esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Recent Research into the Causes and Control of Noise during High-Speed Train Movement
## 3 High-Speed Train Noise Research Methods
### Wind-Tunnel Test  
Compared to train noise experiments, wind tunnel experiments can be conducted in an indoor environment, independent of external climatic conditions, and can simulate external wind and other climatic conditions according to the hardware facilities of the wind tunnel, thus realizing the controllability of environmental factors. In aerodynamic research, wind tunnels are cutting-edge experimental testing tools. Both Ferrari and NASA have built wind tunnels to improve the aerodynamic performance of Formula 1 cars and space shuttles. As shown in Figure 3, wind tunnel facilities for high-speed trains typically employ large tunnels that use giant vanes at the entrance to generate strong airflow, with special grilles to reduce vortices in the airflow before it enters the laboratory. The noise collected in the wind tunnel test does not include mechanical noise, so it is possible to improve evaluations of the aerodynamic noise generated by the train.  
As shown in Figure 3, to reduce the cost of wind tunnel experiments, scaled-down models are often used for experiments. In 1991, [PERSON] et al. [51] used a 1/76 scale HST train model and a 1/20 scale TGV001 train model to evaluate the effects of different types of ground simulations on train drag. [PERSON] et al. [52] experimentally investigated three different types of trains at a 1/10 scale using a German-Dutch wind tunnel, and the results of the study showed that high Reynolds numbers on the surface significantly improve the reliability of wind tunnel experiments on trains. [PERSON] et al. [53] conducted wind tunnel experiments using a 1/5 scale model of the Shinkansen train, and they estimated the contribution of each source of noise generated by a Shinkansen train to the roadside noise level based on the experimental data. [PERSON] et al. [54] proposed a numerical simulation method for wheel-track noise of the high-speed train and verified the accuracy of this method through wind tunnel experiments. [PERSON] et al. [55] performed a large eddy simulation to research the unsteady flow near the pantograph of the DSA380 high-speed train and predicted the aerodynamic noise caused by the pantograph. Although wind tunnel experiments are cheaper and have a higher safety factor, current wind tunnel experiments are more demanding in terms of experimental environment and equipment for non-full-size flow field simulations due to the similarity criterion.  
Figure 3: Train isometric model.
## 1 Introduction
[30; 31] investigated the properties of materials using scanning electron microscope (SEM) and X-ray diffraction (XRD), which are instructive in the study of internal nozzle wear by severe waterjets. Their efforts laid the foundation for the wide application of impinging jets and wall jets in industrial production. With respect to double-walled confined jets, most investigations focus on air jets. For example, [PERSON] et al. [32] investigated the heat transfer and flow characteristics of a rectangular channel with a small scale slit-vent circular cylinder by Large Eddy Simulation (LES). In addition, [PERSON], et al. [33] studied the influence of impinging under expanded jets on local heat transfer with a nozzle pressure ratio ranging from 2.4 to 5.1. The flow structure distributions captured with the shadowgraph technique were compared with the local Nusselt number. Proposed correlations for the local heat transfer showed a good agreement with the experimental results for larger nozzle to plate distances. However, as a typical kind of confined jet, a double-walled waterjet is rarely studied in the literature. This type of waterjet is widely used in coal seam cutting, deep cutting and other field applications [34; 35]. Compared with free waterjets, double-walled confined jets produce impact and shear effects [36; 37] and have a tensile effect which is closely related to the flow field. Therefore, a study of the double-walled jet flow can both clarify its internal velocity distribution and provide a theoretical guidance for its engineering applications. In the present work, the mean velocity and turbulent intensity of double-walled confined waterjet are measured by means of particle image velocimetry (PIV). Considering the extremely complex velocity features of the flow field, PIV represents an excellent tool, given its capacity to fully reconstruct volumetric data of the target flow field. In order to measure the velocity of double-walled confined waterjets, experimental research is carried out. The distribution of mean velocity and turbulent intensity on both horizontal and vertical planes is used to characterize the flow field. Moreover, the present paper has analyzed the influence of a confined width on the submerged waterjet velocity distribution.
