esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Overview of Studied Numerical Cases  
### Base-Isolated Building Structures  
Three base-isolated buildings of 5, 10, and 15 stories are considered in this study. A Lumped Mass Model (LMM) is adopted for the superstructure, which behaves linearly. The isolation layer is formed by Natural Rubber Bearing (NRB), Lead Rubber Bearing (LRB), and Oil Damper (Oil), as shown in Figure 2. The combination of both NRB and LRB devices is assumed to perform a bilinear hysteresis behavior. The force developed in the Oil Damper device is assumed to depend only on the relative velocity of its edges. The three buildings were designed according to Japanese engineering practice [21] and modeled using the software STERA_3D (Structural Earthquake Response Analysis 3D) [22].  
Figure 1: Principle of the proposed hybrid seismic analysis [18].  
Tables 1 and 2 provide all the necessary properties to reproduce the same models. More design information and even the STERA_3D models are available [23].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Building 1**} & \multicolumn{2}{c}{**Building 2**} & \multicolumn{2}{c}{**Building 3**} \\ \hline
**Ref.** & **B01\_05F\_LMM\_SI** & **B02\_10F\_LMM\_SI** & **B03\_15F\_LMM\_SI** \\ \hline
**Period** & \multicolumn{2}{c}{**T\_n,1 = 0.5 (s)**} & \multicolumn{2}{c}{**T\_n,2 = 1.0 (s)**} & \multicolumn{2}{c}{**T\_n,3 = 1.5 (s)**} \\ \hline
**Story** & \multicolumn{2}{c}{**Stiffness**} & **Weight** & **Stiffness** & **Weight** & **Stiffness** & **Weight** \\
**Level** & \multicolumn{2}{c}{**(kN/mm)**} & \multicolumn{2}{c}{**(kN)**} & \multicolumn{2}{c}{**(kN/mm)**} & \multicolumn{2}{c}{**(kN)**} & \multicolumn{2}{c}{**(kN/mm)**} & \multicolumn{2}{c}{**(kN)**} \\ \hline
15 th & - & - & - & - & 80 & 3000 \\
14 th & - & - & - & - & 156 & 3000 \\
13 th & - & - & - & - & 225 & 3000 \\
12 th & - & - & - & - & 290 & 3000 \\
11 th & - & - & - & - & 349 & 3000 \\
10 th & - & - & 121 & 3000 & 402 & 3000 \\
9 th & - & - & 229 & 3000 & 451 & 3000 \\
8 th & - & - & 326 & 3000 & 494 & 3000 \\
7 th & - & - & 410 & 3000 & 531 & 3000 \\
6 th & - & - & 483 & 3000 & 563 & 3000 \\
5 th & 241 & 3000 & 543 & 3000 & 590 & 3000 \\
4 th & 435 & 3000 & 592 & 3000 & 612 & 3000 \\
3 rd & 579 & 3000 & 628 & 3000 & 628 & 3000 \\
2 nd & 676 & 3000 & 652 & 3000 & 639 & 3000 \\
1 st & 724 & 3000 & 664 & 3000 & 644 & 3000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lumped-mass model properties of studied buildings superstructures.  
Figure 2: (**A**) Overview of base-isolated building and (**B**) constitutive law of isolation devices.  
Synthetic data (rather than experimental data) were generated to develop the three MLMs (MLM1, MLM2, and MLM3) simulating the behavior of the respective isolation layers (NRB + LRB + Oil Damper) of the studied buildings (B01, B02, and B03), as shown in Table 2. Since the MLM aims to predict the true behavior of target components, property modification factors of design codes may be applied to its predicted values, and the same surrogate model can be reused within different code requirements.
