esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation on Rock Fracturing Characteristics
### Simulation Verification  
In this section, a simulation verification is conducted with the expansion fracturing experiment. Concrete specimens are made for static fracturing tests, and the Abaqus software is used to simulate the expansion fracturing process.  
The test schematic of static fracturing is shown in Figure 4a, the dimensions of the specimen are 400 mm length, 300 mm width, and 300 mm thickness. The hole diameter of the test specimen is 42 mm, and six strain gauges are arranged at 10 mm, 30 mm, and 50 mm from the borehole for strain monitoring. As shown in Figure 4b, concrete specimens are made for static fracturing tests, specimens are demolded after 24 h, the standard curing room is maintained for 28 days, and the mixture proportion used for the specimens is listed in Table 2.  
Figure 4: The test schematic of static fracturing of a concrete sample. (**a**) Schematic diagram of the test; and (**b**) physical picture of specimen.  
Figure 3: Expansion stress–time curve of static cracking agent.  
The numerical model of static fracturing is shown in Figure 5; the cohesion element is inserted in the numerical model, which is dependent on mesh partitioning. Triangular meshes are chosen as the basis for cohesion elements, with a mesh size set to 0.005. Considering the slow expansion and cracking process of the static fracturing agent, this problem can be simulated as a quasi-static issue. Explicit dynamic analysis is selected for the numerical simulation and a displacement of 0.01 mm is uniformly applied to the cohesion element nodes around the pre-drilled circular area. In the simulation, the mechanical parameters of the concrete specimens are shown in Table 3.  
Test and simulation results of expansion fracturing are shown in Figure 6. From Figure 6a,b, the failure forms of concrete in the laboratory test and numerical simulation are basically consistent. From Figure 6c, the strain growth in the simulation is relatively smooth. Due to the presence of microcracks in the concrete specimens during the manufacturing process and the varying degrees of cementation between the internal aggregates and mortar, the cracks will rapidly propagate when the expansion pressure increases to a certain value, causing a sharp
# A Review of the Water Desalination Technologies
## 2 The State of the Art
### Humidification-Dehumidification (HDH)  
The Humidification Dehumidification (HDH) system is a recent carrier gas based thermal desalination technique. In detail, freshwater can be obtained by condensing the air humidity. The essential components are the humidifier and the dehumidifier. [PERSON] et al. proposed two possible solutions, adding a heat pump to improve the energy efficiency [82].  
Both solutions are equipped with three different circuits: air, water and freon. The last one is confined inside the pipes and the main components of the heat pump (compressor, condenser, lamination valve and evaporator). The air is recirculated by a fan in a close loop, going through two chambers, where the humidification and the dehumidification occur. Only the water circuit is open, as saline water is the input, while brine and freshwater are the outputs [82].  
As shown in the schema reported in Figure 32, saline feedwater is firstly refrigerated by the condenser of the heat pump. The cold saline water is used to promote the condensation of air humidity inside the dehumidification chamber (on the right), where freshwater is produced. During this process, the saline water temperature increases. After that, saline feedwater is nebulized inside the humidification chamber (on the left), promoting the evaporation of feedwater.  
In the solution reported in Figure 32, the thermal supply from the condenser is transferred to the saline water after the dehumidifier unit.  
The alternative solution proposed by the same authors is reported in Figure 33, where the thermal supply from the condenser is transferred to the air coming from the humidifier unit [82]. Instead of the forced air circulation, a natural air circulation system has been proposed, where the thermal supply is produced by a solar panel [28; 83; 84]. In any case, HDH desalination is a technology under investigation.  
Figure 32: A Humidification–Dehumidification (HDH) unit using a heat pump with a water refrigerated condenser.
## 6 FIFO and LRU Caching Analysis with Data of Different Sizes
### Approximation Scheme for the Mean Unused Space in FIFO Caches
The results are very similar to the FIFO results in Figure 7. In the case of \(\beta=0\), the FIFO and LRU HRCs are identical, as are the HRCs for both approximation variants. In the third case with a preference for large objects, LRU and its approximations achieve a slightly higher hit ratio. Up to 6% higher LRU hit ratios are observed for the second case, with the same shape of relative deviations for the approximation variants for LRU as for FIFO. Although LRU and FIFO caches are frequently applied, it has often been observed [2; 17; 18] that their web caching performance can be far below the optimum when compared with score- or utility-based methods [10; 15]. The latter can use specific information about data items in their caching decisions. Then objects can be preferred in the cache with the highest ratio of popularity to size \(p_{k}/s_{k}\) as a score to maximize the IRM hit rate according to a knapsack solution. Figure 7: FIFO approximation deviations for a case of uniform data-size distribution.
