esa-sceva/satcom-chunk-collection huggingface dataset:
# Turbo Decoder Design based on an LUT-Normalized Log-MAP Algorithm
## 6 Synthesis Results and Comparisons  
In this paper, a turbo decoder was designed using the Cyclone IV series FPGA EP4 CE115F29C7 chip as the target device, and the QPP interleaver was used for interleaving and de-interleaving. In simulation, we adopted turbo codes with a 3072 information block size and 1/2 code rate, and allocated 12 SISO decoders to work in parallel with a maximum clock frequency \(M\) of 44 MHz. Through Equation (15), we can see that the throughput of the proposed decoder is 36 Mbit/s when the clock cycle of each iteration is 1502 and the maximum number \(N\) of iterations is 5.  
\[\text{Throughput}=\frac{\text{Code Length}\times\text{M}}{\text{ Clock Cycle}\times\text{N}} \tag{15}\]  
The comparisons with other literatures are presented in Table 6. As can be seen, the LUT-Nor-Log-MAP algorithm achieved a decoding throughput of 36 Mbit/s in five iterations. Compared with [24], we had advantages in decoding performance and logic resource consumption, and under the same clock frequency our design was able to achieve a higher throughput. Compared with [25], our algorithm had obvious advantages in throughput. Although the throughput of our design was close to [26] and less than [27], the proposed LUT-Nor-Log-MAP algorithm had better decoding performance than the Max-Log-MAP algorithm [26]. Compared with [27], the logic resource consumption in our design was greatly reduced.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **This Work** & **[**24**]** & **[**26**]** & **[**27**]** & **[**25**]** \\ \hline Code Standard & 3 GPP-LTE & 3 GPP-LTE & DVB-SH & 3 GPP-LTE & 3 GPP-LTE \\ Implementation & Cyclone IV & TTA & ASIC & CMOS & ASIP \\ Algorithm (Log-MAP) & LUT-Nor & Max & Max & LUT & Max \\ Code Length & 6144 & 6144 & 6144 & 4096 & 6144 \\ Iterations & 5 & 6 & NA & 6 & 5 \\ Gate Count (K) & 74.5 & 85.7 & 50 & 900 & NA \\ Clock Frequency (MHz) & 44 & 210 & 346 & 252 & 200 \\ Throughput (Mbit/s) & 36 & 65.1 & 346 & 535 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation results and comparison.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **ADD** & **SUB** & **Comparison** & **Shift** \\ \hline Max & 2 & 0 & 1 & 0 \\ LUT & 2 & 1 & (4 + 1) & 0 \\ Nor & 2 & 0 & 1 & 3 \\ LUT-Nor & 2 & 1 or 0 & (4 + 1 + 1) or 2 & 0 or 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation results and comparison.
## 4 The Performance Parameters and Application Scenarios of 6G Networks
### Application Scenarios
#### 4.2.3 New Smart City  
Many sensors have been put in cars, buildings, industries, highways, residences, and other amenities in order to create a smart city. Notably, 6G is expected to function as a dependable wireless high-speed communication system, enabling application integration and cooperation for more efficient data-driven operations. Communication networks are becoming an essential part of the public infrastructure for smart cities in the rapidly changing digital world. However, most urban public infrastructure's perception, transmission, analysis, and control remain fragmented without a single management platform since several administrative agencies handle the building and operation of the infrastructure.  
By implementing a unified network design, presenting fresh business possibilities, and building a more effective and extensive network, 6G seeks to solve this. Physical and logical networks can be separated thanks to upcoming developments in network virtualization, software-defined networks, and network slicing. At many levels, including effective transmission, smooth networking, internal security, large-scale deployment, and automated maintenance, AI integration into the 6G system will be crucial. A new smart city ecosystem is expected to develop with the help of 6G.
# Research on Grooved Concrete pavement Based on the Durability of Its Anti-Skid Performance
## 4 Conclusions  
This study provides an experimental investigation of the anti-skid performance and abrasion resistance of cement concrete pavements, including the LT grooved method, the curing method using a composite curing agent, anti-skid tests, and abrasion resistance tests. These tests were performed on samples with different grooved schemes, and conclusions could be drawn as follows:  
1. The two techniques used in this study, including LT grooving and a curing method using a composite concrete curing agent, could effectively enhance the anti-skid performance of the pavement.
