esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This simulator integrates a vehicular ad hoc network (VANET) simulator, a V2X communication device, and a vehicle simulator (vehicle model, virtual road, neighboring vehicles, traffic signals, etc.). It can be used to simulate a virtual environment for EMS development and the evaluation of connected (P)HEVs. In this paper, we present an EMS for PHEVs using road information and V2X communication information and evaluate it using the IDHIL simulator. The proposed EMS consists of cooperative eco-driving (CED) and adaptive ECMs (A-ECMSs). CED utilizes road information (curvature, slope, distance, limit speed) and V2X communication information (traffic light, neighboring vehicles). The road information is simplified by dividing the entire road into several parts in advance. Using this information, speed and coasting timing are calculated and provided to the driver through visual guidance. CED guidance provides the driver with instructions to guide the target speed and pedal behavior (acceleration, deceleration, and coasting driving). The A-ECMS utilizes road information to approximate battery usage for the upcoming segment to determine the target SOC. This is reflected in the equivalent factor (EF) to calculate the optimal power distribution ratio. This paper is organized as follows. Section 2 describes the IDHIL simulator and its improvements and additions over previous work. Section 3 describes the EMS, including CED guidance and the A-ECMS. Section 4 shows the results of the IDHIL simulation test. Section 5 discusses the results and mentions future work.
# Independent System Calibration of Sentinel-1B
## 3 Internal Calibration
### Time Delay  
Sentinel-1 SAR data range compression is based on an ideal chirp instead of the real replica derived from internal calibration pulses, thus not reflecting the impact of the instrument on the imaging data. In order to consider at least the impact of the ADC uncertainty on the internal delay, the ideal chirp needs to be shifted in time accordingly. This internal delay offset is derived from the calibration pulses acquired with the same settings as the nominal imaging data. Only one value is calculated per data take and used to shift the ideal chirp. Therefore, it is very important that it remains stable over time and especially within the data take.  
The instrument internal delay has been analyzed for the same long DTs as in Section 3.1. Table 2 shows the results of two long StripMap DTs, each acquired with a different polarization on transmit (H and V). The drift between preamble and postamble for long DTs of about 25 min is 0.17 ns on average with a maximum of 0.22 ns for the SM6 H-pol data take acquired in May 2016. This confirms the high instrument stability in terms of internal delay within one single data take.  
Additionally, the internal delay behavior was evaluated on a daily basis during the Sentinel-1B commissioning phase for both co- and cross-polar channels. The obtained results are depicted in Figure 7 giving an assessment of the internal delay behavior over time. The calibration pulses measured both in the preamble (beginning of DT, top plot) and in the postamble (end of DT, bottom plot) have been evaluated, demonstrating the stability within a data take as well as over the CP. The jumps of the internal delay observed over time during the CP are caused by the reboot of the SAR Electronics Eub-system (SES) (end of May, middle and end of June, beginning of July), which includes the ADC in charge of the signal sampling.  
The different colors in Figure 7 indicate different acquisition modes; squares and circles correspond to the co-polar channel; + and x represent the cross-polar channel.  
The results observed during the CP show a very stable behavior of the instrument time delay within single data takes as well as over extended operation periods. This validates the Sentinel-1 approach, which considers one single delay value derived per data take for shifting the ideal chirp used for SAR data range compression.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Data Take** & **Swath** & **PREAMBLE** & **POSTAMBLE** & \(\boldsymbol{\Delta}\) **Time Delay** \\ \hline \multirow{2}{*}{SM DH} & SM6 & 433.91 ns & 434.11 ns & 0.2 ns \\  & 100 MHz & 433.70 ns & 433.81 ns & 0.11 ns \\ \hline \multirow{2}{*}{SM DV} & SM6 & 433.46 ns & 433.68 ns & 0.22 ns \\  & 100 MHz & 433.51 ns & 433.65 ns & 0.14 ns \\ \hline \multirow{2}{*}{Mean Value} & 433.64 ns & 433.81 ns & 0.17 ns \\  & Standard Deviation & 0.18 ns & 0.18 ns & 0.04 ns \\ \hline \end{tabular}
\end{table}
Table 2: Sentinel-1B instrument internal delay for long DTs acquired with different polarizations and acquisition modes.
