esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Electric Field Distribution around an Instrumented Total Hip Stem
###### Abstract  
Presently, total joint replacement (TJR) is a standard procedure in orthopedic surgery. Adequate osseointegration of the implant components still remains a clinical issue. However, active stimulation of bone tissue to enhance bone ongrowth at the implant surfaces has not been widely investigated so far. For the last several years, invasive electromagnetically induced osseotherapy has been employed in clinical practice, e.g., for the treatment of avascular necrosis, femoral neck fractures, and pseudarthosis. In the present study, the approach of exploiting the electric stimulation effect was transferred to the field of TJR. Therefore, a commercially available total hip stem was instrumented with an electrode on its surface in order to generate an electric field supporting the regeneration of the surrounding bone tissue. The objective was to conduct numerical simulations validated by experimental investigations as a proof of concept for an instrumented electro-stimulative total hip stem. The results revealed that the calculated electric field around a total hip stem fulfills the requirements to stimulate adjacent bone tissue when using clinically applied electric voltages. The derived numerical and experimental data of electric potentials and corresponding electric fields are encouraging for the implementation of active electrical stimulation in uncomented total hip stems to enhance their osseointegration.  
electrical stimulation; numerical simulation; electric field distribution; experimental validation; osseointegration; total hip replacement (THR) +
Footnote †: journal: Applied Sciences
## 3 Coordinate Control Strategy of Distributed Parallel-Series
### Control Strategy of Onshore Converter  
The currents flowing in the upper and lower arms of phase A are \(i_{ap}\) and \(i_{an}\).  
\[v_{dc}=v_{ap}+v_{an}+2L_{0}\frac{di_{dc}}{dt} \tag{13}\]  
According to Equation (11), the capacitor voltage and AC current can be controlled.  
\[\left\{\begin{array}{l}i_{dqref}=K_{p}(v_{c-avgref}-v_{c-avg})+\frac{K_{i}}{ s}(v_{c-avgref}-v_{c-avg})\\ V_{dqref}=K_{pv}(i_{dqref}-i_{dq})+\frac{K_{in}}{s}(i_{dqref}-i_{dq})\\ \end{array}\right. \tag{14}\]  
According to Equation (14), adjusting the DC voltage can achieve DC current \(i_{dc}\) control, that is:  
\[v_{dcerf}=K_{p1}(i_{dc-ref}-i_{dc})+\frac{K_{iI}}{s}(i_{dc-ref}-i_{dc}) \tag{15}\]  
Figure 3 presents the overall control strategy for the system. The generator side rectifier in the wind turbine controls both the DC voltage and AC current in the generator, ensuring they remain at the target values and preserving energy balance. The grid-side converter is responsible for implementing MPPT control and optimizing the power output. Meanwhile, the average capacitor voltage controller in the MMC captures the full wind power and transfers it to the onshore converter, which regulates the DC current in the DC cable.  
According to Figure 3a, the transfer function is built for stability analysis, as shown in Figure 4. Moreover, the response under different control parameters is shown in Figure 5; it can be seen that with a larger \(K_{p}\) and \(K_{i}\), the system will respond faster.  
If the DC cable is longer, the system will respond faster as it is a proportional parameter.  
Figure 4: The transfer function of the control strategy in wind turbines.
## 2 Methodology
### Grid Independence Study
The torque coefficient (Cm), representing the average torque coefficient for the four blades, was used as the key evaluation criterion, and the results are presented in Figure 6.  
As can be seen from the results, with an increase in the number of cells from 178566 to 620011, the torque coefficient increases by an average of 10-15% between steps, which indicates the sensitivity of the model to the quality of the grid. However, starting from 620011 elements, the increments of Cm become less significant--less than 1%--which indicates the achievement of grid convergence and the independence of the results from further refinement of the grid. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mesh Level** & **Total Cells (\(\times\)10\({}^{3}\))** & **Cm** & **Cp** \\ \hline Coarse & 400 & 0.1718 & 0.3436 \\ \hline Medium & 600 & 0.1534 & 0.3068 \\ \hline Fine & 800 & 0.1503 & 0.3006 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Grid independence study. Figure 5: Mesh sensitivity analysis. Based on this analysis, the grid 620011 elements were chosen as optimal. We used the same mesh for the other configurations, since the topology and flow patterns were not very different. Conducting a mesh study separately for each geometry would have been redundant, since the simulation settings and physics of the problem were kept the same.