# Pruning Adapters with Lottery Ticket
## 4 Discussion  
Most neural networks are overparameterized and carry a lot of redundancies. Therefore, distillation [14; 15] and pruning [16; 17] are introduced to streamline the model while perserving accuracy. There are several research directions on training small or sparse networks, pruning before training [18], pruning after training [16], and pruning during training [19].  
Adapters have achieved great results in multi-task [7], cross-lingual transfer learning [20], and infusing knowledge [21]. It indicates that adapters are capable of extracting useful features from tasks. They are applied to fuse knowledge learned from different tasks for a target one. However, adapters are still far from being concise, and fusing multiple adapters may cause inference inefficiency. To tackle this problem, [PERSON] et al. [8] have firstly introduced a way to remove adapters from lower transformer layers.  
Recently, the Lottery Ticket Hypothesis has been the most influential theory for pruning, where [PERSON] and [PERSON] [9] discover that a dense neural network contains subnetworks (winning ticket) that can have the same performance as the original network when trained isolated. They further show that not only the structure of the pruned networks can affect the performance but also the initial weights of these networks. It is also revealed that using a certain pruning strategy, a subnetwork can be found that learns faster than the original one and reaches higher test accuracy. Our proposed approaches are motivated by this theory and aim to identify the winning ticket with a systematical pruning strategies for different levels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Param/Task** & **AVG (Total/Large)** & **Training Time** \\ \hline Full fine-tune & 100\% & 79.60 & 84.64 & 1.00\(\times\) \\ Adapter 128 & 4.15\% & 79.42 & 84.34 & 1.07\(\times\) \\ Adapter 27 & 0.92\% & 79.20 & 84.06 & 1.05\(\times\) \\ Prune Weight (21\%) & 0.87\% & 79.22 & 84.42 & 1.07\(\times\) \\ Prune Neuron (26\%) & 1.08\% & 79.22 & 84.34 & 1.05\(\times\) \\ Prune Layer (37.5\%) & 1.56\% & 79.14 & 84.00 & 1.03\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Winning ticket of different pruning schemes in test results on GLUE test sets using GLUE server and the percentage of parameters trained. Average results on all GLUE datasets and average results on large datasets in GLUE (SST-2, QQP, MNLI, MNLI\({}_{\text{mm}}\), QNLI). Training time of all task in GLUE, with Full fine-tune (4 h) as base.  
Figure 4: Winning ticket of different pruning schemes on GLUE test, with average results on large datasets in GLUE (SST-2, QQP, MNLI, MNLI\({}_{\text{mm}}\), QNLI)
# Successful Tests on Detecting Pre-Earthquake Magnetic Field Signals from Space
## 2 Materials and Methods
### Analysis and Magnetic Field Satellite Anomaly Definition
The data related to geomagnetic indices are provided to the user through the Swarm satellite itself and also through the sites [[https://www.ncei.noaa.gov/products/space-weather](https://www.ncei.noaa.gov/products/space-weather)]([https://www.ncei.noaa.gov/products/space-weather](https://www.ncei.noaa.gov/products/space-weather)) and [[https://wdc.kugi.kyoto-u.ac.jp/](https://wdc.kugi.kyoto-u.ac.jp/)]([https://wdc.kugi.kyoto-u.ac.jp/](https://wdc.kugi.kyoto-u.ac.jp/)) (accessed on 2 July 2024). As a result, Kp, Dst, and F10.7 indices were investigated to evaluate the difference between potential seismo-ionospheric disturbances and solar geomagnetic disturbances. The major difference between ionospheric effects caused by geomagnetic storms and potential seismic effects is that, given a certain time of observation, the former have a global effect and are observed worldwide, while the latter are only observed near the epicenter, so are rather local [46].