## 3 Boost Converter
### Efficiency  
Efficiency \(\eta\) is defined as the power ratio in the load and that supplied by the generator (Figure 1):  
\[\eta=\frac{P_{o}}{P_{g}}=\frac{V_{o}I_{o}}{V_{g}I_{g}}=\frac{A_{Vi}}{A_{Vi}}. \tag{52}\]  
Now, substituting \(A_{Vi}\) by its value given in (43):  
\[\eta=\frac{1-\frac{V_{X}}{A_{Vi}V_{g}}}{1+A_{Vi}{}^{2}\frac{R_{X}}{R_{L}}}. \tag{53}\]  
where \(R_{X}\), \(V_{X}\) and \(A_{Vi}\) are given by (45)-(47) respectively.  
Figure 6: Total loss resistance \(R_{X}\) for the boost converter.
# Space-Efficient Fully Dynamic DFS
## 6 Compression of Data Structures
### Halving Required Space
**Claim 3**: _Finding the leftmost element among ones in \(S_{u}[a^{\prime}...b^{\prime}]\) with a value just \(c\) can be done by one_ rank _and one_ select _queries on \(S_{u}\) for any \(a^{\prime},b^{\prime},c\)._  
Let \(k=\operatorname{rank}_{c}(a^{\prime}-1,S_{u})\). It is observed that the \((k+1)\)-st occurrence of \(c\) in \(S_{u}\) is the answer if it exists and its position is in \([a^{\prime},b^{\prime}]\). So, if \(\operatorname{select}_{c}(k+1,S_{u})\) returns \(\emptyset\) or a value more than \(b^{\prime}\) then the answer is \"not exist\". Otherwise the answer can be obtained by this select query itself. This completes the proof of Lemma 6. Since \(\operatorname{rank}\), select, RN and RP queries on \(S_{u}\) can be solved in \(O(\log n)\) time for each with a wavelet tree for \(S_{u}\), we immediately obtain a data structure of \((m+o(m))\log n\) bits for the queries \(Q\) and \(Q^{\prime}\). Here the required space of \(B_{u}\) is again \(o(m)\log n\) bits and does not matter. **Corollary 2**: _There exists a data structure of \((m+o(m))\log n\) bits such that for any \(w,x,y\), \(Q(T(w),x,y)\) can be solved in \(O(\log n)\) time and \(Q^{\prime}(w,x,y)\) in \(O(\log^{2}n)\) time. This data structure can be built in \(O(m\sqrt{\log n})\) time._  
Please note that the above corollary does not mention working space for the construction of the data structure. Therefore, obtaining space-efficient dynamic DFS algorithm requires consideration of the building process, since in the algorithm of [PERSON] et al. we need to rebuild this data structure periodically. We further consider this in Section 7.
## 5 Overview of 5G-Enabling Technologies
### Integration of Non-Terrestrial Networks (NTNs) with 5G
#### 5.5.2 The 3 GPP NTN Standardization  
Work on the NR NTN by the 3 GPP began in 2017 with Release 15 (Rel-15) in June 2018 and was centered on developing channel models and deployment scenarios for the 5G NTN [145]. Rel-15 concentrated on two main goals: the first was to choose a few typical NTN deployment scenarios and reach a consensus on important factors, including architecture, orbital height, and frequency bands, while the second focused on developing NTN channel models based on terrestrial 3 GPP channel models. Key outcomes of the Rel-15 include the recommendation of S- and Ka-bands as NTN frequency ranges and the development of channel models that support urban, suburban, and rural scenarios. On completion of Rel-15, approaches to 5G NR adaptation for NTN were studied by 3 GPP in Release 16 (Rel-16) [146], with a major goal of determining the minimum requirement for successful integration of 5G NR with NTN. The study reports from Rel-16 led to the initiation of Release 17 (Rel-17) by the 3 GPP. Unlike the earlier releases, the main aim of Rel-17 was to define necessary improvements for LEO and GEO-enabled NTNs while providing complete support for air-to-ground and HAPS networks. The latest study on 5G NR and NTN integration is contained in Release 18 of the 3 GPP for 5G NR and was released in December 2021. It aims to upgrade 5G NR by incorporating more sophisticated technologies, such as AI and ML [147; 148].
