esa-sceva/satcom-chunk-collection huggingface dataset:
# Convex-Hull Algorithms: Implementation, Testing, and Experimentation
## 6 Workhorse Experiments
### Time Performance
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{(**a**)} \\ \hline \(\#\) & **introsort** & **plane-sweep** & **torch** & **quickhull** & **throw-away** \\ \hline \(2^{10}\) & 36.94 & 53.68 & 49.96 & 14.41 & 20.01 \\ \(2^{15}\) & 54.05 & 70.67 & 64.97 & 12.92 & 19.12 \\ \(2^{20}\) & 71.11 & 88.02 & 81.72 & 13.22 & 18.78 \\ \(2^{25}\) & 88.96 & 105.8 & 102.5 & 11.51 & 18.67 \\ \(2^{30}\) & 224.2 & 283.0 & 308.5 & 152.2 & 226.3 \\ \hline \hline \end{tabular} 
\begin{tabular}{c c c c c} \hline \hline \(n\) & **introsort** & **plane-sweep** & **torch** & **quickhull** & **throw-away** \\ \hline \(2^{10}\) & 36.94 & 53.68 & 49.96 & 14.41 & 20.01 \\ \(2^{15}\) & 54.05 & 70.67 & 64.97 & 12.92 & 19.12 \\ \(2^{20}\) & 71.11 & 88.02 & 81.72 & 13.22 & 18.78 \\ \(2^{25}\) & 88.96 & 105.8 & 102.5 & 11.51 &In terms of speed, torch has three success criteria: (1) It should be faster than plane-sweep that it attempts to improve upon; (2) it should perform almost as well as sorting; and (3) it should perform well when compared to other algorithms of industrial standard, as it would otherwise be out-competed by those. For problem instances of low and medium sizes on a square (Table 5) and on a disc (Table 6), torch is placed well in between introsort and plane-sweep. It performs a bit worse in the Windows environment for the bell data set (Table 7), likely because of the earlier-mentioned problem with overlapping work. We notice that torch becomes relatively slower for large instances. This may be due to the fact that it tries to make the linear term of the cost smaller. To achieve this, it may be necessary to process the same points several times. Thus, for large data collections, its advantage over plane-sweep decreases. There is a trade-off between the elimination cost and the obtained gain. The elimination cost grows with an extra factor of \(c\lg n\) per point, albeit with a small constant \(c\), while the saving is a constant per point. As for the third criterion, torch struggles hard with throw-away and quickhull, which are clearly faster, since they eliminate points before sorting, causing the sorting term to grow slower. One should also notice that the programs behave differently on different computers. On the Linux computer, with some exceptions, torch performs better than plane-sweep, but not significantly better. On the Windows computer, the situation is the opposite between the two. Also, quickhull is slower in the Windows environment. This indicates that the orientation tests can be evaluated faster on the Linux computer, which coincide with indications from our micro-benchmarks. The differences can come from several sources, but we consider it likely that different types of CPUs, compilers, operating systems, and hardware architectures have contributed to this. These differences serve as a warning to those who wish to crown a specific algorithm as the champion, since even with reasonable statistical accuracy, there can be a significant difference in how well a given algorithm performs in different environments.
## 4 Results and Discussion
### Results of Single Parameter Evaluation
#### 4.1.3 Aquifer Thickness  
Aquifer thickness (Figure 3c) is determined by combining groundwater depth measurements with borehole data [40]. The data sources include the national important geological borehole database service platform (55 points), Geo-technical Investigation Reports (359 points), and borehole data collected during field investigations (30 holes), totaling 444 points.  
In the bedrock mountain areas, the thickness of the weathered fissure layer is primarily considered, and it is obtained by subtracting the groundwater depth. The average value is then calculated for different regions. In the metamorphic rock and igneous rock mountain areas of Tangshan City, the thickness of the weathering layer ranges from 40-50 m, with some areas reaching up to 60 m. For the bare leaky karst mountains, the thickness of the strong water-bearing zone identified through borehole data is used to determine the aquifer thickness. Again, the average value is calculated for different regions. For example, in the northern part of Yutian County, Tangshan City, the thickness of the Jixian dolomite strong water-bearing zone is 40-45 m. In the plain area, the shallow aquifer thickness in the coastal region is relatively thin, mostly less than 10 m. This is determined based on calculations using borehole data and groundwater depth measurements.
