esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation
### Discussion  
When a deep-buried tunnel collapses, a collapsed arch is generally formed and no roof fall occurs. The cause of the deep-buried loess tunnel and its 120 m roof described in this study (the first case in China) was only preliminarily examined. More in-depth research needs to be carried out. The collapse treatment measures proposed in this paper are highly reliable and ensure a one-time passage through the collapsed section, but the cost is high and the construction period is long. The cost of this scheme was about 16.64 million CNY (including 14.64 million CNY for collapse treatment and 2 million CNY for secondary excavation construction) and the construction period was 135 days. Better treatment measures need to be proposed. However, the research results in this article offer a certain reference value for the treatment of tunnel collapse accidents when crossing loess under similar conditions. They also provide reliable support for the design of parameters and construction methods for similar section tunnel excavations in the future. From the loess tunnel collapse case described in this article, the following recommendations can be proposed. 1. Exploration stage. Determine the geological and hydrogeological engineering conditions of the location of the tunnel. On this basis, the influence of the local climate in particular should be taken into account, while the timing of the rainy season and the amount of rainfall should be ascertained to guide the design and construction. 2. Design stage. According to the survey results and tunnel design specifications, two tunnel support parameters should be proposed. One is the tunnel support parameter through the dry section, and the other is the tunnel support parameter through the water-rich section. 3. Construction stage. Increase the frequency of on-site monitoring and measurement, and implement the primary support and secondary lining in time. Learn to be flexible \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Monitoring Position**} & \multirow{2}{*}{**Axial Force F\({}_{x}\)/kN**} & \multicolumn{2}{c}{**Bending Moment**} & \multirow{2}{*}{**Failure Type**} & \multirow{2}{*}{**K**} & **Meets Safety Factor** \\  & & & & & **Requirement?** \\ \hline Vault & \(-\)534.32 & 77.32 & Tensile & 1.2 & No \\ Left 45\({}^{\circ}\) & \(-\)2501.31 & \(-\)43.80 & Compressive & 2.3 & Yes \\ Left 60\({}^{\circ}\) & \(-\)2788.58 & \(-\)60.58 & Compressive & 2.1 & Yes \\ Left 90\({}^{\circ}\) & \(-\)772.67 & 44.05 & Compressive & 5.9 & Yes \\ Left football & \(-\)406.47 & \(-\)222.08 & Tensile & 0.3 & No \\ Right 45\({}^{\circ}\) & \(-\)2540.80 & \(-\)37.80 & Compressive & 2.3 & Yes \\ Right 60\({}^{\circ}\) & \(-\)2835.99 & \(-\)61.98 & Compressive & 2.1 & Yes \\ Right 90\({}^{\circ}\) & \(-\)758.49 & 45.87 & Compressive & 5.9 & Yes \\ Right football & \(-\)418.17 & \(-\)224.23 & Tensile & 0.3 & No \\ Invert center line & 775.72 & \(-\)0.43 & Compressive & 8.2 & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Safety factor of each position in the original design scheme.
# Design and Analysis: Servo-Tube-Powered Liquid Jet Injector for Drug Delivery Applications
## 2 Materials and Methods
### Jet Production  
As previously illustrated, the jet stagnation pressure emanating from a jet injector depends on a force balance between the driving force imparted on the plunger by the power source and the resistive force of pressure buildup in the chamber due to the bulk modulus of the working fluid, as well as any friction the device exhibits. This has been well-modeled by [PERSON] and [PERSON] [9]. The equations are:  
\[\frac{dp}{dt}=\frac{(B+p)\frac{dx}{dt}-\frac{BA_{a}}{A_{p}}\sqrt{\frac{2p}{\rho_ {o}}}}{L-x_{p}}, \tag{1}\]  
\[\frac{d^{2}x}{dt^{2}}=-\frac{F_{p}}{m_{p}}-\frac{A_{p}}{m_{p}}-\frac{dx}{dt} \frac{F_{f}}{m_{p}\left|\frac{dx}{dt}\right|}, \tag{2}\]  
where \(B\) is the bulk modulus of the fluid, \(\rho_{o}\) is the initial density, \(p\) is the pressure and \(A_{p}\) and \(A_{o}\) are the piston and orifice area determined from the diameters of the piston and nozzle orifices, respectively. For water, \(B=2.18\) GPa and \(\rho_{o}=1000\) kg/m\({}^{3}\) are used. \(x_{p}\) and \(dx/dt\) are the position and velocity of the piston, respectively, and \(L=80\) mm is the length of the liquid column inside the injector. \(m_{p}\) is the mass of the piston. \(F_{p}\) and \(F_{f}\) are the driving force and frictional force, respectively. For more details, see [9; 25].  
