esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
At present, the rapid tunneling technology in China's coal mines is in an accelerated development phase. The stride-type hydraulic support system, despite its effectiveness in reducing manual labor and improving safety, is characterized by slow advance rates and low support efficiency [1, 2, 3]. Additionally, the multiple applications of support on the tunnel roof can result in roof fracturing, which endangers the miners' safety [4, 5, 6]. Therefore, this paper introduces a novel hydraulic combined support system that employs an innovative strata movement method. While effectively fulfilling its support function, this system is capable of preventing roof fracturing caused by repeated support applications, enabling simultaneous operations of \"boring ahead and bolting behind\", as well as \"boring and bolting concurrently\".  
After a structural design is completed, verifying the stability and safety of hydraulic supports is crucial for ensuring the smooth progress of underground production [7, 8, 9, 10, 11]. With the increasing depth of coal mining, the issues of mining pressure manifestation become increasingly prominent. Under the action of impact loads, hydraulic supports may suffer structural damage, which can further trigger safety accidents such as roof falls and rib spallings [12, 13, 14, 15, 16, 17, 18, 19]. To enhance the stability and safety of hydraulic supports, modern engineering design has incorporated finite element software for transient dynamic analysis,allowing for the more accurate design and analysis of their structures [20; 21; 22; 23; 24; 25]. [PERSON] [26] conducted theoretical, simulation, and experimental research on stride-type hydraulic advance support equipment. [PERSON] [27] established a dynamic model of hydraulic support based on the joint constraints method and conducted dynamic simulation studies on this hydraulic support.  
Against the background of the ongoing development of rapid coal mining tunneling technology, hydraulic supports, as crucial equipment, directly influence tunneling efficiency and safety with their performance. To date, existing research has, to some extent, explored the mechanical properties of traditional hydraulic supports [28; 29; 30]. Nevertheless, when it comes to the dynamic characteristics of hydraulic combined supports under complex conditions, research efforts are still insufficient. This study sets out to delve into the dynamic response characteristics of a newly designed hydraulic combined support under diverse impact loads through transient dynamic simulation analysis, unraveling its stress and displacement patterns. The findings will underpin the optimization of the design and safety assessment. Leveraging cutting edge simulation techniques and multidimensional analysis in line with actual working conditions, this research seeks to bridge the existing research gap and propel the advancement of hydraulic support technology.
## 3 Results and Discussion
### Hydrogen Sorption Properties
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Space** & **Phase** & **Unit Cell** & **Crystallite** & **V, nm\({}^{3}\)** & **\(\Delta\)V/V\({}_{0}\)** \\  & **Group** & **Content, \%** & **Parameters, nm** & **Size, nm** & **V, nm\({}^{3}\)** & \(\Delta\)V/V\({}_{0}\)** \\ \hline \multicolumn{8}{c}{As prepared} \\ \hline TiZrVNbTa & Im\(\overline{3}\)m & 100 & a = 0.3301(5) & \(>\)200 & 0.0359(3) & - \\ \hline \multicolumn{8}{c}{After hydrogenation} \\ \hline TiZrVNbTa & Fm\(\overline{3}\)m & 100 & a = 0.4539(3) & \(>\)200 & 0.0935(2) & 30.2 \\ Pd@TiZrVNbTa & Fm\(\overline{3}\)m & 100 & a = 0.4538(1) & \(>\)200 & 0.0935(1) & 30.2 \\ TiZrVNbTa \({}^{*}\) & Fm\(\overline{3}\)m & 95 & a = 0.4517(5) & \(>\)200 & 0.0922(4) & 27.3 \\  & & a = 0.4795(7) & & & & \\  & Pnnn & 5 & b = 0.4829(4) & \(>\)200 & 0.0799(3) & 22.1 \\  & & & c = 0.3450(2) & & & \\ \hline \multicolumn{8}{c}{After dehydrogenation} \\ \hline TiZrVNbTa & Im\(\overline{3}\)m & 100 & a = 0.3321(2) & 100 & 0.0366(6) & 1.9 \\ Pd@TiZrVNbTa & Im\(\overline{3}\)m & 100 & a = 0.3314(4) & 100 & 0.0364(2) & 1.4 \\ TiZrVNbTa \({}^{*}\) & Im\(\overline{3}\)m & 95 & a = 0.3304(2) & 40 & 0.0361(5) & 0.6 \\  & Im\(\overline{3}\)m & 5 & a = 0.3395(7) & 50 & 0.0391(2) & 8.2 \\ \hline \hline \end{tabular} \({}^{*}\)-arc-melted alloy [27].  
