esa-sceva/satcom-chunk-collection huggingface dataset:
# Approximation Algorithm for Shortest Path in Large Social Networks
## 7 Conclusions  
Based on hierarchical networks, we propose a parallel approximate shortest path algorithm which is efficient and maintains high approximation accuracy on large scale networks. The algorithm condenses central nodes and their neighbors into super nodes to iteratively construct higher level networks until the scale of the top level meets a set threshold value. The algorithm approximates the distances of the shortest paths in the original network by means of super nodes in the higher-level network. The performance of our algorithm was tested on four real networks. Results from our tests show that our algorithm has a runtime per query within a few milliseconds and at the same time delivers high accuracy on large scale networks. Compared with other algorithms, our algorithm runs twice as fast as LBFS and over 10 times faster than CDZ.  
The proposed algorithm mainly focuses on undirected and unweighted networks. In the future, we seek to focus on directed and weighted networks by exploring the approximate distance between a node and its central node based on hierarchical networks. We will also consider an adaptive algorithm for dynamic networks.  
**Author Contributions:** Conceptualization, D.N.A.M.; Data curation, L.W.Y.; Investigation, D.N.A.M.; Software, D.N.A.M. and L.W.Y.; Supervision, H.G.; Writing--original draft, D.N.A.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work was partially supported by the National Natural Science Foundation of China under Grant No. 61433014, by the National High Technology Research and Development Program under Grant No. 2015 AA7115089.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multicolumn{6}{c}{**Threshold**} \\ \cline{2-7}  & **Parameter** & **40** & **60** & **100** & **140** & **180** \\ \hline \multirow{2}{*}{Email-Enron} & \(T_{q}\) (ms) & 0.46 & 0.661 & 1.170 & 2.021 & 3.875 \\ \cline{2-7}  & \(p\) & 1.03 & 1.024 & 1.02 & 1.02 & 1.02 \\ \hline \multirow{2}{*}{itdk0304\_rlinks} & \(T_{q}\) (ms) & 2.31 & 3.65 & 4.72 & 8.04 & 15.56 \\ \cline{2-7}  & \(p\) & 1.03 & 1.026 & 1.02 & 1.019 & 1.018 \\ \hline \multirow{2}{*}{DBLP} & \(T_{q}\) (ms) & 5.49 & 7.521 & 8.78 & 17.214 & 36.820 \\ \cline{2-7}  & \(p\) & 1.03 & 1.022 & 1.02 & 1.018 & 1.018 \\ \hline \multirow{2}{*}{roadNet} & \(T_{q}\) (ms) & 10.89 & 13.10 & 16.79 & 33.41 & 61.58 \\ \cline{2-7}  & \(p\) & 1.026 & 1.02 & 1.02 & 1.018 & 1.018 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different thresholds.
## 3 Federated Kalman Filter Design
### Kalman Filter Algorithm for Inertial/Odometer Integrated Navigation
#### 3.6.1. Structure of the Federated Kalman Filter  
In this study, the federated Kalman filter model consisted of two sub-Kalman filters and one main global filter. The GPS and odometer subfilters performed the local Kalman optimal estimation on the positioning information measured by the GPS and the odometer to obtain \(\hat{\mathrm{X}}_{1}\) and \(\hat{\mathrm{X}}_{2}\), respectively, and simultaneously obtain the estimation error covariance moment array \(\mathrm{P}_{1}\) and \(\mathrm{P}_{2}\). The main filter fused the local optimal estimation with the state 15-D information, \(\hat{\mathrm{X}}_{1}\) and \(\hat{\mathrm{X}}_{2}\), of the main filter to obtain the global optimal estimation, \(\hat{\mathrm{X}}\), and the global estimation error, \(\mathrm{P}\). The main filter feeds back \(\mathrm{X}\) and \(\mathrm{P}\) to the subfilters, and at the same time uses the information distribution factors \(\beta_{1}\) and \(\beta_{2}\) to weight the GPS and odometer subfilters, respectively. The Kalman federated filter architecture is shown in Figure 4.
