esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Research on Mechanical Performance of SSRC Columns under Eccentric Compression
## 1 Introduction
Three variables (namely reinforcement type, reinforcement ratio, and eccentricity (\(e\))) were employed to investigate their effects on the failure mode, strain, and deflection of SSRC columns. Finally, based on the current codes [25; 26; 27], the calculations for the ultimate load and maximum crack width of SSRC members were analyzed and discussed.
## 2 Numerical Simulation Method of Riser Vortex Shock Vibration
### Calculation Model Verification
#### 2.5.2 Numerical Model Accuracy Validation  
In order to verify the accuracy of the numerical model, five groups of different uniform flow superimposed on the same oscillating flow were used to simulate the vortex-induced vibration of the riser. The flow rates are 0.10 m/s, 0.15 m/s, 0.20 m/s, 0.25 m/s and 0.30 m/s, respectively, with the corresponding Reynolds numbers ranging from 2000 to 6000. The dimensionless values of YRMS/D for the root means square value of the transverse flow displacement at section 6 of the risers of the five groups were compared with the experimental results of [PERSON] et al. [20], as shown in Table 3 below.  
As can be seen from Table 2 above, the dimensionless root mean square value of the transverse-flow displacement of the riser obtained by numerical simulation in this paper is slightly larger than the original experimental results, but the errors are all less than 8%. It is considered that the numerical model constructed in this paper possesses good accuracy.  
Influence of Superimposed Flow Velocity Ratio on Vortex-Induced Vibration Characteristics of Opposites  
Based on the vortex-solid coupling numerical model of the riser, the vortex-induced vibration response of the riser was studied by changing the velocity ratio of the superimposed flow.
## 1 Introduction
Moreover, potential future system with optical ISL and optical frequency references called Kepler, was proposed by the German Aerospace Center. Such system topology might effectively reduce modelling errors in the Earth parameters. It is expected that Kepler improve global geodetic reference frames with the focus on the Earth rotation parameters. However, the evaluation of the relative weighting of different measurement techniques taking part in orbit determination should be conducted, including also testing various settings and properties of observations. In this simulation study, we focus on introducing ISLs in the orbit determination of a Galileo-like constellation together with GNSS measurements and relative weighting of both types of measurements. We are interested in the analysis of different processing solutions depending on the choice of ground station set, weighting approaches, or connectivity scheme. We used three sets of GNSS ground stations and four ISL connectivity schemes for which we exploited methods of relative weighting based on variance component estimation (VCE) together with two values of ISL range bias. We present the impact of weighting properties on accuracy of orbit determination with reference to the number of ground stations and ISL range bias estimation depending on the connectivity scheme. This study aimed to address the following questions: (1) how the choice of VCE approach impacts the orbit and clock estimations, (2) how the weighting approach affects bias estimation, and (3) how the weighting approach and bias might change the estimation results with various sets of ground stations comparing to GNSS-only solution (i.e., without support of ISLs). The outcomes of this research might be useful in establishing future processing schemes for combined orbit determination supported with ISLs. The remainder of this paper is organized as follows: in Section 2 the methodology of the simulation is described, including observation models, ISL connectivity schemes, simulation properties, and a detailed description of the VCE methods. In Section 3 we assess the VCE approaches, results of ISL range bias estimation, and impact of the number of stations on both, i.e., weighting and the bias estimation with reference to the GNSS-only solution. In Section 4 we discuss the results in terms of possible processing strategies of relative weighting and ISL range bias estimation, and we conclude the paper.
## 3 Construction of LPTN-Based VML
### Fractional Heat Generation Model  
[PERSON]'s analytical method for heat generation rate calculation, derived from extensive experimental measurements and empirical formulations, has been validated across numerous studies under moderate loads, medium rotational speeds, and optimal lubrication conditions [49]. This method establishes that for deep groove ball bearings subjected exclusively to radial loads, the heat generation rate is calculated as the product of total frictional torque and bearing rotational speed. To explicitly characterize the intrinsic relationship between unknown parameters in thermal loss calculations and system inputs (rotational speed \(n_{s}\) and radial load \(F_{r}\)), the formulation is expressed as a function of these operational parameters:  
\[Q_{tol}(n_{s},F_{r})=\left[M_{f}(F_{r})+M_{v}(n_{s})\right]\times\frac{2\pi n _{s}}{60} \tag{1}\]  
where \(M_{f}(F_{r})\) and \(M_{v}(n_{s})\) are the friction torque related to the radial load and thermally induced preload on the bearing, calculated according to the following formula, respectively.  
