esa-sceva/satcom-chunk-collection huggingface dataset:
# Multidisciplinary Design Optimization of
## 2 Working Principle of the Pump
### Motion Analysis  
A coordinate system, as shown in Figure 2, is established to analyze the piston motion. When the piston locates on the TDC, the rotation angle \(\psi\) of the spindle is defined as \(0^{\circ}\) and the piston's displacement in \(X\) direction is the minimum displacement \(X_{\text{min}}\):  
\[X_{\text{min}}=-R\text{tan}\gamma \tag{1}\]  
where \(R\) is the radius of the piston distribution circle; \(\gamma\) is the swash-plate's inclination.  
When the cylinder rotates with \(\psi\), the piston's displacement in the \(X\) direction is:  
\[X=-R\text{cos}\psi\text{tan}\gamma \tag{2}\]  
The relative displacement of the piston in the \(X\) direction is:  
\[H=X-X_{\text{min}}=R\text{tan}\gamma(1-\text{cos}\psi) \tag{3}\]  
where the rotation angle of the cylinder is \(\psi=\omega t\); \(\omega\) is the angular velocity of the cylinder.  
Therefore, the piston's velocity in the \(X\) direction is:  
\[V_{x}=\frac{dH}{dt}=R\omega\text{tan}\gamma\text{sin}\psi \tag{4}\]  
For the pump in this work, \(R=29\,\text{mm}\), \(\gamma=12.5^{\circ}\), \(n_{0}=1500\,\text{r}/\text{min}\). According to Equations (1)-(4), the motion equations of pistons in the \(X\) direction are:  
\[X =-6.43\text{cos}157t \tag{5}\] \[V_{X} =1009.38\text{sin}157t \tag{6}\]  
Figure 1: The swash-plate axial piston pump: (1) Swash plate; (2) Pistons; (3) Connecting rod; (4) Shell; (5) Variable displacement mechanism; (6) Valve plate; (7) Cylinder; (8) Slipper; (9) Slipper retainer; (10) Spindle.
# Study on the Stability and Control of Gob-Side Entry Retaining in Paste Backfill Working Face
## 6 Industrial Test
### Strengthened Support Scheme of GER  
During the curing process of the paste backfill, its mechanical properties gradually improve. Initially, the fill's strength and stability are relatively weak, and its bearing capacity is low, posing risks to the stability of roadway structure. Unlike hydraulic supports used in mining faces, the support equipment in GER should not obstruct the transportation function of the retained roadway. Therefore, portal hydraulic supports (as shown in Figure 16) are used to strengthen the support within roadway, compensating for the initial weakness of the material's strength. At the same time, these supports provide higher active support forces, controlling roof subsidence and ensuring a safe, spacious working environment. This overall improves the stability and safety of roadway structure.  
Unlike hydraulic supports used in working face, the support equipment for GER has more specific requirements. The support equipment must be arranged within the roadway in a way that does not interfere with the transport function of the entry, while still providing reliable support. Compared to single hydraulic support, portal hydraulic support offers superior load-bearing capacity and stability, effectively ensuring the safety of workers and equipment. Moreover, the geological conditions of the GER working face are relatively complex. Portal hydraulic supports exhibit high adaptability, meeting the support requirements under various geological conditions. These supports reduce the need for multiple mining teams, simplify the production system layout, alleviate mining face sequencing pressures, and decrease labor intensity.  
Figure 14: Control concept of GER.  
force should not be less than 2880 KN. The layout of the GER strengthening support is shown in Figure 17.
## 4 Evaluation of Algorithms
### Proposed Power-Aware Algorithm (PA)  
Algorithm 2 presents the pseudo code for the PA algorithm. In the proposed PA algorithm, the VM is allocated to the server, which showcases the minimum increase in the overall power consumption. The main highlight of the proposed PA algorithm is the inclusion of the outgoing tasks' minimum remaining execution time to predict the dynamic power in host selection (Line 13-23). Algorithm 2 receives host capacity and the VM's resource request through HostQ and ReqQ. The feasible hosts (with maximum utilization) for VM placements are identified as SelectedHost and the difference in dynamic power before and after VM placement is calculated in Line 8 of Algorithm 2. The power consumption \(P2\) is not constant throughout the execution of the VM, as it depends on the next incoming and outgoing task of the machine to which it is allocated. The decision is performed based on the new VM execution time and the next outgoing task's remaining execution time, as explained in Lines 13-23 of Algorithm 2.  
