esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology and Network Design
### Signalling System Requirements  
Conceptually, the wayside signalling systems are located at a fixed location, usually deployed in technical rooms at the main stations, which are generally close to urban areas. These urban areas are where MNOs focus on mobile network deployments.  
They transmit messages to each other encapsulated in UDP/IP segments to be transmitted to the physical layer. Nowadays, the most common physical technology used is fibre optical cables. This study presents the results of exchanging this commonly used technology for mobile communication networks based on MNOs.  
Table 1 shows the communication requirements for this signalling system. Therefore, to analyse the feasibility of this paper's proposal, it is necessary to measure a set of KPIs that verifies how MNO networks can affect bit rate, latency and jitter, considering the physical layer and application layer response times and timeouts required by the signalling equipment.  
Looking at the parameters listed in Table 1, there is very little information on the requirements to fulfil an adequate performance communication for signalling systems. For that reason, a complete characterisation of the communication is included as part of this study. Nevertheless, it is clear that signalling systems are limited by latency and period, with the required data rate not being the bottleneck since 4G and 5G can provide a much higher data rate.  
Specifically, the message reception timeout parameter describes the maximum time allowed to send an order, execute it and notify back to the IXL can be 1.8 s, which can be  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**Communication Requirements for Signalling Equipment**} \\ \hline Communication protocol & UDP/IP \\ Message reception timeout & 1.8 s \\ Message sending period & 300 ms \\ Signalling bit rate & 4 kpbs \\ Availability & \textgreater{}99\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Communication requirements for railway signalling equipment.  
conditioned by the network round-trip time. It is important to note that this parameter involves mechanical elements on the tracks as part of the task, such as moving a railway point on the track.  
Regarding the jitter, this is conditioned by the time of the message sending period. As it is a UDP communication protocol, a jitter greater than 300 ms would cause a notable disorder in the message, and it would be considered a lost message. Moreover, the theoretical bit rate generated by communication between signalling devices is 4 kbps.
# A Hierarchical Decision Fusion Diagnosis Method for Rolling Bearings
## 3 Case Study
### Diagnostic Results of Rolling Bearings  
#### 3.3.1 Fault Detection Results of Rolling Bearings  
Each time-domain feature of each sample of wavelet packet reconstruction signals of rolling bearings is input into the input layer of BPNN, and the fault diagnosis of rolling bearing is carried out by the stratified diagnosis method.  
In the rolling bearing fault detection layer, it is determined whether there is a fault in the rolling bearing. According to the sample set of Table 2, the diagnostic accuracy of the fault detection layer based on a single time-domain feature (serial number 1-11) and decision fusion based on a time-domain feature (serial number 12) is tested and compared, as shown in Figure 8.  
It can be seen from the diagram that feature 4, feature 5 and feature 7 have a good effect on the diagnosis of wavelet packet reconstruction signal when fault detection is based on a single feature, the diagnosis rate is more than 98% and the diagnosis effect based on other features is relatively poor. When the decision fusion method (number 12) is used to diagnose the fault detection layer, the diagnosis rate increases to 100%, which is a higher diagnostic accuracy than the single-feature-based diagnosis results.
## 3 Study Area and Data  
### Study Area  
Henan Province, situated in the central-eastern region of China, spans the middle and lower sections of the Yellow River. Its geographical coordinates range from 31\({}^{\circ}\)23\({}^{\prime}\) to 36\({}^{\circ}\)22\({}^{\prime}\) north latitude and 110\({}^{\circ}\)21\({}^{\prime}\) to 116\({}^{\circ}\)39\({}^{\prime}\) east longitude, covering an area of approximately 166,000 square kilometers. The province comprises 55.7% plains and 44.3% mountainous and hilly terrains. It experiences an average annual temperature between 10.5 \({}^{\circ}\)C and 16.7 \({}^{\circ}\)C, with annual rainfall varying from 407.7 to 1295.8 mm. Primarily within the warm temperate zone, the climate transitions to subtropical in the southern areas. Characterized by a continental monsoon climate, the region shifts from the northern subtropical zone to the warm temperate zone. Additionally, from east to west, there is a transition from plain to hilly and mountainous climates. This study focuses on the extensive mountainous and hilly regions in western Henan Province, including the Luoyang Basin, stretching from the province's western border to the eastern border of Zhengzhou City. The terrain of the study area is diverse, primarily consisting of mountains and hills, as well as plains and basins. The elevations of the mountains range mostly between 1000 and 2000 m. The northern side is characterized by loess hills, with elevations typically ranging from 500 to 700 m. The constant erosion of rainwater has formed various landforms such as valleys, ravines, loess hills, terraces, and loess plateaus. The western and southern sides are mostly mountains and hills, with numerous high mountains forming dense water networks. The plains and basins are formed at the junctions of these high mountains and water networks,  
Figure 1: Artificial neural network model structure.  
concentrated along the floodplains of the Yi, Luo, Ru, and Jian rivers, as well as in alluvial plains such as Lushi and Zhangtou.  