2. From the experimental study of the anti-skid performance of concrete pavement with different groove schemes, LT had relatively good skid resistance compared to other schemes.
3. The improvement in durability observed for concrete cured using the sprayed composite curing agent indicates that this can be an effective method to maintain concrete pavement in practical applications.
4. Based on the results of skid and abrasion resistance tests, the longitudinally-transversely grooved sample (5, 5, 30, 6, 4, 10) provides better skid resistance and durability and can be suggested to be adopted as the optimal dimensions for good anti-skid durability for concrete pavement. This sample's dimensions, including transverse groove width, transverse groove depth, transverse groupe spacing, longitudinal groove width, longitudinal groove depth, and longitudinal groove spacing, were 5, 5, 30, 6, 4, and 10 mm, respectively.  
[PERSON] conceived and designed the experiments; [PERSON] performed the experiments and analyzed the data; [PERSON] and [PERSON] contributed reagents and materials; [PERSON] wrote the paper.  
This research was supported by the Fundamental Research Funds for the Central Universities in China (No. 310821163502), the Transportation Department of Hebei Province (Grant No. T-2012107 and Y-2012014), the Transportation Department of Jiangxi Province (Grant No. Ganjiaokejiao [2015], and the Transportation Department of Hubei Province of China (No. Eijaokejiao [2012] 857). In addition, the authors would like to thank the reviewers of this paper for their ever-present support and valuable advice.  
The authors declare no conflict of interest.
# Two Medoid-Based Algorithms for Clustering Sets
## 3 Medoid-Based Approaches
### Example  
The process of computing medoids can be demonstrated using the cluster example introduced in the Section 2.1. First, the global histogram \(H\) of the frequencies of elementary items in the dataset is defined:  
\(H\):  
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|} \hline A & B & C & D & E & F & G & H & I & J & \(\ldots\) \\ \hline
35 & 20 & 22 & 20 & 24 & 12 & 10 & 8 & 22 & 7 & \(\ldots\) \\ \hline \end{tabular}  
The objects \(X_{1}\) and \(X_{2}\) of the cluster have two items in common (A, B) and five other items. The modified distance \(d_{j}\) can be calculated by (4):  
\[d_{j}(X_{1},X_{2})=1-\frac{f_{H}(A)+f_{H}(B)}{f_{H}(A)+f_{H}(B)+5}=1-\frac{35+2 0}{35+20+5}=1-\frac{55}{60}=0.08\]  
It should be noted how the classical Jaccard similarity, i.e., \(\frac{|X_{1}\cap X_{2}|}{|X_{1}\cup X_{2}|}=\frac{2}{7}=0.29\) is significantly increased by the usage of the global weights \(f_{H}(A)\) and \(f_{H}(B)\), to \(\frac{55}{60}=0.92\), and conversely for the distance. Something similar occurs when using (1) with local weights in [8]. Figure 1 collects all the pairwise distances between the data objects of the cluster.  
From the matrix in Figure 1, it emerges that the medoid representative of the cluster is the \(X_{5}\) data object, which has a minimal sum (0.14) of the distances to all the other points in the cluster. Moreover, the contribution of the cluster to the function cost \(SDM\) is also 0.14.