Utilizing a servo-controlled actuator makes it possible to decouple this set of ordinary differential equations, and therefore, it is no longer necessary to consider Equation (2), as the PID loops for position and velocity within the amplifier will ensure that it is possible to maintain any desired plunger velocity (\(dx/dt\)) to shape the pressure output of the device according to Equation (1). For a given velocity profile, the corresponding pressure and pressure changes can be easily obtained. The effects of changing friction due to O-ring friction or increased power demands for more viscous fluids no longer need to be considered. The errors caused by outside factors will be compensated at 8 kHz. Figure 6 illustrates the stagnation pressure behavior of the injector under a given velocity mapping with time.  
The velocity map depicted in Figure 6 was tailored to yield a pressure pulse with a peak of 13 MPa and to have constant pressure output rather than a noticeable peak to show the controllability of the device and its ability to overcome the back-driving forces present within the injection chamber. The 13 MPa peak will illustrate the ability of the device to puncture human tissue, as it is at the upper limit of what is prescribed by [PERSON] et al. [55].  
Figure 5: Sample pulse train from FPGA to input of servo amplifier.
## 1 Introduction
[28; 29] proposed a propagation matrix-based AVA inversion method constrained by the Generalised Cross-Validation criterion, and, after incorporating the propagation matrix AVA inversion method into PP-PS joint inversion, the experiments showed that the joint inversion could obtain better inversion results for all three parameters. This study aimed to advance propagation matrix analysis by shifting from the frequency-slow spatial domain to the frequency-angular domain. This work introduces a novel propagation matrix method, formulated under the assumption of uniform seismic wave amplitude distribution within each layer. The proposed methodology effectively addresses the inaccuracies that arise when conventional propagation matrices are used to simulate AVA responses in non-uniform multilayered media across varying incidence angles. The robustness and validity of this innovative approach have been rigorously demonstrated using theoretical solution comparisons for AVA seismic responses. Further validation is achieved by applying the new propagation matrix method to both synthetic and real-world seismic datasets, thereby confirming its practical utility and effectiveness in enhancing multiparameter AVA characterisation and inversion processes.
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 4 High Performance Computing: Fundamentals Concepts and Models  
This section briefly introduces some fundamental concepts and terminology associated with HPC methodologies and technologies.  
In the last decades, modern computing hardware is inherently parallel, and thus parallelism has become ubiquitously available. Specifically, parallelism exists on different levels in HPC platforms. Its use is particularly appropriate when complex computations have to be accomplished rapidly on huge data sets. However, HPC uses hardware with different characteristics and specific programming environments to handle complex computational problems [154; 155; 156; 15  
Figure 5: A three-dimensional representation of the vertical (**left**) and east–west (**right**) component of the ground displacement related to the Wolf volcano eruption occurred on 25 May 2015. Time-series of the corresponding deformation (cm/year) are also depicted; the red bar indicates the start of the eruption.  
We first consider the main currently available parallel computing architectures (Section 4.1). Afterwards, parallel programming models and tools are addressed (Section 4.2). Performance models and cloud computing (CC) are also discussed (Sections 4.3 and 4.4).
# Performance Analysis of a Color-Code-Based Optical Camera Communication System
## 3 Test and Results
### Bit Error Ratio Test
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **Frame** & \multirow{2}{*}{**Transmission Time of**} & \multicolumn{2}{c}{**Distance between Transmitter and Receiver**} \\
**Rate** & **per** & & **2400 Bits (s)** & **120 cm** & **220 cm** & **320 cm** \\ \hline
48 & 1 & 50 & 0 bit & 2 bit & 13 bit \\
240 & 5 & 10 & 0 bit & 9 bit & 33 bit \\
480 & 10 & 5 & 6 bit & 42 bit & 48 bit \\
960 & 20 & 2.5 & 196 bit & 707 bit & 910 bit \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of erroneous bits depending on the data rate (RGB). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **Frame** & \multirow{2}{*}{**Transmission Time of**} & \multicolumn{2}{c}{**Distance between Transmitter and Receiver**} \\
**Rate** & **per** & & **2400 Bits (s)** & **120 cm** & **220 cm** & **320 cm** \\ \hline
48 & 1 & 50 & 0 bit & 2 bit & 13 bit \\
240 & 5 & 10 & 0 bit & 9 bit & 33 bit \\
480 & 10 & 5 & 6 bit & 42 bit & 48 bit \\
960 & 20 & 2.5 & 196 bit & 707 bit & 910 bit \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of erroneous bits depending on the data rate (HSV). color increases, all bits in the RGB color space increase, whereas in the HSV color space, only the bits reserved for brightness increase. This means that the HSV color space is less susceptible to adverse effects caused by the environment or hardware. Additionally, the CMOS camera used in the test phase loses color values as it heats up.
