esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Prediction Methods for Axial Strength of Ground Connections with Shear Keys
## 4 Comparison between Calculation and Test Results
### API (2007)  
The API specification considers that the shear strength of the grouted connection under axial load is only linearly related to the product of grouted material strength and the shear key height-to-distance ratio, _h/s_. Irrespective of the radial stiffness of the grouted connection, the corresponding design formula is obtained by linear regression with the safety factor added. A comparison between the regression formula calculation results and the test results is shown in Figure 15a. The formula fitted by the API code is representative, regardless of the value of shear strength. It can be used to predict and evaluate the axial strength in the absence of geometric parameters of grouted connections. However, the difference between the calculated value from the formula and the experimental value is obvious. Only 2 out of 51 specimens have a deviation ratio of less than 10%, and the maximum deviation ratio is 168%. A comparison between the calculated results obtained from the revised design formula and the test results is shown in Figure 15b. All the scattered points are distributed under the diagonal line, and the shear strength calculated by the formula is less than the test values; especially when the strength is high, the design formula is conservative.  
Figure 14: Comparison of shear strength calculated using HSE and tested results.  
Figure 15: Comparison of shear strength calculated using API and tested results: (**a**) API fitting formula; (**b**) formula under extreme conditions.
## 7 Results and Discussions
### Optimal Configuration from the HPRALC, MPRAMC, and LPRAHC-Based Configurations  
Three different efficiency-based scenarios--\"HPRALC\", \"MPRAMC\", and \"LPRAHC\"--were analyzed in order to establish the optimum set for the system.\"
# A New Calibration Method for Broadband Complex Resistivity Measurement System of Rocks and Ores
## 1 Introduction
Furthermore, because of inevitable electromagnetic coupling in traditional four-electrode systems, [PERSON] and [PERSON] [30] extended the frequency range to MHz by combining the four-electrode method (low frequency) with the two-electrode method (high frequency). During field exploration, many researchers have focused on the arrangement and selection of electrode configurations. These approaches help suppress the apparatus effect under high-frequency conditions [26,31,32]. To extract effective induced polarization (IP) responses from measurement data, inversion models and methods can also be employed [33]. By leveraging differences in the transmission equations of the induced polarization field and the electromagnetic induction field, the two effects can be separated [32; 34]. New measurement devices, methods, and electrodes have been continuously developed for use in high-precision CR measurement systems [22; 34; 35]. Currently, specialized instruments for measuring the complex resistivity of rocks andores are not widely available. Most researchers rely on alternatives, such as impedance analyzers or LCR bridge instruments. These include the Solartron 1260A impedance analyzer from Solartron (Bognor Regis, UK) and the HP4194A impedance/gain-phase analyzer from Hewlett-Packard (Palo Alto, CA, USA). These instruments are primarily designed for measuring electronic components and typically use low-value sampling resistors (<1 k\(\Omega\)). Their calibration methods are based on the forward connection of signal sources and primarily account for the distributed capacitance of the acquisition channels. However, in testing high-impedance samples, which can have values up to 10 M\(\Omega\), using low-value sampling resistors leads to reduced voltage acquisition accuracy. Traditional calibration methods struggle to meet the precision requirements for such high-impedance samples. To address the limitations in high-frequency complex resistivity measurement systems, this study introduces a novel measurement instrument designed for a resistance range of 50 \(\Omega\) to 10 M\(\Omega\). A new calibration method is proposed, using both forward and reverse connections of the excitation signal source to calculate the distributed capacitance of internal acquisition channels, and the parasitic capacitance of sampling resistors, and to improve data calibration accuracy. The influence of contact impedance on measurement results is analyzed, and two sample testing devices are developed to extend the instrument's measurement range and improve its precision for high-impedance samples. Through extensive testing with multiple circuit models and actual samples, the proposed system and calibration method achieve a measurement accuracy of 1% for resistances up to 10 M\(\Omega\). This performance surpasses that of the Solartron 1260A impedance analyzer and traditional calibration methods. The proposed system shows significant advantages for rock physical property measurements and spectral-induced polarization data processing.
