esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Computation Offloading with Task Scheduling Minimizing Reallocation in VANETs
## 7 Conclusions  
We proposed _vehicular adaptive offloading_ (VAO) for the dynamic vehicular environment, which uses DAG scheduling for optimizing task execution. Unlike other well-known scheduling methods, HEFT and Max-Min, VAO focuses on reducing task reallocation, taking into account the dynamic topology of VANETs. For that purpose, VAO does not offload tasks to neighbors whose routes are much different from the RV's route. The simulation results showed that VAO decreases the probability of task reallocation by 45.4%, as compared with HEFT. Our simulation reveals that more SVs are required than the width of DAG to optimize CO but many more vehicles than the width are not helpful. When the number of searched vehicles is sufficient, the schedule length of VAO, including all reallocated tasks, is 14.4% shorter than that of HEFT.  
We have two future works. First, during the simulation, we occasionally observed that the schedule length of a task set increased when CO was used, as compared with the case where all the tasks were performed in one vehicle. This is mainly because of unexpected task reallocation events, so, if we can predict the chance of task reallocation occurring, inefficient CO can be avoided. We think that AI methods like LSTM (Long Short-Term Memory), which can analyze time-series data on number and distribution of vehicles, might be helpful to predict task reallocation events. Second, we did not consider the situation where multiple vehicles request CO at the same time, resulting in possible conflict at their shared nearby vehicles. This might be solved if a nearby vehicle informs each CO RV of the computing power that can be provided to the RV exclusively.  
Table 3 summarizes the characteristics of the algorithms observed in this paper.  
**Author Contributions:** Conceptualization, M.G. and S.A.; methodology, M.G.; software, M.G.; validation, M.G., Y.Y. and S.A.; formal analysis, Y.Y.; investigation, M.G.; resources, M.G.; data curation, M.G.; writing--original draft preparation, M.G.; writing--review and editing, Y.Y.; visualization, M.G.; supervision, S.A.; project administration, S.A.; funding acquisition, S.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C1011184).  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
###### Abstract  
This study examines the impact of plasma intensity on the process unused rate, expressed as 1-Ui, within semiconductor manufacturing. Since the influence of plasma level on 1-Ui is inconsistent without considering gas, the experimental data were analyzed after grouping plasma levels by intensity. Plasma intensity is classified into three categories: low (<100), medium (500-700), and high (>1000). The dataset includes seven columns and 8324 entries representing seven gases--NF3, SF6, CH2F2, CHF3, C4F6, C4F8, and CF4. To analyze the relationship between plasma intensity and 1-Ui, we conducted a series of ANOVA tests followed by post hoc analyses to identify statistically significant differences in 1-Ui across the defined plasma intensity levels.  
plasma intensity; semiconductor manufacturing; process unused rate; 1-Ui; ANOVA; non-linear regression Article
## 1 Introduction
Alumina has demonstrated resilience in high-temperature environments and can be effectively applied as a coating through the sol-gel method [34; 36; 40; 41; 42; 43]. However, despite these benefits it is important to note that this type of coating has great impact in terms of rising costs, more complicated manufacturing and implication in the supply chain. Research by [PERSON] et al. [44] showcased the efficacy of a sol-gel alumina layer, generated via a modified Yoldas sol [41], in providing effective protection for Inconel-718 even after 4000 h of exposure at 800 \({}^{\circ}\)C in static laboratory air. Additionally, a 400 nm thick transition alumina layer, applied using the sol-gel method on commercial power plant steel X20, exhibited excellent protection against oxidation in laboratory air up to 650 \({}^{\circ}\)C [35]. The infusion of manganese and chromium ions into the alumina layer, leading to the formation of mixed oxides, contributes to the enhanced protective properties of the coating, preventing oxidation. Furthermore, the avoidance of local defects, attributed to substrate preparation through polishing, is identified as a significant factor in improving the coating process [34; 35]. Enhancing the corrosion fatigue behavior of coated steels involves achieving high resistance to cracking, low porosity in the coating [45], and inducing compressive residual stresses within the substrate surface [30; 46]. [PERSON] et al. [47] observed that anodic coatings generally improved the corrosion fatigue behavior of X20 Cr13, offering cathodic protection in case of coating breakdown. No corrosion occurred in the substrate when using coatings less noble than the steel, as anodic dissolution took place in the coatings [47; 48]. Alumina coating provided cathodic protection for both X20 Cr13 and X5 CrNiCuNb16-4. [PERSON] et al. [48] noted that the fatigue behavior of AISI4043 (36 CrNiMo4), a medium carbon, low alloy steel, remains similar to that in air. Fatigue cracks initiated at alumina particles in the substrate steel rather than corrosion pits formed during testing. The corrosion fatigue strength of ceramic-sprayed medium carbon steel (S45C) was slightly higher than that of the substrate steel at medium stress levels, but the coating's sealing nature had little effect on improving corrosion fatigue strength at higher stress levels. Once coating cracks formed, corrosion fatigue strength became equivalent to that of the substrate steel. [PERSON] et al. [46] indicated that tungsten carbide WC-17 Co and WC-10 Co-4 Cr thermal spray coatings applied by high velocity oxygen fuel coating (HVOF) process result in higher fatigue strength. In contrast, the fatigue strength of AISI 4340 steel associated with chromium electroplating was significantly reduced. The study introduces the prospect of reducing corrosion rates and pit initiation by applying an alumina coating via the sol-gel process to coupons of 1.4021 and fatigue specimens of 1.4542. However, experiments indicate that this approach is not favorable in a CCS environment. Nonetheless, the results provide valuable insights into corrosion management during carbon compression.