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
## 4 Results and Discussion
### DD Network Solutions
#### 4.3.3 Tropospheric Estimates  
Taking IGS tropospheric products as the reference value, the differences between the estimated ZTDs and the IGS products were calculated. The stations SOD3, WTZR, and SPTU were also used to illustrate the estimation accuracy of three different positioning modes. The ZTD errors are presented in Figure 10. The data gaps in Figure 10 were caused by missing observations or the reference data. It can be seen that the ZTD estimates present wider discrepancies as the latitude decreases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Region**System** & **G (\%)** & **R (\%)** & \multicolumn{3}{c}{**G + R**} \\ \cline{3-5}  & & **G (\%)** & **R (\%)** & **G + R (\%)** \\ \hline High latitude network & 70.3 & 63.9 & 69 & 62.8 & 66.5 \\ \hline Middle latitude network & 53.1 & 56 & 51.5 & 54.8 & 53.0 \\ \hline Low latitude network & 74.7 & 26.4 & 74.6 & 33.9 & 57.5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The ambiguity fixing rate of DD processing (G, R, and G + R in black body denote the data processing mode, respectively. No bold G, R, and G + R represent the ambiguity fixing rate of GPS+GLONASS processing mode, respectively).  
Figure 10: The ZTD error series of station SOD3, WTZR, and SPTU estimated with DD processing.  
The RMSE of all the stations is shown in Figure 11. The mean RMSE of each network and their comparison between different processing modes are shown in Table 8.  
The statistical results show that, in all the three networks, the ZTD estimates of GLONASS are obviously worse than that of GPS, which are 15.85%, 12.26%, and 32.24% lower in the high, middle, and low latitude networks, respectively. The accuracy of GPS+GLONASS in the high latitude network is 2.08% worse than that of GPS but slightly better in the middle and low latitude networks, about 1.06% and 4.00%, respectively. The consistency between the estimated results and the IGS products decreases as the latitude decreases for GPS, GLONASS, and GPS+GLONASS.
# Development of an Electric Pulse Device for Coal Grinding
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**The Negative Electrode in the Electric Pulse**} & \multicolumn{2}{c}{**The Negative Electrode in the Electric Pulse**} \\
**Diameter of the** & & **Grinding Device Is a Solid Flat Metal Plate** & **Grinding Device Is a Flat Metal Grid Plate with** \\
**Resulting Product** & & **Without Holes** & **Openings of 2000 \(\upmu\)m** \\ \cline{2-5}
**(d, \(\upmu\)m)** & **Average Mass of the** & & **Average Mass of the** & **Output of the Finished** \\
**Obtained Product** & **(m\({}_{\text{L}}\), g)** & **Product (K, \%)** & **Obtained Product (m\({}_{\text{L}}\), g)** & **Product (K, \%)** \\ \hline
**d, \(\upmu\)m** & & & **N = 500 discharge, U = 32 kV, C = 0.4 \(\upmu\)F** & \\ \hline
200–400 & 17.6 \(\pm\) 0.8 & 4.4 & 44.8 \(\pm\) 0.9 & 11.2 \\ \hline
100–200 & 15.6 \(\pm\) 0.7 & 3.9 & 32.8 \(\pm\) 0.7 & 8.2 \\ \hline
80–100 & 6 \(\pm\) 0.2 & 1.5 & 16.4 \(\pm\) 0.4 & 4.1 \\ \hline
**d, \(\upmu\)m** & & **N = 500 discharge, U = 32 kV, C = 0.8 \(\upmu\)F** & \\ \hline
200–400 & 22.8 \(\pm\) 0.7 & 5.7 & 55.6 \(\pm\) 1.2 & 13.9 \\ \hline
100–200 & 20.4 \(\pm\) 0.5 & 5.1 & 46.4 \(\pm\) 1.1 & 11.6 \\ \hline
80–100 & 10.8 \(\pm\) 0.6 & 2.7 & 21.6 \(\pm\) 0.4 & 5.4 \\ \hline
**d, \(\upmu\)m** & & **N = 500 discharge, U = 32 kV, C = 1.2 \(\upmu\)F** & \\ \hline
200–400 & 30 \(\pm\) 0.9 & 7.5 & 69.6 \(\pm\) 1.5 & 17.4 \\ \hline
100–200 & 23.2 \(\pm\) 0.9 & 5.8 & 64.8 \(\pm\) 1.3 & 16.2 \\ \hline
80–100 & 15.2 \(\pm\) 0.5 & 3.8 & 26.8 \(\pm\) 1.1 & 6.7 \\ \hline
**d, \(\upmu\)m** & & **N = 500 discharge, U = 32 kV, C = 1.6 \(\upmu\)F** & \\ \hline
200–400 & 35.2 \(\pm\) 1.1 & 8.8 & 76.4 \(\pm\) 2.7 & 19.1 \\ \hline
100–200 & 26 \(\pm\) 0.9 & 6.5 & 74.8 \(\pm\) 1.2 & 18.7 \\ \hline