Complexity Analysis  
The time complexity of the algorithm can be analyzed by considering '\(n\)' VM requests in ReqQ and '\(m\)' number of hosts in HostQ. Lines 3-4 need to execute O(\(n\)) times. Lines 1-24 need to execute O(\(m\)) times. The sort functions in Line13 and 20 need to execute O(\(m\) log(\(m\))). The complexity of the algorithm is conquered by the number of VM requests \(n\), sort operation and the number of host \(m\). The complexity of the algorithm is estimated as O(\(nmlog(m)\)).  
Input:_HostQ_, _ReqQ_  
Output:_TargetVMQ_  
1 while _VM_ in _ReqQ_ do  
2 For _host_ in _HostQ_ do  
3 _Cur -uti_-- Get current utilization;  
4 _Rem -uti_ -- Maxu--Cur-uti_;  
5 If _rem-uti_\(\leq\)_VM_-_uti_ then  
6 _P1_--Get the dynamic power of the host;  
7 _P2_--Get dynamic power of the host after _VM_ placement using Equation (7);  
8 _AP_\(\leftarrow\)_P2_--P1_;  
9 _R1_--Get minimum _remaining-task-execution-time_ of VMs the _host_;  
10 _AP_\(\leftarrow\)Execution-time of _VM_\(\leftarrow\)_R1_;  
11 _Selected-Host_\(\leftarrow\)add(_host_);  
12 If _Selected-Host_\(\
eq\)NULL then  
13 Sort the _Selected-Host_ in non-decreasing order of _AP_;  
14 _chosoy-host_--Get the host from _SelectedHost_ with positive _AP_;  
15 _Selected Host_\(\leftarrow\)Get the difference between _SelectedHost_ and _chosyhost_;  
16 _S-exp-pow_--_Selected Host [_first_]_._P2_\(\times\)Execution-time of _VM_;  
17 _Sel-host_1_--_Selected-Host[_first_];  
18 _Pow_1_--Get the total dynamic power without the task corresponding to _R1_ with placement of _VM_;  
19 _C-exp-Pow_--P2_\(\times\)_R1_+_Pow_1_\(\times\)_AR_;  
20 Sort _chosy-host_ in non-decreasing order on _CexpPow_;  
21 _Desthost_--Get the minimum of _Sel-host_1_._S-exp-pow_ and _choosy-host[first]_._C-exp-Pow;  
22 _TargetVMQ_-add(_VM_,_Desthost_);  
23 else  
_TargetVMQ_-add(_VM_,Null);  
25 Return_TargetVMQ_ ```
**Algorithm 2**Power-Aware algorithm (PA)
## 2 Related Works
The classification is performed using the mean, standard deviation, skewness, and kurtosis data of the energy consumption. The finding show that the training-testing ratio influenced the performance of KNN, while MLP achieved better performance than RF. An hourly energy consumption prediction in residential buildings using the MLP proposed in [44] has been reported to achieve small prediction error. In [45], a hybrid deep learning algorithm using a convolutional neural network (CNN) and a multilayer bidirectional gated recurrent unit (MLBi-GRU) are used for residential building energy prediction. The data went through a refinement stage prior to the prediction to remove any abnormalities from the data so that better predictions with less errors are obtained. The author of [46] proposed a multi-objective GA (MOGA) to design ensembles of radial basis function (RBF) neural networks for the prediction of smart home's power usage. The MOGA searched for a non-dominated RBF. PSO has been applied for RBF parameter optimization in [47]. The RBF optimized by PSO had less errors in the prediction of energy consumption in comparison to RBF with parameters that had not been tuned by PSO. A multilayer feedforward neural network (MFNN) was paired with several metaheuristic algorithms, namely multi-objective PSO (MOPSO), multi-objective GA (MOGA), and NSGA-II in [48], for the minimization of annual energy usage for cooling and heating as well as the thermal discomfort hours in Moroccan residential building. Table 1 summarizes the literature presented above, listing the algorithms used, the determinants used for occupant's comfort, and the energy consumption issue tackled by them. The limitations of the works are also noted in the last column. The research trends observed and the success of the intelligent algorithms, particularly the metaheuristics algorithms, motivate this study. Based on the review conducted, even though ABC had been adopted [31], the application of an improved ABC is observed to provide better results especially in terms of energy consumption [49]. Similarly, an improved FA is reported to provide a better solution than the original FA [33; 34]. On the other hand, IW-ABC has not been applied for indoor comfort index maximization and energy consumption minimization prior to this. Therefore, it is expected that this improved ABC is able to provide better results for this problem. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Algorithm** & **Comfort** & **Energy Consumption** & **Remarks** \\ \hline