Figure 2 below presents detailed views of two specific landslide sites within the study area.
# Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction
## 2 Methodology
### AttBiGRU  
In view of the various influences that may lead to changes in spatiotemporal correlations, we have embraced a weighted fusion methodology in our framework to amplify the important information from the input data and avoid limitations arising from over-reliance on a single feature, as shown in Equation (7).  
\[Out=W_{1}*Out_{1}+W_{2}*Out_{2}+W_{3}*Out_{3} \tag{7}\]  
where \(Out\) represents the output of multiple branches. \(Out_{1}\), \(Out_{2}\), and \(Out_{3}\) represent the outputs of RDSC modules, MDSC module based on \(G_{c}\) and MDSC module based on \(G_{s}\), respectively. \(W_{1}\), \(W_{2}\), and \(W_{3}\) are the weight parameters of each branch, which are automatically updated through backpropagation during the training process. \(*\) represents the product of Hadamard.  
The AttBiGRU takes \(Out\) as input and provides powerful bidirectional modeling and prediction performance for the proposed model through the combination of attention mechanism and BiGRU. As the latest advancement in the field of sequence modeling following RNN, gated recurrent unit (GRU) inherits its advantages and has the potential to surpass it in various processing applications. Nevertheless, the unidirectional nature of GRU imposes constraints on its capacity to access global information, thereby engendering potential losses or errors in information accumulation. Bidirectional gated recurrent unit (BiGRU) emerges as a solution by processing input sequences in both the forward and reverse directions, enabling it to identify critical factors or patterns that might be missed when only processing the data in a single direction. The attention mechanism of AttBiGRU can capture key bidirectional dependencies and context information, thus allowing the model to maintain a long-term memory for useful information in prediction tasks, as depicted in Figure 8.  
The fused features are passed through AttBiGRU to obtain \(Out_{AttBiGRU}\). Then, via flattenning and fully connected layer, we obtain the final predicted passenger flow results, as shown in Equation (8).  
\[Prediction=f\Big{(}W_{fc}*Out_{AttBiGRU}+b_{fc}\Big{)} \tag{8}\]  
where \(Out_{AttBiGRU}\) is the output of AttBiGRU, and \(f\) denotes the fully connected network. \(W_{fc}\) and \(b_{fc}\) are the weights and biases of fully connected layer, respectively.  
Figure 8: The structure of AttBiGRU.
## 3 Results and Discussion
### Structural Division of Shock Wave Structure in the Ejector
The oblique shock waves are generated after the 0.5 m, which corresponds to the Mach number contour in Figure 4a. The intensity of oblique shock wave reaches its maximum at 0.51 m and keeps constant between 0.52 m and 0.54 m. The results show that the bifurcation structure is generated at the root of the positive shock with the decrease of the Mach number values. Then, the oblique shock is formed. The energy of the primary jet core decreases which resulting in the separation degree of the boundary layer becomes stronger, and the mixing degree of the two streams is sufficiently affected. The efficiency of the steam ejector will decline with the non-fully mixing process between the two streams. Figure 5 shows the Mach number distribution curve along the ejector axis. The Mach number values fluctuate continuously. There are six peak values and six trough values in the shock train region. However, there is only one inviscid normal shock wave in the subsonic diffuser section with only one peak value. It shows that the shock train is composed of the bifurcation shock waves causing the fluid oscillations. The concrete manifestation is that the static pressure along the axis drops first and then remains constant while the velocity increases first and then decreases, as shown in Figures 3 and 5. That indicates the shock train plays an important role in restoring pressure in the case of some energy loss. Therefore, the shock train area can be judged and defined according to the fluctuation range and oscillation amplitude of the pressure or the velocity. In the subsonic diffuser section, a single normal shock wave appears near the outlet of the throat section, which is generally accompanied by the transonic behaviour and followed by a subsonic region. The normal shock wave can prevent the disturbance caused by the change of the back pressure propagating upstream. When the position of the normal shock wave is closer to the outlet of the subsonic diffuser, the critical back pressure is higher, the influence of the back pressure is less and the system operation will be more stable. However, a few oblique shock waves with small amplitude following by the normal shock wave are generated, which would reduce the energy exchange of the mixing process between the two streams [54]. The oblique shock region composing of the normal shock wave and a series of the bifurcated shock waves are different from the pseudo-shock region composed by the shock train and the co-velocity region. The former will prevent the disturbances caused by the back pressure from propagating upstream. The latter is an important area for mixing of the two streams. Moreover, the shock wave structures are the main components of the entire flow structure inside the ejector. Figure 3: The structure of shock wave in the steam ejector. Figure 4: **(a)** The contour of Mach number at the primary fluid pressure; **(b)** Radial distance on different X-axis position. Figure 5: The distribution curve of Mach number at centerline.