\end{table} 
Table 2: Phase composition and structure parameters of the EBM-PDME TiZrVNbTa alloy. with the necessity to overcome the energy barrier to initiate the primary act of interaction of an alloy with hydrogen. The desorption under the conditions described above leads to the reverse phase transition from fcc to the original bcc cell, similar to that observed for the non-coated alloy. Yet, the residual unit cell parameter (\(\Delta\)V/V\({}_{0}\)) = 1.4% indicates a more complete dehydrogenation process due to a certain catalytic effect of palladium during dehydrogenation.
###### Abstract  
This study proposes a novel wavelength-tuning common-path digital holographic microscopy technique for quantitative phase imaging of functional micro-optics components. The proposed technique is immune to vibration and can reduce system error. In the proposed configuration, a parallel glass plate was inserted into the light path to create two identical test beams, which passed through a specially designed window filter. In this process, one beam serves as the object beam, while the other is diffracted to produce an ideal spherical wave front (the reference beam). A wavelength tunable laser was used as the light source to generate phase-shifting digital holograms. Structural information for the functional micro-optics components was then extracted using a classical four-step phase-shift algorithm. The viability of the proposed technique was assessed by measuring a micro-optics array.  
digital holographic microscopy; phase measurements; wavelength tuning; phase shifting +
Footnote †: journal: Applied Sciences
## 3 Test Results and Analysis
### Analysis of the Microscopic-Scale Evolution of Stress-Strain Softening Type Curves  
#### 3.2.1 Flexible Boundary Displacement Shear Zone  
Figure 9 shows the displacement cloud of the shell unit nodes on the flexible boundary of the stress-strain softening curve specimen. As shown in Figure 9, combined with the macro-mechanical characterization, the initial state, phase transition state, peak state, and critical state were taken to analyze the four states. The trend of the specimen displacement of the flexible boundary for the upper and lower parts of the displacement is large, the middle displacement is small, the trend of lateral deformation occurs in the phase transition state, the amount of displacement gradually increases, and the lateral deformation reaches the maximum value in the critical state, which presents a bulging damage pattern. Figure 10 shows the rotation process of the particles inside the stress-strain softening curve specimen, and the red dashed line in the figure marks the shear zone. As shown in Figure 10, when the specimen is in the initial state, due to the influence of the upper and lower wall stresses, elastic deformation occurs at this stage, the particles are mainly extruded from each other, the specimen is compacted, there is no obvious rotational deformation, and macroscopic compaction manifests as shear shrinkage deformation. With loading, the loading effect on the particles gradually increased, and the particles at different positions underwent different degrees of rotational deformation, particle position adjustment, and rearrangement. At this time, the macroscopic volumetric strain changed from compression to expansion, and the specimen entered a phase change state and exhibited a shear expansion trend, but the rotational deformation of the particles was still not obvious. When the axial strain reaches approximately 9%, the stress level reaches its peak, the specimen enters its peak state, the rotational angle between the particles increases, and the rotational angle is constantly adjusted to move to the beginning of the occurrence of misalignment and overrun. In the middle of the specimen, uneven bulging occurs, and the regularity of the arrangement of the particles becomes increasingly obvious, leading to localized regionalization and the formation of a staggered shear zone. When the specimen enters the critical state, the rotational deformation between the particles reaches the extreme value, forming an \"X\"-shaped shear band starting from the two ends of the specimen [24]. At this time, the shear strength of the specimen is reduced, and there is an obvious bulging deformation. Combined with the macroscopic triaxial test (stress-strain curve results), it can be concluded that a shear band is fully formed when the specimen reaches the peak stress after the strain softening stage.
