationary and nonlinear data for its universality, and it is widely used in processing displacement signal [23]. The time-domain signal can be converted into frequency-domain signal, that is, the spectrum of signal, by Fourier Transform (FT) [24]. It is believed that any continuously measured time signal can be expressed by the FT as infinite superposition of sine wave signals with different frequencies. The FT is widely used in the field of signals processing [25]. The original time domain signals can be obtained from the frequency domain signals by Inverse Fourier Transform (IFT) [26]. The EMD and the FT (combined with IFT) can be used respectively to process the displacement signal of the bridge measured by a UAV, and the bridge modal parameters are extracted from the processed signals through Operational Modal Analysis (OMA) [27; 28]. In OMA process, the structural natural frequency can be identified by Power Spectral Density (PSD) [29], while the ratio of the PSD Transmissibility (PSDT) [30] is used to obtain the bridge mode shape [31]. In this paper, EMD and FT are used to eliminate the random component in the signals of a bridge model measured by a UAV. The bridge vibration is video-recorded by a UAV, the target points on the bridge model in the video are tracked by KLT, and their displacement contains false component. Then the EMD and FT are used to process displacement signals, respectively. The modal parameters of the bridge model can be obtained by analyzing the processed signals with OMA. Finally, taking the fixed camera measurement as a reference, the processing effects and the modal parameter identification accuracies of EMD, FT, and DF are compared.
# Extended Depth-of-Field of a Miniature Optical Endoscope Using Wavefront Coding
## 6 Conclusions and Outlook  
Nowadays, the optical endoscope is a widespread and versatile diagnostics and inspection tool in massive industrial fields. In particular, the small-diameter endoscope for performing visual examinations of the interiors of cavities or tubes is highly attractive, which possibly provides more information regarding the small components. However, the common optical endoscopic modality always operates with a small DOF, thereby being unable to capture sharp long range images and limited in usefulness in practical applications. Here we describe such a miniature endoscope only 3 mm in diameter that integrates group lens, a phase mask and an image sensor, which is expected to enlarge the DOF from 3 mm to 2.7 mm without sacrificing the spatial resolution.  
Moving forward, by coding a stream of light with a phase mask and then decoding it with a special algorithm, wavefront coding produces perfectly focused images with an astounding DOF in optical system. The distinct advantages of enhancing the DOF of an optical endoscope using wavefront coding include high-resolution, low-cost and light weight, although it always induces a small reduction in signal-to-noise ratio at the best focus value. Beyond that, the wavefront coding technology also holds great potential for correcting the intrinsic optical aberrations that typically occur in standard endoscopes [21].  
Conceptualization, L.Y. and J.W.; Data curation, M.C. and S.Z.; Formal analysis, M.C. and T.Y.; Funding acquisition, J.W. and L.Y.; Investigation, L.Y., M.C. and J.W.; Methodology, L.Y.; Project administration, H.X.; Resources, J.W. and M.Z.; Writing--original draft, L.Y.; Writing--review & editing, H.X. All authors have read and agreed to the published version of the manuscript. This work was partially supported by the National Key R&D Program of China (Grant No. 2018 YFF0212105 and 2018 YFF01013203). The authors declare no conflict of interest.
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 5 Discussion  
The effectiveness of the method proposed in this paper comes from the processing and matching of low-scoring boxes. As shown in Figure 4, the tracking visualization results verify the effectiveness of the method proposed in this paper. In the sequence of MOT17-07,  
Figure 3: Comparison of results before and after postponing NMS. The position pointed by the red arrow is an untraceable object after being occluded.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **MOTA\(\uparrow\)** & **IDF1\(\uparrow\)** & **FP\(\downarrow\)** & **FN\(\downarrow\)** & **IDs\(\downarrow\)** \\ \hline Baseline + IOU & 66.3\% & 70.4\% & 56,794 & 102,512 & 2072 \\ Baseline + NMS + IOU & 75.1\% & 71.2\% & **40,654** & 96,341 & 2043 \\ Baseline + NMS + UR & 75.4\% & 71.2\% & 42,372 & 98,784 & 2032 \\ Baseline + NMS + UR + BI & **75.7\%** & **71.4\%** & 42,134 & **90,833** & **2026** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation results on MOT20.  
