esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Properties of Disconnectable Coupling Joints for Steel Bracing under Eccentric Load
## 4 Bearing Capacity under Eccentric Load
### Parameter Analysis of Bearing Capacity
#### 4.2.6 Influence of Channel Steel Thickness  
Figure 16a lists the effect of channel steel thickness on the bearing capacity of DC joints. The increase of channel steel thickness improves the bearing capacity of DC joints. The beneficial effect reduces gradually. When the load is less than 2200 kN, the impact of channel steel thickness on the bearing capacity of the DC joints is weak. However, the effect amplifies as the load increases. The relationship of channel steel thickness with initial compression steel and yield load is shown in Figure 16b. The channel steel thickness positively correlates with initial compression stiffness and yield load, and the correlation  
Figure 14: Impact of steel wedge height on bearing capacity.  
Figure 15: Impact of steel tube wall thickness on bearing capacity.  
gradually decreases. When the channel steel thickness increases from 8 mm to 10 mm, the initial compression stiffness and yield load increase by 61.9 kN/mm and 311 kN, respectively, and their increase rates are about 1.7% and 8.8%, respectively. The initial compression stiffness and yield load increase by 34 kN/mm and 190 kN, respectively, as the thickness increases from 16 mm to 18 mm, and their increase rate is only about 0.9% and 5.3%, respectively.
## 2 Materials and Methods
### Numerical Ultrasound Propagation Model
#### 2.1.4 Material Inhomogeneity: Cortical Pores  
To study the effect of material inhomogeneity, cortical pores were included in the curved bone plate models (Figure 2d). Previous ex vivo studies in human cortical bone reported cortical porosity (_Ct.Po_) and cortical pore diameter (_Ct.Po.Dm_) values between 2% and 22% and 7 and 95 \(\upmu\)m, respectively [28; 29; 30]. Cortical pores were defined as circular pores with _Ct.Po.Dm_ = 60 \(\upmu\)m and varying pore densities, resulting in models with _Ct.Po_ values ranging from 0% to 20% with an increment of 2%.  
For the simulation models with cortical pores, transmission measurements were performed to calculate the reference speed of sound _Ct.v\({}_{11}\)Ref._ An unfocused single-element transducer with a width of 0.3 mm emitted ultrasound waves with a center frequency of 4 MHz and a \(-\)6-dB bandwidth of 60%. The unfocused ultrasound wave traveled though the bone and the transmitted ultrasound wave was captured by a single element detector with a width of 0.3 mm, which was placed below the bone. The transducer and detector were placed at the beam axis of the reference MF simulation. In addition, a simulation was performed with the same configuration without the bone to measure the reference signal transmitted though water. The time-of-flight of the ultrasound wave transmitted through water _TOF\({}_{H20}\)_ and bone _TOF\({}_{bone}\)_ was defined at the time of the maximum of the signal envelope. The _Ct.v\({}_{11}\)Ref_ of the bone models with pores were calculated using the following equation from [31].  
\[\mathit{Ct.}\ ^{*}_{11}\ ^{\mathit{Ref}}\ =\ \frac{\mathit{Ct.Th}_{bone}}{ \frac{\mathit{Ct.Th}_{bone}}{\mathit{Hi}_{20}}\ +\ \big{(}\mathit{TOF}_{bone}\ -\ \mathit{TOF}_{H_{20}}\big{)}}, \tag{1}\]  
with _Ct.Th\({}_{bone}\)_ = 4 mm.
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 5 Results and Analysis
### Daylight Readings  
The daylight (morning) readings collected consisted of 101 data points and the water level ranged from below \(-2\) m to above \(2\) m. The water level measurement was offset to match the pre-existing system which was calibrated against the mean sea level (MSL) during the construction of the water gate facility. The measurement data plots generated by the system against the ultrasonic sensor are shown in the figures below. The root-mean-square error (RMSE) during the daylight was \(0.41\) m averaged over the entire testing period. The breakdown of the RMSE for each weather condition for both day and night periods is tabulated in Table 1. Although the installation of the polariser significantly improved the stability and consistency of the results, the reflections appearing from different angles had the tendency to cause the camera to miscalculate the depth, resulting in a greater RMSE error during the day.
## 5 Experimental Data Collection  
The seismic signal collection was conducted at the elephant orphanage in Pinnawala, Sri Lanka (Figure 7), where there are about twenty-five elephants in a free-ranging area.  