# Optimization of BP Neural Network Model for Rockburst Prediction under Multiple Influence Factors
###### Abstract  
Rockbursts are serious threats to the safe production of mining, resulting in great casualties and property losses. The accurate prediction of rockburst is an important premise that influences the safety and health of miners. As a classical machine learning algorithm, the back propagation (BP) neural network has been widely used in rockburst prediction. However, there are few reports about the influence study of different training sample sizes, optimization algorithms and index dimensionless methods on the prediction accuracy of BP neural network models. Therefore, 100 groups of typical rockburst engineering samples were collected locally and abroad, and considering the relevance, scientificity and quantifiability of the prediction indexes, the ratio of the maximum tangential stress of surrounding rock to the rock uniaxial compressive strength (\(\sigma_{\circ}/\sigma_{\rm c}\)), the ratio of the rock uniaxial compressive strength to the rock uniaxial tensile strength (\(\sigma_{\rm c}/\sigma_{\rm t}\)) and the elastic energy index (\(W_{\rm wt}\)) were chosen as the prediction indexes. When the number of samples was 40, 70 and 100, sixty improved BP models were established based on the standard gradient descent algorithm and four optimization algorithms (momentum gradient descent algorithm, quasi-Newton algorithm, conjugate gradient algorithm, Levenberg-Marquardt algorithm) and four index dimensionless methods (unified extreme value processing method, differentiated extreme value processing method, data averaging processing method, normalized processing method). The prediction performances of each improved model were compared with those of standard BP models. The comparative study results indicate that the sample size, optimization algorithm and dimensionless method have different effects on the prediction accuracy of BP models, which are described as follows: (1) The prediction accuracy value \(A\) of the BP model increases with the addition of sample size. The average value \(A_{\rm ave}\) of twenty improved models under three kinds of sample sizes increases from \(A_{\rm ave}\) (40) = 69.7% to \(A_{\rm ave}\) (100) = 75.3%, with a maximal value \(A_{\rm max}\) from \(A_{\rm max}\) (40) = 85.0% to \(A_{\rm max}\) (100) = 97.0%. (2) The value \(A\) and comprehensive accuracy value \(C\) of the BP model based on four optimization algorithms are generally higher than those of the standard BP model. (3) The improved BP model based on the unified extreme value processing method combined with the Levenberg-Marquardt algorithm has the highest value \(A_{\rm max}\) (100) = 97.0% and value \(C\) = 194, and the prediction results of five engineering cases are completely consistent with the actual situation at the site, so this is the best BP neural network model selected in this paper.  
rockburst prediction; mining safety prevention; BP neural network; model optimization; sample size; optimization algorithm; index dimensionless 2023
# Investigation of the Influence of Splitter Blades on the Resonance Conditions of Impellers
## 3 Numerical Analysis
### Mistuned Case
#### 3.3.2 Hazard Evaluation for Excitations of Various Engine Orders  
Here, mode 192 is chosen as the target mistuned mode for the following analysis. It corresponds to the ND4 mode in MBF5.  
When evaluating the harmfulness of the excitations with various engine orders in the conventional way, the ND components need to be analyzed for the mistuned mode first. Then, the engine orders of excitation corresponding to these ND components can be determined by the conventional resonance conditions using Equation (29). After that, the harmfulness of these excitations can be ranked based on the corresponding amplitudes of the ND components [36; 37]. Here, the nodal diameter spectrum method proposed by [PERSON] et al. is used to analyze the ND contents of the mistuned mode 192 and the result is shown in Figure 7a. The ND components are sorted by amplitudes in descending order: ND4, ND6, and ND3. According to Equation (29), the engine orders of excitation which can arouse the corresponding ND components are \(n_{1}=4,13\ldots,n_{2}=6,11\ldots\) and \(n_{3}=3,14\ldots\), respectively. Thus, the harmfulness of these excitations can be ranked as follows: \(n_{1}\), \(n_{2}\), and \(n_{3}\).  
According to Section 2, the evaluating process is now conducted as follows: first analyze the harmonic contents of the target mistuned mode. This can be done by performing spatial harmonic content analysis of the blade-based RMV. Then, determine the engine orders of excitation based on the proposed resonance condition using Equation (27). After that, rank the harmfulness of these excitations according to the corresponding amplitudes of the harmonic components. The spatial harmonic contents analysis result is shown in Figure 7b and the largest three harmonics are: \(h_{1}=15\), \(h_{2}=2\), and \(h_{3}=4\). According to Equation (27), the corresponding engine orders are \(n_{1}^{\prime}=15,19\ldots\), \(n_{2}^{\prime}=2,32\ldots\) and \(n_{3}^{\prime}=4,30\ldots\) respectively. Thus, the harmfulness of these excitations can be ranked as follows: \(n_{1}^{\prime}\), \(n_{2}^{\prime}\), and \(n_{3}^{\prime}\).  