## 4 Performance Analysis of Basic SC Unit
### Computational Accuracy of Basic SC Unit
The results indicate that, regardless of variations in the adder's input count N and scaling factor k, the computational errors remain essentially the same, with no significant distinctions. Consequently, the adder proposed in this study demonstrates remarkable flexibility without any compromise in computational accuracy. Figure 11: The RMSE of the multiplier unit. Figure 12: greenThe RMSE of proposed adder and [36; 37; 38; 39; 40]. Additionally, during the computation process, the proposed adder requires the subtraction of a random sequence with a constant probability value. Experimental evaluations were conducted to assess the computational accuracy when integrated with two distinct types of reference sequences: regular sequences (e.g., 0101 or 001001) and random sequences generated by the SNGs. The figure shows that the adder based on regular sequences demonstrated higher computational accuracy, which can primarily be attributed to the inherent errors within random sequences generated by the SNGs. Therefore, the adder architecture adopted in this study eliminates the need for supplementary SNGs, instead leveraging direct and regular reference sequences to enhance its performance. This approach not only simplifies the system design but also significantly reduces the overall resource consumption, thereby obtaining a substantial improvement in computational efficiency.
## 6 Application  
Through the preliminary user study, we believe that the current system has two practical applications. One is to extract line drawings of the landscape according to the style of manga drawing, and the other is to extract the line drawings from the existing illustrations and provide them to the students who want to learn coloring.  
Figure 10 shows the results of extracting line drawings from real landscape photos. The details of buildings and trees are completely preserved. It can directly replace traditional photo filters and even eliminate the requirement of manually setup parameters.  
Because the comic line drawing style ignores textures and light effects on human and animal skin, the vision effect of painting did not look good. It is appropriate to learn the pencil sketch painting style for human or animals, but this is beyond the scope of this paper. In addition, a more important application is to use our system to provide analytical data for subsequent drawing simulations. We can also try to extract the basic color layer and add light, shadow, or texture blending effects to the basic color layer.  
Figure 9: More experimental results: There are five groups of images. The first image of each group is the original image; the second one is generated by the model similar to U-Net with GAN ([PERSON] et al. [9]); and the third one is generated by our model.
## 2 Results and Discussion
### Sensory Evaluation  
The average scores of all the types of Muscat Hamburg wine in the sensory evaluation are shown in Table 4. Wines with higher scores have more desirable attributes. Significant differences (\(p<0.001\)) between the different wines were found in the aroma analysis (aroma purity, intensity and quality), taste analysis (taste purity, taste duration and taste quality) and harmony. Carbonic maccerated Muscat Hamburg wine received the highest scores in aroma analysis, taste analysis and harmony (26.3, 37.5 and 10.2, respectively), followed by white Muscat Hamburg wine (24.2, 35.4 and 9.5, respectively) and red Muscat Hamburg wine (22.4, 34.1 and 8.6, respectively). The results of aroma analysis were in accordance with the previous quantitative analysis of aroma compound, which showed that Muscat Hamburg wine prepared by carbonic macceration had the highest content of esters and terpen. The lower content of phenols, which greatly influence the taste and harmony of red wine, in the red Muscat Hamburg wine could by why this sample received the lowest scores in the taste and harmony categories of the sensory evaluation [29]. Carbonic maccerated Muscat Hamburg wine received the highest score overall with 86.8 points and was rated as \"excellent\". The classically prepared white wine, which received 82.1 points, was listed second and rated as \"very good\", while the classically prepared red wine received 77.6 points and was rated as \"good\". These results indicate carbonic  
Figure 1: The impact of different fermentation technologies on the odor activity values (OAVs) of the odorant series for Muscat Hamburg wine. ***\(p<0.001\).  
macceration produces wines with distinctive of properties, superior quality, and a harmonious balance compared with wines produced by conventional techniques [11; 30].  