## 4 Motion Planning Algorithm Based on Co-Optimization of Communication and Sensing  
If the exact position and velocity of the target are known, optimal observer trajectories can be accurately designed. However, the target state is not known in advance, so the trajectory generator must continually re-plan the trajectory based on the results of the target state estimation. Hence, the multiple UAV cooperative target tracking problem is a real-time dynamic optimization problem. We use the combined communication and sensing utility function to plan optimal trajectories for the UAVs in the framework of receding horizon optimization.
## 2 Literature Review
### The Studies of Public Opinion towards CAVs
#### 2.1.3 Willingness to Use or Pay  
As an important indicator of acceptance, a drivers' intended use of CAVs has attracted certain scholar attention. To date, most studies have relied on questionnaire surveys to provide important insights for the people's willingness to buy or use CAVs (see the detailed summary by refs. [39; 40]). In terms of demographic characteristics, the main findings show that the willingness to use CAVs could be affected by age, gender, income, and education factors [41; 42; 43]. In terms of the psychological characteristics, trust and perceived benefit are positive predictors, and perceived risk and perceived dread are negative predictors [44; 45]. In addition, other factors such as economic factors [46; 47; 48], such as the price of CAVs [49; 50], also play an important role. To provide the CAV experience through interactions with a system, [PERSON] et al. (2018) [51] investigated the psychosocial factors associated with the intended use of CAVs based on a simulator study. Their results showed that the attitude toward the behavior, subjective norms, and perceived behavioral control are significant predictors of intentions to use CAVs. A similar result was also found by [PERSON] et al. (2022) [52].  
In brief, the current studies have extensively investigated the trust, attitude, and willingness to use/buy CAVs, and have made lots of reliable conclusions. However, the problems of the willingness to share roads with CAVs and confidence or risk perception when interacting with CAVs have not been given enough attention. On this basis, in-depth research should be conducted to evaluate public opinion towards the various scenarios in mixed traffic with CAVs.
## 1 Introduction
The channel hole acts as a substrate of a 2D planar flash cell, and forming channel holes is the most critical process in determining not only various characteristics (such as reliability, performance, and quality) but also the manufacturing yield of 3D NAND flash memory. For example, if we fail to form the uniform channel hole, flash memory will not work correctly as a memory device or will die (i.e., it will be a bad or failed chip) within a short time. However, since forming channel holes requires a highly complex process, it is a great challenge to achieve a uniform channel hole profile in trillions of channel holes within a 3D VNAND flash chip. Furthermore, as the number of vertically stacked layers increases (i.e., the vertical height of 3D flash memory becomes higher to increase flash capacity), forming the channel hole becomes more complex and challenging due to its high aspect ratio [23]. Hence, in a high-capacity 3D flash chip, there can be a large number of new failures resulting from various abnormal profiles in channel holes during NAND flash memory manufacturing (we collectively call these failures _channel hole defects_). In this paper, we propose novel test approaches to efficiently screen out new failures in 3D NAND flash memory, focusing on different channel hole defects resulting from abnormalities in the channel hole profile. We classified the channel hole defects into four types depending on the abnormal shape in the channel hole profile: not-open, bowing, tilting, and bending. For each channel hole defect, we derived failure mechanisms based on electrical and physical analysis. In addition, we attempted to electrically screen out the channel hole defects by designing test methodologies (e.g., inventing new test patterns or setting efficient stress conditions). To validate the effectiveness of our proposed techniques, we conducted comprehensive experiments using 320 real 3D flash chips. Experimental results show that our new techniques can efficiently detect and screen out channel hole defects in 3D NAND flash memory without unexpected side effects such as flash reliability or performance degradation. The rest of this paper is organized as follows. Before our techniques are presented, we review the organizational basics of 3D NAND flash memory and the manufacturing process in Section 2. In Section 3, we report our key findings for each channel hole abnormality. Wedescribe the proposed screening methodologies in Section 4. Experimental results follow in Section 5, and Section 6 concludes with a summary and future work.