# A Study of Friction Nonlinearity and Compensation for Turntable Servo Systems
## 2 Friction Modeling and Identification  
### Friction Modeling  
Considering the accuracy of the friction model, the difficulty of parameter identification, and the available experimental conditions, this study selected the widely applied model of \"static friction + Coulomb friction + viscous friction.\" This model is illustrated in Figure 1, with its mathematical expressions provided in Equation (1). Static friction is the force required to initiate relative motion from rest. When the external force applied is less than the maximum static friction, the static friction force numerically equals the external force; however, when the external force exceeds the maximum static friction, the static friction force is equal to the maximum static friction. Coulomb friction operates under the conditions of non-zero velocity. It is characterized by a frictional force that is dependent on the normal force and the direction of relative motion but is independent of velocity. Viscous friction is a linear model that takes the effects of the lubricating medium into account, where the frictional force is directly proportional to the velocity of the relative motion between objects.  
\[T_{f}=\left\{\begin{array}{ll}T_{e}&V=0,|T_{e}|\leq T_{s}\\ T_{s}\operatorname{sgn}\left(T_{e}\right)&V=0,|T_{e}|>T_{s}\\ T_{c}\operatorname{sgn}\left(v\right)+B_{v}V&v\
eq 0\end{array}\right. \tag{1}\]In the formula, \(T_{s}\) represents the maximum static friction torque, \(T_{e}\) denotes the external torque (electromagnetic torque), \(T_{c}\) is the Coulomb friction torque, and \(B_{v}\) is the coefficient of viscous friction. The following section identifies these parameters through an offline method.
## 2 Methodology
### Equivalent Circuit of the Oval-Square SRR  
The proposed design structure of the metamaterial was made up of a series of SRR. The metamaterial can be acknowledged from the perspective of a resonant LC circuit with a resonance frequency, \(\mathrm{f_{R}}=\frac{1}{2\pi\sqrt{L_{\mathrm{T}}C_{\mathrm{T}}}}\), where L\({}_{\mathrm{T}}\) is the total inductance, and C\({}_{\mathrm{T}}\) is the total capacitance of the proposed SRR. A metal strip that represents effective inductance and a gap that represents effective capacitance characterize the SRR. Those elements are passive elements that will receive, store, or dissipate energy in the circuit. ADS Software was utilized to design and validate the equivalent circuit model for the proposed oval-square SRR. Figure 4 shows the circuit model of the proposed metamaterial. The outer square-shaped ring is represented by inductance and capacitance and is marked with L1, C1, L4 and C4, whereas the second square-shaped ring is represented by L2, C2, L3 and C3. Furthermore, the outer oval-shaped ring was symbolized with L13 and L16, the middle oval-shaped ring symbolized with L14 and L15, and finally, the inner oval-shaped ring is shown by L17, L18, C13 and C15. L19, L20 and C14 were assigned to the middle split horizontal strip. The passive components labelled L5 until L12 are the coupling inductors that are connected in series, whereas the components labelled C5 until C12 are the coupling capacitors that are also connected in series.  
The S\({}_{21}\) results from the simulation and the equivalent circuit are compared in Figure 5. The resonance frequencies for the equivalent circuit model are given at 5.53 GHz and 8.70 GHz with magnitudes of \(-47.08\) dB and \(-29.40\) dB, respectively. Meanwhile, the resonance frequencies of S\({}_{21}\) for the simulation results are 5.52 GHz and 8.81 GHz with magnitudes of \(-43.03\) dB and \(-17.19\) dB, respectively. Because the equivalent circuit is made up of a series of passive elements, the energy dissipation is customized by the inductance and capacitance, the magnitude of S\({}_{21}\) tends to be smaller. Despite having a difference in the magnitude of the S\({}_{21}\), the resonance frequencies of the simulation and equivalent circuit results are almost the same.  
Figure 2: Wave propagation simulation, (**a**) unit cell metamaterial, (**b**) boundary conditions, and (**c**) \(4\times 4\) arrays metamaterial.  
Figure 4: The equivalent circuit model of the proposed metamaterial.  
Figure 3: Fabricated unit cell and arrays metamaterial, (**a**) Rogers RO4003C, (**b**) FR-4 and the illustration of experimental set up, (**c**) VNA.