In general, carbonic macerated Muscat Hamburg wine received highest score, it is related to unique aroma, such as floral, fatty and fruity. The floral aroma is mainly determined by cryophyllene and \(\alpha\)-terpineol, the fatty one may be connected with n-decanoic acid and (\(E\))-2-nonenal, while ethyl laurate have a major role in the fruity aromatic series. Other aroma compounds in Muscat Hamburg wine have some effect of odor to a certain degree. Ultimately, the highest sensory evaluation is connected closely with the aroma compounds present.
### Instrument Working Principle and Expectations  
Our instrument concept, the Magnetic Plasma Analyzer (MPA), comprises of two planar permanent magnets placed parallel to each other (face to face), creating a quasi-uniform magnetic field between them. The charged plasma particles follow different trajectories through the magnetic field depending on their energy. A position-sensitive detector is mounted on the side of the magnetic chamber. As a result, particles with different energies are detected at different positions on the detector. MPA detects all particles from a given look direction in the same acquisition step, independent of their energies. Indeed, the expected counts are independent of any voltage modulation (unlike ESAs or Faraday cups). Instead, the number of counts only depends on the physical parameters of the instrument and sensor to first order: aperture size, magnetic field strength and pixel size. This is the key advantage of MPA's working principle: the simultaneous measurement of all velocities in one acquisition step leads to a higher time resolution than ESAs. For our instrument concept, we define a pixel as a set length range on the position-sensitive sensor that can be sampled with one given anode. Small pixel sizes allow for a larger number of energy channels (better resolution) but result in fewer counts on each pixel. In this paper, we discuss the optimal geometry of the MPA instrument to measure proton and \(\alpha\)-particle VDFs, the two most abundant ion species in the solar wind. We then evaluate the performance of the instrument assuming Maxwellian and \(\kappa\)-distributions of the incoming particles. We assume a perfectly uniform magnetic field inside the instrument and zero magnetic field outside the instrument (except in our SIMION simulations in Section 4.2).
# A Novel Link-to-System Mapping Technique Based on Machine Learning for 5G/IoT Wireless Networks
## 5 Numerical Results and Analysis
### AWGN Curve  
Figure 5 shows BLER performance for 4G AWGN and 5G AWGN. 4G LTE-A systems and 5G NR systems use Turbo-code and LDPC as channel coding schemes, respectively. The size of TB and their coding rates varied slightly according to channel coding schemes, as shown in Table 3. After applying the channel coding shown in Table 3, the performances of 4G AWGN and 5G NR AWGN were evaluated. 5G NR AWGN outperformed 4G AWGN, especially better for small CQL values.  
3 GPP 4G LTE and 5G NR-standards are specified to set the target BLER of 0.1, i.e., 10%. As SNR increased, BLER decreased. SNR could be measured immediately on SLS, but the calculation of BLER was burdensome because CRC decoding had to be performed. When measured BLER is lower than target BLER, a UE can choose the index of high CQI to support high data rate. Under an unknown BLER, we found the value of SNR satisfying the target BLER of 0.1 through the simulation results and induced Equation (12) for CQI and SNR.  
From the AWGN curve shown in Figure 4, SINR threshold at BLER = 0.1 (10%) can be derived, as shown in Table 5. In practical 4G/5G systems, if an estimated BLER exceeds 0.1, TB transmission is considered as failure. Otherwise, TB transmission is considered as success. We plot SINR threshold at BLER = 10% for the range of all CQIs in Figure 6. From this plot, we derive a linear equation as follows:  
\[SNR_{th}(i)=1.938\times i-9.682\ [dB],i=1,\cdots,15, \tag{12}\]  
where \(i\) means CQI index. \(SNR_{th}(i)\) is SINR threshold for CQI index \(i\) at 10% BLER.
# The Cut-Off Frequency of High-Pass Filtering of Strong-Motion Records Based on Transfer Learning
## 2 Database  
Raw ground motion records, supplied by the Center for Engineering Strong Motion Data (CESMD) [38], were adopted in this work. A total of 4461 raw records in V1c format were selected for this paper. The traditional method was applied to these raw records to obtain the appropriate high-pass cut-off frequency for each record.
