esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Simulation Setting  
The parameters used for the related localization simulation are listed in Table 3. In our simulation experiments, we assume that underwater sensor nodes drift with water currents and they are able to self-organize in the network. The anchor nodes and the ordinary sensor nodes are distributed in a (1,000, 1,000, 600) volume [2]. The percentage of anchor nodes varies from 3%, 5%, 8%, to 12% in our simulation. The numbers of underwater sensor nodes vary from 100 to 500 [39]. They are also equipped with pressure sensors and acoustic modems. Most of the available acoustic modems have large range values because they are designed to work in applications where the distance between nodes are in the kilometer range [2], but long range modems achieve lower data rates. In RLS, we can successfully reduce significantly the number of transmitted bits compared with the existing localization methods like those described in [7, 8, 31]. Micro-modems are a special kind of underwater acoustic large range modem developed by the Woods Hole Oceanographic Institution [40], where it utilized by the RLS. Compared with the available modems such as Aquacomm modem developed by [32] with a range of 200 m, micro-modems have a large range of around 1,000 m [41]. Hence, they are suitable for applications in deep ocean water. We assume the nodes are synchronized and the distance can be measured by TOA [2]. We give the average of 100 simulation runs. Performance of the proposed localization scheme is analyzed in terms of localization success, localization accuracy, energy consumption and localization response time.  
Localization success is defined as the ratio of the localizable nodes to the total nodes. Mean error ratio presents the localization accuracy and it is the average distance between the estimated position and the real coordinates of all nodes. As in [45, 46], for our simulations we normalize this absolute localization error to the node communication range R [31]. Energy consumption is defined as spent energy for transmitting and receiving messages by both anchors and sensors divided by the number of localized nodes. Another useful metric is average response time for localization of a detector sensor. It is expected a small amount for localization schemes considering to the application which require very fast reaction.  
\begin{table}
\begin{tabular}{c c} \hline Parameter & Value \\ \hline Localization Domain Area & 1,000 m \(\times\) 1,000 m \\ Maximum Depth & 600 m \\ Beacon Percentage & 3\%–5\%–8\%–12\% \\ Ordinary Node Number & 100–500 \\ Speed Of Sound & 1500 m/s \\ Error in Speed of Sound & 0.07 m/s [42] \\ Error in Depth & 0.1 m \\ Standard Derivation Of Time stamping & 15 \(\upmu\)s [43] \\ Packet Size & 80 bits \\ Data Rate & 80 bps \\ Transmit Power & 35 W [44] \\ Receive Power & 0.3 W [44] \\ Simulation Run & 100 \\ \hline \end{tabular}
\end{table}
Table 3: Simulation Parameters.  
Figure 6(a) shows the initial random sensor and anchor distribution, Figure 6(b) illustrates the grid anchor deployment and random sensor distribution.
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
## 4 Results
### Experimental Setup
#### 4.1.2 Baseline Method  
We compare the proposed network-based reconstruction with two state-of-the-art iterative algorithms in correcting reflection artifacts. These iterative algorithms, known as the ATR and adjoint method respectively, are designed to remove the reflection artifacts from the mathematical point of view. The reasons for choosing these two methods are three-fold. Firstly, in contrast to the universal back-projection formula [5] which is mathematically valid only in homogeneous media, these methods can be applied to general heterogeneous media. The universal back-project formula is a special case of the TR method if one sets the sound speed to be constant and applies the Kirchhoff solution formula for the 3D acoustic wave equation. Secondly, the TR and adjoint methods are applicable with arbitrary closed surfaces of sensor arrays. In a typical TR or adjoint process, the measured ultrasound signals are re-transmitted in a temporally reversed order back to the tissue. This can be numerically simulated by solving the wave propagation model backwards to the initial moment while using the measured ultrasound signal as the boundary condition, regardless of the geometry of sensor arrays. Thirdly, TR and adjoint methods are popular in both research and application, for instance [8; 9; 49; 50] for various TR methods and [51; 52; 53] for various adjoint methods. Their implementation has also been included in open source packages such as the k-wave MATLAB package [54]. A sketchy introduction to these iterative algorithms is included in the Appendix A for the convenience of the readers.
