esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Mid-Infrared Supercontinuum Generation in Chalcogenide Glass Fibers
## 3 MIR SC Generation in ChG Fibers
### SC Generation in ChG Step-Index Fibers
For the first approach, an output power of 550 mW can be achieved in a 23-cm-long ChG fiber pumped by OPO and OPA systems with a pulse duration of -450 fs and, notably, a high repetition rate of 42 MHz [127]. Similarly, by pumping a 11-cm-long fiber with 330 fs pulses at a repetition rate of 21 MHz from OPA, a high brightness of 17 mW for the generated SC was achieved [117]. For the second approach, in a 3-m-long ChG fiber pumped with a relatively low repetition rate of 100 kHz, a large output power of 143 mW was also obtained due to the long pulse duration of 1 ns [128]. Furthermore, the highest ever reported SC output power of 565 mW from a 2-m-long ChG fiber resulted from pump pulses combined with a long pulse duration and large repetition rate, of 40 ps and 10 MHz, respectively [99]. When the pump conditions are given and unable to be changed, ChG fibers with a large cores, which can relax the focusing of the OPA beam can be adopted to increase the coupling efficiency and improve the output power [117]. The two other ways to achieve a high output power are by reducing the fiber loss resulting from the impurity band and by shortening fiber length used for SC generation [129; 130].
# WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
## 5 Results
### Results from Multi-Technology Fusion  
#### 5.1.1 Case 1: High-Density Deployment with Good UWB Conditions  
In this first experiment, the set-up represents a scenario with a high-density of UWB reference points, such that at any point the visibility of at least four anchors is guaranteed. Moreover, all the anchors are in a location with good propagation conditions, favouring a low ranging error. Figure 3a represents the scenario, with the UWB anchors and the captured location data for one trajectory (yellow and orange dots). Figure 4 shows in 2D the performance of system with the estimated locations (blue dots) against the ground truth points (yellow dots). The UWB (green diamonds) and WiFi (pink triangles) illustrate where the devices are placed. Figure 5 shows the location accuracy achieved with UWB, WiFi FTM and the fusion of both in this case compared with the ground truth. In Figure 5, fusion median (yellow line), standard deviation (rectangle height), and outliers (circles) improves significantly from UWB and WiFi isolated cases. UWB localisation is better than WiFi in this case. Despite having UWB anchors in good conditions, the positioning error has an average near one meter. This meter-level accuracy is due to multipath effects that affect both UWB and WiFi FTM (which has a lower accuracy). When using fusion, the accuracy improves, with a lower average and much lower variance.  
Figure 3: High density (**a**) and low density (**b**) scenario set-up distribution.
# Exploring a New Adaptive Routing Based on the Dijkstra Algorithm in Optical Networks-on-Chip
## 2 Internal Insertion Loss Analysis of ONoCs  
The traditional network uses an electrical signal to transmit information. It has small bandwidth and needs a lot of energy so as to hardly meet the communication demand. Compared with electronic interconnections, optical interconnects can not only meet the future system bandwidth requirements, but also show the advantages of lower latency and smaller power consumption. Electronic interconnections are fit for short distance communication, whereas optical interconnections do well in long distance communication. Therefore, in order to make full use of the advantages of optical interconnections and electronic interconnections, the hybrid optical mesh NoC [21] was proposed. It consists of an electrical connection layer, an optical connection layer, and a connection layer through-silicon-vias (TSVs) between the two layers. TSVs are used to transmit the signals from the electronic layer to the optical layer. In this paper, we use the hybrid optical mesh NoC shown in Figure 1. Besides, the electrical interconnection layer and the optical interconnection layer work together. The former contributes to transmitting control information to links and the later is mainly aimed at transmitting sensor data.