The elephants' behavior was closely observed before the experiment. It was clear that in hot weather, elephants tended to be inactive and spend most of the day under trees. However, in normal weather, they became active and engaged. Seismic data were collected  
Figure 6: Determination of the best feature–algorithm combination for machine learning using the LazyPredict platform. Here, SED means spectral energy distribution.  
Figure 7: Free-ranging area in Pinnawala Elephant Orphanage. The observation hall is the marked rectangle closer to the right bottom corner.  
under average weather conditions using three equispaced geophones buried near the fence that is marked with straight lines in Figure 7. Elephant behavior was video-recorded during the few-hour data capture. At the time the food truck arrived, one elephant rumbled quite noticeably as shown in Figure 8, where the elephant is seen literally standing on two legs, exerting maximum pressure on the ground that would enhance the seismic rumble propagation through the ground.  
The seismic data that were aligned with audio and video records of the rumble were extracted from the recorded seismic signal. The spectrogram clearly showed the frequency patterns of strong elephant rumbles as shown at the 10 s mark of the top two illustrations of Figure 9.
# Experimental Study on Permeability Evolution of Sandstone during Triaxial Compression Damage
## 1 Introduction
[22] carried out triaxial compression tests under different confining pressures and seepage pressures and used 3D digital image correlation and scanning electron microscopy to monitor the strain localization behavior of sandstones under different conditions and its effect on permeability evolution. After reviewing the aforementioned research findings, it is evident that there is a limited amount of research focused on the relationship between the mechanical parameters, permeability characteristics, and damage state of rocks under hydromechanical conditions. To address this gap, the present study utilizes a Rock Top multi-field coupled tester and employs the steady-state permeability testing method to conduct an extensive investigation into the permeability evolution patterns and mechanical parameters of sandstone subjected to various hydromechanical conditions during the triaxial compression damage process. Moreover, a detailed analysis of the rock's permeability under specific strain thresholds at each stage of stress-strain was conducted. The results of this study offer valuable insights into the safeguarding of deep rock engineering.
## 2 Materials and Methods
### Modeling of Motor-Driven Steam Control Valve Opening Angle  
From a rigorous analytical standpoint, a preceding investigation revealed that the opening angle of a motor-driven steam control valve has a direct influence on the flow rate of hot steam, which subsequently impacts the temperature [17]. Consequently, it becomes imperative to develop a model for the opening angle of the steam-regulating valve to facilitate a more precise characterization of the temperature dynamics. The regulating valve control system, as depicted in Figure 3, comprises crucial components such as an embedded controller, a valve drive system, a data processing unit, and a pressure sensor [18]. Once the desired flow rate is set in the logic-programmable controller, the opening angle controller acquires the real-time opening angle value of the valve and measures the pressure differential across the valve using a pressure sensor. These data are then transmitted to the upper computer, which utilizes interpolation calculations on the temperature data table to deduce the appropriate valve opening angle corresponding to the target temperature. This derived value is then relayed back to the opening angle controller, which generates a signal to adjust the valve's opening angle. Subsequently, the controller transmits this signal to the valve drive system, instructing it to manipulate the valve to achieve the desired opening angle. During this process, the motor's angular displacement is effectively transformed into linear displacement through a worm gear and screw mechanism, thereby enabling precise control of the valve's opening angle [19].  
Figure 3: Control system schematic for steam control valves.  
Regarding the control signal generated by the PLC and subsequently converted by A/D to adjust the opening angle of the steam valve, this component is represented by a higher-order transfer function incorporating a delay element [20,21]. Nevertheless, the active disturbance rejection control inherent in this higher-order transfer function poses convergence challenges, making it difficult to attain the intended control effect. Upon closer examination of this higher-order transfer function, it becomes apparent that it can be simplified into multiple constant gain values. Furthermore, based on a practical analysis of the system's behavior, it is reasonable to omit this component from further consideration.