[30] & GA and fuzzy & & & \\  & controller & & & \\ \hline
[31] & ABC and fuzzy & & & \\  & controller & & & \\ \hline
[32] & ABC with knowledge & & & \\  & base and fuzzy & & & \\  & controller & & & \\ \hline
[33; 34] & Hybrid FA-GA and & & & \\  & fuzzy controller & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Algorithms and Usage for Comfort and Energy Consumption Improvement.
# Spatio-Temporal Distribution of Ground Deformation Due to 2018 Lombok Earthquake Series
## 2 Materials and Methods
The DInSAR process produced images in radian unit (angular phase units) in the range \(-\pi\) to \(\pi\), causing ambiguity problems. Although the deformation pattern could be observed, the main information regarding the deformation value could not be read properly. In order to get a deformation image containing metric value, an unwrapping process had to be carried out, and the absolute phase angle unit had to be changed into metric units in LOS (Line of Sight). Unwrapping was one of the important steps to obtain high-quality phase unwrap results. The filtering process was done first, and the signal would be smoothed by exporting the subset results into the SANPHU format. Then the unwrapping phase was performed using the SNAPHU Unwrapping plugin. Image unwrapping was completed using the MCF (Minimum Cost Flow) method with the consideration that the processed image has a large area with a small coherence value [52; 53; 54; 55]. A phase to displacement process was conducted to transform phase into deformation [4]. In order to find out the magnitude of deformation, a calculation of the displacement of the earth's surface was performed according to the equation along the line of sight (LOS) sensor [56; 57]:  
\[\Delta\oslash_{\text{defo}}=4\pi\Delta\text{R}/\lambda \tag{1}\]  
where the value of \(\lambda\) is the wavelength of Sentinel-1 image, \(\Delta\oslash_{\text{defo}}\) is the phase difference value and R is the wavelength distance. The result of deformation at this stage had not been georeferenced. Furthermore, the georeferencing process needed to be carried out so that the image had the correct coordinates on the map. Geocoding was performed by using DEM as a georeferenced model. In order to match the medium resolution of Sentinel-1, we applied bilinear interpolation for SRTM 1 Sec HGT. Image coordinates were converted into the Geographic Coordinate System (WGS84). Indeed, radar coordinates were Range Doppler coordinates with some distortions, such as foreshortening and shadows, due to side-looking geometry. These coordinates should be converted into Geographic Coordinates System to match the coordinates of the Earth's surface. This process relied on Range Doppler Terrain Correction, in which we employed Range Doppler orthorectification method based on orbit data, radar recording time, slant range to the Earth's surface and referenced DEM. This produced final georeferenced deformation maps which contained uplift and subsidence value in meter. Methodological limitation of this study was caused by lack of time series terrestrial measurement. We were unable to acquire ground GPS points prior to each of the earthquake series (29 July; 5 August; 9 August; 19 August 2018), although our GPS survey after the disaster was successful. Therefore, we relied only on a satellite-based approach to achieve the aims of this study.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 3 Standalone Remote Sensing (RS) Technologies
### Vision-Based (Imaging) Technologies  
Many pieces of research on automated progress reporting have focused on creating 3D models or point cloud models for both indoor and outdoor environments. These point cloud data are generated by using image frames or laser scanning data of the site. Generally, laser scanners can provide more accurate point cloud data but with more processing effort [1]. However, in recent years due to the availability of high-resolution cameras, more powerful computers, and new advancements in image processing techniques more accurate and real-time point clouds are generated by only using image frames.
