esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Cutting Force Model for Ultrasonically Assisted Grinding of Hard and Brittle Materials
## 3 Obtaining \(K\) and \(K_{\mathrm{s}}\)  
### Experimental Setup  
Experiments were carried out on a self-developed ultrasonic vibration system which was able to generate ultrasonic vibrations in a frequency range of 16 kHz. A YDCB-05 piezoelectric dynamometer (DUT, China) was used to measure the cutting forces, the resolution was \(\pm\)0.001 N, and both the linear error and the reproducibility error were less than \(\pm\)1%. A LK-H025 laser displacement sensor (KEYENCE, Japan) was utilized to measure the vibration amplitude while the tool was in rotation, and the repeat accuracy was 0.1 \(\upmu\)m. The grinding wheels used in the experiments were provided by CPTC company. The grinding wheels were diamond core tools of a metal bond. They had an outer diameter of 10 mm and an inner diameter of 4 mm, and a radius of transition fillet in four different sizes: 0.2 mm, 0.7 mm, 1 mm and 1.2 mm. The workpiece material was the K9 optical glass with the dimensions of 15 \(\times\) 30 \(\times\) 60 mm. The mechanical properties of the workpiece are shown in Table 2. Slot grinding was used in the experiments, as shown in Figure 14.  
The relationship between volume correction parameters (\(K\) and \(K_{\mathrm{s}}\)) and input variables (\(n_{r}\), \(A\), \(v_{F}\), \(a_{p}\), \(r_{c}\)) is shown in Equation (29) and Equation (30), and the input variables may have influence on the value of the volume correction parameters. Therefore, the experiments on the influence of the input variables were carried out. The experimental design is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbols** & **Definitions** & **Unit** \\ \hline \(A\) & Vibration amplitude & m \\ \(R_{i}\) & Inner radius of the tool & m \\ \(R_{o}\) & Outer radius of the tool & m \\ \(r_{c}\) & Radius of the transition fillet & m \\ \(v_{F}\) & Feed rate & m/s \\ \(a_{p}\) & Cutting depth & m \\ \(f\) & Ultrasonic vibration frequency & Hz \\ \(n_{r}\) & Rotation speed of the tool & r/min \\ \(K_{IC}\) & Fracture toughness & Pa\(\cdot\)m\({}^{1/2}\) \\ \(E\) & Elastic modulus & Pa \\ \(H_{V}\) & Hardness & Pa \\ \(h_{e}\) & Protrusive height of the & m \\  & abrasive grains & m \\ \hline \hline \end{tabular}
\end{table}
Table 1: The units of parameters in the formulas.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Modulus of elasticity \(E\) (GPa) & 85.9 \\ Fracture toughness \(K_{\mathrm{IC}}\) (MPa\(\cdot\)m\({}^{1/2}\)) & 0.8 \\ Hardness \(H\) (GPa) & 7.2 \\ Poisson’s ratio \(\
u\) & Data \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of K9 optical glass.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & \(n_{r}\) **(t/min)** & \(A\) **(\(\upmu\)m)** & \(v_{F}\) **(mm/min)** & \(a_{p}\) **(mm)** & \(r_{c}\) **(mm)** \\ \hline
1 & 2000, 2500, & 4 & 30 & 1.5 & 1
## 4 Results and Discussion
### Design Criteria of Mesoscopic Evaluation Indices for the Main Skeleton  
3.1 Relationship between Mesoscopic Evaluation Indices and the Macroscopic Index of the Main Skeleton  
The typical characteristics of DSAM are as follows: coarse aggregates in the asphalt mixture constitute the main skeleton; fine aggregates, asphalt, and other admixtures constitute the asphalt mortar that fills the voids of the main skeleton formed by the coarse aggregates and binds as well as restrains the main skeleton so that the asphalt mixture forms a high-strength integrated material. This means that the strength of asphalt mixture is mainly composed of two parts: the main skeleton composed of coarse aggregates and the asphalt mortar, which binds and restrains the main skeleton. The relationship between the mesoscopic evaluation indices (\(\overline{n}_{c}\) and C value) and macroscopic index (RD) of the main skeleton is shown in Figure 11. At the end of the loading stage, when the main skeleton has exerted its maximum capacity to resist the load and has reached the critical value, its corresponding mesoscopic and macroscopic indices are those listed in Table 11.  
Figure 11: Relationship between \(\overline{n}_{c}\), C value, and RD.  
Figure 10: Gradual change trends of \(\overline{n}_{c}\) and C value.  
Based on Figure 11 and Table 11, the following can be concluded.  
