esa-sceva/satcom-chunk-collection huggingface dataset:
# Single-Phase, Bidirectional, 7.7 kW Totem Pole On-Board Charging/Discharging Infrastructure
## 5 Conclusions  
Charging infrastructure is being developed to compensate for the fuel crisis through EV transportation support. Additionally, the charging infrastructure is utilized to support the grid during peak power demand to minimize the overall installation cost and owner's benefit. Therefore, in charging or G2V mode, a GSBD detem pole and BSBD DC-DC converter are included to operate in rectifier and buck mode, respectively. On the other hand, in discharging or V2G mode, GSBD and BSBD converters are employed in inverter and boost mode, respectively. In this G2V and V2G mode, the grid-side converter includes SiC (WBG device) as a switch capable of utilizing the higher switching frequency during PFC operation. The use of SiC devices keeps the PFC operation in CCM mode and avoids the issue of reverse recovery current. Additionally, the use of a higher switching frequency lowers  
Figure 19: Performance curve of the PF in V2G mode.  
Figure 17: Dynamic performance of the output voltage and current in V2G mode.  
Figure 20: Performance curve of the active and reactive power in V2G mode.  
the size of the interfacing boost inductor as well as lesser space and thermal management. The analysis on the switching frequency design also helps to choose its optimum value to operate the PFC for EV charging with higher efficiency and lower THD. Furthermore, A 7.7 kW BDCI is implemented in the MATLAB/Simulink environment. The dynamic performances of the system strengthen the effectiveness of the control strategies used in G2V and V2G mode at the designed parameters and selected system specifications in BDCI. Additionally, the system performance also shows that the targeted power factor and THD are achieved and within the IEEE standards.  
Conceptualization, V.K. and K.Y.; methodology, V.K. and K.Y.; software, V.K. and K.Y.; validation, V.K. and K.Y.; formal analysis, V.K.; investigation, V.K.; resources, V.K. and K.Y.; writing--original draft preparation, V.K.; writing--review and editing, V.K. and K.Y.; visualization, V.K. and K.Y.; supervision, V.K. and K.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning.  
Not applicable.  
The author would like to thank the Ministry of Science, ICT & Future Planning under grant number NRF-2020R1F1A1048246.  
The authors declare no conflict of interest.
## 2 Literature Review
### Research Gap  
The scenarios in the related literature are summarized in Table 1. In general, the current fresh agricultural product industry has conducted research on carbon emission reduction decision-making and advertising, and reached relevant conclusions [21; 23; 35; 42]. However, the research on carbon emission reduction and low-carbon advertising in the fresh agricultural product industry is still in its infancy, and there is a lack of research that considers the impact of carbon emission reduction and advertising on the sales of fresh agricultural products at the same time. In addition, most existing research is based on a two-level chain, not a three-level chain [21]. There are a few significant contributions in this work that contrast with the preceding literature. In view of the fact that low-carbon production has become the trend of future development, this paper takes the three-level fresh agricultural product supply chain system composed of a manufacturer, a retailer and a third-party logistics service provider TPLSP as the research object. According to consumers' low-carbon preference and the national low-carbon emission reduction call, manufacturers adopt carbon emission reduction technology to launch low-carbon fresh agricultural products and study the impact of advertising on the sales of low-carbon fresh agricultural products after adopting carbon emission reduction technology, the supplier's carbon emission reduction decision, and the optimal advertising decision. This study contributes to the existing literature by analyzing the impact of carbon emission reduction and low-carbon advertising on the supply chain of fresh agricultural products.  
This paper proposed a three-level fresh agricultural product supply chain system model composed of manufacturers, retailers and third-party logistics service providers TPLSP as the research object. The innovations of the model proposed in this paper include the innovative introduction of low-carbon advertising into the supply chain of fresh agricultural products. It innovatively analyzes the promotion of low-carbon advertisements in the proposed model under three different advertising contract modes that can directly increase the income of participants in the supply chain of low-carbon fresh agricultural products.