\[M_{f}(F_{r})=f_{1}F_{r}d_{m} \tag{2}\]  
In the formula, \(f_{1}\) is the coefficient related to the type of bearing and the load it is subjected to. For the deep groove ball bearings under only radial load conditions, its value is \(0.0009(P_{0}/C_{0})^{0.55}\). \(P_{0}\) and \(C_{0}\) are, respectively, the equivalent static load and rated static load, obtained from the product design manual of the bearing.  
The frictional torque \(M_{v}(n_{s})\) determined by bearing type, rotational speed, and grease properties, is calculated as follows:  
\[M_{v}(n_{s})=\left\{\begin{array}{cc}10^{-7}f_{2}(v\times n_{s})^{2/3}{d_{m} }^{3}v\times n_{s}>2000\\ 160\times 10^{-7}f_{2}{d_{m}}^{3}v\times n_{s}<2000\end{array}\right. \tag{3}\]  
In the formula, \(f_{2}\) is the coefficient related to the type of bearing and lubrication method. For grease-lubricated deep groove ball bearings, the value is 1.5\(\sim\)3.5, which is used as the unknown parameter to be identified. The relationship between the kinematic viscosity \(v\) of lubricating grease base oil and working temperature is as follows [43].  
\[v_{i}=v_{0}e^{-\beta(T_{i}-T_{0})} \tag{4}\]In the formula, \(\beta\) is the viscosity-temperature coefficient of the lubricant, and \(T_{i}\) and \(T_{0}\) are the working temperature and initial temperature, respectively, and \(v_{0}\) is the viscosity of the lubricant at the initial temperature.  
\(Q_{tol}\) describes the total heat generation of the bearing components. When solving the LPTN model, the total heat generation is distributed to the outer ring, rolling elements, and inner ring in a ratio of 1:2:1 [43], namely nodes 2, 3, and 4 in Figure 5.
# Optimisation of Synchronous Grouting Mix Ratio for Shield Tunnels
## 3 Results and Discussion
### Uniform Test
Newtonian fluid model:  
\[\tau=\eta\gamma \tag{1}\]  
Bingham model:  
\[\tau=\tau_{0}+\eta_{0}\gamma \tag{2}\]  
M-B model:  
\[\tau=\tau_{0}+\eta_{0}\gamma+c\gamma^{2} \tag{3}\]  
H-B model:  
\[\tau=\tau_{0}+\mathrm{K}\gamma^{n} \tag{4}\]  
The rheological test results of the remaining 15 groups of slurries were fitted nonlinearly according to the H-B model, and the rheological indices of each test group are summarised in Table 8.  