## 3 Materials and Methods
### Data Preprocessing and Analysis
#### 3.3.2 Data Labeling  
To classify cyberbullying, the extracted text and image data needed to be labeled. Hence, the text data were then categorized into four cyberbullying classes. This was done based on the knowledge acquired from prior studies such as [35; 36; 37; 38]. The identified categories were as follows:  
* Non-bullying (class 0): This category contains text data related to no insulting, defamatory, offensive, or aggressive language.
* Defamatory (class 1): This category contains data related to defamatory language-based cyberbullying, which involves actions where individuals insult or damage another person's reputation. This includes spreading false information about someone or engaging in character defamation, such as sexual harassment or body shaming.
* Offensive (class 2): This category includes offensive language-based cyberbullying. It occurs when individuals use derogatory terms or insults, such as \"f\"cker\", \"bi\"ch\", or \"you are a dog, dumb, or stupid\", to attack or demean another person.
* Aggressive (class 3): This category includes data related to threats, violent behavior, and abusive actions aimed at someone, such as \"I will kill you\" or \"You should die\".  
When it comes to labeling extracted image data, knowledge acquired from prior studies, such as [39; 40; 41], was considered. The identified categories were as follows:  
* Non-bullying (class 0): This category includes images that do not feature any defamatory, sexual, offensive, or aggressive content.
* Defamatory (class 1): This category includes images that contain sexual or nudity-related content.
* Offensive (class 2): This category includes images that show a middle finger or combine human faces with animal faces.
* Aggressive (class 3): This category includes images that depict violence, such as beating someone or brandishing weapons.  
Table 2 shows the number of samples in each class for both the public and private datasets after data labeling. In the beginning, the public dataset contained 7854 multi-modal samples. We removed all samples that were either not in English or not relevant to our research and data labeling.
## 1 Introduction
In previous studies, the stray parameters of the MMC valve, the nonlinear characteristics of surge arresters, and the electric field characteristics of the MMC valve have not been considered in the fault current loop. As a result, the overcurrent of the MMC valves calculated using these methods might be incomplete in terms of operational conditions and might lack sufficient accuracy. When overcurrent anomalies occur in the MMC-HVDC system, those methods are incapable of rapidly identifying and locating the fault factors. This deficiency poses risks for subsequent engineering commissioning and testing. Therefore, it is essential to incorporate these factors into the overcurrent studies to improve the accuracy and reliability of MMC protection strategies. In this paper, a field-circuit coupling analysis method is proposed for overcurrent research on the MMC valve. The method integrated the electric field characteristics of valves, stray parameters, and transient characteristics of nonlinear devices during the fault process with the overcurrent analysis. By invoking the electric field calculation model, the results of the MMC valve electric field strength and the stray capacitances are synchronously coupled into the system analysis model. Firstly, the development process and influencing factors of overcurrent in the MMC valve within offshore wind power MMC-HVDC systems are analyzed. Subsequently, the stray parameters induced by the valve structure and valve hall layout were calculated. A comprehensive overcurrent analysis model for the MMC valves, incorporating field-circuit coupling analysis, is established. This model includes the electric field characteristics of the MMC valves, stray parameters, and the nonlinear model of the surge arresters in the MMC-HVDC system. Finally, the proposed field-circuit coupling method was used to accurately calculate the overcurrent in converter valves. The field-circuit coupling analysis method presented in this study enables precise calculation of overcurrent. When overcurrent anomalies occur in the MMC-HVDC system, the field-circuit coupling analysis method in this paper can rapidly identify and locate the fault factors causing the anomalies. Based on these calculations, a rational overcurrent suppression strategy for an offshore MMC valve is proposed. The effectiveness and practicality of the proposed method were verified through comprehensive case studies conducted on the \(\pm\)500 kV offshore MMC-HVDC valve overcurrent calculation and suppression. The numerical results demonstrate the efficacy and accuracy of the method presented in this paper.