## 2 Materials and Methods
### MTS Cytotoxicity Assay  
Cell viability and in vitro cytotoxicity was performed using MTS assay, as it offers several advantages over other cytotoxicity tests such as MTT [29]. Intensity of the color varies based on the amount of formazan and is proportional to the number of viable cells; reduction of this intensity is indicative of cellular damage. To perform this assay, 20 uL of CellTiter 96\({}^{\text{0}}\) Aqueous One Solution Reagent, which is composed of the novel tetrazolium compound MTS and an electron coupling reagent phenazine methosulfate (PMS), was added to each well containing cells treated with EFA. After 3 h in culture, the cell viability was determined by measuring the absorbance of a coloured, water-soluble formazan at 490 nm using a microplate reader. Results are expressed as a percentage of viability in control cells incubated in medium alone
## 4 Discussion
### Discussion of Major Quantitative Findings and Qualitative Feedback from the Users
#### 4.1.2 Attitude to Use  
In terms of ATU for the interactive interface, our quantitative findings suggest that users of joystick control have a better ATU than users of hand gesture recognition. Also, users who apply the joystick control for more than 20 min have a relatively better ATU for the interface than the users who use the joystick control for less than 15 min. According to our qualitative inquiries, this result might be attributed to the fact that users believe using a joystick controller is rather easier to operate and control and helps complete the tasks efficiently, whereas hand gesture recognition makes them irritable, painful, impatient, and tried. Therefore, users give negative feedback for hand gesture recognition, similar to the research finding by [PERSON] et al. [7] (see Table 7).
## 4 Experimental Results and Analysis
### Discussion  
In the first boundary layer control experiment, the suction method has been proven to effectively improve the working conditions of the optical cavity. The results of the three experiments show the following:  
1. Managing the boundary layer of the optical cavity and improving the pressure distribution, especially at the back part of the optical cavity, have been achieved to a certain extent.
2. Increasing the pumping volume of the boundary layer within a certain range can further reduce the thickness of the boundary layer and the pressure on the optical cavity, while increasing the chemical reaction platform power. However, excessive pumping volume reduces the power.  
Figure 18: Pressure distribution of the reaction cavity and supersonic diffusers.  
Among the three experimental parameters, the mainstream ejection mode was the most sensitive to pumping volume. When the pumping volume was increased to 6%, the chemical reaction platform power decreased significantly. The orifice plate was not highly sensitive to the pumping volume. When the pumping volume increased from 2% to 8%, the impact on the chemical reaction platform power was apparent.  
Subsequently, a supersonic diffuser based on active gas pressurization sprayed high-pressure supersonic active gas in the channel, which can realize efficient pressure recovery and cut off the propagation path of the disturbance by mixing it with the incoming gas. In this study, a supersonic diffuser based on active gas pressurization was studied experimentally, and the following conclusions were drawn:  
1. When the supersonic diffuser operates at the design point, it can start smoothly and operate normally under an initial back pressure of 3.85 \(\mathrm{P_{D}}\). If the flow rate of the active gas is properly increased, the diffuser performance can be further improved.
2. The results of experiment 1# show that the incoming flow of the chemical reaction platform was more sensitive to the blocked area of the downstream channel. If the blocked area is excessively large, the incoming flow of the chemical reaction platform will be blocked, resulting in abnormal operation of the reaction platform.
3. The results of experiments 2# and 3# show that the diffuser can be started smoothly at an initial back pressure of approximately 3.9 \(\mathrm{P_{D}}\) and stabilized the flow field in the optical cavity well, preventing the propagation of downstream disturbance and creating necessary conditions for a normal reaction in the optical cavity.
## 4 Formulation of Maximum Likelihood Estimator  
To estimate the position of a passive target the Maximum Likelihood estimator is employed, since the probability density functions of measurement errors are known. A zero-mean Gaussian distribution is assumed for the TDOA measurement errors in the formulation of the localization issue [2]. Therefore, the likelihood function is obtained as  
\[L(\bar{\mathbf{r}}|\mathbf{x})=\frac{1}{\left(2\pi\right)^{N/2}\det\left( \mathbf{C}\right)^{1/2}}\exp\left(-\frac{1}{2}(\bar{\mathbf{r}}-\mathbf{d}( \mathbf{x}))^{T}\mathbf{C}^{-1}(\bar{\mathbf{r}}-\mathbf{d}(\mathbf{x})) \right), \tag{8}\]  
Then, taking the logarithm of the Equation (8), log-likelihood function is yielded  
\[\ln L(\bar{\mathbf{r}}|\mathbf{x})=k-\frac{1}{2\sigma^{2}}\Big{(}(\bar{ \mathbf{r}}-\mathbf{d}(\mathbf{x}))^{T}(\bar{\mathbf{r}}-\mathbf{d}(\mathbf{ x}))\Big{)}, \tag{9}\]  
in which \(k=\ln\left(\frac{1}{\left(2\pi\right)^{N/2}\det\left(\mathbf{C}\right)^{1/2}}\right)\) is neglected as a constant independent of \(\mathbf{x}\). As the solution to the minimization problem, we obtain the estimated target position \(\hat{\mathbf{x}}\)  
\[\hat{\mathbf{x}}=\underset{\mathbf{x}\in\mathbb{R}^{2}}{\operatorname{argmin} }\left(J_{ML}(\mathbf{x})\right), \tag{10}\]  
with objective function of ML estimator  
\[J_{ML}(\mathbf{x})=(\bar{\mathbf{r}}-\mathbf{d}(\mathbf{x}))^{T}((\bar{ \mathbf{r}}-\mathbf{d}(\mathbf{x}))). \tag{11}\]  
The corresponding objective function \(J_{ML}(\mathbf{x})\) of the formulated ML estimation problem is depicted on Figure 2.  