The coefficients of determination (R\({}^{2}\)) for the yield shear stress and viscosity coefficient were 0.943 and 0.633, respectively. The regression model equations are as follows:  
\[\tau_{0}=-38.134x_{1}+109.046x_{2}-14.142x_{3}-8.444x_{4}+24.284x_{5}+23.691, \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No. Test**} & \multicolumn{4}{c}{**Bingham**} & \multicolumn{4}{c}{**M–B**} & \multicolumn{4}{c}{**H–B**} \\ \cline{2-11}  & \(\tau_{0}\) & \(\eta_{0}\) & **R\({}^{2}\)** & \(\tau_{0}\) & \(\eta_{0}\) & \(c\) & **R\({}^{2}\)** & \(\tau_{0}\) & **K** & \(n\) & **R\({}^{2}\)** \\ \cline{2-11}  & **[Pa]** & **[\(\times\)10\({}^{-4}\) Pa-s]** & & **[Pa]** & **[\(\times\)10\({}^{-4}\) Pa-s]** & **[\(\times\)10\({}^{-5}\)]** & & **[Pa]** & **[\(\times\)10\({}^{-4}\) Pa-s]** & & & \\ \hline
1 & 4.18 & 155.30 & 0.97163 & 4.11 & 197.40 & \(-\)4.14 & 0.97589 & 4.11 & 287.10 & 0.87 & 0.97331 \\
2 & 8.20 & 388.50 & 0.97377 & 8  
\[K=-1859.13x_{1}-2822.7x_{2}+605.901x_{3}+1294.31x_{4}+1264.72x_{5}+1719.418 \tag{6}\]  
where \(\tau_{0}\) is the yield shear stress (Pa); \(K\) is the viscosity coefficient; and \(x_{1}\), \(x_{2}\), \(x_{3}\), \(x_{4}\), and \(x_{5}\) are the water-cement, bentonite-water, cement-sand, water reducer-cement, and latex powder-cement ratios, respectively. Equations (5) and (6) show that the yield shear stress of the prepared shield tunnel synchronous grouting slurry was affected by the five test factors from large to small: bentonite-water ratio > water-cement ratio > water reducer-cement ratio > cement-sand ratio > latex powder-cement ratio; among them, the bentonite-water ratio has the greatest impact on the yield shear stress. The viscosity coefficient of the prepared shield tunnel synchronous grouting slurry was affected by the five test factors from large to small: bentonite-water ratio > water reducer-cement ratio > water-cement ratio > latex powder -cement ratio > cement-sand ratio; among them, the bentonite-water ratio has the greatest impact on the viscosity coefficient. The yield shear stress of the slurry increases linearly as the bentonite-water and latex powder-cement ratios increase and decreases linearly as the water-cement, cement-sand, and water reducer-cement ratios increase. The viscosity coefficient of the slurry increases linearly as the cement-sand, water reducer-cement, and latex powder-cement ratios increase and decreases linearly as the water-cement and bentonite-water ratios increase.
## 2 Materials and Methods
### Preparation of the Samples for Deep Sequencing  
Genomic DNA was isolated from primary AML cells carrying the shRNA library with the QIAamp(r) DNA Blood Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. Two rounds of PCR were performed using Titanium(r) Taq PCR Kit (Takara Clontech, Kusatsu, Japan) to amplify the library barcodes (primers #4 and #5, Table S1; with up to 50 \(\upmu\)DNA in a 100 \(\upmu\)L reaction volume) and to attach the adapters for Illumina sequencing (primers #6 and #7, Table S1; 1 \(\upmu\)L of a 1:500 dilution of PCR1 as input). The PCR products were purified with the ISOLATE II PCR and Gel Kit (Bioline Meridian Bioscience Inc., Cincinnati, OH, USA) according to the manufacturer's instructions, quantified using Qubit Fluorometer (Invitrogen(tm), Thermo Fischer Scientific, Waltham, MA, USA) and submitted to the Deep Sequencing Facility (CRTD, Dresden, Germany), where multiplexed libraries were prepared. The sequencing was performed with the NextSeq 500 sequencer (Illumina Inc., San Diego, CA, USA) at the sequencing depth of \(1\times 10^{7}\) reads per sample.
## 2 System Composition and Principle  
The new ROV system mainly includes the structural system, composed of the ROV and the control box, the control system, composed of the lower computer control program, and the upper computer software. The overall structure of the new ROV system is shown in Figure 1.  
The new ROV is mainly composed of the ROV's buoyancy body in the upper part and the electromagnetic adsorption chassis in the lower part. Because of the special structural design of this ROV, it can complete the conversion of different motion forms. When it is on the ground, the motor on the chassis can be controlled to drive the tire to complete forward, backward, turning, and other actions or the electromagnetic adsorption force on the chassis can be controlled to complete the wall climbing motion on the metal wall. The electromagnetic adsorption chassis of the ROV can be disassembled, and the ROV's buoyancy body after disassembly can operate independently underwater. The portable ground control box is mainly composed of a control panel, a display screen, an embedded computer, a lithium battery, and other modules. It is connected to the ROV through an umbilical cable to realize power supply and control of the ROV.  
In the control system, the upper computer software visualization window in the portable ground control box can observe the ROV's video, depth, angle, and other information fed back by the lower computer in real time. The lower computer program is mainly used to analyze and execute various instructions of the upper computer and transmit the instructions to the thrusters, lighting, and other equipment. The upper computer and the lower computer communicate through the power carrier.