Our team recently identified a transcription factor, AtWRKY46, that curtails this futile NH\({}_{4}\)+ efflux in two distinct ways: Firstly, AtWRKY46 directly downregulates _AtNUDX9_, preserving the levels of N-glycosated proteins and subsequently impeding futile NH\({}_{4}\)+ efflux. Secondly, AtWRKY46 directly suppresses the IAA-conjugating genes _AtGH3.1/GH3.6/UGT75D1/UGT84B2_, ensuring the maintenance of free IAA levels and inhibiting the futile NH\({}_{4}\)+ efflux from the roots [10]. As previously established, NH\({}_{4}\)+ uptake leads to an increase in H+ efflux, and IAA also promotes H+ efflux by phosphorylating the proton pump. Therefore, it is of interest to further identify the relationships between free IAA, H+ and NH\({}_{4}\)+ efflux. Our recent results further show that H\({}^{+}\) secreted in the extracellular compartment can promote the NH\({}_{4}\)\({}^{+}\) efflux, while the PM H\({}^{+}\)-ATPase knockout mutants _Ataha1-7_ and _Ataha2-5_ exhibit lower NH\({}_{4}\)\({}^{+}\) efflux compared with wild-type. Intriguingly, a mutation in AtPIN5, an IAA transporter responsible for IAA translocation from the cytosol to the ER lumen, leads to enhanced cytosolic IAA accumulation, consequentially increasing H\({}^{+}\) efflux and decreasing NH\({}_{4}\)\({}^{+}\) flux. The application of the PM H\({}^{+}\)-ATPase inhibitor, vanadate, in the medium further attenuates both H\({}^{+}\) and NH\({}_{4}\)\({}^{+}\) efflux in _Atpin5_. This suggests that AtPIN5 may play a pivotal role in synchronously modulating H\({}^{+}\) and NH\({}_{4}\)\({}^{+}\) efflux by regulating free IAA levels in the cytoplasm [20]. Additionally, our study identified a transcription factor, OsEIL1, in rice, which has been demonstrated to directly associate with the promoter of _OsVTC1.3_ (a homolog of _AtVTC1_). This binding promotes transcription, maintaining the levels of N-glycosylated proteins in roots, which in turn mitigates unnecessary NH\({}_{4}\)\({}^{+}\) efflux. Notably, this regulatory paradigm is absent in Arabidopsis, potentially offering an explanation as to why rice exhibits greater NH\({}_{4}\)\({}^{+}\) tolerance compared to Arabidopsis [21]. In summation, significant advancements have been made in discerning the molecular underpinnings of NH\({}_{4}\)\({}^{+}\) toxicity in recent times. This not only provides a theoretical groundwork for future explorations into plant NH\({}_{4}\)\({}^{+}\) response mechanisms but also holds promise for enhancing plant NH\({}_{4}\)\({}^{+}\) resilience in agriculture. D.W.D expresses gratitude for the support received from the National Natural Science Foundation of China (Grant No. 32030099). The author declares no conflict of interest.
# Modeling Shearing and Bending Behavior of Kiwifruit Branches Using the Discrete Element Method
## 2 Materials and Methods
### Calibration for DEM Model Parameters  
The calibration of the DEM model parameters was achieved through simulations of shearing and three-point bending tests, with the objective of identifying the optimal parameters that minimize the discrepancy with laboratory experimental data. The fixture's outline was measured and precisely replicated in CAD software SolidWorks 2018 at a 1:1 scale. Subsequently, the fixture model was exported to the DEM software PFC3D 6.0 for simulation purposes. As depicted in Figure 5, models of the kiwifruit branch and bonding keys were specifically created for the three-point bending and shearing tests, respectively.  
The calibration process consists of three stages: First, a Plackett-Burman design screening test is conducted to identify the significant parameters affecting the mechanical response of the DEM model, with the factor levels detailed in Table 2. Second, a steepest ascent test is employed to refine the value range of these significant parameters. Finally, a response surface methodology test is utilized to develop a predictive model correlating the DEM parameters with mechanical response, with the goal of determining the optimal parameter set that minimizes the deviation from laboratory experimental results.