# Multi-Vehicle Tracking Based on Monocular Camera in Driver View
## 3 Proposed Algorithm  
### Over View of Tracking Framework  
As shown in Figure 1, the proposed tracking method is composed of the joint detection and embedding (JDE) [7] module and the data association (DA) module. JDE is based on a one-stage object detection network which simultaneously outputs the detection results and the appearances of detected targets. DA is a post process which is used to match the detection result from the JDE module with the tracking information from the previous frame.
## 3 Results  
The orthogonal array for the L-36 and L-8 experiments was established according to Section 2.3, and the simulation model was established following the methods in Section 2.1  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factor** & **Description** & **Levels** \\ \hline G & Path G direction & \(-1\): reverse, 1: forward \\ H & Path H direction & \(-1\): reverse, 1: forward \\ F & Path F direction & \(-1\): reverse, 1: forward \\ E & Locating scheme & 3 levels: 0: mean, 1: N-I, 2: N-O \\ S & Welding sequence & 3 levels: 0: median, 1: N-I, 2: N-O \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sequence optimization results.  
for the two reference assemblies, Section 2.2. For Assemblies I and II, the summary statistics of the gathered data are presented in Table 2. The descriptive statistics for the root mean square (RMS) of the displacement in all the nodes by changing the parameters of weld path direction, weld sequences, and locating schemes, according to the DOE plan, are summarized in Table 2. The range of the displacement is 0.4714 mm, compromising 80% for the maximum displacement achieved. For the second assembly, the range of the displacement achieved is 4.293 mm, which compromises 84% of the maximum displacement achieved. This overview provides insight into how much a potential optimization of the parameters can improve the geometric accuracy after welding.  
Preliminary screening with the regression model revealed that the higher-order interactions could not be taken into consideration due to perfect multicollinearity between the parameters as the outcome of the VIF analysis. For this reason, the models were reduced to take into account only the interactions among the significant parameters. For Assembly I, the ANOVA model considered the main factors, including the welding sequences, the locating schemes, and the weld path direction of weld segments F and G. Weld segment H's direction was insignificant and was left out from the model. The two-way interaction between the welding sequences and the locating scheme were also included in the linear regression model as predictors. To evaluate multicollinearity among the predictors, the VIF was calculated for each predictor. All VIF values were below the threshold of 5, indicating low to moderate multicollinearity within the model. The VIF results are presented in Table 3. For Assembly II, due to the geometric complexity and simulation time, the number of experiments was reduced, and therefore only the main effects were studied.
## 3 Results
### Integration of the Various Blocks of Data  
ComDim was performed using the six blocks of data: the microbial ecosystem data (metabarcoding) through the two MDS dimensions of the OTU abundance analysis (\"OTU.dim\", two variables), the sensory data (\"SENSO\", eight variables of odor/color), the log\({}_{2}\)(n)-transformed VOC data (\"log2 VOC\", 17 compounds in common), the microbiological data (\"MICROBIO\", six variables), the biogenic amines (\"BIOGAMI\", six variables) and the TVBN measurements (one variable).  
From Table 2, three dimensions were retained. The first dimension accounted for 45% of the total variation in the multiblock data set. For this dimension, TVBN had the highest specific weight, or salience (52%), followed by the blocks BIOGAMI (22%) and MICROBIO (18%) (data not shown). The second dimension was specifically associated with the block OTUdim (salience of 91%), whereas the third dimension accounted for both the log\({}_{2}\)VOC and SENSO blocks (saliences of 52 and 41%, respectively).  
