esa-sceva/satcom-chunk-collection huggingface dataset:
# Stereo-DIC Measurements of a Vibrating Bladed Disk: In-Depth Analysis of Full-Field Deformed Shapes
## 4 Results
The results for the testing frequency at 6239 Hz are reported in Figure 5, again in the cylindrical reference frame coordinates, showing a really smooth map for all the three directions. Figure 4: Displacement maps for the 2568 Hz excitation frequency, along the directions of the cylindrical reference system of the blisk. Figure 3: DFT of the single point LDV measurement obtained by blisk’s random excitation. The colorbars highlight that the axial direction is dominant in this harmonic response, showing that mainly Blade 1 is responding to the excitation. Nevertheless, radial and tangential displacement maps show that also Blade 6 is responding, at least in the in-plane direction, even if the displacement amplitude is much lower than the out-of-plane direction. This specific dynamic response emphasizes the need of 3D full-field measurements in this research field. Moreover, at this higher frequency a much more complex blade deformation is observed with respect to the previous at 2568 Hz mode. A nodal line can be noted on the blade surface, meaning that the points across this line of the same blade move out of phase. This behavior surely has an effect on the in operation stress levels and can also determine different resonance combination with the inlet fluid. A deeper insight in this deformed shape can be obtained, for example, by focusing the tangential displacement map to a range of \(\pm 3\)\(\upmu\)m, as shown in Figure 6. This plot better emphasizes the presence of the nodal line in Blades 1 and 6. The same deformed shape feature can be additionally noted on Blades 5 and 7, even if with smaller amplitudes. Figure 5: Displacement maps for the 6239 Hz excitation frequency, along the directions of the cylindrical reference system of the blisk. Figure 6: Displacement map for the excitation at the 6239 Hz with focused view of the tangential displacement. Another high-frequency test investigated was 6357 Hz, and the results are reported in Figure 7. The displacement maps highlight that the blades presented quite similar deformed shape as the previous testing frequency at 6239 Hz, with one nodal line on the blade itself. Nevertheless, at this working frequency a different combination of blades is excited; since the main amplitude was found at Blade 11, the relevant responses were measured for Blades 2 and 7, while the other blades were approximately not responding. This is a peculiarity of the mistuned blisks, which presents several modes at different (but close) frequencies, featuring similar deformed shapes of the blades but different combinations of the responding blades. In order to completely and precisely describe these harmonic responses, with single point techniques such as LDV, or even with CSLDV, a really long testing time would be needed, since several locations on each blade should be subsequently measured. Just a discrete description of the shape could be obtained otherwise, by observing a specific but limited set of positions.
## 7 Conclusions  
This study systematically investigated the dynamic characteristics of a novel pump valve with secondary cushioning mechanism and its impact on drilling pump performance. A comprehensive three-dimensional transient model was developed and validated to simulate actual operational processes. Fluid dynamics within the pump chamber, valve kinematics, pressure-volume characteristics, and flow dynamics under various operational conditions were analyzed through coupled numerical simulations. The principal findings are summarized as follows:Sealing Efficacy Verification: complete sealing integrity was demonstrated through near-zero clearance flow velocities, validated by symmetrical flow suppression patterns.
* Hysteresis Mitigation Strategy: the secondary cushioned mechanism design reduces closure delays by 22%, directly enhancing volumetric efficiency.
* Parametric Optimization Guidelines: spring stiffness and preload were established as primary control parameters, with structural factors dominating closing dynamics. A wave spring stiffness enhancement of 24% achieves pressure stability through optimized damping, reducing perturbations by 90%. Increasing the number of strokes can effectively increase drilling pump displacement while exacerbating critical component failures. These findings provide actionable design principles for minimizing energy losses and improving reliability in drilling pump systems. Future work should solve the observed flow deviation problem through experimental verification, as well as optimize the design of the secondary buffer mechanism to extend the service life of the pump valves.  