# Advancements in Data Analysis for the Work-Sampling Method
## 2 Materials and Methods
The work-sampling method is characterized by a high degree of activity and economy, which is expressed in the following advantages:  
* We can record several workplaces simultaneously and track a relatively large number of activities (time efficiency);
* The time and cost of observations are significantly lower than those of continuous recording with a stopwatch (from 35 to 80%); we obtain the information we need quickly, using fewer resources, and at a lower risk and cost (cost-effectiveness);
* The objectivity of recording the actual situation has an accuracy that is satisfactory in practice; work sampling provides statistically valid data for analyzing work patterns;
* Since the recording technique minimizes the influence of the observed workers on the recording results, the probability of false results is much lower than with continuous recording (less intrusive as it involves periodic observations over a period of time);
* Training analysts for recording is simple, fast, and straightforward; we do not need any special equipment (no timing devices) to carry out recordings;
* It can be applied to various types of work environments (flexibility);
* The study takes longer, minimizing short-term fluctuations;
* The recording can be interrupted or resumed, if necessary, as it does not affect the result. Of course, this method also has its disadvantages:  
* The recording of observations can involve a certain amount of subjectivity, which can lead to inconsistencies in data collection and interpretation;
* Difficulties in recording individual workplaces, especially if they are located further away;
* We cannot capture individual differences between workers as we are observing a group of workers;
* We cannot set standards by sampling work activities;
* It provides an overview of activities without detailed insights into specific tasks or processes;
* It does not capture short-term fluctuations that could be important for identifying inefficiencies or bottlenecks in the process;
* It is practically impossible to ensure adequate accuracy for activities that account for less than 1% of the share, as a very large number of observations (over 150,000) would be required;
* It is very difficult to identify the causes of employee work interruptions and absenteeism, as an understanding of the specific context and work environment is required. The last one is exactly the area we are mainly contributing to in this article (described in Section 4).
# Research on Driving Style Recognition of Autonomous Vehicles Based on ACO-BP
## 3 Methodology
### K-Means Cluster Analysis  
The K-means clustering algorithm is widely utilized in unsupervised machine learning to partition a dataset into K-distinct clusters or classes [36]. It aims to achieve clustering by minimizing the squared distance between data points and the center of the cluster to which they belong. The current research mostly divides driving styles into three categories: conservative, ordinary, and aggressive. Therefore, in order to obtain the ideal clustering results, the clustering centers were set to 3. The specific clustering results are shown in Table 4.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Sample Number** & \(\mathbf{Y_{1}}\) & \(\mathbf{Y_{2}}\) & \(\mathbf{Y_{3}}\) & \(\mathbf{Y_{4}}\) \\ \hline
1 & 0.3145 & \(-0.096\) & \(-0.6892\) & 0.3413 \\
2 & 0.6533 & \(-1.6115\) & \(-1.0683\) & 1.1215 \\
3 & 0.4303 & \(-0.0566\) & \(-0.722\) & \(-0.2946\) \\
4 & 0.5054 & \(-0.3687\) & \(-1.2341\) & \(-0.3897\) \\ ; & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
301 & \(-0.3487\) & \(-0.6473\) & 0.612 & 1.1598 \\ \hline \end{tabular}
\end{table}
Table 3: Principal component scores.  
Figure 5: Contribution rate graph.  