# Nonlinear Stability Analysis of Shallow-Buried Bias Tunnel Based on Failure Mode Improvement
## 4 Surrounding Rock Pressure Calculation
### Internal Energy Consumption  
The internal energy consumption is calculated as follows:  
\[\dot{W}_{AB}=c_{t}\cdot v_{0}\cdot\cos\varphi\cdot l_{AB} \tag{25}\]  
\[\dot{W}_{BO}=c_{t}\cdot v_{01}\cdot\cos\varphi\cdot l_{BO} \tag{26}\]  
\[\dot{W}_{BC}=c_{t}\cdot v_{1}\cdot\cos\varphi\cdot l_{BC} \tag{27}\]  
\[\dot{W}_{A^{\prime}B^{\prime}}=c_{t}\cdot v_{0}\cdot\cos\varphi\cdot l_{A^{ \prime}B^{\prime}} \tag{28}\]  
\[\dot{W}_{B^{\prime}O^{\prime}}=c_{t}\cdot{v_{01}}^{\prime}\cdot\cos\varphi \cdot l_{B^{\prime}O^{\prime}} \tag{29}\]  
\[\dot{W}_{B^{\prime}C^{\prime}}=c_{t}\cdot{v_{1}}^{\prime}\cdot\cos\varphi \cdot l_{B^{\prime}C^{\prime}} \tag{30}\]  
\[\dot{W}_{CD}=\frac{c_{t}\cdot v_{1}\cdot\cos\varphi\cdot l_{OC}}{\sin 2\varphi }\cdot(\exp(\eta\cdot\tan 2\varphi)-1) \tag{31}\]  
\[\dot{W}_{CCD}=\frac{c_{t}\cdot v_{1}\cdot\cos\varphi\cdot l_{OC}}{\tan 2\varphi }\cdot(\exp(\eta\cdot\tan 2\varphi)-1) \tag{32}\]  
\[\dot{W}_{DE}=c_{t}\cdot v_{1}\cdot\cos\varphi\cdot\exp(\eta\cdot\tan 2\varphi )\cdot l_{DE} \tag{33}\]  
\[\dot{W}_{C^{\prime}D^{\prime}}=\frac{c_{t}\cdot{v_{1}}^{\prime}\cdot\cos \varphi\cdot l_{O^{\prime}C^{\prime}}}{\sin 2\varphi}\cdot(\exp(\eta^{\prime}\cdot\tan 2 \varphi)-1) \tag{34}\]  
\[\dot{W}_{O^{\prime}C^{\prime}D^{\prime}}=\frac{c_{t}\cdot{v_{1}}^{\prime} \cdot\cos\varphi\cdot l_{O^{\prime}C^{\prime}}}{\tan 2\varphi}\cdot(\exp(\eta^{ \prime}\cdot\tan 2\varphi)-1) \tag{35}\]  
\[\dot{W}_{D^{\prime}E^{\prime}}=c_{t}\cdot{v_{1}}^{\prime}\cdot\cos\varphi \cdot\exp(\eta^{\prime}\cdot\tan 2\varphi)\cdot l_{D^{\prime}E^{\prime}} \tag{36}\]  
The internal energy consumption \(\dot{W}_{AB},\dot{W}_{OB},\dot{W}_{BC},\dot{W}_{A^{\prime}B^{\prime}},\dot{W} _{O^{\prime}B^{\prime}},\dot{W}_{B^{\prime}C^{\prime}}\) for \(l_{AB}\), \(l_{OB}\), \(l_{BC}\), \(l_{A^{\prime}B^{\prime}}\), \(l_{O^{\prime}B^{\prime}}\), \(l_{B^{\prime}C^{\prime}}\), respectively, can be calculated based on the principles described in Reference [46]. \(\dot{W}_{CD},\dot{W}_{CCD},\dot{W}_{DE},\dot{W}_{C^{\prime}D^{\prime}},\dot{W} _{O^{\prime}C^{\prime}D^{\prime}},\dot{W}_{D^{\prime}E^{\prime}}\) are the internal energy consumption of \(l_{\over CD},l_{OC}+l_{OD},l_{DE},l_{C^{\prime}D^{\prime}},l_{O^{\prime}C^{ \prime}}+l_{O^{\prime}D^{\prime}},l_{D^{\prime}E^{\prime}}\), respectively, and its calculation formula can be found in Reference [48].
# Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model
## 3 Example Analysis
### Principal Component Data Analysis
#### 3.2.2 Principal Component Extraction  
In particular, the SPSS software was adopted in this paper to conduct the principal component analysis on the data. First, the original data was normalized to eliminate the significant differences. Afterwards, the correlation analysis was conducted on the 10 influencing factors of rock burst disasters. The thermodynamic diagram of the correlation coefficient is exhibited in Figure 4.  
\begin{table}
\begin{tabular}{c c} \hline
**Fitness Quantity of KMO Sampling** & **0.753** \\ \hline \multirow{3}{*}{Bartlett sphericity test} & Approximate chi square & 302.168 \\  & Degree of freedom & 45 \\  & Significance & 0.000 \\ \hline \end{tabular}
\end{table}
Table 4: KMO and Bartlett sphericity test results.  
Figure 4: Thermodynamic diagram of the correlation coefficient.  
After the principal component analysis, the variance contribution rates of the first six selected principal components were 48.142%, 14.054%, 12.678%, 9.751%, 4.259%, and 3.776%, respectively. Remarkably, the cumulative variance contribution rate reached 92.659%, which was greater than 85% and reflected the original information of the indicators to the greatest possible extent. The variance contribution rate and its cumulative contribution rate are exhibited in Figure 5.  
Eventually, a subsequent analysis was performed on the last six principal components to obtain the principal component coefficient matrix. In relation to Formula (9) to Formula (14), the training and testing samples obtained from the principal component analysis are shown in Tables 5 and 6.  
\[Y_{1}=0.1560X_{1}+0.2946X_{2}-0.1189X_{3}+0.4008X_{4}+0.3432X_{5}+0.3711X_{6}+0. 2649X_{7}-0.3637X_{8}\] \[-0.3133X_{9}+0.3976X_{10} \tag{9}\]  
\[Y_{2}=0.6887X_{1}-0.3720X_{2}+0.4412X_{3}-0.0413X_{4}-0.3005X_{5}+0.1984X_{6}+0. 1854X_{7}+0.0604X_{8} \tag{10}\]  
\[\begin{split} Y_{3}=0.13903X_{1}+0.4668X_{2}+0.4434X_{3}-0.0191X _{4}+0.3815X_{5}+0.1785X_{6}+0.1370X_{7}+0.2727X_{8}\\ +0.51121X_{9}-0.1837X_{10}\end{split} \tag{11}\]  
\[Y_{4}=0.2109X_{1}-0.04976X_{2}-0.5881X_{3}-0.1678X_{4}+0.0106X_{5}-0.0370X_{6}+ 0.6918X_{7}+0.1534X_{8} \tag{12}\]  
\[\begin{split} Y_{5}=-0.4653X_{1}-0.1623X_{2}+0.0352X_{3}+0.3573X _{4}-0.3092X_{5}+0.5730X_{6}+0.1732X_{7}+0.4145X_{8}\\ +0.0033X_{9}-0.0485X_{10}\end{split} \tag{13}\]  
\[\begin{split} Y_{6}=0.2292X_{1}+0.1601X_{2}-0.2970X_{3}-0.3435X_{4 }+0.1813X_{5}+0.2904X_{6}-0.3921X_{7}+0.5549X_{8}\\ -0.3503X_{9}+0.1141X_{10}\end{split} \tag{14}\]  
Figure 5: Variance contribution rate and its cumulative contribution rate.