# Review on the Recent Numerical Studies of Liquid Atomization
## 4 Particle Tracking
### Breakup Models
#### 4.2.5 Rayleigh-Taylor Model  
The KH-RT breakup model was meant for high We and low-pressure systems. Generally, it divides the dominant mechanisms to be KH instability within the liquid core and RT instability outside. The length of the liquid core L is defined as [54]:  
\[L=C_{L}d_{0}\sqrt{\frac{\rho_{l}}{\rho_{g}}} \tag{30}\]  
where \(C_{\text{L}}\) is the Levich constant and \(d_{0}\) is a reference diameter. For conical injections, \(d_{0}\) is the minimum of the droplet diameter and the inner diameter of the nozzle. For surface injection from a droplet, the equivalent hydraulic diameter of the surface area is used as \(d_{0}\). For droplets distributed in a Rosin-Rammler manner, the maximum diameter of the Rosin-Rammler becomes the \(d_{0}\). The liquid core just outside a nozzle is the continuous liquid jet, which can be regarded as consisting of a series of blobs, so this breakup model also applies to primary breakup.  
Within the liquid core, the KH breakup model is dominant while the maximum growth rate and wavelength can be calculated from Equations (28) and (29). Outside the liquid core, the RT instability typically grows faster. The maximum growth rate of the surface wave \(\Omega_{\text{RT}}\) and the corresponding wavelength \(\Lambda_{\text{RT}}\) are given by [55]:  
\[\Omega_{RT}=\sqrt{\frac{2\left(-g_{t}(\rho_{l}-\rho_{g})\right)^{3/2}}{3\sqrt {3\sigma}(\rho_{l}+\rho_{g})}} \tag{31}\]  
\[\Lambda_{RT}=2\pi C_{RT}/\sqrt{\frac{-g_{t}\left(\rho_{l}-\rho_{g}\right)}{3 \sigma}} \tag{32}\]  
where \(C_{\text{RT}}\) is the breakup radius constant with a value of 0.1. The rate of change of droplet radius \(a\) in the parent parcel can still be obtained by Equations (26) and (27). In both KH and RT breakup models, child parcels are generated only when the accumulated shed parcel mass exceeds 5% of the parent parcel mass.
## 2 System Description and Analysis
### ACV Model
#### Hydrodynamics  
Hydrodynamics function is given as:  
\[\tau_{h}=[X_{h},Y_{h},K_{h},N_{h}]^{T} \tag{8}\]  
and \(X_{h},Y_{h},K_{h},N_{h}\) denote the projection of hydrodynamics in four DOF. They are:  
\[\begin{split} X_{h}&=X_{drag}\\ Y_{h}&=Y_{drag}+Y_{r}r+Y_{r}\dot{r}+Y_{|r|r}|r|+Y_{| |r|}|v|r+Y_{\hat{v}}\hat{v}\\ K_{h}&=K_{drag}+K_{r}r+K_{\dot{r}}\dot{r}+K_{|r|r|}|r |+K_{|\hat{v}|r}|v|r+K_{\hat{v}}\hat{v}\\ N_{h}&=N_{drag}+N_{r}r+N_{\dot{r}}\dot{r}+N_{|r|r| }|r|+N_{|\hat{v}|r}|v|r+N_{\hat{v}}\hat{v}\end{split}\]  
where \((\cdot)_{drag}\) are ship model hydrodynamic. \(Y_{\hat{v}}\), \(K_{\hat{v}}\), \(N_{\hat{v}}\), \(Y_{r}\), \(K_{r}\), \(N_{r}\), \(Y_{\dot{r}}\), \(K_{\dot{r}}\), \(N_{r}\), \(Y_{|r|r}\), \(K_{|r|r}\), \(N_{|\hat{v}|r}\), \(Y_{|r|r}\), \(K_{|r|r}\), and \(N_{|r|r}\) are hydrodynamic coefficients.  
\[\begin{split}\left[\begin{array}{c}X_{drag}\\ Y_{drag}\\ K_{drag}\\ N_{drag}\end{array}\right]&=\left[\begin{array}{c}\frac{1}{2}C_{xh}\rho_{ h}U^{2}L_{a}^{2}\\ \frac{1}{2}C_{xh}\rho_{h}U^{2}L_{a}^{2}\\ \frac{1}{2}C_{xh}\rho_{h}U^{2}L_{a}^{2}\\ \frac{1}{2}C_{xh}\rho_{h}U^{2}L_{a}^{2}\end{array}\right]\end{split}\]  
where \(U=\sqrt{u^{2}+v^{2}}\) is the velocity of ACV, \(C_{xh},C_{yh},C_{xh}\) are the hydrodynamic dimensionless coefficients, \(\rho_{h}\) is the water density, \(L_{a}\) is the cushion length.  