## 2 Materials and Methods  
Si wafers with 500 \(\upmu\)m thickness were used as substrates. The deposition of Mo thin film was done in an AJA sputtering system, with a direct current (DC) power of 160 W supplied to the target. The sputtered Mo thin film had a bilayer structure similar to that described in Reference [6], i.e., a 100 nm thick, loosely packed bottom layer (sputtered with Ar flow rate at 30 sccm, and chamber pressure at 15 mTorr), and a 900 nm thick densely packed top layer (sputtered with Ar flow rate at 15 sccm, and chamber pressure at 1.5 mTorr), adding to a total thickness of 1 \(\upmu\)m. Annealing of as-sputtered Mo thin films was carried out in a micro-controlled MTI furnace. The quartz tube used to anneal the samples was pre-baked with an N\({}_{2}\) flow of 2 LPM at 400 \({}^{\circ}\)C for 1 h, to remove residual contamination and water vapour. The annealing temperature was varied from 700 \({}^{\circ}\)C to 1100 \({}^{\circ}\)C in 100 \({}^{\circ}\)C steps for 1 h, respectively, in an N\({}_{2}\) atmosphere. The up/down temperature ramping rate was maintained at 10 \({}^{\circ}\)C/min. In the following discussion, the as-sputtered Mo thin film is denoted as As-Mo, whilst annealed Mo thin films are labelled Mo-T\({}_{a}\), where T\({}_{a}\) is the annealing temperature in \({}^{\circ}\)C.  
Figure 1: Schematic diagram of (**a**) mesa-structured and (**b**) vertical structured Si quantum dot (QDs) solar cells (not to scale). Current flows are also indicated.  
The surface morphology and average roughness of as-sputtered and annealed Mo thin films were examined through a combination of: Atomic force microscopy (AFM) using a Bruker Dimension Icon SPM (Bruker, [PERSON], MA, USA), and field emission scanning electron microscopy (FESEM) using a FEI Nova NanoSEM 230 (Thermo Fisher Scientific, Hillsboro, OR, USA). The FESEM was also utilized to check the Mo/Si interface through cross-sectional imaging on the cleaved samples. The evolution of the crystalline structures of Mo thin films was investigated through X-ray diffraction using a PANalytical Xpert Materials Research diffractometer with a Cu K\(\alpha\) source. Finally, the change in electrical properties of Mo thin films, with respect to annealing temperature, which is correlated to the evolution of crystalline properties, was studied based on a 4-point-probe (4 PP) measurement using a Jandel Model RM3 setup.
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 4 Implementation and Performance Evaluation
### Analysis on Experimental Results
#### 4.3.2 The Average Communication Latency  
Referring to the proposed network architecture for communication cycles and transmission time slots in Figure 18, each node compresses physiological signals and schedules the transmission to the CH. Then, TDMA is utilized to schedule the data transmission from the CH to the inter-CH and from inter-CH to relay. The signal sending rate is related to the priority level, which implies the scheduling arrangement of the sending duration. Observe that the time slot durations of inter-CHs are allocated based on priority weightings. Notice that the clustering scheme coordinates the transmission order, which may reduce the delay of data collection. In the case of 15 s of monitoring, one CH will have 6350 bytes of data, and the transmission rate is 150 kbps. The distance between the inter-CH and the CH is 2 m, and the distance between the inter-CH and relay is 1 m. As shown in Figure 20, the longer the monitoring time, the more data will be generated, thus resulting in a longer delay.
## 2 Results and Discussion
### Volatile Composition in Wine Made of Dried and Fresh Goji Berry
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Died Goji Berry fermented Wine**} & \multicolumn{3}{c}{**Fresh Goji Berry fermented Wine**} \\ \cline{3-8}
