esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Industrial Robots Control and Programming Approaches
## 4 Innovative Control Approaches
### External Trajectory Generator
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Feedback** & **Environment** & **IR Vendor** & **Application** & **Cycle Rate** \\ \hline
[39] & Position (vision) & PLC-based & KUKA (RSI) & Adaptive pick-and-place in robotic lines & 12 ms \\
[44] & Position (tracker) & PC-based & MABI (UCI-App) & Path correction in machining & 8 ms \\
[40] & Position (vision) & PC-based & Fanuc (DPM) & Pose correction with cost-effective strategies & N.D. \\
[41] & Position (tracker) & PC-based & KUKA (RSI) & Path correction in drilling/milling & 12 ms \\
[42] & Position (tracker) & PLC-based & KUKA (RSI) & Accurate path following & 4 ms \\
[45] & Force (F/T sensor) & PC-based & ABB & Force-controlled grinding/deburring & 4 ms \\
[46] & Force (F/T sensor) & PLC-based & KUKA (RSI) & Hybrid force-controlled milling & 4 ms \\
[48] & Force (F/T sensor) & PC-based & Comau & Hand-guided robot motion & 2 ms \\
[49] & Force (F/T sensor) & PC-based & KUKA (RSI) & Hand-guided robot motion & 12 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Examples of external trajectory generators from literature.
# Enhanced YOLO- and Wearable-Based Inspection System for Automotive Wire Harness Assembly
## 2 Materials and Methods
### Model Selection
#### 2.2.3 Adaptive Spatial Feature Fusion Seeks Better Fusion Solutions  
During the image acquisition process using wearable electronic glasses, the assembly workers continually engage in actions such as bending, reaching, and plugging, causing the distance of the hand in the field of view to vary. This leads to changes in image size and the formation of target images at different scales. The Adaptively Spatial Feature Fusion (ASFF) network utilizes spatial filtering to suppress inconsistencies in spatial features across different scales during fusion, retaining only useful information for combination. By adaptively fusing deep and shallow features, ASFF effectively utilizes feature information at different scales, significantly mitigating the loss of target feature information. In YOLOv5, three different levels of feature information, each with different resolutions and channel numbers, are integrated using ASFF. ASFF_Detect, which replaces the original Detect module of YOLOv5, first aligns the features of other layers to the same resolution and  
Figure 5: Detection system composition framework.  
channel number before fusing and training them together, aiming to find the optimal fusion solution. These adaptively fused features from different layers ensure that conflicting information is filtered out, while more discriminative information is retained and dominates. The general workflow of ASHF is illustrated in Figure 6.  
ASFF requires the integration of feature information from three different levels, and the first step is to adjust them to the same size. Therefore, changes are needed in both up-sampling and down-sampling for each scale. For up-sampling, \(1\times 1\) convolution is initially used to convert the channel numbers of other layers to that of the current layer, followed by interpolation to increase the resolution. For \(1/2\) down-sampling, a \(3\times 3\) convolution with stride = 2 is used to modify the channel numbers. For \(1/4\) down-sampling, a layer of maximum pooling with stride = 2 is added before convolution. Once adjusted to a uniform size, they need to be integrated and trained. The formula for integrating features from the three different levels is as follows:  
\[y_{ij}^{l}=a_{ij}^{l}*x_{ij}^{1-sl}+\beta_{ij}^{l}*x_{ij}^{2-sl}+\gamma_{ij}^{l }*x_{ij}^{3-sl},a_{ij}^{l},\beta_{ij}^{l},\gamma_{ij}^{l}\in[0,1] \tag{1}\]  
In the formula, \(x_{ij}^{n-l}\) represents converting the number of feature map channels of (0,1) from \(n\) to 1. \(y_{ij}^{l}\) represents the final output feature map. \(a_{ij}^{l}\), \(\beta_{ij}^{l}\), \(\gamma_{ij}^{l}\) is the weight of integrating features at three different levels and satisfies \(a_{ij}^{l}+\beta_{ij}^{l}+\gamma_{ij}^{l}=1\).