# Anomalous Humidity Dependence in Photoacoustic Spectroscopy of CO Explained by Kinetic Cooling
## 3 Results  
### Humidity Dependence in the Experiment  
Using the 2_f_-WM-QEPAS setup described in Section 2.1, the photoacoustic signal from 20 ppm of CO in N\({}_{2}\) was recorded at a pressure of 200 mbar as well as 800 mbar while the humidity was varied. The laser wavelength was locked to the peak of the R9 transition of CO throughout. To correct for changes of the quality factor \(Q\) and resonance frequency \(f_{QTF}\) of the tuning fork with pressure and humidity, \(Q\) and \(f_{QTF}\) were measured for each pressure and humidity (see Figure S2 in the electronic supporting material). The excitation frequency was adjusted for every data point in Figure 2 and the photoacoustic signal was normalized by \(Q\).  
Figure 2: Amplitude (**left**) and phase (**right**) of the QEPAS signal of 20 ppm CO in N\({}_{2}\) at 200 mbar and 800 mbar at varying humidities (given as volume fraction). The temperature of the gas was 296 K.  
The amplitude of the QEPAS signal showed a pronounced minimum at a humidity of 0.19%v. The minimum in amplitude was accompanied by a change of phase from dry N\({}_{2}\) to humidified N\({}_{2}\) of 155\({}^{\circ}\) (800 mbar) and 125\({}^{\circ}\) (200 mbar). The phase change was more abrupt and the minimum in amplitude was more pronounced for a sample pressure of 800 mbar when compared to 200 mbar.
## 4 Results and Discussion
### Comparative Evaluation
#### 4.7.2 Evaluation Based on MyNursingHome Dataset  
Figure 10 delineates the assessment of FP-SFOA-DQN-FL based on the MyNursingHome dataset with respect to evaluation indicators. Figure 10a signifies the evaluation of FP-SFOA-DQN-FL in accordance with accuracy. By increasing the time step from 20 s to 200 s, the accuracy profited by the developed technique was 0.925, while the classical approaches attained a loss function of 0.249 for the federated object-detection algorithm, 0.207 for FedCV, 0.183 for DRFL and 0.159 for active learning as shown in Figure 10b. Figure 10c depicts the evaluation of the devised methodology in accordance with MSE. When assuming the time step as 200 s, the MSE obtained by FP-SFOA-DQN-FL was 0.125. Figure 10d implies the evaluation of RMSE. By considering the time step as 200 s, the RMSE received by the proposed FP-SFOA-DQN-FL was 0.034, and the FPR attained by the designed scheme was 0.143, as specified in Figure 10e. Also, the FPR provided by the existing models, such as the federated object-detection algorithm, is 0.279, FedCV is 0.239, DRFL is 0.216, and active learning is 0.192. Figure 10f implies the comparative evaluation of FP-SFOA-DQN-FL in terms of mean average precision. When the time step was 200 s, FP-SFOA-DQN-FL delivered a mean average precision of 0.895. The comparative evaluation of communication cost is depicted in Figure 10g. When the time step was 100 s, the communication cost of the federated object-detection algorithm, FedCV, DRFL, active learning, and FP-SFOA-DQN-FL was 0.150, 0.127, 0.118, 0.102, and 0.074, respectively.  
Figure 9: Comparative estimation based on YOLO object-detection dataset. (**a**) Accuracy, (**b**) Loss, (**c**) MSE, (**d**) RMSE, (**e**) FPR, (**f**) Mean average precision, (**g**) Communication cost.  
Figure 10: Comparative analysis based on MyNursingHome dataset. (**a**) Accuracy, (**b**) Loss, (**c**) MSE, (**d**) RMSE, (**e**) FPR, (**f**) Mean average precision, (**g**) Communication cost.