the target with ID 7 is still accurately tracked even when it is almost completely occluded, which directly verifies the importance of performing NMS later in the tracking stage. In addition, there are different degrees of occlusion problems in several other sequences, and the method in this paper successfully tracks the occluded targets. In particular, the sequence in MOT20 has a larger number of targets, and the targets are more likely to occlude each other, which is a challenge for tracking. This study is still accurate and robust in such cases. Accuracy and robustness mainly come from two aspects: (1) Motion prediction is used in multi-target tracking. Motion prediction enables the prediction results to be combined with the previous tracking results and avoids comparison with the global target frame when matching the tracking trajectory, which can make the results more accurate and robust; (2) An unsupervised pretrained person ReID network is used. The MOT dataset has different scenes, picture quality, and shooting styles on different sequences, so there is a clear domain gap between different test sequences. Unsupervised pre-training of person ReID network improves the model's ability to narrow the domain gap and has accurate recognition accuracy in different scenarios.
## 4 Results and Discussion
### Performance Evaluation of UNSW-NB15
The implications of these results, coupled with the findings presented in Figures 9 and 10, are that the proposed model outperforms existing models with a true positive rate of 0.9982 and a false positive rate of 0.028. The ROC curve, with an AUC of 97.38%, also proves the model's high effectiveness in differentiating between actual threats and false alarms. Thus, the proposed model proves to be highly effective in detecting cyber-attacks with high accuracy and reliability, and it ensures a highly effective solution for eradicating the drawbacks of the conventional models.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 5 Performance Evaluation  
In this section, the performance of the proposed DTPCH-C scheme is evaluated. First, the simulation settings are introduced in detail and the DTPCH-C is analyzed by different ways to divide the grid. Then, the performance of the proposed DTPCH-C is evaluated and compared with the benchmark methods, i.e., the grey wolf optimization (GWO) [14] and dynamic scale UAV weighted clustering algorithm (DSWCA) [20]. The metrics used in the simulation are the packet delivery ratio (PDR), network lifetime, and energy consumption.
# Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines
## 5 Experiment and Evaluation
### Performance of the Proposed Method
#### 5.2.2 Image De-Noising  
In this stage, unwanted noise is removed and valuable information is preserved, such as transmission line edges and transmission line icing textures. Figure 22 shows the image after the de-noising effect (filter noise). The proposed method in this paper for image de-noising has been successfully applied. Based on Figure 22, the image after de-noising is smoother and has less noise, and the edges and texture of the transmission line icing are maintained.  
Figure 21: The result of image color restoration. (**a**) Image 1; (**b**) Image 2; (**c**) Image 3.  
Figure 20: The original image with different illumination conditions and backgrounds. (**a**) Image 1; (**b**) Image 2; (**c**) Image 3.
# Digital Identity in the EU: Promoting eIDAS Solutions Based on Biometrics
## 1 Introduction
### Related Work and Outline of the Paper  
To adequately address these concerns and fully contextualize the contribution of this work within the broader landscape of digital identity research, three main aspects need to be considered. First, we need to consider decentralized biometric techniques, i.e., those that can be efficiently implemented on common mobile devices without requiring specialized hardware or sensors. This area of research has been promoted in recent years by the advent of deep learning techniques, which have far surpassed previous holistic and geometric approaches [13]. Secondly, the schemes underlying biometric authentication must be analyzed. This has to do with how an external Identity Provider connects to the mobile device and how biometric data is collected and manipulated [14]. Finally, the problem of preserving privacy through a balanced approach that considers both the protection of sensitive personal data and collective needs while ensuring an adequate level of security is fundamental.  
Building on this premise, the paper first undertakes an analysis of the emerging notion of digital identity, highlighting how technological progress has shaped its evolutionary path. Particular attention is given to the role of digital identity in accessing network resources and/or conducting digital transactions.  
This aspect is explored in Section 2, where some basic authentication schemes are illustrated, and the complexities associated with the introduction of biometric authentication are briefly outlined in full compliance with current regulations. Section 3 looks at European regulation and, more specifically, at the challenges related to interoperability among EU Member States in the implementation of the eIDAS Regulation. Section 4 proposes a new authentication scheme based on existing biometrics and ID cards, but implementing a high level of security specifically designed for the most demanding applications. This solution, if implemented, could be an opportunity for the European identification system to easily adopt and extend this technology to the whole European area. Section 5 discusses the benefits and concerns of the proposed method, addressing both technological aspects and potential regulatory impacts. Finally, Section 6 presents the conclusions and offers final remarks.