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
## 1 Introduction
Based on this system model, we propose a proportionally fair utility function to evaluate the average EE of nodes. With the aid of stochastic geometry, the average transmission rate and the harvesting energy of nodes in WPCNs are firstly analyzed to evaluate the impact on average EE. Consequently, we could characterize the fairness utility function averaged over BS locations and fading channels, so it does not depend on a specific network realization. Next, noted that the fairness utility function is non-concave for user association bias, we show that the fairness utility function can be relaxed as a concave function with respect to the receive threshold and user association bias. Then, by maximizing the relaxed utility function, we can obtain the optimal receive threshold and user association bias for the tradeoff between information transmission and power transfer. This allows us to derive an efficient iterative algorithm for obtaining the optimal solution. Exploiting alternating optimization for joint association probability and receive threshold, we also propose an efficient iterative algorithm for obtaining the suboptimal solution and reducing the compute complexity of iterative algorithm. The main contributions can be summarized as:  
1. The average transmission rate and harvesting energy of nodes in WPCNs are analyzed and the impact of user association bias and receive threshold on EE of networks is revealed. 2. In the downlink multi-tier heterogeneous WPCNs, there exists an optimal receive threshold for maximizing the EE proportionally fair utility function in any tier. 3. An efficient iterative algorithm for obtaining the optimal solution of proportionally fair utility function for downlink nodes is proposed. The rest of this paper is organized as follows. In Section 2, the system modeled with a stochastic geometry is presented. Considering the transmission rate and harvested energy, the energy efficiency proportional fairness utility function is introduced. The impact of average transmission rate and the harvested energy of nodes in WPCNs on the average EE proportional fair utility function are analyzed in Section 3. The receive threshold and user association bias optimization for the maximization of the system EE proportional fair utility function is designed in Section 4. By exploiting an alternating programming, a novel low-complexity iterative algorithm was proposed to obtain the sub-optimal solution of this problem. The derived results are validated in Section 5 by simulation results, where the impact of various system parameters on the proportionally fair utility function is illustrated. Finally, Section 6 concludes the paper. A list of the symbols employed in this paper is given in Table 1.
## 5 Conclusions  
In this paper, we investigated the optically stimulated luminescence (OSL) ages and the variations in the frequency-dependent susceptibility, diffuse reflectance spectra, TOC concentration and \(\delta^{13}\)C\({}_{\text{org}}\) composition of the Chenqianshan loess section on Shengshan Island, East China Sea, and discussed the paleoenvironmental significance of these indices and environmental influencing factors. The results of TOC, \(\delta^{13}\)C\({}_{\text{org}}\), \(\chi_{\text{fd}}\), and Hm/(Hm + Gt) for the CQS profile displayed a similar pattern. The TOC concentration for the CQS section varied between 0.11% and 0.47%, and the \(\delta^{13}\)C\({}_{\text{org}}\) composition of the CQS loess section varied between \(-20.80\%\) and \(-24.56\%\) during the last glacial period. The \(\xi^{13}\)C\({}_{\rm org}\) composition of the loess layer during the L1L2 (MIS 4) stage was lower than those of the weak paleosol layer during the L1S1 (MIS 3) stage, showing the transition of climate from dry-cold to warm-humid. The average value of C\({}_{4}\) abundance was 21.31%. It indicated that the vegetation of CQS loess deposits was mainly C\({}_{3}\)/C\({}_{4}\) mixed vegetation, and C\({}_{3}\) vegetation was the primary vegetation. The \(\xi^{13}\)C\({}_{\rm org}\) composition of the CQS loess section could more likely indicate the evolution of vegetation and climate change than other proxy indicators. The comparison between the \(\xi^{13}\)C\({}_{\rm org}\) curve of the CQS section and other existing \(\xi^{13}\)C\({}_{\rm org}\) records of the loess sections from central and northern China showed similar trends, and their vegetation successions exhibit synchronous changes during the LG. Based on a comparison of the \(\xi^{13}\)C\({}_{\rm org}\) record, C\({}_{4}\) abundance and \(\chi_{\rm fd}\) of the CQS section and other geological records, it was concluded that the mutual effects of precipitation and temperature caused the change of paleovegetation in loess deposits on islands in the East China Sea during the LG.  