Obviously, different evaluation results are obtained. To validate the proposed resonance conditions and the evaluation results, harmonic response analysis is performed in the following section.
# Dynamic Queries through Augmented Reality for Intelligent Video Systems
## 4 Similarity Analysis
### The Proposed Metric and Its Relationship with the Metric \(L_{1}\)  
This section shows how the proposal in specific circumstances becomes equivalent to the \(L_{1}\) metric. This becomes important because, in several scenarios, it will improve and simplify its computation, speeding up the process of trajectory querying. The scenario in which this becomes equivalent is whenever the similarity criteria are approximated as a linear polynomial.  
The proposed metric can be seen as a composition of the metric \(L_{1}\), but it produces different results in some aspects. Establishing whether two metrics are equivalent makes sense if the generated topological space has the same shape. The main difference between the two metrics is the domain and how the data are taken for comparison.  
The metric \(L_{1}\) takes the data in domain \(\mathbb{R}^{m}\), subtracts for all components, and takes the absolute value. The proposed metric has its domain in \(\mathbb{P}^{m}\), but the difference is the sense of instantaneous change, which coincides with the differences in the behavior of the trajectories. In this sense, metric \(L_{1}\) can be seen as a particular case of the proposed metric when a first-order polynomial function represents the trajectories.  
The proof is as follows: in having two functions \(f_{i}(x)\), \(f_{j}(x)\in\mathbb{P}^{1}\) with the form \(f_{i}(x)=a_{i}x+c\); \(f_{j}(x)=a_{j}x+c\) in the interval \((0,1)\in\mathbb{R}\), suppose \(f_{i}(x)>f_{j}(x)\). The degree of similarity of these functions is calculated by  
\[\begin{array}{c}\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\  
Figure 9 graphically shows the calculation performed using the proposed metric for a component of two different trajectories, where the shaded area is the area to be calculated.  
The same process is repeated for each component of the trajectories, thus leaving the similarity defined by \(\parallel D\parallel\), where \(D\) can be expressed for two components as  
\[D=\begin{bmatrix}d_{g_{x},r_{x}}\\ d_{g_{y},r_{y}}\end{bmatrix}. \tag{12}\]  
The metric expression has been reduced to a simple expression with parameters \(f_{i}\), \(f_{j}\), \(d\), and \(e\), which are functions to be compared and the interval limits in which they coincide. The computational complexity of the last expression is in the order of \(O(k)\), where \(k\) is a constant.
# Fault Diagnosis of Vehicle Gerboxes Based on Adaptive Wavelet Threshold and LT-PCA-NGO-SVM
## 2 Principle of the Method
### Principle of Wavelet Threshold Denoising  
Wavelet threshold denoising is a technique that involves applying noise reduction steps to the collected signal, resulting in a corresponding low-noise signal. By carefully selecting an appropriate threshold value and threshold function, various wavelet bases are employed to adjust the wavelet coefficients. This adjustment ensures the retention of high wavelet coefficients while eliminating the low coefficients. The resultant wavelet coefficients are then reconstructed to obtain low-noise signals, facilitating subsequent analysis. The operational equation for this process is as follows:  
\[f(t)=x(t)+e(t) \tag{1}\]  
where \(x(t)\) is the original signal and \(e(t)\) is the noise signal. The steps of wavelet thresholding denoising are as follows:  
1. Wavelet decomposition. Conduct wavelet decomposition of the noise-containing signal by choosing an appropriate wavelet base and determining the number of decomposition layers (\(i\) layers). This process yields the wavelet coefficients (\(\alpha\), \(cd\)) for the corresponding layers, where \(\alpha\) and \(cd\) represent the approximation coefficients and detail coefficients after wavelet transform decomposition.
2. Thresholding. Process the wavelet coefficients by applying appropriate thresholds and threshold functions.
3. Signal reconstruction. Reconstruct the signal by utilizing the processed coefficients.
## 3 Comparison of Finite Element Simulation of Almen Test and Almen Test
### FE Simulation of Almen Test  
The commercial finite element program, ABAQUS(r), which is suitable for analyzing the non-linear elastic-plastic deformation of metals subjected to multi-collision shot peening is introduced in this section [29]. The explicit time integration scheme is employed to solve the equation of motion for the shot-strip system. The element type used for the Almen strip is CPE4R (plane strain element). The shot dolls are treated as rigid bodies as their hardness is much greater than that of the strip.