## 4 Structural Optimization Design
### Experimental Design  
In the structural optimization design of a large body, it is expected that there are six design variables to be examined, namely the above six dimensions. However, in prac  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Initial Value/m** & **Range of Values/m** \\ \hline \(R_{1}\) & 0.04 & [0.02, 0.06] \\ \(R_{2}\) & 0.02 & [0.01, 0.03] \\ \(R_{3}\) & 0.03 & [0.015, 0.045] \\ \(R_{4}\) & 0.035 & [0.02, 0.05  
tice, not every variable significantly influences the optimization objective, and too many variables with small degrees of influence are examined, which takes longer and requires more effort during optimization. Therefore, it is necessary to select from several design variables, i.e., test factors, through the experimental design to obtain the test factors that have a greater influence on the large body's structural performance (mass, maximum stress, and maximum deformation). The experimental design methods commonly used for structural optimization include orthogonal test design, central composite design, Box-Behnken design, Latin square design, Latin hypercube design, etc. [17]. Orthogonal experimental design is a space-based method commonly used for design problems with multiple factors. Its most striking feature is that the sample points are uniformly and more neatly distributed in the sample space. The results obtained from the experimental design can be compared with each other. This approach typically uses orthogonal test tables to schedule the tests and allows statistical treatment of the test results using extreme difference analysis. Compared with alternative experimental methods, the orthogonal experimental design method uses fewer trials to obtain more comprehensive spatial information about the sample, and the experimental results obtained through the experimental orthogonal design table can be analyzed to determine the effect of each factor on the output response. There are six factors affecting the mass, maximum stress, and deformation of the bulk structure, and the orthogonal test tables with six factors and five levels are chosen according to the range of values of the variables in Table 1, as shown in Table 2.  
Twenty-five trials were performed according to the testing protocol in Table 2, and the results were analyzed for polar differences using the EXCEL tool. The main order of the factors affecting the quality M is \(R_{1}>R_{6}>R_{4}>R_{3}>R_{5}>R_{2}\), the main order of the factors affecting the maximum stress S is \(R_{1}>R_{6}>R_{5}>R_{4}>R_{2}>R_{3}\), and the main order of the factors affecting the maximum deformation U is \(R_{1}>R_{6}>R_{5}>R_{4}>R_{3}>R_{2}\). The analysis results show that \(R_{1}\) (care support plate thickness) and \(R_{6}\) (thickness of the center longitudinal plate) have the maximum impact on the mass, maximum stress, and maximum deformation; \(R_{2}\) has the least impact on all three and reduces the effort of optimizing the design. A total of five parameters, \(R_{1}\), \(R_{3}\sim R_{6}\), were chosen as design variables for the optimization of the large-body structure.
## Appendix A Path Planning Algorithms
### Probabilistic Roadmap  
For the path given on the right in Figure 15, a Probabilistic Roadmap (PRM) style algorithm was used. Resolution was set to 5 cells per meter with a maximum of 500 nodes, a maximum number of neighbors of 3, and a maximum neighbor distance of 1 m. These values may be tuned for improved results. The map uses an occupancy grid to differentiate between free and occupied space.  
For this algorithm, random nodes are first generated in the simulation space. Nodes are then connected to neighbors within the specified range and the shortest path is found from the start goal to the goal node using the connected edges. Further background on this algorithm can be found in [37]. Specific tuning values used for the simulations are given in Table 12.