# A Thorough Evaluation of GaN HEMT Degradation under Realistic Power Amplifier Operation
## 5 Experimental Results
### Tuned-Load Class-AB Stress at 30 \({}^{\circ}\)C  
The stress condition has been applied to the DUT at 30 \({}^{\circ}\)C for a total of 60 h. We report the measured data in Figure 6, where both the waveforms and the performance are shown over the stress time. We can observe a slight variation in the load line (Figure 6a), mainly due to a reduction in the drain current, visible in Figure 6c. This translates into a decrease in the output power from 5.14 W/mm to 5.00 W/mm (-2.7%), and in the drain efficiency, from 65% to 64.4%, as shown in Figure 6i,j, respectively. Concerning the input side, no significant change was observed for the gate voltage waveform, whereas the peak of the gate current tended to decrease with the stress time from 9.3 mA/mm to 8.5 mA/mm (-8.6%), as depicted in Figure 6f, reducing the average gate current consequently from 634 mA/mm to 562 mA/mm (-11.4%, Figure 6g).  
It is worth highlighting that the average currents, especially on the gate side, presented some sudden increases. This behavior was caused by the temporary suspension of the stress condition to perform the intermediate control measurements. During this time, the device experienced some recovery because of the change in the applied operating condition. Nevertheless, such a recovery was not permanent, since, as clearly shown in Figure 6g, after a brief transient, the data aligned to the previous data. This behavior was independent of the applied stress condition and, therefore, is present in all the results shown in the following.  
In Figure 7, we report the data collected from the DC control measurements executed in between stress sessions. We note a mild reduction of the saturated drain current from 851 mA/mm to 833 mA/mm (-2.1%, Figure 7b) and a small variation of the threshold voltage, here represented in Figure 7c, where the gate voltage that provides a drain current of 100 mA/mm is reported as function of the stress time.  
Figure 5: Investigated stress conditions described in Table 1, i.e., tuned-load class AB (red continuous lines) and class E (blue dashed lines): (**a**) load lines, (**b**) gate voltage waveforms, (**c**) gate current waveforms, (**d**) drain voltage waveforms, and (**e**) drain current waveforms.
## 4 Data Trend Term Removal Algorithm
### Weighted Least Squares Polynomial Trend Term Fitting  
In the regression analysis of dynamic test data, the least squares method is frequently employed to determine the variations in regression parameters. This is because the linear regression model has good statistical properties, being bias-free and consistent due to the use of the least squares method for the estimation of regression parameters under the conditions of conforming to the Gauss-Markov hypothesis.  
Let the unknown quantity to be solved be \(x_{1},x_{2},\cdots,x_{n}\), which can be obtained from \(n(n\geq m)\) directly measured values \(y_{1},y_{2},\cdots,y_{n}\) through the following functional relation:  
\[\begin{array}{l}y_{1}=f_{1}(x_{1},x_{2},\cdots,x_{m})\\ y_{2}=f_{2}(x_{1},x_{2},\cdots,x_{m})\\ \cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\\ y_{n}=f_{n}(x_{1},x_{2},\cdots,x_{m})\end{array}. \tag{21}\]  
If \(x_{i}\) is the true value, the true value \(y_{i}\) can be calculated from the known function above. If the measured value is \(y_{i}^{*}\), the corresponding error is \(\delta_{i}=y_{i}-y_{i}^{*}\), \((i=1,2,\cdots,n)\). The least squares method can be formulated as follows:\[\|\delta\|_{2}^{2}=\sum\limits_{i=0}^{n}\delta_{i}^{2}=\sum\limits_{i=0}^{n}{[y_{ i}^{*}-y_{i}]^{2}}=\text{min}. \tag{22}\]  
For measurements with unequal precision, a weight factor \(\omega(x_{i})\) of each measurement value should be added; that is,  
\[\|\delta\|_{2}^{2}=\sum\limits_{i=0}^{n}\delta_{i}^{2}=\sum\limits_{i=0}^{n}{ \omega(x_{i})[y_{i}^{*}-y_{i}]^{2}}=\text{min}. \tag{23}\]  
The weighted least squares method is expressed as follows:  
\[\|\delta\|_{2}^{2}=\sum\limits_{i=0}^{n}\delta_{i}^{2}=\sum\limits_{i=0}^{n}{ \omega(x_{i})[S^{*}(x_{i})-f(x_{i})]^{2}}, \tag{24}\]  
where \(\omega(x)\geq 0\) is the weight function on \([a,b]\). The problem of using the least squares method to fit a curve is to find the function \(y=S^{*}(x)\) in \(S(x)\) that results in the minimum value of \(\|\delta\|_{2}^{2}\).