Conceptualization, S.R., L.Z. and X.Z.; methodology, S.R. and Y.S.; software, H.W.; validation, S.R. and Z.W.; formal analysis, S.R.; investigation, S.R. and C.Y.; resources, H.L.; data curation, S.R. and C.W.; writing--original draft preparation, S.R.; writing--review and editing, S.R., L.Z. and X.Z.; visualization, S.R.; supervision, X.Z.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 41371032 and 41871015).  
Not applicable.  
Some or all data, or models generated or used during the study, are available from the corresponding author by request.  
We are very grateful to [PERSON] for their support of environmental magnetism, and [PERSON] for analyzing the organic carbon isotopes data. We would also like to thank [PERSON] for his help in the luminescence experiments. We are also appreciative of [PERSON] for the beneficial discussions.  
The authors declare no conflict of interest.
# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
## 3 Improved Spatiotemporal Multi-Channel Network (IQGMCL)  
### Relevant Features of IQ Signals  
In literature [25], Dr. [PERSON] verified the validity of convolutional network structure with IQ-related features in automatic modulation recognition applications from the number, size, and depth of the convolutional layer and network operator. Based on the IQCLNet filter design method proposed by Dr. [PERSON], the relevant feature of IQ signals is introduced into the original MCLDNN network. According to the features of IQ signals with timing and phase features, a convolution layer with a convolution kernel size of (2,1) is first used to extract the relevant features between IQ signals. On the one hand, it reduces the dimension of data. On the other hand, it reduces the frequency of domain changes of data in subsequent processing. Then, referring to the small convolution kernel idea adopted in the VGG network, the (1,3) convolution layer is used to extract the time sequence features further. In this way, the features introduced into the training model are not only the time sequence features extracted from the spatiotemporal multi-channels but  
Figure 1: RML2016.10a data graph.  
also the related feature information. The model training accuracy is better. The IQCLNet network structure principle is shown in Figure 2.  
The data format of the original IQ sampling signals is N \(\times\) 2, where N corresponds to the time length of the signal, and 2 corresponds to the in-phase component I and the orthogonal component Q. The two dimensions do not have the same properties, so symmetric operations cannot be carried out in the two dimensions like image processing. Therefore, at present, the convolution neural network mainly uses one-dimensional convolution to extract the time sequence features or uses two-dimensional convolution to extract the features vaguely in the processing of IQ signals. However, such operations ignore the relevant features between IQ, resulting in the loss of phase information and reducing the efficiency of information extraction. Introducing the relevant feature of the signals not only increases the number of training features but also improves the efficiency of information extraction.
## 2 Model Introduction and Verification
### Typhoon Model  
According to the previous research, this paper uses the Holland typhoon model to construct the typhoon wind field with a frequency of 10 years. The model typhoon center pressure is 947 hPa, and the maximum wind speed is 47.26 m/s. The governing equation of the model is as follows:  
\[P_{r}=P_{c}+(P_{n}-P_{c})\exp(-(R_{\max}/r)^{B}) \tag{9}\]  
\[V_{r}=[\frac{B}{\rho_{a}}(\frac{R_{\max}}{r})^{B}(P_{n}-P_{c})\exp[-(\frac{R_{ \max}}{r})^{B}]+(\frac{rf}{2})^{2}]^{0.5}-\frac{rf}{2} \tag{10}\]Equation (9) is the pressure distribution formula of the Holland typhoon model, where \(P_{c}\) is the lowest pressure in the typhoon center and \(P_{n}\) is the background pressure, at 1013 hPa: \(R_{\text{max}}\) is the maximum wind speed radius of the typhoon; \(r\) is the distance from somewhere to the typhoon center: \(B\) is the scale parameter of the typhoon model. Equation (10) is the wind speed distribution formula of the typhoon model, where the air density (\(\rho_{a}\)) is 1.2 kg/m\({}^{3}\): \(f\) is the parameter of Coriolis force.  