## 6 Discussion
### Research Significance and Implications  
With the rapid development of the Internet and e-commerce, virtual enterprise and outsourcing have become popular ways for modern enterprises to cope with economic globalization. Interorganizational workflow technologies are widely used by business enterprises for process re-engineering, process management, and process automation. At present, most research on interorganizational workflow focuses on workflow modeling, i.e., transforming business processes into executable computer languages. The aim of such research is to improve the Workflow Management System (WfMS), and research findings are seldom directly used to analyze and guide the practice of interorganizational process management.  
In this study, interorganizational workflows were analyzed and compared from an organizational perspective in order to investigate interorganizational rework and iteration phenomena. The study mainly relates to participant organizations of TMOs in a construction project. However, the analysis methods and findings can be extended to other fields and other types of interorganizational collaborative workflow, e.g., in manufacturing, e-commerce, and e-government fields. Moreover, they are not only applicable to TMOs in construction projects, but also to participants of New Product Development (NPD), supply chain management, virtual enterprises, and more. The analysis methods and findings used in this study can also be used to analyze and guide the practice of interorganizational collaborative workflows and improve the performance of interorganizational process management. These analysis methods and findings can help to:  
1. Determine the appropriate interoperability form and implementation mode of participant-based interorganizational workflows by optimization and comparison.
2. Analyze and calculate the time spent and workload for each participant in an interorganizational workflow process.
3. Define measures and methods for improving interorganizational workflow management.  
Additionally, the analysis methods and findings of this study can also help to develop a participant-oriented WfMS. This could be used to: develop tools for calculating the time spent and workload of participants in interorganizational workflows, develop a system with an optimization function for the implementation mode of interorganizational workflows, and produce a workflow implementation platform and management system for participants in an optimized implementation mode. The development of a participant-oriented WfMS is one of our research interests.
# _k_-Center Clustering with Outliers in Sliding Windows
## 3 \(k\)-Center with \(z\) Outliers  
Let \(S\) be a (possibly unbounded) stream of points from some metric space, and let \(N\) be the selected window length. For any point \(p\in S\), its _Time-To-Live_\(\operatorname{TTL}(p)\) is \(N\) when \(p\) arrives, and it decreases by \(1\) at each subsequent step. We say that \(p\) is _active_ when \(\operatorname{TTL}(p)>0\), and that it _expires_ when \(\operatorname{TTL}(p)\) becomes \(0\). For convenience, the analysis will also consider expired points with negative TTLs. At any time \(t\), the current window \(W\) consists of all arrived points with positive TTL; hence, \(|W|=N\).  
In this section, we present coreset-based algorithms that, at any time \(t\), are able to return accurate approximate solutions to the \(k\)-center problem with \(z\) outliers for \(W\). The section is structured as follows. Section 3.1 describes and analyzes the weighted coreset construction. Section 3.2 discusses how to extract the final solution from the weighted coreset whose radius is, at most, a constant factor away from \(r^{*}_{k,z}(W)\), as long as a slightly larger number of outliers is tolerated. Section 3.3 shows how to remove an assumption made to simplify the presentation. Finally, Section 3.4 shows that, for spaces of bounded doubling dimension, the approximation factor can be lowered to a mere \(3+\varepsilon\) for any fixed \(\varepsilon\) at the expense of larger working memory requirements.
# Numerical Simulation Research on Plane Alignment Parameters of Desert Highway
## 2 Numerical Simulation
### Numerical Method  
The sand particle size in the study area was 0.075-0.25 mm, the sand particle size in the wind-sand flow was set to be 0.1 mm, the sand density was 2650 kg/m\({}^{3}\), and the sand kinematic viscosity coefficient was 0.047 kg/(m-s) [33]. The volume fraction of sand grain phase was 0.03, the inlet wind speed adopted logarithmic flow wind speed, and the maximum wind speed measured in the test section was 18m/s, so the axial wind speed at the entrance of the model was 18m/s, and its typical wind speed profile can be calculated using Equation (1) [34,35]:  
\[v(z)=\frac{v_{*}}{k}\ln\frac{z}{z_{0}} \tag{1}\]  
where \(v(z)\) is the velocity at the height of \(z\), \(v_{*}\) is the frictional wind speed, \(k\) is the Von Karman constant, which is taken as 0.4. \(z\) is the height, and \(z_{0}\) is the rough length.  