# A New Soil Conditioner for Highly Permeable Sandy Gravel Stratum in EPBs
## 3 Results and Discussion
### Permeability Test
#### 3.3.1 Effect of the B/A Modified Slurry Concentration on Permeability  
To study the effect of B/A modified slurry injection ratios on the impermeability resistance of sand gravel, the impermeability of coarse sand with 0-2 mm diameter conditioned by different injection ratio B/A modified slurry (i.e., 0-50% with 10% interval) was obtained by impermeability tests under 2 bar air pressure. The results are shown in Figure 7a. It is observed that, with the addition of B/A modified slurry into a coarse sand sample, its impermeability was significantly enhanced compared with the sample without modifier. With the injection ratio of B/A modified slurry increasing from 10% to 40%, the permeation value reduced from 46.6 mL/min to 16.2 mL/min. With the combination of impermeability of conditioned coarse sand and economic effectiveness, the proper B/A modified slurry injection ratio is 30%. In the following experiments, the injection ratio of the B/A modified slurry is 30% if not specified.  
The blends of B/A modified slurry and different mix ratios of sodium silicate aqueous solution (i.e., B:A:S = 10:1:0-3 with 0.5 interval) were used as coarse sand conditioner to improve its permeability. The permeability tests of conditioned coarse sand samples were conducted under 2 bar air pressure to investigate the effect of sodium silicate concentration on impermeability. Results were shown in Figure 7b. It is observed that the addition of sodium silicate solution can significantly improve the impermeability of coarse sand. For example, the permeability test result of the B:A = 10:1 (volume ratio) sample is 18.5 mL/min, while the result of the B:A:S = 10:1:0.5 sample is 9.4 mL/min. In addition, as the sodium silicate concentration increases, the permeation of the modified coarse sand sample first decreases and then increases. More specifically, as the concentration of sodium silicate solution increases from 10:1:0.5 to 10:1:2, the permeation value decreases from 9.4 to 3.6 mL/min, and when the concentration continually increases to 10:1:3, the permeation value increases to 5.3 mL/min.
# Hydration of Calcium Sulfoaluminate-Based Binder Incorporating Red Mud and Silica Fume
## Materials and Methods  
### Materials  
RM was supplied by KC Corporation in South Korea; it has a density of 0.00321 g/mm\({}^{3}\) and a Blaine fineness of 2350 m\({}^{2}\)/kg. CSA was produced by Sangyong Cement Industrial Co., Ltd., Boryeong. South Korea; it has a density and Blaine fineness of 0.00295 g/mm\({}^{3}\) and 250 m\({}^{2}\)/kg, respectively. SF from Elkem Materials was used. Its Blaine fineness and density were 22,000 m\({}^{2}\)/kg and 0.002 g/mm\({}^{3}\), respectively. The chemical composition of the CSA, RM, and SF was determined by X-ray fluorescence (XRF) spectrometry, and is tabulated in Table 1. Figure 1 shows the X-ray diffraction (XRD) spectra of the raw materials.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & **Oxide Composition (wt.\%)** & & & **Density** & **Blaine Fineness** & **Ig-loss** \\ \cline{2-10}  & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **MgO** & **CaO** & **K\({}_{2}\)O** & **Na\({}_{2}\)O** & **SO\({}_{3}\)** & **(g/mm\({}^{3}\))** & **(m\({}^{2}\)/kg)** & **(\%)** \\ \hline CSA & 4.85 & 13.46 & 0.27 & 1.13 & 49.84 & 0.17 & 0.21 & 29.17 & 0.00295 & 250 & 2.36 \\ RM & 19.21 & 20.92 & 32.74 & 0.81 & 3.87 & 0.27 & 13.15 & 0.26 & 0.00321 & 2350 & 9.41 \\ SF & 95.50 & 1.00 & 0.30 & 0.50 & 0.40 & 1.00 & - & - & 0.00200 & 22,000 & 2.00 \\ \hline \hline \end{tabular} Note. CSA: calcium sulfoaluminate expansive agent; RM: red mud; SF: silica fume.  