## 1 Introduction  
In 1992, [PERSON] et al. demonstrated that the Laguerre-Gaussian (LG) beam with helical wave-front carries the orbital angular momentum (OAM) [1]. It was shown that the LG beam with helical phase term of \(\exp(il\phi)\), have an OAM of \(l\hbar\) per photon, where \(\phi\) is the azimuthal angle, is the Planck's constant divided by \(2\pi\), and \(l\) is the azimuthal index called topological charge which represents the OAM mode. A key property of the OAM beams is that beams with different topological charges are mutually orthogonal. As a result, the beams can be effectively multiplexed and de-multiplexed to increase the capacity of high-speed optical communication, including free-space optical (FSO) and optical fiber communication [2; 3; 4; 5; 6; 7; 8; 9; 10; 11]. In addition, OAM has been widely applied to many other fields, such as optical tweezers [12; 13], quantum information processing [14; 15; 16], super high-density data storage [17], and so on.  
For a practical OAM-based FSO communication link, atmospheric turbulence is a serious challenge, which distorts both intensity and phase of the received light field, because it will introduce intermodecrosstalk, thereby degrading the performance of the communication system [18; 19; 20; 21; 22; 23; 24]. Thus, compensation of the distorted optical field becomes necessary and important. Several compensation techniques, such as MIMO equalization [25], adaptive optics system [26; 27; 28; 29] and phase compensation based on Gerchberg-Saxton (GS) algorithm [30; 31], have been preliminarily explored in the past years. However, due to the random effect of the atmospheric turbulence, the variation trend of turbulence, which is associated with the refractive index, structure parameter and the number and location of simulated random phase screen insertion in the transmission distance, are still worthy of detailed discussion.  
In this paper, we have emulated the influencing effects of the atmospheric turbulence on the transmitting single and multiplexed OAM beams by inserting random phase screens into the transmission path with different turbulence strength levels by changing the refractive-index structural parameter of \(C_{n}^{2}\) and the number of phase screens of \(N\). Then, the GS algorithm is adopted to calculate the pre-compensation phase and correct both intensity and phase of transmitted LG beams, from which the OAM beams can be effectively retrieved.
## 5 Conclusions  
In summary, we developed a miniature photoacoustic endomicroscopy imaging probe with a multimode fibre to deliver the excitation laser and a transparent ultrasound sensor to detect ultrasound signals. SVD was employed to eliminate the trigger-induced noise and improve the spatial resolution. With high-fidelity OR-PAM images obtained with carbon fibre phantoms, the developed imaging probe promises to provide in situ tissue characterisation towards optical biopsy.  
Conceptualization, T.Z. and W.X.; methodology, T.Z., M.Z. and W.X.; software, T.Z.; validation, T.Z. and M.Z.; formal analysis, T.Z., M.Z. and W.X.; investigation, T.Z. and W.X.; Data curation, T.Z. and W.X.; resources, S.O. and W.X.; writing--original draft preparation, T.Z. and M.Z.; writing--review and editing, All; supervision, W.X.; funding acquisition, S.O. and W.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Academy of Medical Sciences/the Wellcome Trust/the Government Department of Business, Energy and Industrial Strategy/the British Heart Foundation/Diabetes UK Springboard Award [SBF006/1136], Wellcome Trust, United Kingdom (203148/Z/16/Z, WT101957) and Engineering and Physical Sciences Research Council, United Kingdom (NS/A000027/1, NS/A000049/1). For the purpose of open access, the authors have applied a CC BY public copyright license to any author-accepted manuscript version arising from this submission.  
Not applicable.  
The data that support the findings of this study are available on request from the corresponding author.  
The authors thank [PERSON] from University College London, UK, for measuring the optical transmittance of the sensor. We thank [PERSON] and [PERSON] from the Northwestern Polytechnical University, China for insightful discussions on transparent sensor development.  
S.O. is shareholder and co-founder of Hypervision Surgical.