Numerical simulations were carried out by the CFD code ANSYS Fluent (version 2020 R1). The desert wind-blown sand flow is generally in a saturated state, and the sand volume fraction is large, so it should be simulated by a two-fluid model. Therefore, the calculation model adopted the Euler two-fluid unsteady model, and the solution algorithm adopted simple [36,37]. The wind speed in the study area was not greater than 50 m/s, and the sand was mainly affected by gravity. The wind-sand flow can be regarded as an incompressible fluid. The heat exchange between wind-sand flows can be ignored, so the energy equation was not considered. The two equations of the continuity equation, momentum equation, and the \(k-\epsilon\) turbulence model were considered when studying the characteristics of wind-sand movement. The calculated residuals are controlled to \(10^{-5}\) to ensure that the results are convergent and accurate.
# Fast Airfoil Selection Methodology for Small Unmanned Aerial Vehicles
## 5 Results
### Construction of the Airfoil \(\ast\)Map-Diagram\(\ast\)  
The necessary visual aid to help the designer in the airfoil selection process is the maximum lift coefficient versus ideal lift coefficient diagram or \(C_{lmax}-C_{il}\) diagram. This diagram is a form of an airfoil map. \(C_{li}\) values lies at x axis and \(C_{lmax}\) values at y axis. Within the diagram, points exist, each one representing a certain airfoil. The designer can focus on a certain region of the diagram to further analyze the airfoils by comparison.  
The main differentiation that the proposed \(C_{lmax}-C_{il}\) diagram (Figure 8; Table S2 in.xlsx format) introduces over against the corresponding high Reynolds number diagrams that can be found in the literature, consists of the following statements:  
1. It introduces two different Reynolds numbers across the two axes, instead of one. \(C_{lmax}\) is calculated at Re = 100,000, while \(C_{li}\) at Re = 200,000;
2. Aside of the points that represent each airfoil, a faded line appears to the left and/or right of each point. This line represents the bucket width variable that we discussed earlier.  
Remarks regarding Figure 8.  
1. The big bucket widths are now visually verified;
2. The \(C_{ii}\) points' position is not mandatory to be in the middle of the bucket. The exact position has to do with the physical shape that the bucket has each time;
3. The four airfoils (S1123, FX63-137, MH114 and SG6043) that appear at the upper part of the diagram (those with high \(C_{lmax}\)), are indeed characterized by their designers as High Lift, Human power aircraft airfoil, Ultralight Airfoil, and High L/D, respectively (see Table 5). All of these airfoils are of high camber. In combination with Table 9, \(\Delta C_{Lf}\) measurement, it can be stated that flap deflection does not have a great effect on the lift coefficient improvement of those high camber airfoils. The S8064 airfoil which has the lowest \(C_{lmax}\) is a counterexample, as it is characterized by its designer as a fast RC aircraft;
4. \(C_{li}\) dispersion is located around the 0.5-0.6 region and \(C_{lmax}\) dispersion is located around the 0.9-1 region.
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 5 Future Trends
### Increased Adoption of Wireless Sensors  
As previously stated, enhanced autonomy and reliability in PV-DT would require the use of wireless sensors. This would enable better coverage of large-scale utility installations while making maintenance less laborious. However, the use of wireless sensors will be highly dependent on efficient low-latency networks, such as the recent 5G and 6G bands that have been adopted. This would ensure the synchronization of data from all sensors as required for data reliability.  