## 3 Experiments and Analysis
### Surface Modeling and Contour Line Roughness  
The original point cloud was subjected to preprocessing and Delaunay triangulation, thereby creating a 3D surface model of the borehole. This reconstruction process was implemented on the basis of the PCL library. The reconstruction is shown in Figure 6. To obtain the contour lines of the drill holes, and calculate the roughness, the model is vertically sliced using a plane rotated along the center axis (z-axis). The angle of rotation of the plane ranges from 0 to 180\({}^{\circ}\). The number of extracted contour lines is dependent upon the selected rotation step. In this study, the chosen rotation step is 45\({}^{\circ}\), which results in 4 profiles and 8 contour lines. The extracted contour lines were coordinate transformed according to Equation (3) to form height undulations in the radius direction, as shown in Figure 7.  
These contour lines were sampled at 1 mm intervals to calculate the \(RF\) factor of the hole in different orientations, and the data are presented in Table 1. As shown in Table 1, the hole is rougher in the 90\({}^{\circ}\) orientation, with an average value of 0.0360.  
Figure 6: Reconstruction of the borehole surface model.  
Figure 7: Rough contour lines for bored piles in different directions.
# A Tweak-Cube Color Image Encryption Scheme Jointly Manipulated by Chaos and Hyper-Chaos
## 2 Preliminaries
### The Logistic-Fraction Hybrid Chaotic Map
#### 2.1.3 Approximate Entropy  
Approximate entropy (ApEn) is a nonlinear dynamic parameter used to quantify the regularity and unpredictability of time series fluctuations [27]. It can also measure the complexity of a chaotic map. Moreover, the larger the calculated ApEn value, the more complicated the sequence generated by the chaotic map is. We set the control parameters 11.44, 3.99, and 0.31 for the LFHCM, Logistic map, and Fraction map separately. Then, we calculated the ApEn of the three maps with different thresholds, as shown in Table 2. It can be seen that the proposed LFHCM has larger ApEn values, which proves that it can produce more complex sequences.
# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
###### Abstract  
Many concepts for future generations of wireless communication systems use coherent processing of signals from many distributed antennas. The aim is to improve communication reliability, capacity, and energy efficiency and provide possibilities for new applications through integrated communication and sensing. The large bandwidths available in the higher bands have inspired much work regarding sensing in the millimeter-wave (mmWave) and sub-THz bands; however, the sub-6 GHz cellular bands will still be the main provider of wide cellular coverage due to the more favorable propagation conditions. In this paper, we present a measurement system and results of sub-6 GHz distributed multiple-input-multiple-output (MIMO) measurements performed in an industrial environment. From the measurements, we evaluated the diversity for both large-scale and small-scale fading and characterized the link reliability. We also analyzed the possibility of multistatic sensing and positioning of users in the environment, with the initial results showing a mean-square error below 20 cm on the estimated position. Further, the results clearly showed that new channel models are needed that are spatially consistent and deal with the nonstationary channel properties among the antennas.  