## 5 Experimental Evaluations
### Experimental Setup  
We carried out comprehensive computer simulations in accordance with the 3 GPP specifications and open-source data that had been approved by telecom operators [47], as implemented in related works [48]. We used the MobFogSim simulator, which is dynamic and supports a wide range of IoT/mobile device platforms. Table 2 contains a description of the input parameters as well as the simulation settings.
## 3 Numerical Application
Table 3 gives the relative errors (norm-2 error) computed by Equation (9) on points 1, 2, and 3 of the results of the NTM model, those of the proposed reduced model (size = 28), those of the conventional block-Arnoldi reduced model (size = 20) and those of the conventional block-Arnoldi reduced model (size = 30). From the numerical results of the two conventional ROMs, it is easy to observe that a high-order reduced model produces a more accurate result compared to a low-order one. However, the high-order conventional reduced model is less accurate than the proposed one, which validates the merits of the proposed ROM scheme. Moreover, the time discretization time for all models is 80,000. For the NTM model of the prototype IGBT, the CPU times for a transient process are 64,436.92 s, while those for the proposed model are 8038.95 s. The CPU times for implementing the proposed scheme are about 35 s; the total used by the proposed model for the same transient simulations (8073.95 s) is less than 13% of those of the original NTM. Figure 4: Time-domain transient response on point 1. (**a**) Zoom in (0–0.12 s). (**b**) Whole excitation duration (0–0.2 s). Figure 5: Time-domain transient response on point 2. (**a**) Zoom in (0–0.12 s). (**b**) Whole excitation duration (0–0.2 s). Figure 6: Time-domain transient response on point 3. (**a**) Zoom in (0–0.12 s). (**b**) Whole excitation duration (0–0.2 s). Figure 7: Absolute errors of the computed temperatures observing point 1, using the conventional block Arnoldi algorithm and the proposed scheme, compared to those of the original NTM model under the tested broad-frequency-excitation signal. (**a**) Zoom in (0–0.12 s). (**b**) Whole excitation duration (0–0.2 s). Figure 8: Absolute errors of the computed temperatures on observing point 2, using the conventional block Arnoldi algorithm and the proposed scheme, compared to those of the original NTM model under the tested broad-frequency-excitation signal. (**a**) Zoom in (0-0.12 s). (**b**) Whole excitation duration (0-0.2 s). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Analysis Point**} & \multicolumn{2}{c}{**NTM Model**} & \multicolumn{2}{c}{**Proposed**} \\ \cline{2-5}  & **max (K)** & **min (K)** & **max (K)** & **min (K)** \\ \hline Point 1 & 40.9190 & \(-\)37.1739 & 40.9114 & \(-\)37.1928 \\ Point 2 & 12.3376 & \(-\)9.8100 & 12.3112 & \(-\)9.8353 \\ Point 3 & 19.9404 & \(-\)17.9279 & 19.9244 & \(-\)17.9785 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparisons of the transient response on different observing points by using the proposed and NTM models. Figure 9: Absolute errors of the computed temperatures on observing point 3, using the conventional block Arnoldi algorithm and the proposed scheme, compared to those of the original NTM model under the tested broad-frequency-excitation signal. (**a**) Zoom in (0–0.12 s). (**b**) Whole excitation duration (0–0.2 s).