Conceptualization, Y.W.; methodology, Y.W.; validation, Y.W.; investigation, Y.W.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Sichuan Science and Technology Support Project, grant number 2020 YFC0260.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality requirements.  
The authors declare no conflicts of interest.
# Adaptive BBU Migration Based on Deep Q-Learning for Cloud Radio Access Network
## 3 Proposed System Model
### System Scenario  
As previously illustrated in Figure 1, this paper uses the centralized architecture of BBU. To oversee the performance of all virtual resources, BBUs are grouped together as a BBU pool in a single location and managed by a cloud manager. Cooperative radio resource allocation and large-scale collaborative processing are made possible by the virtualized BBU pool. The iCanCloud framework [41], a simulation platform for modeling and simulating cloud environments, is used to simulate the system. The OMNeT++ platform has been used to develop iCanCloud. The simulation framework simulator INET is used by the network systems. The iCanCloud simulator kernel includes a range of modules designed to replicate the behavior of specific components, organized according to their functions. The iCanCloud platform's modular architecture consists of three main sections: the user model, cloud manager, and cloud infrastructure as shown in Figure 2.  
Figure 1: Architecture of cloud radio access network.  
Figure 2: Modular architecture of iCanCloud.
## 3 Ultra-Long-Range Scanning and Acquisition Technology
### Initial Pointing
Therefore, in this study, we designed an adapter structure for the PAT terminal, on which dual GPS receivers are installed. Through manufacturing tolerances, we ensure that the line connecting the two GPS receivers is perfectly parallel to the elevation axis of the PAT terminal. Using this method, the absolute azimuth angle can be measured, which allows the optical axis of the telescope to be calibrated by selecting any star for star calibration. The schematic of the dual-station GPS installation is shown in Figure 3. The dual GPS measurement devices are mounted on an extension rod parallel to the elevation axis of the optical communication PAT terminal adapter plate. The extension rod and adapter plate are manufactured to ensure their parallelism, with the length of the extension rod being 1900 mm. Through differential calculation, the angle between the line connecting the two GPS receivers and true north can be determined. When the distance between the two receivers is greater than 1 m, the measurement error is less than 0.2\({}^{\circ}\).
# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
###### Abstract  
Changing climate is expected to cause more extreme weather patterns in many parts of the world. In the Carpathian Basin, it is expected that the frequency of intensive precipitation will increase causing inland excess water (IEW) in parts of the plains more frequently, while currently the phenomenon already causes great damage. This research presents and validates a new methodology to determine the extent of these floods using a combination of passive and active remote sensing data. The method can be used to monitor IEW over large areas in a fully automated way based on freely available Sentinel-1 and Sentinel-2 remote sensing imagery. The method is validated for two IEW periods in 2016 and 2018 using high-resolution optical satellite data and aerial photographs. Compared to earlier remote sensing data-based methods, our method can be applied under unfavorite weather conditions, does not need human interaction and gives accurate results for inundations larger than 1000 m\({}^{2}\). The overall accuracy of the classification exceeds 99%; however, smaller IEW patches are underestimated due to the spatial resolution of the input data. Knowledge on the location and duration of the inundations helps to take operational measures against the water but is also required to determine the possibilities for storage of water for dry periods. The frequent monitoring of the floods supports sustainable water management in the area better than the methods currently employed.  