# Measuring Polarization in Online Debates
## 2 User-Based Model for Reddit  
In this section, we present a computational model to represent a Reddit debate that allows us to study the polarization of the debates based on the interactions between the participants. In Reddit, debates are generated from a main (root) comment that contains a link to some news and a set of comments, where each comment, except for the root comment, answers exactly one previous comment, usually by another user or author. These answers between comments lead to positive, negative or neutral interactions, and their analysis will allow us to classify users into three groups: users that are in agreement with the root comment of the debate, users that are in disagreement with it, and undecided users. In order to analyze the agreement and disagreement between user comments in Reddit debates, recently in [13], we defined an analysis system that represents a Reddit debate as a two-sided debate graph. In this graph, comments are divided into two groups, the ones that agree with the root comment of the debate, and the ones that disagree with it. The edges of the graph represent disagreement between the comments of the two groups. So, in this work, we use the two-sided debate model as a starting point to build the user-based model to study the polarization of the debates based on the interactions between the participants. Following [13], we first formalize the notions of a comment and Reddit debate for a root comment. **Definition 1** (Reddit comment and debate). : _A Reddit comment is a pair \(c=(m,u)\), where \(m\) is the text of the comment and \(u\) is the user's identifier of the comment._  
_Let \(c_{1}=(m_{1},u_{1})\) and \(c_{2}=(m_{2},u_{2})\) be two comments. We say that \(c_{1}\) answers \(c_{2}\) if \(c_{1}\) is a reply to the comment \(c_{2}\)._  
_Let \(r=(m_{r},u_{r})\) be a comment such that \(m_{r}\) contains a link to some news. A Reddit debate on \(r\) is a non-empty set \(\Gamma\) of Reddit comments such that \(r\in\Gamma\) and every comment \(c\in\Gamma\), \(c\
eq r,c\) answers a previous comment in \(\Gamma\). We refer to \(r\) as the root comment of \(\Gamma\)._  
Given the structure of a Reddit debate \(\Gamma\) on a root comment \(r\), the next step is to extract the relationships between the comments in \(\Gamma\). We represent \(\Gamma\) as a labeled tree, where each comment gives rise to a node, and edges denote answers between comments and are labeled with a value in the real interval \([-2,2]\). The label for an edge \((c_{1},c_{2})\) denotes the _sentiment_ expressed in the text of the comment \(c_{1}\) in response to the text of the comment \(c_{2}\) so that the value \(-2\) denotes a total disagreement and the value \(2\) a total agreement. We use the sentiment value \(0\) to denote both answers expressing a neutral position with respect to the opinion expressed in \(c_{2}\), and answers expressing, at the same time, agreement with part of the opinion expressed in \(c_{2}\), and disagreement with another part of \(c_{2}\). **Definition 2** (Debate Tree). : _Let \(\Gamma\) be a Reddit debate on a root comment \(r\).
# Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis
## 2 Theoretical Background
### Wasserstein Distance  
Wasserstein distance [36] is a function used to measure the distance between two probability distributions in metric space. Compared with other common measures to measure the difference of probability distribution, such as total variation, KL divergence and JS divergence, Wasserstein distance has obvious advantages. First of all, it is a natural measure of the distance between a discrete distribution and a continuous distribution. Secondly, it gives the concrete implementation scheme of transforming from one distribution to another. Moreover, finally, after the distribution changes, it can maintain the aggregate form of the distribution itself. The general mathematical expression for the Wasserstein distance is as follows:  
\[W(p_{1},p_{2})=\underset{\gamma\in II(p_{1},p_{2})}{\inf}E_{(x,y)\sim\gamma}[ \parallel x-y\parallel] \tag{9}\]  
where \(II(p_{1},p_{2})\) represents the set of joint distributions of \(p_{1}\) and \(p_{2}\). The mathematical meaning of the formula is simply described as follows:  
When \(\gamma\in II(p_{1},p_{2})\), the solution of \((x,y)\) is subject to joint distribution \(\gamma\), with respect to the expectation of \(\parallel x-y\parallel\), the minimum expected value of all solutions is the Wasserstein distance.  