## 5 Conclusions  
This paper presents a novel proposal for the construction of a two-tier planning model, with the objective of achieving both economic efficiency and flexibility in ADN. The model integrates the temporal and spatial trend regulation characteristics of the ESS and SOP, and is verified by an improved IEEE 33 system through arithmetic simulation. The results are as follows:  
* The coordinated planning of SOP and ESS offers superior benefits in terms of enhancing the flexibility and economy of ADN, as well as improving the voltage level of ADN, in comparison to separate individual planning. In comparison to the original plan, the degree of flexibility has been enhanced by 36.92%, the economy of ADN has been improved by 2.58%, and the voltage level has been increased by 33.5%.
* From the perspective of large-scale penetration of DG, the proposed indicators of DG consumption rate and DG volatility can be employed to comprehensively assess the flexibility of the power side of ADN. Furthermore, they can be utilized as an optimization target to formulate a more reasonable SOP and ESS configuration scheme.  
In a subsequent study, social and environmental factors will be taken into account to establish a multiobjective optimization model that simultaneously satisfies the economics and flexibility of the distribution network, as well as the social and environmental benefits, in order to achieve a more comprehensive and reasonable guidance for the siting and sizing of SOP and ESS.  
Conceptualization, J.L. and Y.Z.; methodology, J.L. and C.L.; software, Y.Z. and Z.R.; validation, J.L. and Y.Z.; formal analysis, Y.Z. and Z.R.; investigation, F.Z.; resources, Z.R.; data curation, J.L.; writing--original draft preparation, Y.Z.; writing--review and editing, G.L., J.L., and C.L.; visualization, J.L. and G.L.; supervision, J.L., Z.R., and C.L.; project administration, J.L. and Y.Z.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China and Inner Mongolia Science & Technology Plan under Grant, grant number 2022 YFB2403100 and 2022 JBGS0043.  
Not applicable.  
The raw data is the IEEE33 node system parameters as shown in Appendix A.
# Low-Power Wireless Data Transfer System for Stimulation in an Intracritical Visual Prosthesis
## 3 System Architecture  
The communication link for transmitting the stimulation signals to the implanted electrodes (downlink), comprises of the external transmitter, the inductive link, channel and the implanted transceiver. Figure 2 shows the entire downlink system with its schematic sub-blocks. Frequencies <3 MHz or >25 MHz will be allocated for inductive powering for minimal skin losses, like the popular 125 kHz ISM band (<3 MHz) or the 401 MHz ISM band (>25 MHz) which gives room for filtering interference.
## 3 Proposed Method
### Route Generation  
#### 3.5.1 Effective Area Acquisition  
For a given query condition, when planning the path from the start point to the end point for users, the possible effective search area is only a small part of the entire road network, and most of the areas cannot appear in the road. However, if we do not process the road network and let the algorithm search the entire road network, the efficiency will be extremely low. In this case, it is necessary to obtain a suitable effective search area from the road network. Therefore, this paper adopts the method in [15] to cut the search area. Specifically, we take the starting point and the ending point as the centre of the circle and \(r\) as the radius to draw a circle. The intersecting part of the two circles is the effective area. \(r\) refers to the distance travelled at the average driving speed \(\overline{v}(t_{0})\) for \(b\) time starting at time \(t_{0}\), i.e., \(r=\overline{v}(t_{0})\times b\). The edges within the valid region are valid edges. Here, the effective edge can be defined as: at time \(t_{0}\), starting from the starting point, the end point can be reached within the time budget, and starting from the end point, the starting point can also be reached within the time budget.
# Load Balancing Strategies for Slice-Based Parallel Versions of JEM Video Encoder
## 2 Description of the New Characteristics of the Joint Exploration Test Model (JEM)  
The JEM codec is based on the HEVC reference software, called HM, meaning that the overall architecture of both codecs is quite similar since they share a hybrid video codec design. However, some of the coding stages are modified in the JEM implementation in order to improve the previous standard [14; 15]. The R/D performance of JEM is better than in HEVC due to the use of these techniques, but this is achieved at the expense of an increased computational cost for the intra-prediction stage. This section describes the main improvements offered by JEM in comparison to the previous standards, as these could lead to a load imbalance when using parallel algorithms such as those proposed in this work.