\end{table}
Table 1: Chemical composition and physical properties of raw materials.
# Geovisualization Techniques of Spatial Environmental Data Using Different Visualization Tools
## 1 Introduction
In our point of view, the findings of papers [24; 26; 28; 35; 38] are of high importance that are about justifying that 2D or even more 3D visualization of data enhances their cognitive interpretation. Due to the increased interest, a comprehensive overview of cognitive infocommunications is already available in the form of a book, which also provides an outstanding theoretical foundation for the topic [45]. The mapping and correct interpretation of environmental data is of great importance in environmental science and environmental policy as well, which is a complex task due to the significant spatial and temporal variability of the data. In our study we aim to facilitate the evaluation of the data with an interactive web map created with open access webGIS tools and geoinformatics software. In this project we analyzed a spatial and temporal database regarding the ground water supply of an Eastern Hungarian settlement. Among others, the novelty of our study lies in the fact that even though the application of various geovisualization technologies are common in environmental sciences, the geoinformatics processing and complex online presentation of urban environmental data have not been investigated yet. Furthermore, the methodological comparison of applied technologies is not included in research aimed at environmental problems. By using the example of the contamination of the groundwater supply of an Eastern Hungarian settlement, in our study we aimed to answer the question whether free geovisualization techniques (QGIS, KML, Google APIs) are suitable to create such automated methods, as a result of which--withthe publication of spatial data on the web--the end user may gain additional information on the investigated subject through their senses. In addition to the above, we investigated whether these 2D or 3D geovisualization techniques are more effective than the printed maps from various sources or not. With help of this techniques we investigated the spatial patterns of the phosphate and nitrate contents of the groundwater of the settlement and the changes which had occurred five years after the establishment of the sewage system in the settlement, compared to the baseline. Based on the above, our purpose is to publish and demonstrate the spatial visualization and processing of spatial environmental databases of a Hungarian settlement, and also highlight the power of geovisualization in terms of cognitive infocommunication, using open-access webGIS tools and geoinformatics software.
## 2 Materials and Methods
### Functional and Data Description  
This section describes the main system functionalities and the data structure involved.  
First of all, system functionalities are reported. A single slave node can perform several commands depending on the system status. Each node has an identification number (ID), which allows the master to distinguish between them. The master unit, in addition to its ability to vary node ID or bus baud rate, has the functionality to test the system operation through a rollcall function. In this function, a broadcast packet is sent from the master to the node net so that system integrity is deduced from the number of responding nodes. Generally, this command is sent either after the power of the system is turned on for an initial test or between two missions.  
The master unit can retrieve several types of data from nodes after a mission, depending on the requested function. The first typology of available data is the raw acceleration throughout the mission (both from ADXL355 and ADXL345). The master can request up to 2048 bytes of payload per packet, formatted as represented in Figure 4a. A second data typology is the cumulative FFT, where FFT is calculated over a temporal average of accelerometric data sampled during the mission. The packet sent consists of a fixed 24.5 kB length equally distributed over 8.2 kB of FFT data for each axis. A single 8.2 kB data chunk, for each spatial axis, is composed of an 8 bytes repetitive sequence. Each sequence is composed in turn by FFT real part for the first 4 bytes and FFT imaginary part for the last 4 bytes. The structure of this packet is reported in Figure 4b.  
Figure 3: Flow diagram representing sensor node operation in its on state.  
Figure 4: Packet structure: (**a**) raw time domain data, (**b**) cumulative FFT, (**c**) spectrogram.  
Finally, the third data type retrievable from the slave node is the spectrogram; it is a single-axis FFT represented with respect to the mission time. The spectrogram allows us to distinguish the building modal harmonics variations over time. This measurement involves time, hence, a programmable time window, as well as a programmable sampling frequency, varying between 100 and 500 Samples Per second (SPs) have been defined. This parameter is very critical, as it affects the measurement resolution and accuracy for damage evaluation on the measured building, as well as the inclination to aliasing-based issues. In addition, results from spectrogram are a 2-dimensional graph: the magnitude of each frequency component is represented by a color gradient. This last feature could cause complications during the reading of the results; hence, in future works we planned to use a 3-dimensional spectrogram.