However, it is very difficult to solve the minimum expected value according to formula (9). As the centroid distribution follows an independent multivariate normal distribution, the Wasserstein distance can be calculated by the following formula:  
\[W(p_{1},p_{2})=\sqrt{\parallel\mu_{p_{1}}-\mu_{p_{2}}\parallel_{2}^{2}+ \parallel\sigma_{p_{1}}-\sigma_{p_{2}}\parallel_{2}^{2}} \tag{10}\]  
where \(\mu\) represents the mean value and \(\sigma\) represents the standard deviation.
# Cascade or Direct Speech Translation? A Case Study +
## 5 Comparative Baseline Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**LANG** & **ASR** & **MT** & **WER** & **BLEU** & **CHRF** & \(\Delta\)**PUNC** \\ \hline EU–ES & E2E IND & IND & 14.43 & 28.4 & 54.9 & +1.0 \\ EU–ES & E2E ALL & IND & 14.12 & 28.4 & 54.8 & +0.8 \\ EU–ES & E2E IND & ALL & 14.43 & 33.3 & 60.7 & +2.4 \\ EU–ES & E2E ALL & ALL & 14.12 & 33.4 & 60.8 & +2.3 \\ EU–ES & E2E IND & FT & 14.43 & 32.6 & 60.6 & +2.4 \\ EU–ES & E2E ALL & FT & 14.12 & 33.0 & 60.8 & +2.6 \\ EU–ES & KAL IND & IND & 12.07 & 29.2 & 55.7 & +0.9 \\ EU–ES & KAL ALL & IND & **11.78** & 29.4 & 55.9 & +1.1 \\ EU–ES & KAL IND & ALL & 12.07 & **34.7** & **61.9 \({}^{*}\)** & +2.6 \\ EU–ES & KAL ALL & ALL & **11.78** & **34.7** & **62.0** & +2.7 \\ EU–ES & KAL IND & FT & 12.07 & 33.7 & 61.5 & +2.5 \\ EU–ES & KAL ALL & FT & **11.78** & 33.9 & 61.7 \({}^{*}\) & +2.6 \\ \hline ES–EU & E2E IND & IND & 8.26 & 20.6 & 58.7 & +1.3 \\ ES–EU & E2E ALL & IND & 8.15 & 20.6 & 58.6 & +1.3 \\ ES–EU & E2E IND & ALL & 8.26 & 22.0 & 60.6 & +1.0 \\ ES–EU & E2E ALL & ALL & 8.15 & 22.0 & 60.6 & +1.1 \\ ES–EU & E2E IND & FT & 8.26 & 21.5 & 60.2 & +1.3 \\ ES–EU & E2E ALL & FT & 8.15 & 21.5 & 60.1 & +1.2 \\ ES–EU & KAL IND & IND & 7.23 & 20.9 & 59.0 & +1.4 \\ ES–EU & KAL ALL & IND & **7.21** & 20.9 & 58.9 & +1.3 \\ ES–EU & KAL IND & ALL & 7.23 & 22.5 & **61.0** & +1.2 \\ ES–EU & KAL ALL & ALL & **7.21** & **22.7** & **61.1** & +1.5 \\ ES–EU & KAL IND & FT & 7.23 & 21.9 & 60.6 & +1.2 \\ ES–EU & KAL ALL & FT & **7.21** & 22.0 & 60.7 & +1.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Evaluation results on cascade variants. The best results, indicated in bold, are statistically significant against all other results, for \(p<0.05\), except where indicated as \({}^{*}\); differences between top-performing systems in bold were not statistically significant. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Lang** & **Model** & **ASR** & **MT** & **WER** & **BLEU** & **BP** \\ \hline EU–ES & CAS & IND & IND & 12.07 & 29.2 & 0.913 \\ EU–ES & CAS & ALL & ALL & **11.78** & **34.7** & 0.978 \\ EU–ES & E2E & - & - & - & 17.0 & 1.000 \\ \hline ES–EU & CAS & IND & IND & 7.23 & 20.9 & 0.954 \\ ES–EU & CAS & ALL & ALL & **7.21** & **22.7** & 0.969 \\ ES–EU & E2E & - & - & - & 12.9 & 1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results on cascade and end-to-end baseline models. The best results, indicated in bold, are statistically significant against all other results, for \(p<0.05\). Interestingly, the end-to-end model produced translations that were closer in length to the human references, as shown by the results in terms of the brevity penalty. Although further analyses of these aspects will be warranted, these results indicate that the end-to-end systems built for these experiments may be modelling aspects of the speech translation process that are not fully captured by their cascade counterparts.