###### Abstract
Unlike traditional pilot-aided schemes utilizing the least square (LS) method to fit the SFO slope curve, the proposed scheme utilizes two phase-conjugated pilots for SFO estimation in each OFDM frame and an averaging processor is designed to improve estimation accuracy. These cause the decrease in operational complexity.
## 3 Results and Discussion
### Calculation of the Growth Rate Variation  
It is not correct to consider the growth rate of the freezing interface equal to the movement velocity of the coil. Parallel to the changes in the size of the molten zone, the movement velocity of the freezing interface (known as the solid growth rate) changes as well. The changes in the freezing interface position during time were obtained via IR thermography analysis, and its variation was added to the movement velocity, making it possible to obtain a more realistic value of the growth rate during the whole refining process. Figure 14 illustrates the growth rate in relation to the position of the freezing interface along the bar. For certain periods along the bar, the growth rate seems to become negative, pointing out that a back-melting effect occurs due to excess heat input or insufficient heat losses. This phenomenon is more obvious at both edges of the crucible where the heat transfer is more influenced by the heated graphite edges.
# A Thermal Transport Study of Branched Carbon Nanotubes with Cross and T-Junctions
## 1 Introduction
All of the CNT branches had the same chirality and length and were attached to the cross and T-junctions to form branched CNTs. The effects on thermal transfer due to branch length, topological defects at the junction, and temperature were studied. Moreover, the collective atomic velocity cross-correlation spectra between branches were used to analyze the phonon transport mechanisms in the branched CNTs.
# Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System
## 5 Simulation Results and Discussion
### Performance Evaluation  
In this section, the performance of the proposed model in the NOMA-OFDM is presented. The simulation work of the proposed Bi-LSTM model based multiuser CE and signal detection is performed using the simulation parameters, as shown in Table 1. The data generation for training the model is discussed in the previous Section 4.1. The SNR value of 30 dB is set up during the generation of training datasets. To generate the datasets and test, the learned model, the CP size 20, and the pilot symbols 64 in each transmitted package are considered. The comparison of CNN, ML, traditional MMSE, and LS methods with the proposed model are performed according to SER versus SNR. In the online testing stage, the [0: 2: 30] dB SNR range is considered for the simulation performance evaluation. Monte Carlo simulation is performed for the evaluation of the SER performance of the proposed Bi-LSTM model.
## 3 Results and Discussion  
### Modal Analysis and Angular Calculation  
The laws of vibration of an object are closely related to its intrinsic frequency. The vibration in the pitch direction of the DCM will significantly affect the quality of the beam [19]. In the DCM of SSRF, the first crystal has no freedom of motion in the pitch direction, so only the vibration in the pitch direction of the second crystal is considered. Therefore, this paper carried out a modal analysis on the overall part of the second crystal of the DCM of SSRF (including the second crystal, an angle adjustment mechanism for the second crystal, and the connected cooling piping). Figure 6 shows the results of the wet modal [20] analysis.  
The angular displacement of the DCM is calculated based on the difference in displacement along the z direction between the two endpoints of the crystal's meridian line, as illustrated in Figure 7. Given the small amplitude of the DCM's vibration, the length of \(l\) (in Figure 7) can be approximated as equal to the crystal's meridian line (150 mm).  
Figure 5: Physical properties of liquid nitrogen and nitrogen.  
Figure 6: Wet modal analysis of the DCM (the pressure in the pipe is generated at a liquid nitrogen flow velocity of 0.995 m/s). The color contours in the figure represent the displacement of the structure relative to the initial position, with red \(>\) yellow \(>\) green \(>\) blue. The red annotations represent the mode order and the corresponding natural frequency.  
Therefore, the angular displacement of the monochromator can be calculated using the following equation:  
\[\alpha=\arctan\left(\frac{\Delta z}{l}\right) \tag{7}\]  
In the equation, \(\alpha\) represents the angular displacement of the DCM, rad; \(\Delta z\) is the displacement difference between the two endpoints of the meridian line, mm; and \(l\) is the length of the meridian line, with \(l=150\) mm.