Based on an examination of the topology of the function \(J_{ML}(\mathbf{x})\) depicted in Figure 2, it is determined that \(J_{ML}(\mathbf{x})\) is a multi-modal function with pronounced nonlinearities. Wherever the global minimum of a function occurs is where the target \(\mathbf{x}\) should be located, due to the difficulty of the \(J_{ML}(\mathbf{x})\) function, it is important to use advanced algorithms for finding the best possible solution globally.
## 2 Materials and Methods
### Volatile Profiling of the Crude Leaf, Stem, and Root Extracts Using GC-MS  
The crude _C. mutans_ leaf, stem, and root ethyl acetate extracts were re-dissolved in HPLC-grade ethyl acetate to appropriate concentrations and filtered using 0.22 \(\upmu\)m syringe filters prior to GC-MS analyses. The GC-MS analysis was carried out according to [9], with some modifications. Briefly, 1 \(\upmu\)L aliquot of each sample was injected into the GC-MS equipment (GC model 7890 and MS model 5975C; from Agilent Technologies, Santa Clara, CA, USA). An HP-5 ms capillary column (30 m \(\times\) 0.250 mm inner diameter \(\times\) 0.25 \(\upmu\)m of film thickness) from Agilent Technologies was used for separation. GC-MS was performed in a splitless mode and pure helium was used as the carrier gas with a flow rate of 1 mL/min. The MS source was pre-set to 230 \({}^{\circ}\)C and MS quad temperature was recorded at 150 \({}^{\circ}\)C. The injector temperature was set to 250 \({}^{\circ}\)C with an injection volume of 1 \(\upmu\)L per sample. The oven temperature was programmed from 220 \({}^{\circ}\)C for 10 min and continuously increased at a rate of 5 \({}^{\circ}\)C/min until a temperature of 300 \({}^{\circ}\)C was reached and then retained for 10 min. The total run time was 36 min. The post-run was fixed at 200 \({}^{\circ}\)C for 5 min, and the ion source temperature was constant at 200 \({}^{\circ}\)C. The mass analyzer was performed in a full scan mode from _m/z_ 40 to 550, and mass spectra were obtained at EI = 70 eV. The identification of the compounds was performed by spectra matching using the National Institute Standard and Technology (NIST) library version 11 (NIST11). The leaf, stem, and root profiles obtained were compared to select for the most suitable plant's part to be used for the subsequent isolation of target compound.
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 2 Materials and Methods
### Remote Sensing Data Acquisition  
Landsat imageries from 1988, 1993, 1998, 2003, 2008, 2013 and 2019 were used in this study to map and quantify LULC changes in the city of Maseru over three decades. These cloud-free imageries were downloaded from the United States Geological Survey (USGS) data repository ([[http://www.usgs.gov/](http://www.usgs.gov/)]([http://www.usgs.gov/](http://www.usgs.gov/)) accessed on 15 October 2019). The images were acquired during the rainy season and between December and March to help distinguish between agricultural fields with bare soil and built-up areas (Table 1).
## 3 Summary of the Artificial Neural Network (ANN) for the CO2 Emission Prediction Model
### Convolutional Neural Network (CNN)  
As depicted in Figure 3, CNN is a type of deep ANN that consists of three main layers: convolutional layers, pooling layers, and fully connected layers [41, 42]. Unlike traditional MLPs, CNNs leverage shared weights to efficiently process high-dimensional data, reducing computational complexity. In CNN architecture, the convolutional layer extracts patterns from the input features, while the pooling layer reduces the spatial dimensions of the intermediate representations. This lowers the number of parameters and improves the training efficiency. Finally, the fully connected layers perform a linear transformation to produce the final output [15, 40].