It is important to note that utility-scale solar farms are not usually installed in urban areas where the 5G and 6G networks are suited. The installations are found in remote regions where adequate land mass is available or even on relatively stationary water bodies. It will therefore be essential to consider the economic impact of extending 5G or 6G to such areas and the type of sensors to be developed and installed. On the other hand, smart homes and cities with PV installations would immensely benefit from such technologies and may drive the market for such products.
## Appendix B  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Code \({}^{1}\)** & **Similar to** & **Similarity** & **Accession** & **c DNA (ug/mL)** \\ \hline EP1 & _Achromobacter_ sp. IR27 & 97\% & GU726513.1 & 55 \\ EP2 & _Bacillus cereus_ strain F2-21 & 99\% & K350029.1 & 1465 \\ EP3 & _Bacillus simplex_ strain Se2 & 99\% & HQ432812.1 & 380 \\ EP4 & _Bacillus subtilis_ strain SUT2 & 99\% & GU971415.1 & 940 \\ EP5 & _Bacillus subtilis_ strain RW134 & 99\% & MH010185.1 & 50 \\ EP6 & _Microbacterium_ syndstra min Csn-105 & 99\% & K7880637.1 & 3640 \\ EP7 & _Delia acidomonas_ isolate R141 & 99\% & D0530127.1 & 20 \\ EP8 & _Bacillus thuringiensis_ strain WV9 & 100\% & JQ936681.1 & 2288 \\ EP9 & _Bacillus subtilis_ strain BGR261 & 99\% & KT074466.1895 & 25 \\ EP10 & _Achromobacter_ sp. strain SYP-8562 & 99\% & K763682.1 & 1560 \\ EP11 & _Bacillus subtilis_ strain RW134 & 99\% & MH0110185.1 & 170 \\ EP12 & _Achromobacter_ sp. strain SYP-B562 & 98\% & KY636382.1 & 35 \\ EP13 & _A Agrobacteriumuter  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Sequence from 5’ to 3’** & **Reference** \\ \hline
27F & AGAGTTTGATCATGGCTCAG & [56] \\
518R & GTATTACCCGGGCTGGTG & [56] \\ \hline
23S30R & CTTCGCCTCTGGTGCCTAGGT & [57, 58] \\ CYA359F & GGGGAAATTTCCGCAATGGG & [58, 59] \\ \hline nif-Df & GATTTTCADGADAADGATATT & [60] \\ nif-Dr & CCAIGGIATICCDTATTTTC & [60] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Primer sequences used for PCR.  
Figure 17: Growth curves for the cultured microorganisms selected for phenotyping. \(X\)-axis presents time of incubation (hours) and \(Y\)-axis values of OD\({}_{600}\). Logarithmic curves are fitted to the data.
# Locating Image Sources from Multiple Spatial Room Impulse Responses
## 5 Conclusions  
We have presented an algorithm that aims at reliably locating image sources by combining multiple source-receiver SRIR measurements. Such measurement combination has benefits over estimating image source positions from single measurements, where estimation errors and physical constraints limit the number of reliably detectable reflections.  
With measurements of multiple SRIRs from a small room and two large concert halls, we have demonstrated how the approach attains improved visualization. As one of the best examples, the balcony reflections in the Amsterdam Concertgebouw could be identified with the presented method. These were not recognized earlier when analyzing individual spatial room impulse responses only.  
Conceptualization, O.P., N.M.-K. and T.L.; methodology, O.P. and N.M.-K.; software, O.P.; validation, O.P.; formal analysis, O.P.; investigation, O.P. and N.M.-K.; resources, T.L.; data curation, O.P.; writing--original draft preparation, O.P., N.M.-K.; writing--review and editing, O.P., N.M.-K., T.L.; visualization, O.P.; supervision, T.L.; project administration, T.L.; funding acquisition, T.L. All authors have read and agreed to the published version of the manuscript.  
[PERSON] was funded by The Academy of Finland grant number 296393. [PERSON]'s contribution has received funding from the European Union's Horizon 2020 research and innovation program under the [PERSON] grant agreement No. 812719.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The concert hall data are not publicly available as the halls do not allow us to distribute the data freely.  