# Joint Beam-Forming Optimization for Active-RIS-Assisted Internet-of-Things Networks with SWIPT
## 3 Proposed Algorithm
### Optimizing \(w_{k},\rho_{k},t_{k}\) Given \(\Theta\) and \(P\)
}^{K}\text{Tr}(H_{k}W_{i})\geqslant\sigma_{v}^{2}\left\| h_{r,k}^{H}P\Theta\right\|^{2}+\widetilde{\sigma} \tag{24}\]  
For (17) and (18), there is the following process due to \(\left\|A\right\|=\sqrt{\text{Tr}(A^{H}A)}\):  
\[\sum_{i=1}^{K}\|\text{YGW}_{i}\|^{2}+\sigma_{v}^{2}\|Y\|^{2}\leqslant p_{ \text{max}},\]  
\[\sum_{i=1}^{K}\text{Tr}\Big{(}\text{YGW}_{i}\text{G}^{H}\text{Y}^{H}\Big{)}+ \sigma_{v}^{2}\|Y\|^{2}\leqslant p_{\text{max}} \tag{25}\]  
\[\sum_{i=1}^{K}\text{Tr}\Big{(}\text{W}_{k}\text{W}_{k}^{H}\Big{)}\leqslant P_{ B},\forall k,\]  
\[\sum_{i=1}^{K}\text{Tr}(\text{W}_{k})\leqslant P_{B},\forall k, \tag{26}\]For (19), due to \(P_{k}^{L}=\eta_{k}(1-\rho_{k})\bigg{(}\sum\limits_{i=1}^{K}\left|\mathrm{h}_{k}^{H} \mathrm{w}_{i}\right|^{2}+\sigma_{v}^{2}\|\mathrm{h}_{r,k}^{H}\mathrm{P}\Theta\| ^{2}\bigg{)},\forall k\), the following process is assumed to be \(\eta_{k}=1\) for all users:  
\[(1-\rho_{k})\bigg{(}\sum\limits_{i=1}^{K}\left|\mathrm{h}_{k}^{H}\mathrm{w}_{i }\right|^{2}+\sigma_{v}^{2}\|\mathrm{h}_{r,k}^{H}\mathrm{P}\Theta\|^{2}\bigg{)} \geqslant t_{k}^{2},\forall k,\]  
\[\sum\limits_{i=1}^{K}\left|\mathrm{h}_{k}^{H}\mathrm{w}_{i}\right|^{2}+\sigma _{v}^{2}\|\mathrm{h}_{r,k}^{H}\mathrm{P}\Theta\|^{2}\geqslant\frac{t_{k}^{2}} {(1-\rho_{k})},\forall k, \tag{27}\]  
\[\sum\limits_{i=1}^{K}\left|\mathrm{h}_{k}^{H}\mathrm{h}_{k}\mathrm{w}_{i} \mathrm{w}_{i}^{H}\right|^{2}+\sigma_{v}^{2}\|\mathrm{h}_{r,k}^{H}\mathrm{Y} \|^{2}\geqslant\frac{t_{k}^{2}}{(1-\rho_{k})},\forall k,\]  
\[\sum\limits_{i=1}^{K}\mathrm{Tr}(H_{k}W_{i})+\sigma_{v}^{2}\|\mathrm{h}_{r,k}^ {H}\mathrm{Y}\|^{2}\geqslant\frac{t_{k}^{2}}{(1-\rho_{k})},\forall k. \tag{28}\]  
For (20), due to the use of SDR and SCA optimization methods, the rank-one constraints are to be relaxed on the original constraints, which can be expressed as  
\[0<\rho_{k}<1,W_{k}\succeq 0,\forall k. \tag{29}\]  
To make (P3) a convex problem, we relax the rank-one constraints. The optimization objective of (P2) is to find the maximum value of \(t_{k}^{2}\), which can be equated to finding the maximum value at the minimum point \(t_{k_{0}}\) in the function \(t_{k}^{2}\). After expanding it with [PERSON]'s first-order series, it is a first-order linear function, which can be solved efficiently by CVX as the same constraints as in (P3), and the solution obtained by substituting \(t_{k}\) as a new \(t_{k_{0}}\) repeats the process of solving and iterating until convergence, which leads to the final \(\mathrm{w}_{k},\rho_{k},t_{k}\). These solutions have a unique rank and can be proved by solving the KKT condition using the Lagrangian dyadic method [36].