There are many formulas to determine the maximum wind speed radius of the typhoon, such as [PERSON], [PERSON], [PERSON], etc. [31; 32; 33], and all put forward corresponding formulas. In this study, the procedure proposed by [PERSON] [34] is adopted to determine the maximum wind speed radius of a typhoon:  
\[R_{\text{max}}=51.6\exp(-0.0223V_{\text{max}}+0.0281\varphi) \tag{11}\]  
where \(\varphi\) is the latitude of the place where the typhoon center is located.  
The maximum wind speed (\(V_{\text{max}}\)) of the typhoon is constructed according to the relationship between wind speed and air pressure proposed by [PERSON] [35], and the empirical formula is given:  
\[V_{\text{max}}=3.7237\times(P_{n}-P_{c})^{0.6065} \tag{12}\]
###### Abstract  
Dynamic factor evaluation method calculation methods outlined by [PERSON] (DAF\({}_{\text{Eisenmann}}\)) and the American Railway Engineering Association (DAF\({}_{\text{Area}}\)) are used to calculate the dynamic factor during design and for trackside measurement, respectively, in nations where the construction of concrete track structures is relatively new. In this situation, dynamic factor calculation methods may be incorrect, and this is demonstrated by comparison of the respective track types' total spring constant. A finite element analysis of a standard design railway track is conducted, and the design total spring constant (TSC, or K) obtained from the time history function analysis is compared to the TSC of existing tracks through trackside measurement results. The comparison result shows that TSC obtained by finite element analysis result is 22% higher than that of the trackside measurement value, indicating that the TSC is conservative in the current track design. Considering the proportional relationship between TSC and dynamic factor, it is estimated that the dynamic factor currently being applied in track design is also conservative. Based on these findings, an assessment of the applicability of different dynamic factors (DAF\({}_{\text{Eisenmann}}\) and DAF\({}_{\text{Area}}\)), theoretical calculation and field measurement (DAF\({}_{\text{Field}}\)) using the probabilistic analysis of wheel loads from the field measurement data is conducted. A correlative analysis between DAF\({}_{\text{Eisenmann}}\) and DAF\({}_{\text{Area}}\) shows that DAF\({}_{\text{Eisenmann}}\) and DAF\({}_{\text{Area}}\) were estimated to be higher by 33% and 27% in ballasted track and by 39% and 30% in concrete track than the dynamic factor derived from field measurement, respectively, which indicates that the dynamic factor currently in use can potentially lead to over-estimation in track design and maintenance.  
dynamic amplification factor; total spring constant of track; field measurement; finite element method; railway system management; railway maintenance
## 4 Analysis Output
### Sea Water Ingress  
The predicted solid hydrates in Figure 11 include C-S-H, portlandite, ettringite, monocarbonate calcite, hydrotalcite, and siliceous hydrogarnet. As monocarbonate is depleted, Friedel's salt is precipitated with an increase in ettringite, and the total volume as the amount of seawater added increases, while portlandite and C-S-H are removed. At higher seawater additions, ettringite is depleted along with portlandite and C-S-H, leading to a decrease in total volume, in which brucite re-precipitates and some M-S-H is formed.  
The change in hydrate phase volume with decreasing pH is shown in Figure 12. The solid hydrates appear to remain stable, especially C-S-H until the pH falls below 12.5. There is a sharp increase in ettringite when the pH is between 12.64 and 12.50, but a sharp decrease below a pH of approximately 12. Below a pH of approximately 12.5, there is a rapid decrease in volume with only hydrotalcite, brucite, M-S-H, and some calcite remaining. As with the effect of carbonation, the ingress of seawater lowers the Ca:Si ratio of the C-S-H which is simulated by the CSHQ DSP gel phase model (see Figure 13).  
Figure 11: Phase assemblage during seawater ingress.  