## Contributions
### Speech Production and Synthesis  
Speech synthesis research for the several Iberian languages has a long tradition, with systems being developed based both in state-of-the-art technologies as well on methods closer to human speech production. For this special issue, very good representatives of both lines of research were selected: one using recent neural architectures for Linguistic-Acoustic Mapping [8] and the other exploring finite element synthesis of vowels [9].  
Current state-of-the-art Text-to-Speech systems use statistical models such as deep neural networks to convert text to speech both in end-to-end and two-stage variants. Recurrent neural networks are a specific neural topology that proved capable of producing high quality speech, but the high computation demands, caused by the recurrent connections, make them unsuitable for low resource environments such as embedded devices or to run locally in small and mobiledevices (e.g., smartphones). The first paper of this section [8], by [PERSON], [PERSON], and [PERSON], focusing on the second part of a two-stage Text-to-Speech system, investigates both speech quality and computational efficiency of two competitive processing architectures: a linguistic-acoustic decoder based on quasi-recurrent neural networks (QLADs) and a self-attention linguistic acoustic-decoder (SALAD), based on the Transformer network. From the results of objective and subjective evaluation, performed with the Spanish speech dataset, QLAD proved to be a high quality and computational efficient replacement for recursive solutions.  
The second paper of this section is also related to acoustic modeling for speech synthesis, but this time the model is directly inspired by human speech production structures and physical phenomena and only for vowels. The paper, by [PERSON] and coworkers [9], adopts realistic 3D acoustic models to include the higher order models of propagation that typically appear above 5 kHz to study the changes in high frequency. Their work is motivated by the limitation of 1D approaches to planar propagation and the little attention of previous research to the high frequency range. The authors experimented with two vocal tract representations (realistic and simplified) derived from MRI data with glottal source configurations covering the whole phonation range. Analysis of the long-term average spectrum of the obtained synthetic speech showed diminished high frequency energy for all phonation types and all the vowels synthesized. This reduction, between 3.6 dB and 7.2 dB, strongly dependent on phonation type and fundamental frequency, may be perceptually relevant.
## 7 Conclusions and Future Prospective
\begin{table}
\begin{tabular}{p{113.8 pt} p{284.5 pt}} \hline \hline Data Sharing and Collaboration & Establishing open-access repositories and fostering collaborative efforts among researchers and institutions can facilitate the pooling of existing datasets. \\ Initiatives such as data challenges and consortium's can incentivize data sharing and standardization. \\ \hline Synthetic Data Generation & Leveraging techniques like generative adversarial networks (GANs) and other data augmentation methods can produce realistic synthetic data to supplement limited datasets, enhancing model robustness and generalizability. \\ \hline Standardized Data Collection Protocols & Developing and adopting standardized protocols for data acquisition ensures consistency and quality across datasets, making it easier to combine and compare data from diverse sources. \\ \hline Incentivizing Data Publication & Encouraging researchers to publish and share their datasets alongside their findings, perhaps through dedicated repositories or journals, can increase data availability. \\ \hline Cross-Domain Transfer Learning & Utilizing pre-trained models on related large datasets and fine-tuning them for specific tasks can mitigate the need for massive domain-specific datasets initially. \\ \hline \hline \end{tabular}
\end{table} 
Table 26: Recommended steps for experts to address the scarcity of high-quality datasets in neural network applications.