The score plots in dimensions 1 and 2, on the one hand, and 2 and 3, on the other hand, are shown in Figure 8A,B. The evolution of the products with the number of days of storage was reflected in different ways. For the SG at 28 days and, to a lesser extent, for the SG at 14 days or VP at 28 days, the increase in the TVBN (along the first dimension) was pronounced. By comparison, the MAP CSS did not show such a pattern at 28 days, but a modification in the microbial ecosystems was noticed, which was reflected by the second dimension. As stated in Section 3.2, the MAP ecosystem composition was characterized by a decrease in _Photobacterium_ in favor of _Lactobacillus_ over time, which was expressed along the second dimension of ComDim. Moreover, a detailed analysis of the OTU with this second dimension made it possible to highlight that the SG ecosystem at 28 days of storage could be associated with specific genera, such as _Carnobacterium_, _Morganella_ and _Bacillus_. Finally, the third dimension of ComDim showed a common contribution of sensory and VOCs data, allowing for the description of SG salmon samples at 0 or 14 days of storage, which was not shown for the MAP and VP CCS. The correlation coefficients between the raw data and this third dimension highlighted the sensory odor \"dill\" as being specific toward SG at day 0, which was associated with VOCs, such as pristane, pentadecane, heptadecane and tridecane. A smoke odor was associated with benzaldehyde and butylate hydroxytoluene compounds.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **\%expl** & **cum\%expl** \\ \hline Dim.1 & 45.56 & 45.56 \\ Dim.2 & 14.35 & 59.91 \\ Dim.3 & 11.15 & 71.06 \\ Dim.4 & 6.45 & 77.51 \\ Dim.5 & 4.15 & 81.66 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Percentage of the variability explained (%expl) and cumulative percentage (cum%expl) explained by the first common components defined using ComDim.
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
## 4 Full-Wave Electromagnetic Simulation Results  
Full-wave electromagnetic simulations, conducted in CST Microwave Studio(r) (CST-MWS) (Release Version 2020.01) for a ULA of 15 broadband antipodal Vivaldi antennas, are used to obtain the far-field transmit beam patterns produced by the proposed transmit beamformer. Here, we consider both multibeam and single-beam beamforming. The transmit signal frequency range of interest is \([f_{L}=1.3,f_{H}=2.8]\) GHz, which has a fractional bandwidth of more than \(B_{f}=70\%\). The ULA of Vivaldi antennas is shown in Figure 6a, and the \(S_{11}\) curve of a single antenna element is shown in Figure 6b, verifying the wideband element response within the frequency range of interest. The interelement spacing is selected as \(\lambda/2\) such that \(\Delta x=\Delta ct\), where \(\lambda\) is the wavelength corresponding to the temporal sampling frequency \(F_{s}=6\) GHz. The passband of the 2-D FIR trapezoidal filter is taken such that its temporal frequency is in this frequency range of interest, i.e., \([0.43\pi,0.93\pi]\) rad/sample. The order and the maximum transition band of the filter, and other parameters of the trapezoid which vary with different cases of beamforming are mentioned in the corresponding places in Sections 4.1 and 4.2. These include angles \(\alpha_{i}\in[-45^{\circ},45^{\circ}]\), \(i=1,2,\ldots,K\) and the half-angle \(\delta_{k}\) with respect to Figure 4. Matlab(r) (Version R2018a) software is used for generating the impulse response of the desired 2-D sparse FIR filter responsible for beamforming with the help of the CVX optimization toolbox and SeDuMi solver. It is then exported and fed into CST-MWS ULA model as port excitation signals for each antenna. To obtain the far-field transmit beam patterns in CST-MWS, far-field monitors are placed within the frequency range of interest. Transmit beam patterns produced by both the sparse and nonsparse 2-D FIR filters are then obtained and compared. For the single-beam case, the results of the proposed method are compared with similar approaches adapted from [32; 39; 40; 41] such that the filters have the same specifications. In particular, we present the average of the DODs, their standard deviation (SD), average gain, and side-lobe levels in the passband frequency range 1.3-2.8 GHz. Side-lobe levels are considered in the range \(\theta\in[-90^{\circ},90^{\circ}]\), and the peak and average side-lobe levels at a particular temporal frequency are computed with respect to the average gain of the main beams. Additionally, 2-D FIR filter performance in terms of the passband ripple and the stopband attenuation, and the DSP adder and multiplier complexity are presented.  
Figure 6: (a) ULA of 15-element antipodal Vivaldi antennas with an interelement spacing of 50 mm; (b) \(|S_{11}|\) plot of single antenna element made of Rogers RO4003 dielectric (dielectric constant: 3.55).