**Volatile** & \multicolumn{1}{c}{**T-test between DW and FW**} & \multicolumn{3}{c}{**\(p\) Value**} & \multicolumn{3}{c}{**\(p\) Value**} \\ \cline{3-8}  & \multicolumn{1}{c}{**Pressing**} & \multicolumn{1}{c}{**Glycosidase**} & \multicolumn{1}{c}{**Interaction**} & \multicolumn{1}{c}{**Pressing**} & \multicolumn{1}{c}{**Glycosidase**} & \multicolumn{1}{c}{**Interaction**} \\ \hline _AldehydeKetones_ & & & & & & & \\ \hline Furifural & **39.11 *** & 0.103 & 0.59 & 0.369 & 0.733 & 0.782 & 0.801 \\ Decanal & 1.26 & 0.176 & **0.029 \({}^{*}\)** & 0.453 & 0.859 & 0.795 & 0.070 \\ Safranal & 3.85 *** & 0.111 & 0.728 & 0.810 & **0.000 *** & 0.176 & 0.270 \\ 2-Butyl-2-ocetcal & **1.25 *** & 0.649 & 0.761 & 0.963 & **0.811** & 0.598 & 0.639 \\ Isobutyl ketone & **16.21 ** & 0.682 & 0.088 & 0.126 & **0.003 *** & **0.003 \({}^{**}\)** & 0.378 \\ TMCHN & **0.00 \({}^{*}\)** & 0 & 0 & 0 & **0.002 \({}^{**}\)** & 0.463 & 0.463 \\ 6 MSHENE & *** & 0.102 & 0.821 & 0.745 & 0 & 0 & 0 \\ 2-Undecanone & **2.86 \({}^{**}\)** & **0.001 *** & 0.774 & 0.598 & **0.002 \({}^{**}\)** & 0.281 & 0.222 \\ 6-Cyclotrical & **1.46 *** & **0.003 \({}^{**}\)** & 0.977 & 0.855 & **0.000 \({}^{***}\)** & 0.836 & 0.055 \\ Dividropseudionone & 2.38 *** & 0.911 & 0.342 & 0.308 & **0.000 \({}^{***}\)** & 0.260 & 0.167 \\ 2-Acetylpyrrole & *** & 0.822 & 0.878 & 0.956 & 0 & 0 & 0 \\ TMHAAL & 5.22 *** & 0.916 & 0.426 & 0.619 & 0.851 & 0.396 & **0.003 \({}^{*}\)** \\ _Total_ & **16.03 *** & 0.065 & 0.191 & _0.182_ & _0.879_ & _0.812_ & 0.259 \\ \hline _Borzene_ & & & & & & & \\ Isodureureure & 3.86 *** & 0.125 & 0.555 & 0.568 & **0.001 \({}^{**}\)** & 0.221 & 0.820 \\ TEMLNE & 2.16 *** & 0.858 & 0.839 & 0.238 & **0.052** & 0.158 & 0.896 \\ DPYXL & 1.62 *** & 0.258 & 0.711 & 0.352 & **0.044 \({}^{*}\)** & 0.776 & 0.950 \\ Naphthalene & 2.20 *** & 0.193 & 0.063 & 0.154 & **0.137** & 0.416 & 0.439 \\ TMDHPE & 2.78 *** & 0.355 & 0.851 & 0.417 & **0.007 \({}^{**}\)** & 0.341 & 0.553 \\ M2 PB & 0.83 *** & **0.019 \({}^{**}\)** & 0.842 & **0.024 \({}^{**}\)** & 0.629 & 0.522 & 0.420 \\ Phenethyl acetate & 3.86 \({}^{**}\)** & 0.055 & 0.857 & 0.577 & **0.008 \({}^{**}\)** & 0.228 & 0.461 \\ \(\beta\)-Methylnaphthalene & 3.25 \({}^{**}\)** & 0.882 & 0.903 & 0.269 \({}^{**}\)** & 0.0583 & 0.619 \\ Benzyl alcohol & 0.75 & 0.884 & 0.969 & 0.840 & **0.027 \({}^{*}\)** & 0.778 & 0.926 \\ Ethyl dihydroincamatze & **0.41 *** & 0.677 & 0.600 & 0.821 & 0.477 & 0.223 & 0.498 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
# Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots
## 3 Results  
### Homogenization of High-Resolution Images  
Figure 2 shows the results of the homogenization of the infrared bands of the May and September images with respect to the July image. It is observed that the original histograms (a), (b) and (d) have different forms. After this operation, the distribution of the histograms of the homogenized images (c) and (e) is similar to that of reference (a). The procedure is applied similarly to the red (R) and green (G) bands.
###### Abstract  
Class III patients have classically been managed through monomaxillary techniques, mainly involving mandibular setback movements. More recently, according to the upper incisor to soft tissue plane (UI-STP) or Barcelona Line (BL) planning protocol, bimaxillary procedures for upper maxilla advancement and reduced mandibular setback have been recommended in order to secure better aesthetic outcomes and avoid upper airway constriction. The present study describes the jaw movements in the sagittal plane performed in class III patients subjected to bimaxillary surgery following the BL protocol. A retrospective evaluation was performed on 124 class III patients subjected to bimaxillary surgery. All subjects underwent upper maxilla advancement. A total of 112 patients received mandible advancement movement (90.3%), nine received mandibular setback (7.25%), and the mandible underwent no movement along the sagittal dimension in the three remaining patients (2.4%). Mandibular advancement was significantly the most frequent treatment option. The presented results suggest that when the BL planning protocol is used as an aesthetic and functional reference, class III occlusion appears mostly related to maxillary sagittal hypoplasia instead of mandible hyperplasia, so bimaxillary advancement surgery should be considered as one of the first-choice procedures for the treatment of these patients.  
angle class III malocclusion, dentofacial deformity; esthetics; mandible; obstructive sleep apnea; orthognathic surgery +
Footnote †: journal: Applied sciences
## 2 System Model
### Scenario 3: Repeated Transmission through a Successful Relay  
The third scenario, as shown in Figure 5, is similar to the second scenario, but we assumed that there were multiple candidates for a relay, and the candidates that successfully received the data acted as a relay. Since there can be multiple candidates that receive the data successfully, some resolution mechanism is required to make only one act as a relay.  