Keywords: inland excess water; flood; water management; radar remote sensing; optical remote sensing; automation +
Footnote †: journal: _Sustainability_
## 3 Hardware
The OP07 operates with a very low input offset voltage of 75\(\upmu\)V, achieved by the trimming of the wafer stage, which eliminates the requirement for suppression by external components, in most cases. Additionally, the OP07 features a low input bias current of \(\pm 4\) nA and a high open-loop gain of 200 V/mV. This combination of high open-loop gain and low offset voltage make the OP07 particularly useful for high-gain instrumentation applications. The amplifier was installed in a non-inverting configuration, as the power detector delivers a positive DC voltage. The OP07 negative feedback is provided by an ICL760, which converts the supply voltage from positive to negative over an input range of 1.5 V to 12 V, resulting in complementary output voltages from \(-1.5\) V to \(-12\) V. Our instrument has been designed to accommodate a wide range of applications; thus, the amplifier gain is configurable. Figure 7 illustrates that the amplifier gain can be modified according to the operator's needs through the use of a 250 K\(\Omega\) potentiometer (R2) and a 1 K\(\Omega\) precision resistor (R3). Furthermore, R1, another potentiometer, is utilized for offset nulling, as described in [24]. The instrument's main and secondary controls communicate with a graphical user interface (GUI) developed on a Raspberry Pi 4 to receive initial measurement parameters, pause the measurement, or reset the microcontrollers to restart. This communication is facilitated through Bluetooth, using two HC-05 Bluetooth modules installed on the main control and secondary control shields. The HC-05 is a class 2 Bluetooth module with a serial port profile, capable of being configured as either a master or slave. The Bluetooth  
Figure 6: Signal conversion system. (**a**) 3D-printed case. (**b**) DC2158A evaluation board and voltage regulator circuit. (**c**) LTC5596 IC, RF in, and DC out. Figure 7: Schematic circuit of the operational amplifier OP07 installed in the instrument. communication protocol used is v2.0+EDR, with a 2.4 GHz frequency in the ISM band and GFSK modulation.
# Space Efficiency of Tall Buildings in Singapore
## 4 Results
### Space Efficiency of Singaporean Towers  
Average spatial efficiency in the surveyed buildings was 80%, with the core area making up 17% of GFA. These metrics ranged from a low of 68% and 5% to a high of 91% and 32%, respectively, as outlined in Appendix C. Table 3 provides a comparative analysis of these averages for space efficiency and core-to-GFA ratios alongside data from similar studies conducted in Turkey [83], Asia [84], the Middle East [85], and North America [86].  
Oasia Hotel Downtown and Marina Bay Sands Hotel Towers 1-3 exemplify significant innovations in architectural design, achieving impressive spatial efficiencies between 86% and 91%. These towers set a new benchmark for minimal GFA ratios by leveraging innovative core designs and cutting-edge structural systems. The placement of vertical transportation elements, like elevators and staircases, is optimized to reduce the core's footprint, thereby increasing the amount of usable space within the tower. This careful core  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Findings** & **Turkey** & **Asia** & **The Middle East** & **North America** \\ \hline Structural & Concrete (68\%) & Concrete (89\%) & Concrete (79\%) & Concrete (70\%) & Concrete (55\%) & Concrete (39\%) \\ material & Composite (30\%) & Composite (11\%) & Composite (79\%) & Composite (30\%) & Composite (39\%) \\  & Steel (2\%) & & Steel (3\%) & Steel (6\%) & Steel (6\%) \\ \hline Structural & Outrigger frame & \multirow{4}{*}{\begin{tabular}{c} Outrigger frame \\ (17\%) \\ Mega column (5\%) \\ Shear-trussed frame \\ (2\%) \\ Shear-walled frame \\ (76\%) \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Outrigger frame \\ (19\%) \\ Shear-walled frame \\ (81\%) \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Outrigger frame \\ (76\%) \\ Tube (17\%) \\ \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{c} Outrigger frame \\ (44\%) \\ Tube (26\%) \\ Buttressed core (3\%) \\ Mega column and \\ core (3\%) \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} Outrigger frame \\ (42\%) \\ Tube (16\%) \\ Mega core (3\%) \\ \end{tabular} } \\  & Shear-walled frame & & & \\ \cline{1-1}  & & & & \\ \cline{  
design strategy greatly enhances both space efficiency and functionality. Additionally, these high-rise buildings utilize outrigger frame and shear-walled systems, which enhance their capacity to handle vertical and lateral forces. This is achieved by strategically incorporating compact cross-sections within the structural elements to effectively manage loads and resist stress. By adopting these modern design techniques, these towers not only optimize space but also improve overall structural integrity and durability.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 5 Future Roadmap: The Promise of AI
### Neural Networks in Encryption: Neural Cryptography  
Neural cryptography is a recent research trend. Different kinds of neural networks have been used for this purpose [9, 220]. Moreover, different cryptographic algorithms have been examined in this area [221]. More importantly, different enabling technologies are used to support neural cryptography. For example, hardware technology can be utilized to embed cryptographic algorithms into neural network accelerators [220].
# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 5 Pattern Matching-Based Load Anomaly Detector
### Pattern Matching Scheme
#### 5.2.1 Result Pattern Matching
Generate the initial parameters: {\(X_{init}\), \(X_{init}\)}, maximum iteration number _maxgen_, population size _sizepop_, and random flight distance range _FR_. Set the iteration variable \(t\) = 0, and perform the training process from Step 2 to Step 6. 2. Set the iteration variable: \(t\) = \(t\) + \(1\). 3. There are three substeps for this step. 1. Calculate the flight distance _Dist_\(i\) of food searching for fruit fly \(i\), and calculate the smell concentration judgment value. 2. Input _Si_ into the SVM to learn the training data (the processed data obtained in the benign data training phase). Use the trained SVM to reclassify the training data and obtain the prediction result series _SVM_\({}_{prediction}\). * Compare the prediction result series \(SVM_{prediction}\) with the real labeling of the training data \(SVM_{label}\). Calculate the smell concentration \(Smell_{i}\). The smell concentration judgment function used here is an accumulation function. \(L\) is the length of \(SVM_{prediction}\); therefore, \(u\) ranges from 0 to \(L\), and the function compares each term in the prediction result series \(SVM_{prediction}[u]\) with the real label of the training data \(SVM_{label}[u]\). Initialize an initial integer a = 0; when \(SVM_{label}[u]\ =\ SVM_{prediction}[u]\), a = a + 1. Finally, return \(a\) as the smell concentration \(Smell_{i}\). * The offspring are generated according to Equations (4)-(6). Then, the offspring are input into the SVM, and the smell concentration value is recalculated. * If \(t\) = _maxgen_, the stop criterion is satisfied, and the best penalty factor for the SVM can be obtained. Otherwise, return to Step 2. In this study, the SVM with the optimal penalty factor obtained from the FOA is called FOA-SVM, and in the implementation phase, both the prediction result and the real load value are classified by FOA-SVM.
# PBR Clutter Suppression Algorithm Based on Dilation Morphology of Non-Uniform Grid
## 4 Experiment result and Analysis
### Testing by Simulated Data  
#### 4.1.1 Scenario for Simulation  
The surveillance scope is set from 60\({}^{\circ}\) to 170\({}^{\circ}\). There are five targets moving with constant velocity in the scope. Table 2 lists the track settings of five targets. Plot these target tracks in polar coordinates, as Figure 7a shows.  
Since the parameter of the illuminator is agile and various, PBR utilizes only part of pulses with specific aims for detection. To make closer to reality, the time interval between pulses utilized is not constant. The whole time length of simulated data is 50 s. The number of valid pulses is set as 667. The time interval between adjacent valid pulses is allocated randomly. Figure 8 shows the pulse interval allocation of simulated data. So, the detection result from the ideal echo of the target is not uniformly continues. Figure 7b demonstrates the target tracks points based on the pulse interval allocation with measurement error.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Start Position (km, degree)** & **Start Position (km) in** & **Track Slope** & **Track Intercept (km)** \\  & **in Polar Coordinates** & **Cartesian Coordinates** & **Track Slope** & **Track Intercept (km)** \\ \hline Target 1 & (86.023,144.5) & (\(-70\), 50) & 5 & 60 \\ Target 2 & (70.456,96.5) & (\(-8\), 70) & 10 & 100 \\ Target 3 & (80.623,82.9) & (\(10\), 80) & \(-3\) & 10 \\ Target 4 & (76.158,113.2) & (\(-30\), 70) & \(-10\) & 80 \\ Target 5 & (70.711,135) & (\(-50\), 50) & 30 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The track settings of five targets.  