# Comparison of Multivariate Analysis Methods as Applied to English Speech
###### Abstract
(2016) developed a new factor analysis method, based on what they called the \"origin-shifted principal component analysis\" [12]. In this method, the starting point for calculating eigenvectors was moved to the acoustically silent point, instead of the gravity center of the data points. This reduced the generation of the noise and improved resynthesized speech. Furthermore, they confirmed that there are indeed three or four common factors in spoken British English, Japanese, and Mandarin Chinese. If the number of spectral factors used for speech resynthesis is three or more, the basic language information will be retained in the noise-vocoded speech. However, it is still necessary to determine in detail how different the results of the normal factor analysis [8] and the modified factor analysis, i.e., origin-shifted factor analysis [12], can be when applied to English speech. In the present study, we used four different factor analyses for this purpose. Another purpose of this study was to determine the acoustic correlates of English phonemes by using the aforementioned factor analyses. We specifically focus on how English obstruents appear in the obtained factor spaces. Using normal factor analysis, [PERSON] et al. (2017) [8] suggested that obstruents are almost exclusively represented on a factor related to a frequency range above \(\sim\)3300 Hz. However, it needs to be confirmed whether the same will be indicated using the origin-shifted factor analysis, following [PERSON] et al. (2016) [12]. The origin-shifted factor analysis, which always selects a subspace including the silent point, should be more sensitive to spectral changes near the silent point, and thus may give new insight into the acoustic natures of relatively weak sounds such as obstruents. Since an obstruent always forms the beginning or end of a syllable in English, sometimes with a consonant cluster, to determine acoustic correlates of obstruents is highly important. Already much is known about sonorous phonemes, like vowels, but less is known about the acoustics related to phonemes lower in the sonority hierarchy (cf. [10]).
# Preliminary Results of Ship Detection Technique by Wake Pattern Recognition in SAR Images
## 2 Method Description  
### Rationale and Originalities  
The wake appearance depends on length, beam and velocity of the ship [16]. When the velocity is very large, as in the case of the vessels object of this study, the ship wake can be significantly narrow. The angle of the wedge generated can be significantly smaller than the Kelvin angle of 38\({}^{\circ}\)56\({}^{\prime}\) due to the interference between the wave systems created by the bow and stern of the ship. Increasing the speed, the transverse waves tend to vanish while the divergent waves tend to increase their amplitude.  
Very few results [11] are available on the wake characterization of go-fast boats. Recent investigations [11; 35] confirm that in both optical and radar images (a) the angular aperture between the wake components is significantly narrower and (b) the classical Kelvin wake explanation of the pattern is insufficient. The authors agree that the go-fast boats' wake shows the turbulent component and the narrow-V wakes, which are produced even when the source at the bow are not cancelled by sinks at the stern.  
The proposed approach takes full advantages from the technique shown in [24], but a different solution is proposed and tested in order to detect the wake produced by go-fast boats. The main innovation is that it separates the wake detection process from the ship trace in the radar image. In fact, each pixel of the image is analyzed to identify if there are wakes crossing the pixel. The detection of the wake is carried out on the basis of merit indexes, which are an additional original aspect of the proposed technique. In fact, two different levels of wake validation are proposed (as detailed in Section 2.2), the first one is relative to the wake presence in a single pixel and the second one is relative to the wake presence in neighbor pixels. This strongly increases the level of confidence that the wake is really imaged.  
For sake of clarity, the main originalities can be summarized as follows:  
* The proposed approach separates the wake detection process from the ship trace in the radar image. In fact, each pixel of the image is analyzed to identify if there are wakes crossing the pixel.
* Only the turbulent and narrow-V components of the wake is searched as a marker of the ship presence
* After the wake candidate reconstruction, two levels of wake presence validation are proposed (as detailed in Section 2.2), the first one is relative to the wake presence in a single pixel and the second one is relative to the wake presence in neighbor pixels. This strongly increases the level of confidence that the wake is really imaged.
* A new index of merit of the wake presence is introduced and refereed to the whole wake and not to each wake component
# A Multi-Scale Video Longformer Network for Action Recognition
## 4 Experiments
### Comparative Experiments  
We conducted comparative experiments to assess the performance of the proposed MSVL in comparison with state-of-the-art methods on the HMDB51 and UCF101 datasets. The results for top-1 and top-5 accuracies, FLOPs, and parameters are presented in Table 6. The top-1 and  
Figure 12: We sampled 8 frames from the “hammering” action, (**a**) representing RGB frames and (**b**) displaying Grad-CAM results for stage 4.  