## 1 Introduction
The existing logistical and resource demands required to measure and characterize lighting on a road means existing lighting big data are very limited. Big data for urban environments in other areas such as crime, traffic safety, air pollution, and travel demand are becoming increasingly available as cities become instrumented with digital devices and infrastructure to make them'smart' [33]. There is no precise big lighting data available for studies and applications at present. Some researchers reported an approach to map artificial lightsscapes for ecological studies [34]. However, the approach assumed that street lighting luminaires are hemispherical isotropic point sources emitting at equal intensity in all directions below the horizontal. In the calculation process, information about the location, height above ground level, and bulb type of luminaires is required, and only metal-halide and low-pressure-sodium light sources were included in that study. The proposed model requires many inputs and cannot guarantee precise lighting data. Big lighting data that could provide information about various lighting metrics (e.g., illuminance levels, mesopic luminance, and melanopic illuminance performance) for an entire city can provide insights into the urban environment, particularly through their combination with other big datasets. New approaches are required to obtain such big lighting data. One feasible way is to use remote sensing technology [35]. The pixel resolution of spatial imagery has been increased from 30-100 m [36] to <10 m [37]. Remote sensing of night lights has recently been enhanced, and the threshold of detection of lit areas reached 0.4 W/m\({}^{2}\)/\(\upmu\)m/sr at several different spectral bands ranging from the visual region to the infrared region (0.44 to 2.2 \(\upmu\)m) [38]. In other research, images were taken from a plane at a height of 900 m. The imagery reaches 10 cm to 1 m pixel resolution, and was used to estimate a light sources' category [39]. Remote sensing technology is a promising technology to map lightscapes and obtain big lighting data. However, there are still some technological bottlenecks at present. First, the pixel resolution requires an improvement to around 1-3 m. Second, although the spectral bands cover the visible light region, they are not continuous. Third, it is difficult to precisely transform and correct the detected nighttime light intensities to illuminance or luminance. In this work, we propose a functional model to reconstruct illuminance distributions of road lighting using a small number feature points. We can obtain more lighting data with very few measured lighting points while reaching the same degree of measurement error as traditional measurements. This technique may be used to obtain big lighting data for lighting performance evaluations and other human-, environment-, and ecology-related impact evaluations.
## 4 Analysis of Shield Tunneling Parameters  
### Chamber Earth Pressure and Thrust Force  
In shield tunneling, the shield machine is mainly pushed by the thrust force. The chamber earth pressure reduces the soil deformation in front of the cutter by balancing the earth pressure in front of the cutter. The chamber earth pressure and thrust force in this study are obtained by real-time monitoring of the shield control system. The chamber earth pressure is the average value of each earth tank pressure. Thrust force is the superposition value of each thrust force.  
The preset value of the chamber earth pressure refers to the calculation method of static earth pressure and loose earth pressure proposed by [PERSON] et al. [20]. The value range of the static earth pressure and Terzaghi loose earth pressure are 0.24-0.30 MPa and 0.23-0.36 MPa, respectively. As shown in Figure 13, the actual chamber earth pressure of the south line is in the range of 0.22-0.27 MPa, and the north line is in the range of 0.22-0.23 MPa, indicating that the preset chamber earth pressure is reasonable.  
Figure 12: Horizontal displacement of stratum soil in monitoring section B: (**a**) SIIP-N-1; (**b**) SIIP-N-2; and (**c**) SIIP-N-3.  
At the same time, it is found from the two figures that the thrust force has a good positive correlation with the chamber earth pressure. Figure 13a shows that when crossing section A, the chamber earth pressure and thrust force fluctuate greatly. The chamber earth pressure rises rapidly from 0.23 MPa to 0.25 MPa when the shield tail comes out, increases by 8.7%, and then stabilizes to about 0.24 MPa. To ensure the stability of the soil pressure in front of the cutter, the thrust force needs to be applied synchronously. It rises rapidly from 9296 kN at the crossing section to 11056 kN (an increase of 18.9%) and stabilizes to about 10750 kN after strong fluctuation.  
As shown in Figure 13b, the fluctuation of chamber earth pressure and thrust force is relatively small. The chamber earth pressure rises rapidly from 0.220 MPa when crossing the section to 0.229 MPa when the shield tail is disengaged, increases by 4.1%, and then stabilizes to about 0.225 MPa. The thrust force changes synchronously, rapidly decreases from 15,686 kN to 14,952 kN when crossing the section, then decreases by 4.9%, and then basically stabilizes to about 13,750 kN.  
From the above analysis, it can be seen that the trends of chamber earth pressure and thrust force of the south line and the north line are different during crossing. Although the geological conditions of sections A and B are similar, the building environment is different. As shown in Figure 1, there are many large buildings near section A of the south line, while there are open spaces around section B of the north line. Therefore, during crossing, the earth pressure in the south line section is significantly released. To ensure the stability of the pressure in front of the cutter, there is a phenomenon of rapid rise.