Figure 12: Change in solid phase volume with pH with increasing seawater ingress.
# Optimal Pricing in a Rented 5G Infrastructure Scenario with Sticky Customers
## 7 Conclusions  
We have analysed a scenario where an infrastructure provider and a group of virtual operators compete for end customers, with customers deciding not only based on prices but also based on other factors that lead them to stick with either operator.  
The absence of a Nash equilibrium for the game resulting from the interaction of MNO and MVNOs has led us to examine scenarios where one of the stakeholders holds an advantageous position by being able to set prices for all parties.  
Aside from the obvious observation that price setters may bend the situation to their advantage and obtain more profits, we have seen that assigning the price-setting power to the regulator (which would maximize the social welfare) may be worse both for the infrastructure owner and the renter. The situation would turn slightly better for the renters when the number of renters grows, because their weight in the overall social welfare would grow so as to push the balance towards them.  
An odd finding is that stickiness does not always move profits in the same way as it moves customers, whereas stickiness to MVNOs increases the MNO's losses when MVNOs act as price-setters, we also observe that the same stickiness to MVNOs increases their losses when the MNO sets prices. Furthermore, stickiness does not impress a clear direction to losses when the regulator has price-setting powers and maximizes the social welfare.  
A limitation of this study is that is only provides numerical results. Though the results have been obtained for a significant range of values of the parameters involved, finding an analytical solution of the profit-maximization equation in an approximate way, would help us to see the influence of each parameter more clearly. Furthermore, that would help us create a theory or set guidelines for price setting and regulation.  
This is also a suggestion for the future work we envisage for the subject. We would also like to explore the range of factors that may lead to stickiness and try their more precise mathematical description.  
**Author Contributions:** Conceptualization, M.F. and M.N.; methodology, M.F. and M.N.; software, M.F. and M.N.; validation, M.F. and M.N.; formal analysis, M.F. and M.N.; investigation, M.F. and M.N.; resources, M.F. and M.N.; data curation, M.F. and M.N.; writing--original draft preparation, M.F. and M.N.; writing--review and editing, M.F. and M.N.; visualization, M.F. and M.N.; supervision, M.F. and M.N.; project administration, M.F. and M.N.; funding acquisition, M.F. and M.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Flow Characteristics and Optimization of the Coupler
### Investigation Methods and Boundary Conditions  
In this section, CFD analysis was performed on the CFFCS and the initial and optimized models of the NFFCS to observe their pressure, flow velocity and turbulent kinetic energy distributions. Among these outcomes, turbulence kinetic energy (TKE) which is one of the most common physical quantities in the turbulence model, is often used to observe the energy loss of liquid flow.  
Figure 11a-c respectively show the mesh on the major internal components and the inlet boundary conditions for the CFFCS model, the initial NFFCS model and the optimized NFFCS model. In this simulation, Ansys Mosaic Meshing Technology was used to generate the mesh in the unique poly-hexcore form shown in Figure 11d. The poly-hexcore mesh provides higher accuracy and faster calculation speed for complex CFD problems [40].  
The international standards ISO 16028 and ISO 4399 [12; 17] give the pressure drop requirements and measurement methods for various sizes of flat-face type coupling systems. In this study, the coupling system size of 10 mm was modelled and observed as an example. The influence of the geometry of the small internal components and springs was considered in this investigation. The boundary conditions for simulations based on the ISO standard were set as mass-flow inlet = 0.3237 kg/s and pressure outlet = reference to atmospheric pressure. The hydraulic oil and turbulence models used are the same as those mentioned in Section 3.1.
## 1 Introduction
As shown in Equations (21)-(23), due to the channel disturbance, the polarization rotation angle shifted, which leads to both channel transmittance and excess noise deteriorated. Therefore, the SKR of the CV-QKD system decreases accordingly, namely deteriorating the performance of the CV-QKD system. In the following part of this paper, several simulations are conducted based on the dual-polarization model established in this section. Furthermore, the system parameters are optimized to reduce the requirement for higher PER.