80–100 & 20.4 \(\pm\) 0.8 & 5.1 & 33.2 \(\pm\) 0.9 & 8.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of the output finished product (K) obtained at different values of the capacitor capacity (C). The reason for the decrease in the intensity of the coal grinding process with an increase in the frequency of pulse discharges may be due to several factors:  
* With an increase in the pulse frequency, the energy transmitted per unit of time may not be distributed efficiently enough, which does not allow for high-quality grinding [45];
* Rapid pulse following can lead to the fact that the grinding process does not have time to complete, and the coal is not subjected to the required number of impacts to achieve the required degree of grinding [46];
* As a result of frequent exposure, the agglomeration of crushed particles may occur, which makes it difficult to further grind them. Studies were conducted to determine the energy consumption of the electro-impulse unit (Table 5). Energy consumption was measured using an electric energy meter. The experimental results indicate that at the maximum discharge voltage (32 kV), capacitor bank capacity (1.6 uF), and pulse discharge frequency (f = 5 discharges/s), the energy consumption of the unit is 2.14 kWh.
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
## 4 Problem Formulation
### Tradeoff between Data Freshness and Fidelity  
Following the analyses in the above two subsections, we can see that the number of sensor nodes \(N\) is essential in affecting both freshness and fidelity metrics. Based on the results we derive so far, the objective problem is formulated by importing a weighting factor \(\alpha\), to jointly optimize the two metrics simultaneously through adjusting the number of sensor nodes and the weighting factor:  
\[\mathcal{P}_{1}:\min_{N\in\mathbb{N}}\,f(N)=\alpha\overline{\Delta}(N)+(1- \alpha)\overline{D}(N), \tag{19}\]  
where \(\overline{\Delta}(N)\) and \(\overline{D}(N)\) are given by Equations (7) and (13).  
We can see that when the weighting factor \(\alpha\) is large, the average AoI part is dominant. This condition can be applied in scenarios such as daily news updating and music hits ranking where timeliness is more important. On the contrary, when \(\alpha\) is relatively small, the fidelity of the system becomes the major concern while the freshness requirement becomes relatively low. Such systems like fuel consumption reporting and indoor temperature monitoring are in this case since the state of these systems often changes slowly over time.  
In order to find the optimal \(N^{\star}\), we first analyze the monotonicity of \(\overline{\Delta}(N)\), \(\overline{D}(N)\) and the tradeoff \(f(N)\) respectively.
# Air Traffic Simulation Framework for Testing Automated Air Traffic Control Solutions +
## 4 Interfaces
### Interface for Traffic Simulation  
The traffic prediction can be performed internally using the estimation algorithm described in Section 3.1. In this case, the simulation engine directly generates predicted aircraft positions. However, the simplicity of this built-in algorithm may limit the fidelity of the predictions, thus constraining its suitability for certain high-accuracy applications. To address this limitation, the modular architecture of the simulation framework supports the delegation of the aircraft behavior modeling to an external component. This allows for the integration of more sophisticated models--such as those that account for aircraft type, which is available via ADS-B message fields.  
When using an external aircraft agent, position estimation is carried out through Interface 1. This interface supports bidirectional data exchange, using a unified data structure, which is the same as the one used by the built-in estimation algorithm. The primary difference lies in how the position data are derived: in this case, the aircraft agent is responsible for generating the estimated positions. Figures 5 and 6 illustrate the data flow and the structure of exchange information, respectively. A detailed explanation of the data fields used in this interface is provided in Table 2.  
Figure 4: Traffic update based on area segregation.