## 5 Discussion
### Operation Pressure Determination of BWSC with Large Height Difference
The green dotted line highlights the corresponding values of the maximum and minimum allowable pressure differences when the elevation difference is 200 m.  
basically be considered equal. This understanding is of great significance for determining the operating pressure range of large-height-difference BWSCs. Based on the above analysis, a pressure range design model is proposed: adopting the upper-limit pressure for shallow caverns and the lower-limit pressure for deep caverns to establish the injection-production operating pressure for BWSCs with a large height difference. This approach provides safety assurances for the transformation and utilization of such caverns. For the given case study, the recommended operating pressure range is 12-25.5 MPa.
## 2 Blind Source Separation Techniques
### Complexity Pursuit Algorithm (CP)  
The complexity of any mixture lies between the simplest and most complex sources of components [31]. Thus, the least complex signal extracted from a collection of mixed signals is assured to be the source signal. Concretely, CP can search for a de-mixing (row) vector \(\mathbf{w}_{i}\) such that the component \(\mathbf{y}_{i}(t)\) is recovered as  
\[\mathbf{y}_{i}(t)=\mathbf{w}_{i}\mathbf{x}(t), \tag{7}\]  
which yields the least complexity, thus approaching the (simplest) source signal.
## 3 Field Measurements and Data Processing
### Field Installation at EC Flux Sites
All data are converted to 30 min and daily values corresponding to the SIF time steps. The meteorological and EC data collected on days with corresponding SIFSpec measurements were selected for future study.
# Economic Superiority of PIP Slip Joint Compared to Conventional Tubular Joints
## 2 Methodology
### Comparative Analysis  
Once the \(NPV\), \(LCOE\), and total lifecycle costs are computed for each joint system, a comparative analysis is conducted to determine the most cost-effective solution [56; 57; 58]. The analysis identifies which system offers the best trade-off between structural performance and economic viability. Key cost drivers, such as fabrication expenses, maintenance frequency, and material costs, are examined to highlight areas where cost reductions can be achieved. The results are presented through graphical representations, featuring several bar charts that compare total lifecycle costs, NPV trends, LCOE, and additional comparisons, as shown in Figures 5-9.  
scoring its cost efficiency. Correspondingly, the NPV values indicate minimal variation, yet they reflect the same expenditure dynamics observed in net income.  
NPV, or net present value, is an essential metric that takes into account the time value of money by discounting future cash flows to present-day values. It considers risk, inflation, and opportunity costs, providing a solid measure of long-term financial viability. Although the grouted joint has a marginally higher NPV, the PIP slip joint excels in other financial indicators, such as payback period, internal rate of return (IRR), and annualized costs, which are further discussed in Section 3.1.  
These results reveal an important insight: while the financial profiles of different joint types seem similar at first glance, a deeper economic evaluation highlights the PIP slip joint's advantages in cost efficiency and risk mitigation. Its lower initial investment, simpler installation process, and reduced maintenance requirements make it a compelling option--especially for offshore wind projects that prioritize shorter construction timelines and lower upfront costs.  
Furthermore, the recent literature supports the practical applicability of slip-joint technology in offshore environments, noting benefits such as vibration-assisted installation and removal, as well as adaptability for modular turbine structures [21; 22]. While differences in net income present value are relatively minor, a comprehensive decision-making framework that considers structural, logistical, and economic factors reinforces the suitability of PIP slip joints as an optimal solution for future wind turbine foundations.