Figure 6: The flow chart of the clutter separation algorithm.  
Besides, due to the flicker of target's RCS in PBR, target can only be detected from part of valid pulses. Figure 7c demonstrates the real target points detected. The signal to clutter ratio in this experiment is defined in Equation (20).  
\[\text{SCR}=\log{(\frac{\text{N}_{\text{sig}}}{N_{\text{clu}}})} \tag{20}\]  
where N\({}_{\text{sig}}\) represents the mean number of the valid target detection points in each frame. \(N_{\text{clu}}\) represents the mean number of the false alarm clutter points in each frame. Set SCR as \(-1.26\) dB. Figure 7d shows the final detection result from CFAR, which is the simulated data for following experiment.  
Figure 8: Pulse interval allocation.  
Figure 7: Production of the simulated data: (**a**) true tracks of five targets; (**b**) target tracks with measurement error; (**c**) target tracks detected; (**d**) detection results from CFAR.
## 4 Application
### Visualization of Traffic Direction  
To visualize the distribution of traffic heading angles in the target waters, the AIS data were plotted as ship headings in cell ranges of sizes 5, 6, and 7, as shown in Figure 8. Panels (b), (c), and (d) show the allocation of AIS data into five-, six-, and seven-level grids, respectively. Panels (e), (f), and (g) show the results of the clustering of the ship headings on each grid at each level. Clustering analysis showed that grid levels 6 and 7 were not  
Figure 7: Comparison of traffic density heatmap as per grid cell interval in area “A”: (**a**) Ship AIS data preprocessing and trajectory refinement. (**b**) Distribution of AIS data on grid level 5. (**c**) Distribution of AIS data on grid level 6. (**d**) Distribution of AIS data on grid level 7. (**e**) Result of density heatmap on grid level 5. (**f**) Result of density heatmap on grid level 6. (**g**) Result of density heatmap on grid level 7.  
subjected to DBSCAN clustering owing to the insufficient number of ships bearing data assigned to each cell interval. The direction arrow of the ship azimuth was difficult to identify in grids 6 and 7; thus, a 5-level grid was used. To visualize the clustering results of ship azimuth in each cell in a straightforward manner, the directional arrows were shown. The direction and length of the traffic direction arrow, which showed the azimuth of the ship, indicated the representativeness and cumulative degree of the ship bearing.
# Early Warning Signals for Bearing Failure Using Detrended Fluctuation Analysis
## 5 Conclusions  
Using DFA, it was shown that long-range correlations exist in bearing vibration signals before their failure. DFA is a reliable method to quantify the long-range correlations in long time series with a linear fluctuation function at different time scales. In several data sets of bearing vibration signals, the crossover phenomenon emerged with two scaling exponents at the short and long ranges. The estimated short-range scaling exponents showed a persistent and gradual increase toward failure among the different data sets. Then, the Kendall's tau coefficient was used to measure the trend in the scaling exponents. The results call for adoption of the Kendall's tau of the scaling exponent as a novel health indicator of the bearings. Furthermore, the proposed method has the potential to be integrated as a new detection technique of bearing failure in condition monitoring systems.  
In the future, the effect of other abnormal conditions (lubricant degradation, wear debris, high temperature, etc.) on the vibration signal dynamics will be examined. In addition, it would be interesting to investigate the vibration signals of other elements in mechanical systems using the proposed approach.  
Conceptualization, A.A.; data curation, A.A.; formal analysis, A.A. and L.S.; investigation, A.A.; methodology, L.S.; supervision, L.S.; validation, A.A. and L.S.; writing--original draft, A.A. and L.S.; writing--review and editing, A.A. and L.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
This work is supported by the deanship of scientific research at the German Jordanian University.  
The authors declare no conflict of interest.