Based on the clustering results, it is evident that with three clusters, there is a small distance observed between each class cluster. By examining the sample counts of the three class clusters, there is no problem of underclassification or overclassification [37]. Therefore, the sample size classification of each class cluster is more reasonable. Moreover, combining the existing qualitative knowledge of driving styles and the magnitude of the corresponding characteristic parameter values of the three driving styles, it can be judged that the three driving styles are aggressive, ordinary, and conservative in order [37]. As shown in Figure 6, the clustering effect diagram of the 3 driving styles, it can be clearly seen that the driving styles are divided into 3 types and the boundaries are obvious, which further proves the rationality of the K-means clustering results. Among them, the first type of clusters is aggressive, and the clustering centroids are (1.7, \(-\)0.31, \(-\)0.43, 0.06); the second type of clusters is ordinary, and the clustering centroids are (\(-\)0.33, 0.67, 0.43, \(-\)0.19); and the third type of clusters is conservative, and the clustering centroids are (\(-\)1.03, \(-\)0.95, \(-\)0.4, 0.3). The results of the K-means clustering analysis, denoted as dataset \(\gamma\), are used as the output of the subsequent driving style recognition model to demonstrate the recognition effectiveness of the model.
## 3 Results and Discussion
### Comparison Between R245 fa/Propane and Butane/Propane Mixtures
#### 3.3.3 Sustainability Index at Optimal Conditions  
The optimal mass composition for the mixtures is identified from the previous section by analysing the cycle efficiencies. For comparison purposes, the dead-state temperature is also varied for this mixture and sustainability index is evaluated. The Sustainability Index gives a good measure of the sustainability on the basis of exergy that represents the imperfections and irreversibility in the ORC. A higher sustainability index means better sustainability of the overall system. It is evaluated as follows [37]:  
\[SI=\frac{1}{1-\eta_{II}} \tag{20}\]  
where \(\eta_{II}\) is the second law efficiency.  
Figure 19: Cycle efficiency at cold temperature 20 \({}^{\circ}\)C for optimal mixtures vs. (**a**) hot source temperature; (**b**) cold source temperature.  
Figure 20: Second law cycle efficiency at cold temperature 20 \({}^{\circ}\)C for optimal mixtures vs. (**a**) hot source temperature; (**b**) cold source temperature.  
Figure 21a,b depict the effect of dead-state temperature on the second law cycle efficiency for both the working fluid mixtures considered in this study. In Figure 21a, it can be seen that second law cycle efficiency is directly proportional to the dead-state temperature. In general, butane/propane (0.8/0.2) has a higher second law cycle efficiency than R245 fa/propane (0.6/0.4); however, at a higher dead-state temperature they are almost identical. Similarly, in Figure 21b, it can be observed that the sustainability index rises as the dead-state temperature rises. The sustainability index of both mixtures are almost identical to each other; at a lower dead-state temperature (5-20 \({}^{\circ}\)C), butane/propane (0.8/0.2) has slightly higher sustainability index than R245 fa/propane (0.6/0.4) but at higher dead-state temperatures (>25 \({}^{\circ}\)C), R245 fa/propane has higher sustainability index.
# A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites
## 2 Related Work
[20] defined resource utilization rate in their article first, then adjusted beam radius to make resource utilization rate tend to 100% in a simplified scene, but the author did not consider the result caused by expanding the beam radius that directly leads to intra-beam interference. [PERSON] et al. [21] first proposed a simplified scenario, then conducted an analysis based on this scenario in their study. Then, they concluded that satellites should provide narrow beam coverage while also prioritizing users with high channel gain and traffic demand to achieve maximum throughput. Basedon the above conclusions, the authors proposed an algorithm that adjusts beam radius and allocation beam position based on the indicators \(\mu_{sk}\) and \(v_{sn}\) that depend on users' gain and traffic demand, and location. The algorithm has a strong physical significance, but it ignores users' QoS and consumes so many computational resources in the background that its on-broad computational capability is limited. On the foundation of previous research, we take the system throughput and total system delay into account and utilize the features of large size beams' coverage capability, and small size beams' interference avoidance capability, high gain in this article. Furthermore, we allocate the three kinds of beams to suitable positions at the correct time so that terrestrial users can enjoy better service quality and achieve system performance improvement. The simulation result shows that employing the proposed algorithm in the baseline model results in a 3.44% increase in system throughput and a 35.5% reduction in system latency. Moreover, in alternative models, the proposed algorithm consistently enhances system performance. The remaining chapters of the article are organized as follows: Chapter 3 focuses on proposed required system parameters and system modeling, which constructs the model required by simulation; Chapter 4 contains a simulation scene and algorithm introduction, four simulations to examine the proposed algorithm in four aspects are conducted; in addition, the relevant simulation results are presented in visualized form and analyzed in detail; and Chapter 5 summarizes this article.