## 9 Response of Hosts to Cyanophage Infection  
In the previous part of the review, we focused on how cyanophages affected their hosts using the AMGs followed by a discussion of the response of hosts to the infection of cyanophages. After cyanophage infection, most host gene expression is suppressed. However, some host gene expression is enhanced.  
In a recent study [124], researchers replaced general medium using medium containing 98 atoms% \({}^{15}\)NO\({}^{3-}\) as the sole N source after the S-SM1 phage had infected _Synechococcus_ WH8102. They found 12 host-encoded proteins that continued to be produced despite the general suppression of host gene expression. Of these 12 proteins, 8 of them have homologs (i.e., AMGs) in the genomes of S-SM1 or other phages and/or on putative viral genome fragments from aquatic viral metagenome datasets. These eight proteins included heme oxygenase, methionyl aminopeptidase, ribosomal protein S21, glycogen phosphorylase, phosphogluconate dehydrogenase (Gnd), transaldolase (TaIC), cysteine synthase A, and the CP12 repressor. Methionyl aminopeptidase removes initiator methionine residues, which is the second step in the N-terminal methionine excision (NME) pathway of protein maturation. NME is necessary for the stable assembly of the D1/D2 reaction center of photosystem II [125], so the continued production of methionyl aminopeptidase may be linked to phage PSII protein expression. Ribosomal protein S21 is necessary for mRNAbinding and translation initiation [126], which can enhance viral fitness. We have discussed the other six proteins in the previous review.  
The synthesis of the other 4 of the 12 host proteins may point to cellular defense mechanisms against phage infection. The Rho termination factor is involved in the silencing of foreign DNA elements as a defense against the deleterious effects of their expression [127]. Ribonuclease J may act to degrade phage transcripts and inhibit infection progress [124].  
This study suggests that phages without some AMGs may also maintain the function of some metabolism pathways, which are necessary for the phages using the hosts' genes (such as S-SM1). This study may also explain why there are more AMGs in the myocardium phases than in the other two types of cyanophages. It is likely that the production of some of the host gene's proteins is not enough for myocyanophages growth and may need more energy, thus having more AMGs present than the other two types of cyanophages.
## 4 Proposed Energy-Efficient Relay Mechanism
### SR and DWT-Based Packet Relaying
Preserving the most relevant data for the reconstruction of the original images may enhance overall monitoring quality, still preserving energy over the network. When applying a 1-level 2D DWT, only 25% of the original data in average will reach the sink, depending on the current energy level of intermediate nodes and the pre-configured energy thresholds. Figure 5 presents the reconstruction of the same image when using different subbands generated by a 1-level 2D DWT. In fact, applications may tolerate images with poorer quality, allowing us to enhance the relaying optimization with more \begin{table}
\begin{tabular}{c c} \hline
**DWT Subband** & **DR** \\ \hline HH & 0 \\ HL & 1 \\ LH & 2 \\ LL & 3 \\ \hline \end{tabular}
\end{table} 
Table 4: Data Relevance for a 1-level 2D DWT. Figure 4: Threshold-based packet relaying, regarding the sensing relevancies of source nodes and DWT subbands. levels of DWT decompositions. If a 2D DWT is applied twice over a single image, 7 subbands are produced and intermediate nodes could consider the relaying of only the LL\({}_{(2)}\) subband (6.25% of the original data).