# All-in-Focus Three-Dimensional Reconstruction Based on Edge Matching for Artificial Compound Eye
## 4 Edge Matching and Propagation Method
### Edge Depth Estimation
#### 4.2.3 Edge Depth Fusion  
\(D^{i}(p)\) obtains the depth map of \(I\) with an adjacent sub-image \(I_{i}\), but the inherent offset between adjacent sub-images leads to an incomplete depth map, as shown in Figure 9. To estimate the complete depth map of \(I\), depth maps of all four neighboring sub-images are fused.  
In the depth fusion step, the depth reliability of \(D^{i}(p)\) is first detected using Equation (6). Then the edge direction of pixel \(p\) is utilized, and if the offset between edge direction and depth search direction is smaller than \(5^{\circ}\), the depth of pixel \(p\) is also considered unreliable, and set to 0.  
\[D^{i}(p)=\begin{cases}d,&C^{i}(p,d)\leq\delta\\ 0,&C^{i}(p,d)>\delta\end{cases} \tag{6}\]  
After that, all four edge depths are fused to obtain the final edge map \(D(p)\), which is fused in the following three cases:  
1. The pixel \(p\) in the four \(D^{i}(p)\) has the same depth. The depth of \(D(p)\) remains the same.
2. The pixel \(p\) in the four \(D^{i}(p)\) has different depths, and the difference is smaller than 2 pixels. The smaller depth is chosen.
3. The pixel \(p\) in the four \(D^{i}(p)\) has different depths, and the difference is not smaller than 2 pixels. The depth of the pixel is considered unreliable, and \(D(p)\) is set to 0.
# Marble and Glass Waste Powder in Cement Mortar
## 3 Results and Discussion
### Correlations Between Properties of Mortar  
Pearson correlation analysis is a valuable statistical method used to assess the strength and direction of the linear relationship between two numeric variables. The Pearson correlation coefficient, denoted as r, ranges from \(-1\) to 1, where r = 1 indicates a perfect positive correlation, meaning that as one variable increases, the other also increases in direct proportion. Conversely, r = \(-1\) represents a perfect negative correlation, signifying that as one variable increases, the other decreases in a perfectly linear fashion. When r = 0, it implies no linear relationship between the variables [73].  
The degree of correlation can be categorized into different ranges: values between 0.9 and 1 indicate a very strong relationship, while those between 0.7 and 0.89 signify a strong correlation. A moderate correlation falls within 0.4 to 0.69, whereas values between 0.1 and 0.39 suggest a weak association. Correlations below 0.19 are considered negligible [24; 73].  
Figure 17 presents the Pearson correlation matrix for key MGWP mortar properties, including compressive strength (CS), UPV, strength loss due to sulfate attack (SL), and water absorption (WA). The results reveal significant interdependencies between these parameters, with strong positive and negative correlations observed. This analysis provides critical insights into how mortar properties influence each other, which is essential for optimizing material performance and improving the durability of cementitious composites.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Assignment**} & \multirow{2}{*}{**Bond**} & \multirow{2}{*}{**Phase**} & \multicolumn{4}{c}{**Absorption Peak (cm\({}^{-1}\))**} \\ \cline{3-6}  & & & **MGWP0** & **MGWP10** & **MGWP30** \\ \hline O-H stretching of Ca(OH)\({}_{2}\) & O-H & Portlandite & 3698 & 3690 & 3694 \\ Stretching of water molecules & O-H & H\({}_{2}\)O capil & 3362 & 3241 & 3387 \\ (asymmetric and symmetric) & C-H & CH\({}_{2}\)/CH\({}_{3}\) & 2919 & 2921 & 2922 \\ H-O-H bending of absorbed water & O-H & H\({}_{2}\)O capil & 1606 & 1618 & 1598 \\ Asymmetric stretching of CO\({}_{3}\)\({}^{2-}\) & C-O & Calcite & 1426 & 1422 & 1425 \\ S-O asymmetric stretching of sulfate & S-O & Ettringite & 1114 & 1114 & 1114 \\ Spotching of Si-O bond of C-S-H & Si-O & Afwillite & 980 & 985 & 1033 \\ Si-O-Si bending vibrations of C\({}_{3}\)S & Si-O & Alite & 870 & 865 & 869 \\ Si-O-Si bending vibrations of C\({}_{2}\)S & Si-O & Belite & 443 & 463 & 458 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of FT-IR absorption peaks and phase assignments for mortar mixes cured at 28 days.  
Figure 16: FT-IR spectra of MGWP mortar cured at the age of 28 days.