We thank [PERSON] for providing us with the 3D model of the variable acoustics room Arni.  
The funders had no role in the design of the study; in the collection, analyzes, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
6 Dof 6 Degrees of Freedom (x, y, z, roll, pitch, yaw)  
RIR  Room Impulse Response  
SRIR  Spatial Room Impulse Response  
ER  Early Reflection  
SDM  Spatial Decomposition Method  
SIRR  Spatial Impulse Response Rendering  
HO-SIRR  Higher Order Spatial Impulse Response Rendering  
TDoA  Time Difference of Arrival  
ToA  Time of Arrival  
DoA  Direction of Arrival  
Figure 14: Found image sources in variable acoustics room Arri when acoustic panels on the -y wall are open (min. 3 ER objects per image source). The number of image sources are greatly reduced on the -y hemisphere.  
Figure 13: Found image sources for five loudspeaker positions in variable acoustics room Arri (min. 2 ER objects per image source). Lowering the number of required ER objects makes the algorithm find more higher-order image sources, but also increases the amount of noise.  
Figure A7: Found image sources for loudspeaker orchestra in Amsterdam Concertgebouw (min. 2 ER objects per image source).
## 4 Implementation and Experimental Setup
### MIT-BIH Arrhythmia Database and Computing Platform  
In this paper, ECG datasets from the MIT-BIH arrhythmia database [41] are used for the performance evaluation of our proposed ensemble CNN algorithms. This database is the widely used database for testing classification performance. It contains 48 records, in which each contain two- channel ECG signals for 30 min duration selected from 24 h recording of 47 patients. The database consists of 19 types of ECG beats, in which 16 of them are related to cardiac arrhythmia.  
From the total MIT-BIH database, we only used around 110,157 ECG beats out of 112,647 total beats which were categorized into 16 classes as shown in Table 1. These beat samples were then further divided into 23,999 beats for training and 86,158 beats for testing. As described in Figure 2, the total of 110,157 beats was also replicated with zero padding. Therefore, there are two sets of experimental data, with and without zero padding.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Symbol** & **Annotation Description** & **Total** & **Training** & **Testing** \\ \hline
1 & N & Normal beat & 75,052 & 11,257 & 63,795 \\
2 & L & Left bundle branch block beat & 8075 & 2826 & 5249 \\
3 & R & Right bundle branch block beat & 7259 & 2540 & 4719 \\
4 & V & Premature ventricular contraction & 7130 & 2495 & 4635 \\
5 & / & Paced beat & 7028 & 2459 & 4569 \\
6 & A & Atrial premature contraction & 2546 & 891 & 1655 \\
7 & f & Fusion of paced and normal beat & 982 & 491 & 491 \\
8 & F & Fusion of ventricular and normal beat & 803 & 401 & 402 \\
9 &! & Ventricular flutter wave & 472 & 236 & 236 \\
10 & j & Nodal (junction) escape beat & 229 & 114 & 115 \\
11 & x & Non-conducted P-wave & 193 & 96 & 97 \\
12 & a & Aberrated atrial premature beat & 150 & 75 & 75 \\
13 & E & Ventricular escape beat & 106 & 53 & 53 \\
14 & J & Nodal (junction) premature beat & 83 & 41 & 42 \\
15 & e & Atrial escape beat & 16 & 8 & 8 \\
16 & Q & Unclassifiable beat & 33 & 16 & 17 \\  & & Total 16-class beats & 110,157 & 23,999 & 86,158 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sixteen classes of cardiac arrhythmia ECG beats from the MIT-BIH Database and its amount of Training and Testing beats.  
Figure 3: Ensemble CNN.  
The proposed algorithm was implemented in Python with Tensorflow with GPU [43] and Keras libraries [44]. The experiments were performed on a computer with Intel Core i7-7700 CPU with a total of eight logical processors, memory of 8 GBytes, graphic card Nvidia GeForce GTX 1060 6 GB DDR5, using Microsoft Windows 10 64 bits operating system. The experiments on the training and testing time using this computing platform is elaborated further in Section 5.