Figure 13: Feature maps for the hammering action generated at each stage.  
top-5 accuracy results, with the best, second best, and third best performances, are highlighted in red, blue, and green, respectively. All networks used only RGB frames as input with a video frame resolution of 224 \(\times\) 224 pixels. For feature extraction backbones, R(2 + 1)D, TSN, I3D, and TSM employed ResNet50. divST represents the version of TimeSformer that incorporates spatiotemporal separated attention, while VideoSwin utilized the Swin-S version. We initially attempted to train TimeSformer and VideoSwin directly on the HMDB51 and UCF101 datasets but achieved suboptimal accuracy. As a result, we decided to finetune them using pretrained weights provided by mmaction2 [43]. The sampling parameters for TimeSformer are as follows: a clip length of 16, a frame interval of 2, and one clip. For VideoSwin, the parameters are 8, 32, and 1, respectively.  
In most cases, it can be observed that our MSVL achieves the best performance compared to other methods. In contrast to methods employing convolutional approaches [11; 12; 19; 20; 44], MSVL excels in extracting overall video features, demonstrating exceptional performance. In comparison to divST, MSVL harnesses 3D Longformer Attention in a joint spatiotemporal manner to extract spatiotemporal features. It also lowers computational complexity and reduces the network's parameter count through a local attention mechanism. On the UCF-101 dataset, MSVL outperforms TimeSformer by a margin of 2.9% while simultaneously reducing the parameter count by 77.6%. VideoSwin also employs a convolution-like mechanism that recombines patches from different positions in the input video features. In comparison to VideoSwin, MSVL achieves a notable reduction in the parameter count and FLOPs by 45.1% and 4.8%, respectively. Despite not undergoing pretraining on large-scale datasets, MSVL achieves highly competitive accuracy compared to VideoSwin. We believe that, if MSVL were pretrained on similarly large-scale datasets (such as K400), its performance would further improve.
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 2 Materials and Methods
### Performance Metrics
#### 2.4.2 Throughput  
Throughput is the data's effective transfer rate, whereas, in this paper, it is measured in bytes per second (Bps). Throughput is the total number of successful packet arrivals observed on the destination device over a certain time interval divided by the duration of the time interval [28; 29]. The main aspect of throughput is knowing the availability of sufficient bandwidth for the application. This determines the amount of traffic an application can receive when it passes through the network. This throughput can be measured after the data transmission occurs and can be calculated using Equation (2):  
\[\text{Throughput}=\frac{\text{total of the packet sent}}{\text{total data sending time}}. \tag{2}\]
## 4 Angiogenic Factors
### Vascular Endothelial Growth Factors (VEGFs) and Placental Growth Factor (PGF)
Molecules such as Bevacizumab show promising results because of their ability to neutralize VEGF-induced angiogenic effect which leads to a decrease of tumor progression [93]. Vandetanib, an inhibitor of both VEGFR and EGFR factors, reveals anti-tumor and anti-metastatic properties [87], while Apatinib, which selectively suppresses VEGFR signaling and results in anti-apoptosis action, is proven as one of the candidates in the treatment of CCA [94]. The efficacy of Apatinib has been recently validated demonstratinganother mechanism through which this molecule influences apoptosis in CCA cells: the authors highlighted that Apatinib is able to reverse the VEGF signal by the inhibition of PI3K/Akt and VEGFR/RAF/MEK/ERK pathways, which are usually upregulated during cell proliferation and differentiation in many organs [46]. A co-administration of pazopanib and tramteninib appears to be an efficient treatment in refractory CCA, preventing the angiogenesis through parallel inhibition of RAF/MEK/ERK cascade VEGFR and PDGFR [95]. In addition, several authors highlighted that treatment with PIGF antibodies has shown beneficial effects in several experimental models for chronic liver disease, including cirrhosis, HCC, and CCA, by improving tumor burden [77]. This is probably due to inhibition of the proliferative effect of PIGF on macrophages and endothelial cells [96]. The up-regulation of PIGF is also correlated to liver-intestine cadherin decrease in ICC leading to angiogenesis, an increase of tumor aggression and consequently to poor outcome, while an inhibition of PIGF in experimental mouse model seems able to reduce the pathological angiogenesis and the vascular supply [76]. Since clinical studies using sorafenibb (Nexavar) for treatment of CCA have shown minimal therapeutic benefit, the use of monoclonal antibodies targeting PIGF may serve as a potential treatment in patients with aggressive liver tumor. Taken together, this evidence supports the fact that a large number of actual preclinical studies have the VEGF pathway as a target due its ability to orchestrate tumor angiogenesis acting not only alone, but also in cooperation with other molecules and mechanisms (Figure 3).