## 4 Experimental Results and Discussion
### Experimental Results
#### 4.4.5 Comparison with Other Attention Gate Methods  
We further compare scAG to state-of-the-art attention gates in segmentation task. We conducted experiments on the CVC-ClinicDB to evaluate the effectiveness of scAG. Our method is compared to the Attention Gate (AG) [17,18] and Global Attention Upsample (GAU) [21]. The results of the experiment are depicted in Table 8. We can see that scAG outperforms other approaches with a dominant advantage. The baseline U-Net yields a dice coefficient of 65.93%. With the integration of AG and GAU, an accuracy of 67.59%, 68.59% is respectively achieved. Our scAG boosts the performance to 71.72%, which is an increase of an approximately five-point gap as compared to the standard U-Net model. The AG is designed to focus on spatial information, but it lacks channel information while GAU is designed to concentrate on the channel concept, but it does not care about spatial concepts. On the other hand, our scAG captures both spatial and channel information, thus achieving superior results.
# Design and Control of a Open-Source, Low Cost, 3D Printed Dynamic Quadruped Robot
## 2 Hardware Design
### Torso Design  
Torso houses various electronics components that includes batteries, control computers, IMU and wireless receiver, which are shown in Figure 9. We use two separate single board computers (SBCs) to handle the motion control and perception tasks separately. Motion control is handled by a Lattepanda Alpha single board computer (SBC) with an Intel M3-8100Y CPU, 8 GB memory and 256 GB NVMe storage. No custom power or communication board are used. For communication to the actuators, we use commercially available CANable open-source USB to CAN adapters [22], flashed with candleight firmware that implements gs_usb protocol, for the low level interface. A total of four CAN adapters are used, one per each leg chain, which allows the close loop torque control of all actuators at the speed of 1 kHz. In addition, the motion control computer is connected to a WIT motion WT901C MEMS IMU with 200 hz data rate for robot posture feedback, and Microsoft XBOX 360 wireless receiver for human control input. Perception is handled by a NVidia Xavier Jetson NX SBC with 8 GB memory and 256 GB NVMe storage, which has 384 CUDA cores that can run computationally intensive deep learning-based perception algorithms which are beyond the capability of CPUs. Intel RealSense D435 RGBD camera is mounted at the front of the robot to provide RGB and depth information to the Jetson NX SBC. We plan to run object and human pose detection algorithms using the data feeds from onboard RealSense RGBD camera to provide situational awareness required for rough terrain traversal and long term autonomy of the robot.  
\begin{table}
\begin{tabular}{c c} \hline \hline Density & 1.2 g/cm\({}^{3}\) \\ Tensile Yield Strength & 65 Mpa \\ Flexural Strength & 75 Mpa \\ Flexural Modulus & 2.102 G/pa \\ Impact Strength & 8.5 KJ/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material Properties of the PLA plastic used for structural parts.  
Figure 8: Finite Element Analysis results of leg parts.  
To power the actuators and computers, we have two separate batteries in the battery compartment located at the bottom center of the torso, which doubles as the carrying handle of the robot. Two single board computers are powered by a 3-cell 5200 mAh LiPo battery with 57.72 Wh of capacity. As the measured peak power consumption of LattePanda Alpha computer is roughly 20 W under full load, and the Jetson Xavier NX 15 W, we expect the computer batteries to last approximately one hour and 40 min in the worst case. Leg actuators are powered by a 6-cell 4200 mAh LiPo battery with 93.24 Wh of capacity, which are directly fed into leg actuators using splitter cables.
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 6 New Science Investigations
### River Flow  
In [143], the authors explored the potential of using GNSS-R data obtained from the Chinese BeiDou constellation, known as BeiDou Navigation Satellite System reflectometry technology (BDS-R), in river flow applications. Specifically, they developed a shore-based river flow velocity inversion model using carrier phase observations, where the interference phase was obtained by integrating the Doppler frequency. The raw intermediate frequency (IF) datasets were processed through an open-loop method to extract the Doppler frequency observation generated by river flow and then a velocity inversion was performed. The experiment was conducted on the south bank of Dashengguan Yangtze River in Nanjing city, Jiangsu Province, for nearly two hours on 22 April 2021.
# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
## 3 Results
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Variable** & **Land Cover** & **Model** & **N** & **Obs.** & **Bias** & **MAE** & **RMSE** & **rRMSE** & **r** \\ \hline \multirow{4}{*}{H} & \multirow{4}{*}{herbaceous} & METRIC & 66 & 135 & 115 & 120 & 144 & 1.068 & 0.452 \\  & & TSEB-PT & 67 & 134 & \(-\)39 & 55 & 71 & 0.528 & 0.509 \\  & & disTSEB & 67 & 134 & \(-\)31 & 55 & 69 & 0.517 & 0.470 \\  & & ESVEP & 62 & 133 & 46 & 78 & 107 & 0.805 & 0.440 \\ \cline{2-10}  & \multirow{4}{*}{woody} & METRIC & 121 & 171 & 31 & 77 & 96 & 0.558 & 0.320 \\  & & TSEB-PT & 122 & 172 & \(-\)67 & 80 & 94 & 0.547 & 0.515 \\  & & disTSEB & 110 & 171 & \(-\)58 & 75 & 91 & 0.533 & 0.424 \\  & & ESVEP & 104 & 155 & 98 & 152 & 225 & 1.452 & 0.258 \\ \hline \multirow{4}{*}{\(\lambda E\)} & \multirow{4}{*}{herbaceous} & METRIC & 58 & 289 & \(-\)151 & 157 & 189 & 0.656 & 0.215 \\  & & TSEB-PT & 59 & 288 & \(-\)42 & 78 & 93 & 0.324 & 0.662 \\  & & disTSEB & 59 & 288 & \(-\)33 & 77 & 91 & 0.316 & 0.676 \\  & & ESVEP & 55 & 285 & \(-\)118 & 126 & 149 & 0.523 & 0.605 \\ \cline{2-10}  & \multirow{4}{*}{woody} & METRIC & 121 & 241 & \(-\)46 & 80 & 99 & 0.411 & 0.738 \\  & & TSEB-PT & 124 & 238 & 36 & 61 & 76 & 0.318 & 0.840 \\  & & disTSEB & 110 & 247 & 38 & 65 & 79 & 0.321 & 0.823 \\  & & ESVEP & 90 & 259 & \(-\)62 & 101 & 137 & 0.529 & 0.522 \\ \hline \multirow{4}{*}{Rn} & \multirow{4}{*}{herbaceous} & METRIC & 68 & 461 & \(-\)23 & 30 & 38 & 0.082 & 0.976 \\  & & TSEB-PT & 68 & 461 & \(-\)36 & 40 & 47 & 0.102 & 0.975 \\  & & disTSEB & 68 & 461 & \(-\)29 & 35 & 42 & 0.091 & 0.976 \\  & & ESVEP & 67 & 460 & \(-\)36 & 39 & 47 & 0.102 & 0.975 \\ \cline{2-10}  & \multirow{4}{*}{woody} & METRIC & 154 & 433 & 5 & 26 & 38 & 0.087 & 0.952 \\  & & TSEB-PT & 154 & 433 & 1 & 29 & 40 & 0.092 & 0.952 \\  & & disTSEB & 132 & 444 & 6 & 26 & 33 & 0.075 & 0.968 \\  & & ESVEP & 151 & 437 & \(-\)16 & 36 & 50 & 0.115 & 0.929 \\ \hline \multirow{4}{*}{G} & \multirow{4}{*}{herbaceous} & METRIC & 68 & 48 & 7 & 42 & 51 & 1.062 & 0.447 \\  & & TSEB-PT & 68 & 48 & 34 & 55 & 64 & 1.351 & 0.296 \\ \cline{1-1}  & & disTSEB & 68 & 48 & 22 & 49 & 60 & 1.261 & 0.234 \\ \cline{1-1}  & & ESVEP & 67 & 46 & 34 & 51 & 62 & 1.328 & 0.395 \\ \cline{1-1} \cline{2-10}  & \multirow{4}{*}{woody} & METRIC & 154 & 40 & 38 & 41 & 49 & 1.221 & 0.507 \\ \cline{1-1}  & & TSEB-PT & 154 & 40 & 51 & 57 & 64 & 1.591 & 0.472 \\ \cline{1-1}  & & disTSEB & 132 & 40 & 41 & 48 & 53 & 1.314 & 0.490 \\ \cline{1-1}  & & ESVEP & 151 & 41 & 64 & 67 & 79 & 1.955 & 0.458 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Crop type dependent errors for METRIC, TSEB and ESVEP modelled fluxes using Decision Tree sharpened temperatures. N, number of valid cases; \(\overline{\text{Obs. }}\); mean of observed values (W m\({}^{-2}\)); bias, mean difference between predicted and observed (W m\({}^{-2}\)); MAE, Mean Absolute Error (W m\({}^{-2}\)), RMSE, Root Mean Square Error (W m\({}^{-2}\)); rRMSE, Relative RMSE (-); r, Pearson correlation coefficient (-).