(1) Asphalt mixture is continuously compacted under the load action; \(\overline{n}_{c}\) increases with the increase in RD, whereas the C value gradually decreases. At the end of the loading stage, the mesoscopic characteristics of the main skeleton indicate that the quantity of contact points only slightly increases, and some coarse aggregates break up; the macroscopic characteristics indicate that nutting develops gradually. At this point, it can be considered that the main skeleton has exerted its maximum ability to resist load and has approached the critical value.  
(2) When the main skeleton reaches the critical value, the main mesoscopic evaluation indices and the macroscopic index for each asphalt mixture are as follows. For 70 # asphalt AC-13C: \(\overline{n}_{c}\)= 1.88, C = 7.48%, RD = 10.26 mm; for rock asphalt AC-13C: \(\overline{n}_{c}\) = 1.82, C = 7.29%, RD = 5.25 mm; for SMA-13: \(\overline{n}_{c}\) = 2.15, C = 4.89%, RD = 3.64 mm. The mesoscopic and macroscopic indices show that gradation is the major determinant of the mesoscopic evaluation indices for the main skeleton of the asphalt mixture, and the asphalt performance mainly affects the development trend of macroscopic nutting.
# Longitudinal Structure in the Altitude of the Sporadic E Observed by COSMIC in Low-Latitudes
###### Abstract  
The longitudinal structure in the altitude of the Sporadic E (Es) was investigated for the first time based on the S4 index provided by the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) in low latitudes. The longitudinal structure is identified as a symmetrically located wavenumber-4 (WN4) pattern within 30\({}^{\circ}\)S-30\({}^{\circ}\)N. The WN4 occurs primarily during the daytime at the June solstice and equinoxes, with the largest amplitude at the September equinox and the smallest one at the March equinox. It moves eastward with a speed of \(\sim\)90\({}^{\circ}\)/ day. The strongest WN4 appears within 10-20\({}^{\circ}\)N and 5-15\({}^{\circ}\)S in the Northern and Southern hemispheres, respectively. At the June solstice and the September equinox, the WN4 is stronger in the Northern hemisphere than in the Southern hemisphere, while the situation is reversed at the March equinox. The altitude distribution of the convergence null in the diurnal eastward non-migrating tide with zonal wavenumber-3 (DE3) for the zonal wind is similar to that of the WN4. This and other similar features, such as the seasonal variation, eastward speed, and the symmetrical locations, support the dominant role of the DE3 tide for the formation of the WN4 structure.  
s 2021 13, 4714. [[https://doi.org/10.3390/rs13224714](https://doi.org/10.3390/rs13224714)]([https://doi.org/10.3390/rs13224714](https://doi.org/10.3390/rs13224714))  
1
## 5 Direct Arylation of 1,3,4-Oxadiazoles Using Compounds Bearing Different Functionalities
In 2011, [PERSON] and co-workers described the reactions of 2-phenyl-1,3,4-oxadiazole with phenols and thiophenols, which were substituted at the _ortho_ position with a 2,2-dibromoethenyl group. The transformations were conducted in the presence of a copper iodide-1,10-phenanthroline catalyst system in a basic solution of lithium _tert_-butoxide. This procedure gave the products in satisfactory yields (Scheme 10; **41a,b**; 62-73%) [80]. In 2011, [PERSON] et al. described the use of phenols bearing an ethynyl substituent at the 2-position as effective reagents for the versatile synthesis of 2-(benzofuran-3-yl)-5-phenyl-1,3,4-oxadiazole derivatives (Scheme 10; **42a-j**; 34-93%) [81]. The same year, [PERSON] et al. published an effective procedure for the formation of benzofuran and benzopyrrole derivatives connected directly to a 1,3,4-oxadiazol ring. They used phenols and amines containing a 2,2-dibromoethenyl substituent at the _ortho_ position (Scheme 10; **43a-g**; 84-95%) [82]. A year later, [PERSON] et al. used non-aromatic rings containing a carbonyl group and two nitrogen atoms, which led to direct condensation at the 1,3,4-oxadiazol ring with simultaneous aromatization of the arrangement (Scheme 10; **44a-c**; 65-75%) [83]. Reactions of differently substituted aromatic amines bearing an ethynyl substituent at the 2-position with 2-aryl-1,3,4-oxadiazoles were described in 2012 by [PERSON]. The study reported the formation of a series of the substituted 2-(1H-indol-3-yl)-5-phenyl-1,3,4-oxadiazoles in moderate yields (Scheme 10; **45a-l**; 18-76%) [84]. **Scheme 10. ** Direct arylation of 1,3,4-oxadiazoles by condensation.