distributed MIMO; ICAS; JSAC; measurements; positioning; reliable communication; sensing; wireless +
Footnote †: journal: Sensors  
0204  
241385
# Technical Interoperability for Machine Connectivity on the Shop Floor
## 4 Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Source** & **Description** & **Protocol** \\ \hline \multirow{2}{*}{Machine onboard computer} & The machine computer can provide most of the important & \multirow{2}{*}{OPC UA} \\  & data related to the grinding process. & \\ \hline \multirow{3}{*}{Vibration system} & The external vibration system consists of vibration sensors & \multirow{3}{*}{OPC UA} \\  & that are mounted on the machine, it sends an aggregated & \\  & version of the vibration data. & \\ \hline \multirow{3}{*}{IO-Link sensors} & IO-Link sensors communicate (using IO-Link) with a & \multirow{3}{*}{REST API} \\  & gateway, called IO-Link master, from which it is possible to & \\ \cline{1-1}  & retrieve sensor data over TCP/IP. A second gateway & \\ \cline{1-1}  & translates the data and makes it available as a web service. & \\ \hline Embedded Sensor & Temperature sensor connected to a Raspberry Pi computer. & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The different connected systems from which the data is collected. Figure 4: The Calvin application is distributed over three physical locations. One in the Raspberry Pi located by the machine that reads the thermometer values, one in the local cloud in the factory reading data from the machine and external sensors, and finally in the central private cloud to distribute and store the data. as the default communication protocol. After deciding on the structure for the topics, a Calvin function block was implemented that publishes the requested datatypes. The data is also streamed to the data analytics center with the help of Kafka [29], which can also fetch stored information from the database if needed. The database is a MongoDB setup with three replicas, a general-purpose NoSQL database. The data is stored as JSON (JavaScript Object Notation) documents with value, timestamp, and origin of the data as key components. An example entry can be seen in Figure 5. The application collects and transmits 97 different data values, most of them from the onboard computer. With a scan rate of 100 milliseconds, these values generate 16.5 updates per second, which translates to approximately 128 kbps of transmitted data including overhead. The data analytics experts can use the circa 1.4 million daily entries to test and evaluate new algorithms and approaches. At the shop floor, the operators can see and act on a few of these data entries, and remote monitoring and control can now easily be expanded upon.
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 3 Dynamic Model of the Transmission System
### Calculation of Component Relative Displacements
#### 3.2.2 Relative Displacements between the Ring gear and Planet Gears  
As an illustration, the relationship between the \(i\)-th planet gear, \(p\), and the ring gear, \(r\), in the differential PGT is shown in Figure 5.  
The elastic compression deformation of the ring gear, \(d\), and planet gear, \(mi\), along the meshing line is as follows:  
\[\delta_{dmij}=(-x_{dj}\sin\psi_{dmi}+y_{dj}\cos\psi_{dmi}+x_{mi}\sin\alpha_{dmi} -y_{mi}\cos\alpha_{dmi}+r_{bm}\theta_{zmi}-r_{bf}\theta_{zdj})\cos\beta_{b} \pm(z_{dj}-z_{mi})\sin\beta_{b}-e_{dmij}(t), \tag{4}\]  
where \(\psi_{dmi}\) represents the angle between the meshing line, \(d_{mi}\), and the horizontal axis; \(\alpha_{dmi}\) represents the pressure angle; \(\tau_{bf}\) represents the base radius of the ring gear, \(d\); and \(e_{dmij}\) represents the transmission error between the ring gear, \(d\), and planet gear, \(m\).  
The elastic compression deformation of the ring gear \(r\) and planet gear \(pi\) along the meshing line is as follows:  
\[\delta_{rpij}=(-x_{rj}\sin\psi_{rpi}+y_{rj}\cos\psi_{rpi}+x_{pii}\sin\alpha_{ rpi}-y_{pi}\cos\alpha_{rpi}+r_{bp}\theta_{zpij}-r_{br}\theta_{zrj})\cos\beta_{b} \pm(z_{rj}-z_{pi})\sin\beta_{b}-e_{rpij}(t), \tag{5}\]  
where \(\psi_{rpi}\) represents the angle between the meshing line, \(r_{pi}\), and the horizontal axis; \(\alpha_{rpi}\) represents the pressure angle; \(r_{br}\) represents the base circle radius of the ring gear, \(r\); and \(e_{rpij}\) represents the transmission error between the ring gear, \(r\), and planet gear, \(p\).
# Wavelet-Based Analysis of Motor Current Signals for Detecting Obstacles in Train Doors
## 2 Dynamic Model of the Train Door
### Mechanical Model of Train Door System  
The train door mechanical system comprised a DC motor and a gearbox to amplify the motor torque. The gearbox shaft was coupled with a lead screw to convert the rotational motion into linear displacement. The door panels were connected to the door mounts, and the bottom of each door panel had two wheels and a rack on the chassis to enable smooth linear movement. The lead screw, nut, and door panels were modeled in MATLAB Simulink by assembling Simscape multibody components. A rotational mechanical friction block was connected to the shaft of the gearbox to realize the friction of the entire door actuation system. The complete assembled components of the Simscape mechanical model of the train door system are displayed in Figure 3.  