## 1 Introduction
However, all these methods have relatively large limitations, whether it is the time-consuming measurement, the relevance of the information obtained about the tested material or, finally, the economic aspect. Radiation methods were often chosen for the evaluation of \"in situ\" structures, but they cannot be applied in most cases, due to the necessary configuration of the radiation source-detector assembly, and also due to the harmfulness of ionizing radiation and thus the necessary protection of persons from primary and secondary ionizing radiation, etc. For this reason, electrodynamic and electromagnetic methods are currently used, the applicability of which has been discussed by the general professional public in recent years [4]. To assess the condition of the protective structure after the impact by a pressure wave using NDT methods, it is important to have a record of the condition of the structure before the impact for comparison. It is crucial to know the material properties of the structure and the uniformity of the distribution of reinforcement or cracks in the material. The development of new NDT methods usable for evaluating the concentration and orientation of steel fibers in composites has been discussed several times among the professional public [5; 6; 7]. The authors themselves have been researching and publishing in this area for a long time [7; 8; 9; 10; 11; 12]. The authors are looking for a combination of suitable NDT methods that would be used in the future to prove the degree of damage to cement-based protective structures after an explosion. The article presents the first part of an experiment designed to verify the applicability of NDT methods,to evaluate the degree of damage of the protective structure in the field. The experiment consists of three parts. The first part is a laboratory verification of the initial material characteristics of the materials used. The first test set of structural elements is made of steel fiber-reinforced concrete. The elements will be evaluated using NDT methods and for comparison by destructive laboratory methods. The second part will be the impact of structures using explosion and evaluation of the condition of structures using NDT methods in the field. The last part will be used to verify the results of the NDT method, which will ensure the residual strength of the structure in the laboratory and try to find the relationship between changes in the results of NDT methods and the residual strength. Radiography was included in the introductory and concluding parts of the experiment to verify the results. Although this method is not suitable for using in-situ, it is the only standardized method of the NDT methods used.
# A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings
## 2 Fault Factors and Indicators of Wheel Hub Bearings
### Fault Indicators of Wheel Hub Bearings
#### 2.2.3 Feature Indicators of Wheel Bearing Vibration Signal  
Vibrational signal analysis is the simplest and most effective detection method to reflect the real-time working state of the wheel hub bearing. The collected vibration signal is mapped to the intuitive and accurate time domain to identify the running situations and the damage degree of the wheel hub bearings. The signal amplitude \(x_{i}\), signal energy \(E_{i}\), and signal kurtosis \(K_{i}\) are selected as the feature indicators of wheel bearing vibration signal in this research. The signal \(x_{i}\) itself reflects the instantaneous amplitude of the fault vibrational signal over time.  
The signal energy \(E_{i}\) reflects the impact intensity of the fault vibrational signal, which is described in Equation (9). The signal energy can accurately and effectively diagnose the damage degree of the wheel hub bearing, so it is one of the most common fault diagnosis indicators in industrial production.  
\[E_{i}=x_{i}^{2} \tag{9}\]  
The signal kurtosis \(K_{i}\) indicator is a dimensionless parameter, described in Equation (10). T kurtosis indicator maintains high sensitivity to early bearing fault signals and is not affected by factors such as bearing size, load, and rotating shaft speed.  
\[K_{i}=\frac{1}{N}\underset{i=1}{\overset{N}{\underset{i=1}{\sum}}}\frac{\left( \left|x_{i}\right|-X_{m}\right)^{4}}{X_{rms}^{4}} \tag{10}\]  
Here, \(X_{m}\) is the signal peak value computed in Equation (11). The peak value is the maximum instantaneous amplitude value of the vibration signal during a certain period of time.  
\[X_{m}=\max\{\left|x_{i}\right|\} \tag{11}\]  
\(X_{rms}\) is the root mean square value of the signal computed in Equation (12).  
\[X_{rms}=\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\underset{i=1}{\sum}}}x_{ i}^{2}} \tag{12}\]