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 4 Experimental Setup  
The performance of distributed deep learning communication libraries was tested using both parameter server and ring all-reduce architectures on both bare metal and container systems. In the parameter server method, broadcast and gather (allgather) subroutines were treated separately, while all-reduce was performed as a single routine. These tests were conducted on various environments, including bare metal, singularity, multiple GPUs within a single-docker container (single docker), and one GPU per docker container (cross docker).  
Singularity is a container technology primarily used in scientific and engineering computing, especially in HPC environments. Docker is a container platform that packages servers and applications into containers, making the execution environment independent and simplifying the deployment of applications across different environments. The aim was to compare the performance differences between bare metal and containerized environments and find the most suitable environment for distributed deep learning training.  
The experiment generated 1 GB of random tensor data in Linux shell and PyTorch, performed each collective communication routine, and measured the latency based on the same scenario in the PyTorch deep learning workload.  
Table 1 below shows the server specifications used in the experiment. For the software, NVIDIA driver 515.48, CUDA 11.3, OpenMPI 4.1.4, MPICH 3.3, NCCL 2.4, PyTorch 2.0.1 and docker 20.10.18 were utilized.
## 3 Results and Discussions
### Dipole Moment Analysis  
In the study of changes in protein conformation via external electrostatic fields, the dipole moment parameter analysis of proteins is a very simple and intuitive method of analysis [31]. The dipole moment of a protein molecule is the product of the distance between the centers of positive and negative charges and the electricity carried by the center of the charge, which is a vector representing the polarity of the molecule. In the natural state, the molecular dipole moment presents as a random, disordered distribution state in the system. In this study, we applied an external electrostatic field so that the dipole moment of the protein molecule could couple with the direction of the applied electric field. From Figure 3, we can see that the dipole moment of the protein molecule without the action of the reference group (310 K, 0 v/nm) was stable at about 400-500 Debye. When the instantaneous dipole moment of the electric field was applied simultaneously in the x, y, x, and z directions, it increased rapidly until the dynamic stability was about 2800 Debye and 2600 Debye, respectively, about 30 ns after the simulation began. The results show that there was a significant coupling between the dipole moment and the direction of the electric field, which affected the behavior and conformational change in the protein in the electric field. When an electric field of 0.5 v/nm was applied to the x, y, and z axes at the same time, the growth amplitude and speed of the dipole moment increased significantly, and the reaction was complete within about 10 ns from the beginning of the simulation and remained dynamically stable at about 2250 Debye. It can be inferred that the electric field intensity had an effect on the coupling reaction speed of the electric field to the dipole moment. When the electric field intensity was strong, the coupling speed clearly accelerated, and the electric field with the same intensity in different directions had different effects. It is worth noting that when an electric field of 0.5 v/nm in size  
Figure 2: (**a–d**) represents the RMSD curves of protein No. 4H9W, residues 61–88, residues 139–149, and residues 169–180 under different numbers and directions of electric fields.  
was applied to the y and z axes, respectively, or in the direction of both axes, the total dipole moment of the protein molecule decreased and fluctuated around the value of 0. The final equilibrium position was even negative when applied in both directions at the same time: a phenomenon that indicates that the protein may have a certain force in the y and z directions in the natural state. The intensity of the electric field in both directions does not cause a coupling reaction with a significant dipole moment, and some fragments may be more compact under the electric field. Combined with the different dipole moment convergence values of other experimental groups, it is not difficult to find that there are differences in forces and stability in different directions within the protein [32].
###### Abstract  
Organic-inorganic hybrid electrolytes based on Al-doped Li\({}_{7}\)La\({}_{3}\)Zr\({}_{2}\)O\({}_{12}\) (LLZO) and two different ionic liquids (ILs), namely \(N\)-ethoxyethyl-\(N\)-methylpiperidinium bis(fluorosulfonyl)imide (FSI IL) and \(N\)-ethoxyethyl-\(N\)-methylpiperidinium difluoro(oxalato)borate (DFOB IL), were prepared with the aim of improvement of inherent flexibilities of inorganic solid electrolytes. The composites were evaluated in terms of thermal, spectroscopical, and electrochemical properties. In the impedance spectra of LLZO composites with 15 wt% ILs, a semi-circle due to grain boundary resistances was not observed. With the sample merely pressed with 1 ton, without any high-temperature sintering process, the ionic conductivity of 10\({}^{-3}\) S cm\({}^{-1}\) was achieved at room temperature. Employing a ternary composite of LLZO, FSI IL, and LiFSI as an electrolyte, all-solid-state lithium metal batteries having LiFePO\({}_{4}\) as a cathode were assembled. The cell exhibited a capacity above 100 mAh g\({}^{-1}\) throughout the course of charge-discharge cycle at C/20. This confirms that FSI IL is an effective additive for inorganic solid electrolytes, which can guarantee the ion conduction.  