# Experimental Study of Scour Hole Depth around Bridge Pile Using Efficient Cross-Section
## Appendix C Discussion: Scour around Bridge Piles  
The threat of local scour around bridge piles has been known for many years. Local scouring is a complex phenomenon resulting from a complicated flow pattern as shown in Figure A1[42]. The primary cause of scouring is the downflow emanating from the presence of the pier's upstream face, which also results in pressure differential. Consequently, the layer of fluid near the pier splits forming a helical flow at the pier base and a horseshoe vortex system ensues. There can also be a splitting of flow at the sides of a pier in the form of counter-rotating vortices called wake vortices [9; 42; 43; 44]. Thus, a pier scour is also affected by wake vortices where a rotating column of fluid can lift the sediment from the bed and create a scour hole downstream of the pier.  
In practice, bridge foundations are designed to withstand the effects of scouring without failing for the worst conditions resulting from floods equal to the 100-year flood or a smaller flood if it causes scour depths deeper than the 100-year flood. These foundations are also checked for the accidental situation of a 500-year super flood [45].  
Because of its prevalence as a cause of bridge failure, scour is highly prioritized by the modern bridge code of practices. Several national and provincial bridge design codes (including the American Association of State Highway and Transportation Officials (AASHTO) [46], Load and Resistance Factor Design (LRFD) [47], Load and Resistance Factor Design (LRFD), Ontario Highway Bridge Design Code (OHBDC) [48] and Canadian Highway Bridge Design Code (CHBDC) [49], include provisions for the hydraulic design. Specific provisions for the design of bridge piles to scouring invite the designers to use code-specified \"approved methods.\" These methods refer to the empirical equations, which have been derived using experimental and field data over the past half-century. These equations are used to calculate the depth under which foundations are placed to avoid failure due to scour. However, these widely used equations tend to over-predict this depth (referred to as equilibrium scour depth).
###### Abstract  
Mechanical and electrical equipment is an important component of urban rail transit stations, and the service capacity of stations is affected by its reliability. To solve the problem of predicting faults in station mechanical and electrical equipment with sparse data, this study proposes a fault prediction framework based on SSA-CNN-LSTM. Firstly, this article proposes a fault enhancement method for station electromechanical equipment based on TimeGAN, which expands and generates data that conform to the temporal characteristics of the original dataset, to solve the problem of sparse data in the original fault dataset. An SSA-CNN-LSTM model is then established to extract effective data features from low-dimensional data with insufficient feature depth through structures such as convolutional layers and pooling layers in a CNN, determine the optimal hyperparameters, automatically optimize the model network size, solve the problem of the difficult determination of the neural network model size, and achieve accurate prediction of the fault rate of station electromechanical equipment. Finally, an engineering verification was conducted on the platform screen door (PSD) systems in stations on Shanghai Metro Lines 1, 5, 9, and 10. The experiments showed that the proposed prediction method improved the RMSE by 0.000699, the MAE by 0.00042, and the R2 index by 0.109779 when predicting the fault rate data of platform screen doors on all of the lines. When predicting the fault rate data of the screen doors on a single line, the performance of the model was better than that of the CNN-LSTM model optimized with the PSO algorithm.  
platform screen door system; fault prediction; sparse and weak feature data; data augmentation; CNN-LSTM +
Footnote †: journal: Applied Sciences