## 2 Theoretical Considerations  
### ASP Control Algorithm  
As illustrated in Figure 1, the block diagram of a 'pure' ASP algorithm using the command-FxLMS [1] can be applied to control the engine sound in a car cabin. The algorithm can offer both canceling engine order noise and enhancing engine order sound by minimizing the pseudo error signal \(e^{\prime}(n)\) as shown in Figure 1, which can be defined by  
\[e^{\prime}(n)=e(n)-c(n)=d(n)+y(n)-c(n), \tag{1}\]  
where \(e(n)\), \(c(n)\), \(d(n)\), and \(y(n)\) are the error signal measured by a microphone located at the driver's ear, the pre-defined target signal, the disturbance signal, and the output signal of the actual secondary actuator at the error microphone, respectively. The target signal \(c(n)\) is a sinusoid whose frequency is equal to that of the reference signal \(x(n)\) and its amplitude is the sound pressure level (SPL), which is stored in a pre-defined target profile based on the engine speed. Hence, when \(e^{\prime}(n)\) in Equation (1) is converged to zero, the error signal \(e(n)\) will track to the target signal \(c(n)\). On the other hand, if \(c(n)\) is set to a tiny value, \(e^{\prime}(n)\) is approximately equal to \(e(n)\), and then the ASP algorithm works to cancel engine order noise.  
Thus, the instantaneous version of the cost function to be minimized in this ASP approach can be defined as [1  
where, \(\alpha\) is a convergence coefficient for adaptation and the derivative of the instantaneous cost function with respect to \(\mathbf{w}(n)\) can be defined with \(\frac{\partial I(n)}{\partial\mathbf{w}(n)}=2\bigg{[}\overset{\star}{\mathbf{r} }(n)e^{\prime}(n)+\beta\mathbf{w}(n)\bigg{]}\), in which the filtered reference signal \(\overset{\star}{\mathbf{r}}(n)\) is the output of the secondary path model \(\hat{S}(z)\) in the block diagram of Figure 1. After some manipulation, the adaptive update equation in Equation (3) for the pure narrowband ASP algorithm, which is based on the command FxLMS, can be rewritten by [1]  
\[\mathbf{w}(n+1)=(1-\alpha\beta)\mathbf{w}(n)-\alpha e^{\prime}(n)\overset{ \star}{\mathbf{r}}(n), \tag{4}\]  
The leakage term \((1-\alpha\beta)\), which is a positive number but less than 1, can suppress the divergence of the control filter output \(u(n)\).
###### Abstract
Based on gray correlation and entropy weighting, this paper proposes an improved GRE-TOPSIS. It calculates the correlation between each pair of nodes through grey correlation and entropy weighting and constructs a weighted adjacency matrix of the network. Finally, we use the weighted TOPSIS method to comprehensively assess the importance of nodes and identify key nodes in complex networks in terms of local and location information dimensions. Compared to previous research, this study breaks through the limitations of single-medium network research and combines gray correlation with entropy weighting, enhancing the objectivity of the TOPSIS method's calculations. In the simulation part of this paper, we establish the model of China's coal-iron-water combined transportation network and apply this algorithm to comprehensively rank 84 nodes, verifying the algorithm's performance. Using network efficiency and three other parameters as measures of network performance, we analyze the changing trends in network performance through simulated deliberate and random attacks. The remainder of this paper is organized as follows: Section 2 completes the modeling of the coal-iron-water combined transportation network. Section 3 introduces the GRE-TOPSIS algorithm, providing a comprehensive evaluation method for node importance. Section 4 presents the experimental setup, including network model construction, node importance evaluation index system construction, comprehensive importance evaluation value calculation, and node importance analysis. Moreover, we further complete the simulated attacks and performance analysis in this section. Finally, conclusions and insights are drawn in Section 5.