s +
Footnote †: journal: Applied sciences  
solid electrolyte; ionic liquid; all-solid-state battery; lithium ion battery
# A Controller Switching Mechanism for Resilient Wireless Sensor-Actuator Networks
## 6 Conclusions  
We proposed a new WSAN capable of enhancing control system resiliency. A controller switching mechanism was introduced to ensure resilience against problems, such as controller failures in the controller network. MADB-based time-outs were used for fault detection, and the LWB protocol was used to provide broadcast communications. The backup controller receives the sensor information from the physical system; as it uses a synchronous transmission, it is not necessary to consider the independent wireless link status for time-varying network, which enables fast switching. For verification, an experiment was conducted by serially connecting a TelosB mote to a computer. Results indicated that the proposed mechanism could quickly recover by switching to the backup controller in the case of controller failure. As future work, we will extend the controller switching mechanism to guarantee the safety of physical systems under cyber-physical attacks. One possibility is to investigate a physical-knowledge-based anomaly detector to detect cyberattacks.  
Conceptualization, B.-M.C. and K.-J.P.; methodology, investigation, B.-M.C. and S.K.; simulation and validation, B.-M.C. and S.K.; writing--original draft preparation, B.-M.C.; writing--review and editing, K.-D.K. and K.-J.P.; visualization, B.-M.C.; supervision, K.-J.P.; project administration, K.-J.P.; funding acquisition, K.-J.P. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by an Institute for Information and Communications Technology Promotion (IITP) grant funded by the Korean government (MSIP) (2021-0-01277, development of attack response and intelligent RSU technology for vehicle security threat prevention) and a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2019R1A2C1088092).  
the authors declare no conflict of interest.
## 2 Categories of Robotic Teleoperation
### Robotic Mechanism-Based and Motion Sensor-Based Teleoperation  
Robot teleoperation can be divided into robotic mechanism-based and motion sensor-based teleoperation, according to the command input device used. Teleoperation systems that use robots as master mechanisms usually apply robotic devices of the same or different structure as the remotely controlled robot as the user command input system [18]. Using the same robots to form the master-slave architecture for teleoperation, motion synchronization can be accomplished by simple joint space mapping, and the user can directly visualize the pose of the slave robot by observing the master robot being controlled [19; 20; 21]. When using robots of different configurations as master and slave components for teleoperation, motion synchronization can be achieved using joint space motion remapping or TCP motion in the Cartesian space, and a scaling factor can be set to match the working space ranges of the master and slave robots.  
The motion sensor-based robotic teleoperation platforms eliminate the need to introduce expensive and complex robot platforms as human-robot interaction command input devices. Commercial optical and IMU-based sensors, depth cameras, motion controllers, and gloves are often used to capture user motion information [22]. Compared to the robotic  
Figure 1: The immersive teleoperation of the Baxter robotic system with two human–robot interaction modes [17]. (**a**) Ego-centric teleoperation mode. (**b**) Eco-centric teleoperation mode.  
master devices, motion sensor-based robotic teleoperation platforms usually have a larger space for motion tracking, the user's movement is not constrained by the mechanical structure, and the relatively lower price reduces the overall cost of the system [23; 24].
# Towards Harmonious Coexistence in the Unlicensed Spectrum: Rational Cooperation of Operators
## 1 Introduction
### The Expansion of Spectrum for Wireless Systems  
To address the issue of spectrum scarcity, cognitive radio (CR) can utilize the unused spectrum bands of a primary user (PU) in an opportunistic manner as long as secondary users (SUs) guarantee noharmful interference [7]. Recent migration from analog to digital television broadcasting has expedited the opportunistic sharing of underutilized TV spectrum bands called TV white space (TVWS) [7]. For example, IEEE 802.22 and IEEE 802.11 af standards opportunistically utilize TVWS, and the US Federal Communications Commission (FCC) and the UK Office of Communication (OFCOM) have updated rules for TVWS and other unlicensed bands [7]. As a cellular application of CR technology, _LTE-CR_ was proposed to extend the LTE time-division duplex (TDD), also known as TD-LTE to support TVWS [8]. In [9], the authors applied cooperative sensing mechanisms of CR to the LTE frequency-division duplex (FDD) with an almost blank subframe (ABS) to achieve the maximum utilization of TVWS. In [10], optimal cooperative sensing under the heterogeneous received signal to noise ratio was investigated. Moreover, standardization bodies recently developed ideas on LTE in the unlicensed spectrum such that LTE-A is looking to achieve spectrum integration through coexistence with multi-radio access technology (RAT) systems in the same unlicensed bands [11].