For example, one can use LBT and different delays before starting transmission in order to select only one successful relay, as shown in Figure 6. Suppose that there are \(K\) candidate relays. If a candidate relay fails to receive the packet from the source, it does not transmit. Upon successfully receiving the packet from the source, the \(k^{th}\)\((k=1,2,\cdots,K)\) candidate relay waits \(\Delta_{\text{min}}+(k-1)\Delta\) seconds and performs carrier sensing. It can start transmission if no other candidate relay is transmitting, meaning that candidate relays between 1 and \(k-1\) will fail to receive. Based on this kind of mechanism, one candidate relay (at most) can send the packet.  
There is some chance that any of the candidate relays may not succeed. If the probability that a candidate relay fails to receive from the source is \(P_{SR}^{Outage}\), then the probability that all \(K\) candidate relays fail is \((P_{SR}^{Outage})^{K}\). This value might be ignored when calculating the final outage probability, especially when \(K\) is large and \(P_{SR}^{Outage}\) is small.  
For simplicity, we assumed that one (and only one) relay can transmit for this scenario. The reception at a destination is successful if the source-to-destination transmission is successful or the relay-to-destination transmission is successful.  
Figure 4: Scenario 2.  
Figure 5: Scenario 3.
# Identical Parallel Machine Scheduling Considering Workload Smoothness Index
## 3 Problem Statement and Mathematical Model  
### Problem Statement  
There are \(n\) jobs with positive processing times \(p_{1},p_{2},\ldots,p_{n}\) to be processed on \(m\) identical parallel machines. All the jobs are released at time 0, and the objective is to minimize the workload smoothness index, i.e., minimizing \(SI=\sqrt{\sum_{i\in\mathcal{M}}(W_{\max}-W_{i})^{2}}\), where \(W_{\max}\) is the maximum workload of the machine and \(W_{i}\) is the workload of machine \(i\). The value of \(W_{i}\) is exactly the total processing time of the jobs assigned to machine \(i\) in any processing schedule. Adopting the classical three-notation method, we denote the considered problem as \(P_{m}||SI\).  
The problem under consideration is based on the following fundamental assumptions.  
1. Each machine can only process one job at a time;
2. The setup time of processing any job on each machine is negligible;
3. All the machines are available for processing jobs from time 0.  
Below, we first present basic parameters and decision variables and then establish one mathematical model for problem \(P_{m}||SI\). We further linearize the objective function and thus provide an equivalent linear programming model.  
1. Input Parameters  
\(\mathcal{M}\): set of machines indexed by \(i\), i.e., \(i\in\mathcal{M}=\{1,2,\ldots,m\}\);  
\(\mathcal{J}\): set of jobs indexed by \(j\), i.e., \(j\in\mathcal{J}=\{1,2,\ldots,n\}\);  
\(p_{j}\): the processing time of job \(j\in\mathcal{J}\);  
\(W_{\max}\): the given maximum workload limitation of machines.  
1. Decision Variables  
\(x_{ij}\): a binary variable equal to 1 if job \(j\in\mathcal{J}\) is processed on machine \(i\in\mathcal{M}\) and 0 otherwise;  
\(y_{ij^{\prime}}\): a binary variable equal to 1 if jobs \(j\in\mathcal{J}\) and \(j^{\prime}\in\mathcal{J}\backslash\{j\}\) are processed on machine \(i\in\mathcal{M}\) and 0 otherwise;  
\(W_{i}\): the workload of machine \(i\in\mathcal{M}\).
# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 7 Simulation Experiments and Analysis of Results
### Fault Tolerance  
In this part of the experiment, we will evaluate the fault tolerance of the system. Fault tolerance refers to the ability of the system to withstand interference and achieve consensus in the presence of malicious nodes. A system with higher fault tolerance will reach consensus more quickly. To test this, we introduced a certain number of malicious vehicle nodes that send misleading messages to disrupt the consensus process. We fixed the total number of nodes participating in the consensus at 60 and gradually adjusted the proportion of malicious nodes from 0%, 5%, 10%, to 30%. We will measure the time required for nodes to reach consensus under varying degrees of interference, using this as a benchmark to assess their fault tolerance. A smaller consensus time indicates a higher level of fault tolerance. The fault tolerance is as follows:  
\[FTA=\frac{1}{\overline{T_{F}}}, \tag{10}\]  
where \(\overline{T_{F}}\) is the mean consensus delay of the algorithm under different proportions of malicious nodes. A lower consensus delay in the presence of interference indicates a stronger fault tolerance. The experimental results are shown in Figures 11 and 12.  