# Passive Control of Base Pressure: A Review
## 2 Various Types of Passive Controls
### Passive Control with Static Cylinder  
It is known from the experiments of passive control in the form of a static cylinder that when the flow is correctly-expanded and under-expanded, the passive control in the form of a stationary cylinder becomes useful. [PERSON] et al. [20] did a wind tunnel experiment (Figure 11) using the static cylinder at supersonic Mach number 2 by varying the NPR from 2 to 9. It is observed that when the NPR is low (i.e., up to NPR 5), there is no effect of control on the base pressure values, whereas, for the higher NPR (i.e., NPR = 6 and above), the control begins to show its effectiveness and the change in the base pressure increases until NPR 9.  
Figure 10: Dimensions of nozzle and duct [19].  
The percentage change in base pressure is 14% at NPR 6, and this trend increases and reaches up to 59% at NPR 9. The jet is over-expanded till NPR 6, and after NPR 6, and at higher NPRs, the flow is correctly-expanded or under-expanded. This is the leading cause of the significant increase in base pressure due to the flow interactions with the shock at the square outlet region, and it makes the vortex weak in the base region. Thus, the NPR increases and over-expansion reduces, and a weak oblique shock is present compared to lower NPR.
## Abbreviations
For a harmonic strip wave excitation along the railway track (y-direction)  
\[\textbf{t}(\textbf{x},\textbf{y})=\textbf{a}\exp\big{(}i\xi_{y}y+i\omega t\big{)} \quad\text{for }|x|<b/2 \tag{10}\]  
the displacements across the track-soil interface (x-direction)  
\[u_{S}\big{(}x,\xi_{y},\omega\big{)}=\frac{F^{\prime}\big{(}\xi_{y},\omega\big{)} }{2\pi}\int_{-\infty}^{+\infty}H_{zz}(\sqrt{\xi_{x}{}^{2}+\xi_{y}{}^{2}}, \omega)p(\xi_{x})\text{exp}(i\xi_{x}x)\text{d}\xi_{x} \tag{11}\]  
are calculated, where the wavenumber transform of the uniform load distribution across the track width \(b\)  
\[p(\xi_{x})=\frac{\text{sin}\xi_{x}b/2}{\xi_{x}b/2} \tag{12}\]  
is used. Moreover, the average displacement  
\[\overline{u_{S}}\big{(}\xi_{y},\omega\big{)}=\frac{1}{b}\int_{-b/2}^{+b/2}u \ \ S\ \ \big{(}x,\xi_{y},\omega\big{)}\text{exp}(i\xi_{x}x)\text{d}x \tag{13}\]  
across the track is calculated. Finally, the soil compliance for a strip load  
\[H_{SS}\big{(}\xi_{y},\omega\big{)}=\frac{\overline{u_{S}}\big{(}\xi_{y},\omega \big{)}}{F^{\prime}\big{(}\xi_{y},\omega\big{)}}=\frac{1}{2\pi}\int_{-\infty} ^{+\infty}H_{zz}\bigg{(}\sqrt{\xi_{x}{}^{2}+\xi_{y}{}^{2}},\omega\bigg{)}p^{2 }(\xi_{x})\text{d}\xi_{x} \tag{14}\]  
is established. The inverse  
\[k_{S}\big{(}\xi_{y},\omega\big{)}=\frac{1}{H_{SS}\big{(}\xi_{y},\omega\big{)}} \tag{15}\]  
is the soil stiffness, which is used for coupling the soil with the track. TThe stiffness of the soil can be approximated by a Winkler soil as  
\[k_{S}\big{(}\xi_{y},\omega\big{)}\ \approx\ k_{S}{}^{*}(\omega)=k_{W}+i\omega c_{W} \tag{16}\]  
Figure 11 shows the comparison of the track stiffness calculated by the wavenumber method and by the Winkler approximation. The agreement between these two results and the results of the combined boundary-element/finite-element method [32] is good.