## Materials and Methods
### Least Square Approximation Method
\begin{array}{l}B_{\rm z}=-0.06584-2.59175\cdot 10^{-8}\cdot d+0.06379\cdot h_{\rm PM }+0.15354\cdot d_{\rm Fe}+0.00065\cdot d^{2}-0.00228\cdot d\cdot h_{\rm PM}-\\ 0.00771\cdot d\cdot d_{\rm Fe}+0.01742\cdot h_{\rm PM}\cdot d_{\rm Fe}-0.00779 \cdot h_{\rm PM}^{2}-0.01836\cdot d_{\rm Fe}^{2}-1.50319\cdot 10^{-5}\cdot d ^{2}-6.64056\cdot 10^{-5}\cdot d^{2}\cdot h_{\rm PM}+\\ 0.00013\cdot d^{2}\cdot d\cdot_{\rm Fe}+0.00048\cdot d\cdot h_{\rm PM}^{2}-3.95197 \cdot 10^{-5}\cdot d\cdot h_{\rm PM}\cdot d_{\rm Fe}+0.00036\cdot d\cdot d^{2} _{\rm Fe}-0.0013\cdot h_{\rm PM}^{2}-d_{\rm Fe}+0.00032\cdot h_{\rm PM}^{3}-\\ 0.00053\cdot h_{\rm PM}\cdot d\cdot_{\rm Fe}+0.00092\cdot d_{\rm Fe}^{2}+9.49058 \cdot 10^{-5}\cdot d^{4}+8.87146\cdot 10^{-7}\cdot d^{3}\cdot h_{\rm PM}\cdot 8 1.2445\cdot 10^{-7}\cdot d^{2}\cdot d_{\rm Fe}-\\ 3.50659\cdot 10^{-5}\cdot d^{2}\cdot h_{\rm PM}^{2}-1.1743\cdot 10^{-6}\cdot d^{2}\cdot h _{\rm PM}\cdot d_{\rm Fe}-2.2085\cdot 10^{-5}\cdot d^{3}\cdot h_{\rm PM}^{2}+1.10169\cdot 10^{-5} \cdot d^{2}\cdot h_{\rm PM}^{2}-d_{\rm Fe}-\\ 6.55623\cdot 10^{-6}\cdot d\cdot h_{\rm PM}\cdot d_{\rm Fe}^{2}+3.20094\cdot 10^{-6}\cdot h _{\rm PM}^{2}-d_{\rm Fe}+6.9314\cdot 10^{-6}\cdot h_{\rm PM}^{2}+4.3272\cdot 10^{-5} \cdot d^{2}\cdot h_{\rm PM}^{2}-d_{\rm Fe}^{2}-\\ 6.48919\cdot 10^{-6}\cdot h_{\rm PM}\cdot d_{\rm Fe}^{2}-2.44857\cdot 10^{-6}\cdot d^{2} \cdot d^{2}\cdot h_{\rm PM}\cdot d_{\rm Fe}^{2}-4.04763\cdot 10^{-6}\cdot d ^{2}\cdot h_{\rm PM}+.198959\cdot 10^{-5}\cdot d^{2}\cdot h_{\rm PM}^{2}+.59895\cdot 10^{-5} \cdot d^{2}\cdot h_{\rm PM}^{2}+.46\end{array}  \tag{2}\]  
where \(B_{\rm z}\) is the axial component of magnetic flux density in the middle of the stator, d is the thickness of the stator with two air gaps, \(h_{\rm PM}\) is the thickness of the PMs and \(d_{\rm Fe}\) is the thickness of the rotor disks. As is mentioned above, the mathematical model is derived from the input data obtained by the simplified FEM, which was conducted with limitations, such as the condition that stator (virtual air gap) thickness is not larger than 4 times the PM thickness (\(d_{\rm s}\leq 4\)\(h_{\rm TM}\)) and with the PM span selected in such way that it covers 120\({}^{\circ}\) electrical. These limitations are selected on the basis of FEM analysis results, which are shown in Section 3.
## 3 Materials and Methods
### General Characterization of HOC and FHOC  
Total moisture content was determined gravimetrically, total protein content was determined by the Kjeldahl method with a protein conversion factor of 6.25 ([PERSON], Konigswinter, Germany), soluble protein content was determined by the spectrophotometric method (Agilent Cary 60 UV-Vis, Agilent Technologies, Santa Clara, CA, USA), total fat content ([PERSON], Soxtherm SE414, Konigswinter, Germany), and total ash content was determined gravimetrically according to methods given in [PERSON] et al. (2014) and [PERSON] et al. (2022) ([PERSON] et al., 2012; [PERSON], 2018). Total carbohydrate content was obtained by calculating the total amount of protein, fat, moisture, and ash in a 100 g sample and subtracting it from 100. Color analysis of the products was performed with a color meter (Konica Minolta CM-5, Osaka, Japan) and the results were given as L\({}^{\star}\), a\({}^{\star}\), and b\({}^{\star}\). Total phenolic content was determined using Folin-Ciocalteu and the results were expressed as gallic acid equivalents ([PERSON], 2018). Extractions from FHOC for phenolic matter determination were carried out using distilled water, methanol, and a mixture of methanol:water (80:20 by volume) as solvents. In the extraction process, 2 g of sample was extracted in 10 mL of solvent in a shaking incubator (IKA KS3000 IC, Staufen, Germany) for 24 h at room temperature and the supernatant obtained after centrifugation (7500 rpm, 25 \({}^{\circ}\)C, 30 min) was used for the determination of phenolics. All compositional analyses were performed in triplicate and the results were expressed as dry weight.