# An 8 kb RRAM-Based Nonvolatile SRAM with Pre-Decoding and Fast Storage/Restoration Time
## 5 Conclusions  
This paper presented an improved 8T2R nvSRAM memory cell structure based on RRAM technology for the application of new memory. Aiming at the problem where the data in the SRAM cannot be restored after a power failure, RRAM technology is introduced into the SRAM. The data in the SRAM are stored in RRAM before power failure. When the power supply is restored, the data are restored to SRAM, which solves the disadvantage of SRAM volatility. Meanwhile, a proposed pre-decoding technique improves the reading and writing speed. The memory cell was realized with a UMC CMOS 28 nm 1P9M process, occupying only 0.97 \(\upmu\)m\({}^{2}\). The simulation results show that when the power supply voltage is 0.9 V, the static noise margin is 0.35 V, the read noise margin is 0.16 V, and the write noise margin is 0.41 V. Additionally, the data storage time is 0.21 ns and restoration time is 0.18 ns. Further, a peripheral circuit was designed, and an 8 kb nvSRAM realized. The time for the system to read 1 bit data is 5.2 ns. Compared with the previous research results, it has lower power consumption and faster data storage and restoration times, thus increasing the stability of data storage. This work only verified the design of the memory cell and peripheral circuit from the aspect of function, which had certain limitations. The nvSRAM array was only simulated. Since a differential structure requires a forming process before reading and writing, and the voltage of forming is high, we should also consider the impact of the forming process, so that the design will be more conducive to the read speed of the SRAM. In the design of nvSRAMs based on RRAM technology, most designs use the high and low resistance states of RRAM to store data. This single mode makes the SRAM and RRAM combination a one-to-many mode. However, research found that RRAM has a multi-value storage feature, that is, a single RRAM can store multi-bit data. This discovery provides a new idea for the design of nvSRAM, that is, multiple SRAM cells can share one or two RRAMs to realize the non-volatile storage function. Conceptualization, C.C. ; funding acquisition, C.C. and J.J.; methodology, J.Y., W.L. and Y.Z. ; project administration, J.Y., W.L. and Y.Z. ; resources, Y.Z. ; software, C.C. and J.J.; supervision, C.C. and J.J.; validation, C.C. and J.J.; writing--original draft, J.Y. ; writing--review and editing, C.C. and J.J. All authors have read and agreed to the published version of the manuscript. This research was funded by Jihua laboratory Fund Project (2021B0301030003-03), Xiamen Youth Innovation Fund Project (3502Z20206074) and Major science and technology projects of Xiamen (3502Z20221022). Not applicable.