The efficiency of the lead screw is calculated by Equation (7) below [25]:  
\[\eta=\frac{\tan\alpha}{\tan(\alpha+\tan^{-1}A(\mu))} \tag{7}\]  
\[\alpha=\tan^{-1}(P/U) \tag{8}\]  
\[U=\pi\times d=\pi\times 18 \tag{9}\]  
where \(\eta\) is the lead screw to nut efficiency, \(\alpha\) is the angle thread pitch, \(P\) is the pitch, \(U\) is the circumference, and \(\mu\) is the coefficient of friction (0.15 for steel).  
The relation between the motor torque and the thrust force generated by the lead screw is as follows [25]:  
\[T=F*\frac{P}{2\pi\eta} \tag{10}\]  
where \(T\) is torque required from the motor (Nm), \(P\) is pitch of the lead screw (mm), \(F\) is the force (N), and \(\eta\) is the lead screw efficiency.
## 2 Computation Model and Methodology  
### The Real Fluid RFM Model  
Within the diffuse interface method (DIM), there are many two-phase flow models available, with a number of transport equations ranging from three to seven depending on the initial equilibrium assumptions. This study utilizes a two-phase fully compressible 4-equation model developed at IFPEN, which assumes that the flow system is in full equilibrium (i.e., equilibrium of velocity, pressure, temperature and chemical potential at liquid-vapor interfaces). This model is, therefore, a homogeneous equilibrium model (HEM). It differs from previous HEM models in the thermodynamic closure, which uses a real fluid equation of state for the calculation of the thermodynamic and transport properties, and in the vapor-liquid equilibrium (VLE) procedure, which provides comprehensive knowledge regarding both liquid and gas composition in addition to the local vapor fraction in all cells of the computational grid. Therefore, the RFM model can be seen as a VOF model in which the interface is captured thermodynamically.
## 2 Materials and Methods
### Analysis of Plasma Fatty Acids  
Plasma fatty acids were determined by the method of [PERSON] and [PERSON] [33] with slight modifications. Briefly, 200 \(\upmu\)L of plasma samples were lyophilized (Scientz-18N, Ningbo Scientz Biotechnology Co., Ltd., Ningbo, China.) for 4 h. After lyophilization, 3 mL of 4:1 CH3 OH/C7H8 solution were added, followed by the step-by-step addition of 200 \(\upmu\)L CH3 COCI in three doses and subsequent vortex mixing. The samples were subjected to ethanolysis in a water bath (Memmert WB14, Schwabach, Germany) at 100 \({}^{\circ}\)C for 30 min and then cooled at room temperature. A total of 5 mL of K2 CO3 6% _w/v_ solution was slowly added in order to terminate the reaction and neutralize the mixture. The samples were centrifuged (Hettich Universal 32, Tuttlingen, Germany) at 1000\(\times\)\(g\) for 10 min and the supernatant organic phase of toluene, including fatty acids methyl esters (FAME), was transferred to GC vials, which were sealed and stored at \(-\)80 \({}^{\circ}\)C until analysis.  
FAMEs were separated on a 60 m \(\times\) 0.25 mm capillary column (0.25 \(\upmu\)m film thickness) DB-23 J&W (Agilent Technologies, Santa Clara, CA, USA), using a Hewlett-Packard 6890 Gas Chromatograph (Agilent Technologies, Waldbronn, Germany) equipped with an MSD-5972 mass selective detector. The analytical conditions were as follows: volume injected; 1 \(\upmu\)L, split ratio; 1:20, carrier gas; high-purity helium (0.8 mL/min), injector temperature;230 \({}^{\circ}\)C, MSD transfer line; 280 \({}^{\circ}\)C, oven temperature; from 130 to 250 \({}^{\circ}\)C with stepped temperature program, total run time; 60 min.  