# A Tweak-Cube Color Image Encryption Scheme Jointly Manipulated by Chaos and Hyper-Chaos
## 5 Performance Analysis
### Correlation Analysis  
The correlation of two adjacent pixels can reflect the degree of diffusion about image pixels. Figures 9 and 10 exhibit the correlation distribution of the three channels, R, G and B, in the horizontal, vertical, main-diagonal and back-diagonal directions of the plain Tree image and cipher Tree image, respectively. Furthermore, we randomly chose 3000 pairs of adjacent pixels from a few original images  
Figure 8: Histograms of the (**a**) plain images in Figure 6a; (**b**) cipher images in Figure 6b.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(\chi^{2}\)} & \multicolumn{3}{c}{**Plain Image**} & \multicolumn{3}{c}{**Cipher Image**} \\ \cline{2-7}  & **R** & **G** & **B** & **R** & **G** & **B** \\ \hline Couple & \(2.1036\times 10^{5}\) & \(3.3786\times 10^{5}\) & \(2.8964\times 10^{5}\) & \(247.6016\) & \(234.6563\) & \(249.8984\) \\ House & \(2.5858\times 10^{5}\) & \(2.9916\times 10^{5}\) & \(3.9404\times 10^{5}\) & \(274.3672\) & \(277.3203\) & \(286.9922\) \\ Tree & \(8.1371\times 10^{4}\) & \(5.7009\times 10^{4}\) & \(1.2982\times 10^{4}\) & \(254.0859\) & \(244.8125\) & \(264.3594\) \\ Lena & \(2.4628\times 10^{5}\) & \(1.1766\times 10^{5}\) & \(3.4033\times 10^{5}\) & \(257.1094\) & \(261.9063\) & \(202.1699\) \\ Peppers & \(5.4984\times 10^{4}\) & \(7.2077\times 10^{4}\) & \(1.1435\times 10^{5}\) & \(279.4395\) & \(268.0254\) & \(273.2910\) \\ Airplane & \(1.6115\times 10^{5}\) & \(1.5940\times 10^{5}\) & \(2.6505\times 10^{5}\) & \(234.2305\) & \(283.9785\) & \(243.3086\) \\ Lena in [30] & - & - & - & \(231.6144\) & \(248.36505\) & \(254.4806\) \\ Pepper in [30] & - & - & - & \(266.1728\) & \(273.2120\) & \(252.6552\) \\ Airplane in [30] & - & - & - & \(275.2378\) & \(271.7001\) & \(228.2813\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Chi-square test of plain and cipher images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Plain Image**} \\ \cline{2-5}
**Image** & **Horizontal** & **Vertical** & **Main-Diagonal** & **Back-Diagonal** \\ \hline Couple & 0.0024 & 0.0018 & \(-\)0.0017 & 0.0027 \\ House & 0.0002 & \(-\)0.0004 & \(-\)0.0016 & 0.0008 \\ Tree & 0.0001 & \(-\)0.0007 & 0.0015 & 0.0013 \\ Lena & \(-\)0.0009 & 0.0008 & 0.0021 & 0.0004 \\ Peppers & 0.0019 & \(-\)0.0010 & \(-\)0.0008 & 0.0021 \\ Baboon & \(-\)0.0003 & \(-\)0.0028 & \(-\)0.0001 & \(-\)0.0013 \\ [PERSON] in [14] & 0.0056 & 0.0065 & \(-\)0.0073 & - \\ [PERSON] in [31] & -0.0068 & \(-\)0.0054 & 0.0010 & - \\ [PERSON] in [32] & 0.0059 & \(-\)0.0042 & 0.0180 & - \\ [PERSON] in [33] & 0.0033 & 0.0027 & 0.0014 & - \\ Peppers in [34] & \(-\)0.0047 & \(-\)0.0024 & \(-\)0.0028 & - \\ Peppers in [35] & 0.0002 & 0.0022 & 0.0010 & - \\ [PERSON] in [35] & 0.0004 & \(-\)0.0047 & 0.0001 & - \\ \hline \hline \end{tabular}
\end{table}
Table 7: Correlation coefficients of cipher images.  
Figure 10: Correlation coefficients of cipher Tree image in each direction: (**a**) R channel; (**b**) G channel; (**c**) B channel.