## 3 Results
### Micronucleus Assay  
An in vitro micronucleus assay was carried out to verify the QSAR prediction for DKP genotoxicity. In this test, the cytotoxicity of the investigated compound was first evaluated, as it is a known confounding factor for micronuclei scoring. It was expressed as CBPI, RI and %cytotoxicity, as demonstrated in Table 4. The corresponding raw data for cell scoring are provided in Supplement S6: Micronucleus assay--cell scoring (Table S5). The RI value represented the percentage of the number of cells that divided in the treated culture, compared to the number of cells that divided to form binucleate and multinucleate cells in the control culture, and it was used to calculate the % cytotoxicity. The CBPI indicated the average number of nuclei per cell. As depicted in Table 4, we found that, under the applied experimental conditions, DKP exerted a strong cytotoxic effect at high concentrations (screening series) in comparison with the control in all treatments (short S+, short S- and extended). Cytotoxicity was the most evident in extended treatment (99.71% and 90.30% for 0.67 mg/mL and 2 mg/mL, respectively). Since it exceeded 55 \(\pm\) 5% (defined by the OECD 487 guideline as a reliability criterion), cells treated with 0.67 and 2 mg/mL DKP were not included in determining the percentage of micronuclei among binucleated cells. In screening series, in both short treatments, the induction of micronuclei at a concentration of 0.22 mg/mL was not observed, suggesting no clastogenic activity of DKP. However, in the extended treatment, the induction of micronuclei relative to the negative control was three-fold (11.86:33.33%, \(p\) = 0.0086, \(\alpha\) = 0.05), suggesting a statistically significant genotoxicity of DKP via aneogenic effect. However, this result cannot be considered as clearly positive because of potential disturbances from the high cytotoxicity of DKP. Therefore, additional tests, preferably in in vivo settings, are necessary to define the genotoxicity of DKP more conclusively. The experiment was then repeated for extended treatment mode at physiological concentrations because, only for the extended treatment in the screening series, the genotoxic effect was observed. At micromolar concentrations, no cytotoxicity occurred and the number of micronuclei in binucleated cells was not increased when compared to the
# A Novel Framework for Identifying Customers' Unmet Needs on Online Social Media Using Context Tree
## 6 Discussion
### Comparison between the Proposed Method and Conventional Methods for Identifying Customers' Needs
#### 6.2.3 Cost and Reusability  
The proposed methodology requires a lot of costs. It requires an infrastructure to collect and store social media data. In addition, some software programs are needed to develop, such as a natural language processing toolkit for languages, which appear in the collected data, and a data exploration tool for researchers. Therefore, the proposed framework can be more expensive than other methods. However, once such a system is prepared, it is possible to easily extract the users' contexts for any subject. Although the initial investment cost is high, it has a great advantage in terms of reusability.
## 7 Conclusions  
The paper proposes an improved ALOS method considering three degrees of freedom: the cross-track error, the curvature of reference path and the forward speed of AUV. Based on the simplicity and practicality of conventional LOS, the variation rules of time-varying look-ahead distance under 3 DOF are derived in detail, which lays a foundation for good path following. Under 3 DOFALOS, AUV obtains behavior similar to an excellent helmsman, i.e., if AUV approaches a very steep path, the small look-ahead distance is provided. If AUV approaches a straight line, the large look-ahead distance is employed. Meanwhile, the closer to the reference path, the larger the look-ahead distance. In addition, the higher the forward speed of AUV, the weaker the adjustment strength of look-ahead distance. Simulations and experiments show that 3 DOFALOS has good convergence and curve guidance. Moreover, the proportional guidance method is introduced to eliminate the influence of drift angle on path following. Under proportional guidance considering the real-time drift angle, AUV's combined motion is on the desired path to avoid the deviation. Simulation and experiment show that the proportional guidance method can improve the deviation effectively. Therefore, the feasibility and effectiveness of 3 DOFALOS are proved. 3 DOFALOS is suitable for precise path following of complex curves in the presence of ocean currents.  
In the future, many external factors still remain to be studied, and the choice of look-ahead distance should be more intelligent to achieve the optimal value of independent learning. In fact, there are indeed many other factors that affect the selection of look-ahead distance. Under multiple degrees of freedom, it will be a complicated and huge work to deduce the influence of each degree of freedom. Therefore, the choice of intelligent look-ahead distance should be a correct direction. Furthermore, guidance law in the three-dimensional space path following should also be improved to achieve accurate underwater navigation.  
Conceptualization, Y.S.; Data curation, F.L.; Formal analysis, F.L.; Funding acquisition, B.H.; Investigation, F.L.; Methodology, F.L. and J.W.; Project administration, B.H. and P.Q.; Resources, B.H. and P.Q.; Supervision, Y.S. and P.Q.; Validation, F.L.; Writing--original draft, F.L.; Writing--review and editing, Y.S., J.W., D.W. and Q.Y.  