Identification of the FAME peaks was accomplished using a mixed FAME standard (Sigma L9405, St Louis, MO, USA) and by reference to the NIST mass spectra library. The mixed FAME standard was injected periodically to check for any changes in retention times, while it also served for the calculation of response factors, which were applied to the areas derived from the chromatographic traces.
## 3 Results and Discussion
### Numerical Results  
The distribution of the circumferential and axial strains is shown in Figure 10, and the results for the circumferential and axial stresses are presented in Figure 11. Pictures expanded from \(\mathcal{U}\) part to the whole area are shown for clarity.  
Analysis of the numerical results shows the presence of a significant non-uniformity of strain distribution in the vicinity of circular notches. However, the most significant strain gradients appear in the vicinity of the edges of the circular notches. Within the area occupied by strain gauges, the strains vary only slightly. Thus, the value of circumferential deformation in the entire study area varied in the range from \(-0.00026\) to \(0.011\). Within the sensor installation area, the circumferential deformation varied from \(0.0036\) to \(0.0049\).  
This allows us to use the average values of strains determined from the changes in the resistance of the sensors to find the elasticity characteristics of the pipe material. The analysis of stress distribution in the cross section with minimum area reveals a significant stress concentration. Thus, the average stress on the line \(y=0\) amounted to \(180\) MPa, and the maximum stress on the contour of the hole \(306\) MPa. This fact should be taken into account when processing the results of fracture experiments on specimens with circular notches.  
Figure 11: The distribution of circumferential (**a**) and axial (**b**) stresses.  
Figure 10: The distribution of circumferential (**a**) and axial (**b**) strains.
# Interpolation-Filtering Method for Image Improvement in Digital Holography
## 5 Experimental Validation of the Proposed Method  
### Experimental Setup for Recording Digital Holograms  
For experimental verification of the proposed method, off-axis digital holograms of objects were recorded. The scheme of the experimental setup is presented in Figure 1. A CNI laser, SSP-SLM-561-FN (80 mW, 561 nm), was used as a radiation source. To make the beam more uniform, a spatial low-pass filter consisting of microlens and an aperture with a diameter of 50 \(\upmu\)m was used. Cubes for separation and convergence of the reference and object beams had reflection and transmittance at 50%. A digital scientific camera Retiga R6 (CCD; 14-bit ADC; 2688 \(\times\) 2200 pixels; pixel size 4.6 \(\times\) 4.6 \(\upmu\)m\({}^{2}\)), the noise parameters of which were measured and given in [101], recorded holograms. The camera was controlled from a computer. Digital holograms of reflecting objects were recorded.  
To show the effectiveness of the proposed method, holograms of one and several objects were registered. An example of a hologram fragment with the size of 200 \(\times\) 200 pixels is shown in Figure 9a. The reconstructed images from 3D-scenes are shown in Figure 9b,d,e. The depths of the scenes are 1 mm (Figure 9b) and 5 mm (Figure 9d,e). For the second scene, we show reconstructions of two cross sections of the scene corresponding to the positions of the main elements, two coins in Figure 9d,e. The image sizes are 880 \(\times\) 1100 (Figure 9b), 660 \(\times\) 740 (Figure 9f), and 830 \(\times\) 460 (Figure 9g) pixels. Also, in the reconstructed images (Figure 9b,f,g), the two zones selected for SC calculation are labeled. For better observation of the degree of noisiness, an enlarged view of the noisiness is shown in Figure 9c, which shows an enlarged image of zone 1 for the first object.  
Figure 9: Example of the recorded hologram fragment (**a**) and reconstructed images: the 1 st scene (**b**) and enlarged image of its zone 1 for the speckle-contrast calculation (**c**), sections of the 2 nd scene (**d**,**e**), and its enlarged fragments with zones for the speckle-contrast calculation (**f**,**g**).