Then we can get the hydrodynamic vector variable:  
\[\tau_{h}=M_{h}\dot{v}+D_{h}v+D_{nh}(v)v \tag{9}\]  
where \(M_{h}\) is called the added mass and \(D_{h}\) is a constant coefficient matrix that represents hydrodynamic damping. \(D_{nh}(v)\) is also an equation of hydrodynamic damping, but it is associated with the ship speed \(v\). They are given as follows:  
\[M_{h}=\left[\begin{array}{cccc}0&0&0&0\\ 0&Y_{\hat{v}}&0&Y_{\hat{r}}\\ 0&K_{\hat{v}}&0&K_{\hat{r}}\\ 0&N_{\hat{v}}&0&N_{\hat{r}}\end{array}\right]D_{h}=\left[\begin{array}{cccc}0& 0&0&0\\ 0&0&0&Y_{r}\\ 0&0&0&K_{r}\\ 0&0&0&N_{r}\end{array}\right]\]  
\[D_{nh}(v)=\left[\begin{array}{cccc}\mu_{x}u&\mu_{x}v&0&0\\ \mu_{y}u&\mu_{y}v&0&Y_{|r|r}\left|v\right|+Y_{|r|r}\left|v\right|\\ \mu_{h}u&\mu_{k}v&0&K_{|\hat{v}|r}\left|v\right|+K_{|r|r}\left|v\right|\\ \mu_{h}u&\mu_{n}v&0&N_{|r|r}\left|v\right|+N_{|r|r}\left|v\right|\end{array}\right]\]where  
\[\mu_{x} =\frac{1}{2}C_{xh}\rho_{h}L_{a}^{2},\mu_{y}=\frac{1}{2}C_{yh}\rho_{h}L _{a}^{2}\] \[\mu_{k} =\frac{1}{2}C_{kh}\rho_{h}L_{a}^{2},\mu_{n}=\frac{1}{2}C_{nh}\rho_{h }L_{a}^{2}\]
## 5 Compressive Resistance of SDC Sandwich Wall
### Theoretical Method  
For simplification, the following methods and assumptions may be applied:  
1. Based on the strength superposition method, the compressive resistance of the SDC composite wall consists of four parts: The compressive resistance of the concrete core, steel plates, reinforcement enhancements, and rubber layer. \[\mathrm{N_{u}}=\mathrm{N_{c}}+\mathrm{N_{s}}+\mathrm{N_{rein}}+\mathrm{N_{ rub}}\] (16)
2. The compressive resistance of the rubber layer during the compression of the SDC wall can be calculated as follows: \[\mathrm{N_{rub}}\ =\ \sigma_{\mathrm{rub}}\mathrm{A_{rub}}\ =\ \mathrm{E_{ rub}}\varepsilon_{\mathrm{rub}}\mathrm{A_{rub}}\] (17) where \(\sigma_{\mathrm{rub}}\), \(\varepsilon_{\mathrm{rub}}\), and \(\mathrm{E_{rub}}\) denote the stress, strain, and elastic modulus of the rubber layer. Since the elastic modulus of rubber (7.84 MPa) is far less than that of concrete (\(3.35\times 10^{4}\) MPa) and steel (\(2\times 10^{5}\) MPa), \(\mathrm{N_{rub}}\) is ignored in Equation (16). And Equation (16) becomes the following: \[\mathrm{N_{u}}\ =\ \mathrm{N_{c}}+\mathrm{N_{s}}+\mathrm{N_{rein}}\] (18)
3. The effect of binding bars can be considered in the calculation of compressive resistance of the concrete core instead of reinforcement enhancements. And the influence of steel sheets and sleeves can be taken into consideration by the equivalent compressive resistance. In terms of binding bars, the confining influence of them on the concrete core is ignored in both AISC 360 and Eurocode 4 in order to improve safety reserves of the SCS composite wall. However, [PERSON] [7] proposed that all the steel across the concrete failure plane can be assumed to provide confinement. The confinement stress \(\sigma_{\mathrm{c}}\) was defined as equivalent stress distributed over the corresponding area on average, which can be expressed by \[\sigma_{\mathrm{c}}\ =\ \frac{\mathrm{nT}}{\mathrm{WH}}\] (19) where \(\sigma_{\mathrm{c}}\) denotes the confinement stress of concrete; \(\mathrm{n}\) denotes the number of connectors in a composite structure; \(\mathrm{W}\) and \(\mathrm{H}\) denote the width and height of the wall; \(\mathrm{T}\) denotes the tensile resistance of connectors. Therefore, \[\sigma_{\mathrm{c}}\ =\ \frac{\mathrm{T}}{\mathrm{s}^{2}}\] (20) where \(\mathrm{s}\) denotes the spacing between the neighboring connectors. Figure 21: Comparison of numerical results with predicted results. Furthermore, [PERSON] [45] also proposed a formula to predict the confinement effect on the concrete of binding bars, as given in Equation (15). Given the above methods, the effect of binding bars in the SDC wall is considered in the compressive resistance of the concrete, as shown in Equations (15) and (27). However, steel sheets and sleeves, not only provide confinement on concrete but also provide compressive resistance directly.
## 3 Results and Discussion  
### Geometric Structures  
The optimized structures of PTMCp with TM = Sc \(-\) Co as the central metal ions are obtained. Considering more than one spin state may exist in a molecule, we examine the geometric properties of 19 structures. According to the numerical results, these structural shapes can be roughly divided into two categories. One is the sandwich type, as shown in Figure 1a. The porphyrin molecules are slightly curved, while the four nitrogen atoms are almost in the same plane. The mean plane for the nitrogen atoms is defined as the porphyrin plane. There are slight differences (0-0.2 A) in TM-C(Cp) bond lengths within the same structure. The Cp plane and the porphyrin plane are almost parallel, and the TM ion is sandwiched by them closer to the latter. The other type is the biplane type with a nonzero angle between the Cp ring plane and the porphyrin plane and the TM ion basically in the porphyrin plane, as shown in Figure 1b.  
The structures of complexes with early transition metals (TM = Sc, Ti, and V) fall into the sandwich type, and they show little difference for different spin states of the same molecule. Both \(\text{d}_{\text{TM-Cp}}\) and \(\text{d}_{\text{TM-P}}\) decrease with the increase of the atomic number, which may originate from the enhanced coupling between the TM ion and the two-ligand ring as a consequence of the increase in the number of electrons in its d orbital (Table 1). In cases of TM = Cr \(-\) Co, on the contrary, the structures of different spin states become obviously different. Only three of them are sandwich type, including PCrCp (doublet), PMnCp (singlet), and PFeCp (sextet), while the rest are biplane type. In these complexes, the Cp ring takes an electron from the TM ion to form the stable \(4\pi+2\) conjugated structure, which is reflected in MtCp [42]. In addition, we find that the TM ion is the main magnetic source for most of magnetic molecules. In general, the magnetism of transition-metal ions depends on the configuration of d orbital electrons. The magnetism of PSeCp (triplet) is derived from the ligands because Sc\({}^{3+}\) has no d electrons, for PT  
Figure 1: At B3 LYP functional level, \(\text{d}_{\text{TM-Cp}}\): distance of TM ion from centroid of Cp, \(\text{d}_{\text{TM-P}}\): distance of TM ion from mean plane of nitrogen, (**a**) sandwich type, (**b**) two-plane type. White sphere: H atom; gray sphere: C atom; green sphere: TM atom; blue sphere: N atom.  
provide 2 uB magnetic moment because Ti\({}^{3+}\) has only one d electron, and the same is true of PMnCp(quintet). The spin multiplicity of the ground state of PTMCp increases linearly with the increase of the metal atomic number, except for PCoCp.