Figure 10: Communication overhead.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Nodes** & **PBFT** & **GPBFT** & **CBFT** & **AE-PBFT** \\ \hline
10 & 174 & 145 & 42 & 21 \\
20 & 647 & 362 & 225 & 72 \\
30 & 1449 & 853 & 513 & 155 \\
40 & 2565 & 1373 & 908 & 271 \\
50 & 4053 & 2090 & 1523 & 417 \\
60 & 5458 & 2815 & 2116 & 595 \\
70 & 7180 & 3663 & 2923 & 808 \\
80 & 12,835 & 5291 & 4148 & 1043 \\
90 & 27,883 & 6535 & 5178 & 1320 \\
100 & 53,076 & 7801 & 6502 & 1622 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Communication overhead of different algorithms.  
Analysis of the results reveals that as the proportion of malicious nodes increases, the consensus delay for PBFT rises sharply, while the consensus delays for the other three algorithms remain relatively stable. Compared to the best-performing CBFT, AE-PBFT demonstrates a delay that is an order of magnitude lower, indicating superior performance. Additionally, we define fault tolerance as the inverse of the average delay for each algorithm under interference, suggesting that a smaller value indicates stronger fault tolerance in the system. The results in Table 9 clearly show that AE-PBFT exhibits significantly greater fault tolerance than the other algorithms. This indicates that AE-PBFT can function effectively in an environment subjected to malicious interference. The rapid iteration of credit points enhances the efficiency of node screening, and the adaptive mechanism allows for quick responses and adjustments to external conditions, making it more resilient in harsh environments.  
Figure 11: Consensus latency under malicious nodes.  
Figure 12: Fault-tolerance ability.
# GNSS Accuracy Under White Gaussian Noise Jamming +
## 1 Introduction
### Layout of This Paper  
This paper is organized in the following way. First, the theoretical background on which the prediction of the pseudo-range accuracy is based is given. This theoretical background includes the effect of a jammer on the \(C/N_{0}\) and the tracking error of the DLL. Next, the experimental setup is explained, after which the results are provided. Finally, the paper is drawn to a close in the Conclusions.
# Uncertainty of the 2D Resistivity Survey on the Subsurface Cavities
## 3 Results and Discussion
### Inversion Error  
The presence of noisy data points can create a large discrepancy between the inverted and the resistivity model, producing artefacts in the inverted resistivity models [58]. In this study, we used the statistical 95% confidence interval to identify those noisy data points. Figure 4 exhibits the model predicted versus synthetically measured apparent resistivity datasets for the target cavity T\({}_{2}\). About 5% of the data points situated on the upper and lower limit of the scatter plots were specified as outliers. The predicted resistivity was significantly overestimated for smaller values of measured resistivity data points that may be related to the background medium (host limestone unit). On the other hand, the predicted resistivity was considerably underestimated for the extreme values of measured resistivity, probably due to the smoothing of the resistivity contrast between the background medium and the cavity by the inversion method [59].  
Figure 3: The anomaly effect of the arrays with regard to the air-filled cavity survey depths.  
The inversion model with a smaller error usually provides a more accurate image; however, it does not necessarily mean that an inverted model with a smaller RMS error can give a realistic final model. This is because the inversion algorithm will sometimes tend to overfit the measured data for a larger number of iterations. For large resistivity contrasts, the inversion algorithm can progressively increase the inverted resistivity value from one iteration to the next without a considerable change in RMS error [60]. Consequently, a best-inverted model is often attained at a relatively low iteration number [61]. In cavity modeling, we used the model outputs with a maximum number of 4 iterations to reduce data overfit as further iterations did not improve the results. The result also depicted a considerable RMS error for inversion of high-contrast resistivity data. It is well-known that the surface resistivity method can acquire high-contrast resistivity data for shallower structures (e.g., cavity) than the deeper ones. As a result, the inversion of shallower cavity models exhibited higher RMS error than the deeper models (Figure 5), yet the inversion processes were stopped as long as the absolute data misfit value closes perturbation noise level (3%). Resistivity modeling may also reveal high model resolution, although the inversion provides large RMS error and vice versa. For example, the DD array showed a higher model resolution than the PP array; however, the DD array produced a larger RMS error than the PP array (Figure 5).  
Figure 4: The scatter plot of the model predicted and synthetically measured apparent resistivity data for the arrays: (**a**) dipole-dipole (DD) array, (**b**) pole-dipole (PD) array, (**c**) Wenner–Schlumberger (WS) array, and (**d**) pole-pole (PP) array. Overestimated and underestimated data outliers are displayed on the upper and lower boundaries.