This work is partially supported by the Natural Key Research and Development Program of China (2016 YFC0301400) and the Natural Science Foundation of China (51379198).  
The authors declare no conflict of interest.
## Conclusions: Low Temperature
### Intermediate Temperature: \(22\,^{\circ}\)C-\(32\,^{\circ}\)C
#### 7.2.1 Ftsp  
In Figure 5, the results of the test executed with FTSP in an environment with a temperature variation between \(22\,^{\circ}\)C and \(32\,^{\circ}\)C can be observed.  
For the first \(1,000\) s, a stable behavior can be observed, obtaining an average synchronization error of \(1.488\)\(\mu\)s. A temperature variation is created at instant \(1,000\) s. The influence on the synchronization error is evident, and it can be observed that the average synchronization error increases drastically at \(1,100\) s, \(1,250\) s and \(1,500\) s. After instant \(1,500\) s, the average synchronization error stabilizes to \(4.624\)\(\mu\)s. At the end of the experiment, the average synchronization error is about \(3.582\)\(\mu\)s.  
These results show the great influence of temperature in FTSP, and once again, new approaches are required to improve this issue.  
\begin{table}
\begin{tabular}{c c c c} \hline Parameter & FTSP & AT-FTSP & A2T-FTSP \\ \hline Error & 3.582 \(\mu\)s & 2.302 \(\mu\)s & 1.706 \(\mu\)s \\ Standard Deviation & 6.156 \(\mu\)s & 3.850 \(\mu\)s & 2.522 \(\mu\)s \\ Maximum Error & 87.500 \(\mu\)s & 36.500 \(\mu\)s & 16.222 \(\mu\)s \\ \(<95\)\% Error & 13.3 \(\mu\)s & 10 \(\mu\)s & 7 \(\mu\)s \\ \hline \end{tabular}
\end{table}
Table 4: Results with temperature variation: \(22\,^{\circ}\)C–\(32\,^{\circ}\)C.  
Figure 5: Intermediate temperature results.
# Multi-Identity Recognition of Darknet Vendors Based on Metric Learning
## 4 Evaluation
### Compare with eDarkFind Model  
[PERSON] et al. [17] proposed eDarkFind, an unsupervised algorithm for identifying multiple identities of darknet vendors, which also compared vendor accounts pairwise and used the cosine similarity of extracted features to determine whether two accounts were controlled by the same real vendor. However, they only used data related to drugs and medication from three darknet marketplaces (Dream Market, Wall Street, and Tochka), while the dataset used in this study covered all products from 21 darknet marketplaces.  
We applied eDarkFind to the dataset we collected in this study, extracted the features provided by eDarkFind, and calculated the cosine similarity of vendor features. The evaluation metrics are compared in Table 4. It can be seen that the method based on Siamese neural networks had better performance than directly using cosine similarity to judge the identity of dark web vendors. The poorer performance of the eDarkFind model is due to the following reasons:  
1. It only focuses on vendors selling drugs and only uses data from three markets, so it performs well on the original dataset;
2. It relies entirely on feature calculation, and once the number of vendors increases, the distinguishability of the features may not be clear.
## 3 Methodology
### Total Interpretative Structural Modeling (TISM)
#### 3.1.1 Step 1: Identification of Enablers  
The first step in the TISM process is to identify the enablers for the PFSC. Fifteen enablers were identified by VKCA using Vosviewer network visualization software through systematic literature (Bibliometric analysis), as mentioned in Section 2.1. All fifteen of the enablers are listed in Table 1.