# Study of System Integral Energy Efficiency of a Hybrid Pneumatic Power System
## 2 Materials and Methods  
### Experimental Setup of Our HPPS  
The framework of our HPPS is shown in Figure 1. This system contains an internal combustion engine (ICE) (1) that is used to drive an air compressor (2), a one-way valve (3) that controls the air flow to a high-pressure air storage tank (4), a control valve (5) that adjusts air flow to a merger pipe (6), and an air motor (7) that obtains air flow from both the ICE and air storage tank. The torque sensor (8), pressure sensor (P), flow sensor (F), and temperature sensor (T) are connected with an A/D transformer and a computer (11) to measure the system specification.  
The ICE drives a compressor to charge an air tank. The compressed air flow and exhaust flow are then mixed at merger pipe. The merger flow is controlled by a stepper motor, which adjusts the merger position to create a venture geometry. By combination of both flows, the merger pipe enhances the kinetic energy of the compressed air flow. At the output of the merger pipe, with high pressure and temperature, the merger flow drives an air motor. This not only saves the chemical energy from the fuel, but also reduces air pollution.
## 2 Materials and Methods
### Data
#### 2.2.2 MERRA2 Daily Meteorology Reanalysis Data  
Second Modern-Era Retrospective analysis for Research and Applications (MERRA2) meteorology reanalysis data with a 0.5\({}^{\circ}\)\(\times\) 0.625\({}^{\circ}\) spatial resolution have been widely used in GPP modeling [5; 58; 59]. The data were downloaded from GMAO/NASA ([[https://disc.gsfc.nasa.gov](https://disc.gsfc.nasa.gov)]([https://disc.gsfc.nasa.gov](https://disc.gsfc.nasa.gov)) (accessed on 13 January 2021)). Four MERRA2 variables were used for this study: (1) daily surface incoming shortwave flux (SWGDN, W/m\({}^{2}\)), (2) daily minimum air temperature at 2 m (T2 MMIN, K), (3) daily mean air temperature at 2 m (T2 MMEAN, K), and (4) relative humidity (RH, %). The SWGDN was discovered within the \"Single-Level Radiation Diagnostics\" collection (M2T1 NXRAD), T2 MMIN and T2 MMEAN were found in the \"Single-Level Diagnostics\" collection (M2 SDNXXIJ), and RH was discovered within the \"Assimilated Meteorological Fields\" collection (M2I3 NPASM).  
In order to run GPP estimation models at the same spatial resolution as OLCI products, we adopted the nonlinear spatial interpolation method developed by [PERSON] et al. [60] for downsizing the MERRA2 meteorology reanalysis data from a 0.5\({}^{\circ}\)\(\times\) 0.625\({}^{\circ}\) resolution to a 300 m OLCI pixel resolution [43; 59]. This method utilizes the fourth power of the cosine function and the weighted distance in the closest four grid cell for computing the value of every output pixel with an OLCI resolution [61]. The spatial interpolation scheme may improve the meteorological input data accuracy of every 300 m pixel, eliminating sudden changes between the two sides of the MERRA2 boundary [62].
# Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method
## 4 Experimental Verification and Analysis  
The diagnostic performance of this method under variable working conditions is verified by contrast experiments with bearing datasets from Case Western Reserve University (CWRU) and Paderborn University (PU).
## 3 Testing Procedures
### Dynamic Setup
#### 3.2.1 Device Positions  
To perform the dynamic measurements, both devices were placed at different positions of the vehicle to test their behavior and performance in different environments. Four locations were proposed inside the car where the devices were placed. Then, in each of these locations, the devices were placed at different orientations to analyze how the position and orientation impact the performance of the GPS parameters under study.  
The four positions inside the car are the front dashboard (a-d), rear dashboard (e-f), under the seat (g,h), and over the seat (i-k), as can be seen in Figure 3.  
These different positions simulate the other places where a person can drop an IoT geolocation device, so how each technology behaves concerning these positions can be compared. All of these positions and orientations are interesting because an IoT device will not always be facing the sky but in any orientation since the device may be dropped randomly. Therefore, when placing the device in these locations in the car, there are other ways of facing the device, such as the device facing the sky, facing downward, facing the front of the vehicle, or facing the rear of the car (Figure 3).