## 2 Materials and Methods
### Data  
#### 2.2.1 MODIS Data  
In this paper, BRDF-corrected surface reflectance data bands (Band1, Band2, Band3, Band4, Band5, and Band7) from MCD43A4.061, Leaf Area Index (LAI), and fPAR from MCD15A2H.061 were used as the explanatory variables of our downscaling models. We did not use the band Band6 of MCD43A4 because it had too many missing data in our study area and the performance of the downscaling models with or without Band6 did not change much. We did not select too many explanatory variables (e.g., evapotranspiration ET and land surface temperature LST), because too many variables may increase uncertainty and reduce the usability of satellite data caused by data gaps. Of the explanatory variables, reflectance data has been widely used in previous work on _SIF_ downscaling [13; 14; 15]; fPAR is the import component of _SIF_[22], and LAI is related to the escape factor of the _SIF_ yield. Prior to model training, MODIS data were temporally aggregated to 16-day for prediction and then spatially aggregated to 10 km 16-day for training and validating downscaling models. The spatial resolution of the aggregated MODIS data corresponds to \(20\times 20\) pixels of original MODIS data in the Sinusoidal coordinate reference system. During the aggregation process, pixels of MCD43A4 data with Mandatory_Quality = 255 were discarded, for MCD15A2H, pixels with SCF_QC = 100 were discarded. The coarser aggregated pixels containing fewer than 200 pixels from the original MCD43A4 or MCD15A2H were also discarded. Moreover, the MCD12Q1.006 land cover dataset was aggregated by assigning the vegetation type with the highest area percentage. All the aggregated pixels labeled as urban and built-up lands, permanent snow and ice, barren, and water bodies were also excluded.
## 2 Materials and Methods
### Data Collection
#### 2.1.2 MODIS Data Processing  
We downloaded the fraction of absorbed photosynthetically active radiation (FPAR) and leaf area index (LAI) (MCD15A2H) data of Moderate Resolution Imaging Spectroradiometer (MODIS) at a spatial resolution of 500 m (from MODIS Global Subsets website [[https://modis.ornl.gov/cgi-bin/MODIS/global/subset.pl](https://modis.ornl.gov/cgi-bin/MODIS/global/subset.pl)]([https://modis.ornl.gov/cgi-bin/MODIS/global/subset.pl](https://modis.ornl.gov/cgi-bin/MODIS/global/subset.pl))), with which LUE was calculated according to Formula (1) in the FLUXNET sites:  
\[\text{LUE}=\frac{GPP}{PAR\times FPAR}=\frac{GPP}{0.48\times SW\times FPAR} \tag{1}\]where GPP and SW were from the FLUXNET data, and FPAR was from the MODIS data at a resolution of 500 m. 0.48 represents the ratio of photosynthetically active radiation to the total incoming solar energy. At each site, 9 (\(3\times 3\)) pixels around the central position were collected for quality-control processes. The first quality-control process included (1) gaining 9 values in each site; (2) deleting invalid data according to the quality control flags; (3) counting the number of valid values in each site; and (4) retaining the valid data if the number exceeds 5, or deleting the valid data if the value did not.  
MODIS LAI and FPAR depend on the reflectance in the red, near-infrared (NIR), and sometimes shortwave infrared (SWIR) bands at the surface level, which are often very sensitive to atmospheric effects, including clouds, aerosols, water vapor, and ozone [55; 56]. Although many of these effects can be removed using real-time or near real-time atmospheric observations [57], the remaining effects can sometimes be very large. These remaining effects generally cause more increases in the red band than in the NIR band, consequently resulting in the reduction of LAI and FPAR. In this case, there would be abnormally low values in a seasonal trajectory. Therefore, we conduct the second quality-control process, which includes deleting abnormally low values based on seasonal curves [58]. In parameterization approaches, parameters, for example, maximum LUE, vary with vegetation type.  
In the regression tree approach, vegetation type can be an input data used to calibrate the approach. We used the International Geosphere-Biosphere Programme (IGBP) vegetation type map from yearly global 5-km MODIS Landcover (MCD12C1) to address different types of vegetation. In addition, we downloaded MOD17 8-day/0.05 degree GPP as an existing global GPP product to analyze our GPP from [[http://files.ntsg.umt.edu/data/NTSG_Products/MOD17](http://files.ntsg.umt.edu/data/NTSG_Products/MOD17)]([http://files.ntsg.umt.edu/data/NTSG_Products/MOD17](http://files.ntsg.umt.edu/data/NTSG_Products/MOD17)) [59].