# Lyndon Factorization Algorithms for Small Alphabets and Run-Length Encoded Strings +
## 1 Introduction  
A string \(w\) is a rotation of another string \(w^{\prime}\) if \(w=uv\) and \(w^{\prime}=vu\), for some strings \(u\) and \(v\). A string is a Lyndon word if it is lexicographically smaller than all its proper rotations. [PERSON], [PERSON] and [PERSON] [1] introduced the unique factorization of a string in Lyndon words such that the sequence of factors is nonincreasing according to the lexicographical order. The Lyndon factorization is a key structure in a method for sorting the suffixes of a text [2], which is applied in the construction of the Burrows-Wheeler transform and the suffix array, as well as in the bijective variant of the Burrows-Wheeler transform [3; 4]. The Burrows-Wheeler transform is an invertible transformation of a string, based on sorting of its rotations, while the suffix array is a lexicographically sorted array of the suffixes of a string. They are the groundwork for many indexing and data compression methods.  
[PERSON]'s algorithm [5] computes the Lyndon factorization in linear time and in constant space. Various other solutions for computing the Lyndon factorization have been proposed in the past. A parallel algorithm [6] was presented by [PERSON] and [PERSON], while [PERSON] et al. described an external memory algorithm [7]. Recently, [PERSON] et al. and [PERSON] et al. introduced algorithms to compute the Lyndon factorization of a string given in the grammar-compressed form and in the LZ78 encoding [8; 9].  
In this paper, we present two new variations of [PERSON]'s algorithm. The paper is an extended version of the conference paper [10]. The first algorithm has been designed for strings containing runs of the smallest character. It works best for small alphabets like the DNA alphabet [a, c, g, t] and it is able to skip a significant portion of the string. The second variation works for strings compressed with run-length encoding. In run-length encoding, maximal sequences in which the same data value occurs in many consecutive data elements (called runs) are stored as a pair of a single data value and acount. When there is a run-length encoded string \(R\) of length \(\rho\), our algorithm computes the Lyndon factorization of \(R\) in \(O(\rho)\) time and in constant space. This variation is thus preferable to [PERSON]'s algorithm when the strings are stored or maintained with run-length encoding. In our experiments, the new algorithms are considerably faster than the original one in the case of small alphabets, for both real and simulated data.  
The rest of the paper is organized as follows. Section 2 defines background concepts Section 3 presents [PERSON]'s algorithm, Sections 4 and 5 introduce our variations of [PERSON]'s algorithm, Section 6 shows the results of our practical experiments, and the discussion of Section 7 concludes the article.
# Application of Convolutional Neural Network to Defect Diagnosis of Drill Bits
## 4 Results and Discussion
### t-SNE Analysis for 1D CNN  
For further analysis, a t-SNE analysis was performed. t-SNE is a kind of visualization algorithm which transforms the high-dimensional Euclidean distance between data points into a conditional probability representing similarity [29]. This analysis maps high-dimensional data into low-dimensional data while keeping the features of the distance between data points. It visualizes if a data set has good divisibility by mapping high-dimensional data to a 2D data set. t-SNE technology is also employed to visualize the learned features of a classification accuracy analysis. The obtained results are presented in Figure 9.  
Figure 9 shows the t-SNE results for the test data set before and after applying the 1D convolution and 2D convolution process receptivity. Figure 9a,c present the original data set. Figure 9b,d show the features obtained after the convolutional layers. Compared to the original data set, it clearly shows that all the data points have been gradually separated. In Figure 9b,d, the \"healthy\" samples are separated from the others clearly. Some overlapping or mixed data points can be observed between defect #1 and defect #2. The results match the classification results shown on the confusion matrix, where the \"healthy\" sample has 100% accuracy, and there are some classification errors in defect #2 and defect #3.
# Anomaly Detection in 6G Networks Using Machine Learning Methods
## 6 Experimental Results and Discussion
### Recommendations  
The dataset must go through a preprocessing stage to prepare for the CFS-RF feature selection step. As part of each class in the dataset, after the CFS-RF step, it is exposed to analysis in order to confirm and choose only the most potent influencing characteristics that affect the outcomes [80]. Finally, CFS-RF chooses the dataset's most useful feature subset, which includes 40 features for CIC IDS201, 35 for UNSW NB201, and 30 for NSL KD [81]. The classifier stage then begins. In this scenario, both SVM and RF classifiers operate sequentially as an adaboosting algorithm [82], and they compile information using the typical voting method to function concurrently as a bagging algorithm [83].