## 4 Experiments
### Preprocessing  
The OuluVS2 data set includes extracted lip images, however, the image size is not consistent. In order to apply DNNs, we resize all images to 64 \(\times\) 64. Based on our preliminary experiments with different image sizes, considering classification accuracy and computational cost, we use the image size of 64 \(\times\) 64. Furthermore, we normalize a frame length to 64; if the length is less than 64 we conduct upsampling, otherwise we suppress some frames. In addition, we convert all color images to gray-scale ones. Similar to visual frames, we normalize the audio frame length to 115; if the length is less than 115 we add last frame, otherwise up to 115 frames are used.  
In the OuluVS2 corpus, there are 1050 (35 speakers \(\times\) 10 utterance \(\times\) 3 times) sentences available. However, the data size is not enough for DNN model training. To compensate for the lack of training data, we apply data augmentation in the audio and visual modalities. In the audio modality, we add acoustic noises in DEMAND to the original utterance data. The details, including noise type and signal-to-noise Ratio (SNR) conditions, are shown in
# Advances in Random Fiber Lasers and Their Sensing Application
## 1 Introduction
Passive fiber could be utilized to stabilize the pump source, but with the imperfect signal-to-noise ratio (SNR), which is less than 30 dB [66]. In 2017, [PERSON] et al. used a broadband incoherent amplified spontaneous emission (ASE) source as the pump and implemented a stable output that more than 100W. However, there still exists some incomplete pump wave conversion even at the maximum pump power, leading to the low SNR less than 30 dB [67]. After that, researchers have found that the line-width of the ASE source should be responsible for the uncovered pump waves. In 2019, [PERSON] et al. optimized the bandwidth (~10 nm) of the ASE source and obtained high power output with high SNR of 39 dB, which the spectral of purity is 99.96% [68]. The optimized scheme with bandwidth-adjustable ASE pumping provides an effective way to obtain high power and high SNR RFLs. Moreover, there are other practical approaches for optimizing the output beam [69; 70] of RFLs. It will be covered in more detail in the later section. With optimized output 3 mission characteristics, RFLs have gotten wide applications in telecommunication, remote sensing, and distributed sensing. This review concentrates on the advances in studies of RFLs and their applications in sensing. Until now, varieties of optical fibers have been used in the systems of the RFLs, which could provide optimal schemes for their applications. Just like conventional lasers, emission power, wavelength, and line-width are of great importance for the RFLs. Moreover, the optimization of RFLs is of practical importance for their widespread applications in industry. Sensing is one of the most important potential applications for RFLs, such as remote sensing and distributed optical fiber sensing. In the end, we summarize the progress of RFLs in the last decade and make the prospect for future research.
## 5 Experiments
### Dataset  
Except for the HCA-AMR2.0 corpus, we reference three discourse analysis corpora, GUM [35], STAC [36], and RST-DT [7], to further verify the effectiveness of the proposed models and expound the distinctive advantages of HCA-AMR2.0 over these discourse analysis corpora:  
* _HCA-AMR2.0:_ The first HCA corpus was annotated on sentences in the AMR 2.0 dataset. The source data included discussion forums collected for the DARPA BOLT AND DEFT programs, transcripts and English translations of Mandarin Chinese broadcast news programming from China Central TV, _Wall Street Journal_ text, translated _Xinhua_ news texts, various newswire data from NIST OpenMT evaluations, and weblog data used in the DARPA GALE program.
* _GUM:_ The Georgetown University Multilayer corpus was created as part of the course LING-367 (Computational Corpus Linguistics) at Georgetown University, and its annotation followed the RST-DT segmentation guidelines for English. The text sources consist of 35 documents of news, interviews, instruction books, and travel guides from WikiNews, WikiHow, and WikiVoyage.
* _STAC:_ The Strategic Conversation dataset is a corpus of strategic chat conversations in 45 games annotated with negotiation-related information, dialogue acts, and discourse structures in the segmented discourse representation theory (SDRT) framework.
* _RST-DT:_ RST Discourse Treebank was developed by researchers at the Information Sciences Institute (University of Southern California), the U.S. Department of Defense, and the Linguistic Data Consortium (LDC). It comprises 385 _Wall Street Journal_ articles from the Penn Treebank annotated with discourse structure in the RST framework.  
For the clause segmentation subtask, we selected all three discourse analysis datasets evaluated on the discourse segmentation task as a comparison, and the data files are all in conll format. For the clause parsing subtask, we only selected the RST-DT dataset evaluated on the discourse parsing task as a comparison, as previous works of discourse parsing experiments on the other two datasets used _accuracy_ as the evaluating metric, other than _Parseval_. The interrelation data files in HCA and RST-DT were preprocessed by [PERSON]'s system [37]. The important dataset statistics related to the experiments are listed in Table 7.