# The Response of Vegetation Dynamics to Climate in Xinjiang from 1991 to 2018
###### Abstract  
Vegetation change is one of the most prominent features of terrestrial ecosystems responding to climate change. Further exploration of vegetation characteristics in this context is essential for accurately understanding and predicting ecosystem processes. Xinjiang, an arid region, is highly sensitive to slight climate changes, which can significantly affect vegetation dynamics. Therefore, determining the relationship between climate and vegetation is of paramount importance. Based on this, this study focused on Xinjiang, selecting remote sensing data (including NDVI, LAI, and GPP) as evaluating indices, and the spatiotemporal characteristics of vegetation response to climate from 1991 to 2018 were analyzed using synchronized meteorological data, examining the relationship between vegetation and climate. The results indicated that NDVI, LAI, and GPP all increased during the period, with _slopes_ of 0.52, 0.14 m\({}^{2}\)/m\({}^{2}\), and 1.19 g C m\({}^{-2}\) yr\({}^{-1}\), showing significant spatial heterogeneity in distribution. The net vegetation area increased by more than 20,000 km\({}^{2}\), with cropland experiencing the largest increase. Vegetation in northern Xinjiang showed a more significant positive response to increased precipitation and temperature, while vegetation in southern Xinjiang responded more complexly and exhibited negative correlations with climatic factors. The results emphasized the varied responses of vegetation to climate variables, with temperature having a more complex effect on vegetation change, while precipitation showed more distinct differences between the various vegetation indices. These findings provide important insights into the ecological sustainability of Xinjiang under warming and humidification.  
normalized difference vegetation index; leaf area index; gross primary productivity; climate change; Xinjiang 2024 152065
## 3 Analysis of the Results
### Performance on the Support Schemes in the Tunnel Cross Section
Figure 10 shows the maximum horizontal displacement at the boundary on the left and the right wall. Under the unsupported condition, the horizontal displacements of the left and the right wall were 21.88 cm and 24.54 cm, respectively. Under the radial and the non-radial support conditions, the left wall horizontal displacements decreased to 14.63 cm and 15.73 cm, respectively, and the right wall horizontal displacements decreased to 15.80 cm and 17.13 cm, respectively. The results describe that the displacement of the roadway was  
Figure 8: Horizontal stress distributions of the rock mass around the tunnel of the support face: (a) unsupported, (b) radial support, and (c) non-radial support. Figure 7: Vertical stress distributions of the rock mass around the tunnel of support face: (a) unsupported, (b) radial support, and (c) non\(-\)radial support. effectively reduced by the support installation. The radial support was obviously better than the non-radial support. The failure zone is usually analyzed in tunnel stability studies because it directly characterizes the behavior of the surrounding rock. Figure 11 presents the failure zone distributions of the rock mass around the roadway. ImageJ image processing software is used to measure the area of the failure zone. The failure zone areas under unsupported, radial, and non-radial support conditions were 139.96 m\({}^{2}\), 104.72 m\({}^{2}\), and 113.45 m\({}^{2}\) respectively. The radial and the non-radial support reduced the failure zone areas by 25.18% and 18.94%, respectively, which demonstrates that the existence of the support was beneficial to the stability of the surrounding rock, and the radial support had a greater influence on stabilizing the surrounding rock than the non-radial support. The blot force is given in Figure 12. Studying the bolt force could be used to evaluate the possibility of the local failure of the bolts. The plots show the maximum bolt forces of the radial and the non-radial support were 3.41 MN and 3.14 MN, respectively, which demonstrates that the blot failure possibility of the radial support was slightly higher than that of the non-radial support. Moreover, note that the maximum bolt force was a slight distance away from the bottom of the bolt, where the stress concentration took place. Figure 10: Horizontal displacement distributions of the surrounding rock of the support face: (a) unsupported, (b) radial support, and (c) non-radial support. Figure 9: Vertical displacement distributions of the surrounding rock of the support face: (a) unsupported, (b) radial support, and (c) non\(-\)radial support.
## 3 Path-following Control Based on Improved LOS  
### Traditional LOS  
The LOS method is designed to steer a controlled object toward a specific point on a path and then continue to move toward the next point while following it. This method is widely applied in path following and precision guidance. In the traditional LOS method, a fixed acceptance circle is used to efficiently follow the reference path. However, when the reference path segment changes, the fixed acceptance circle can hinder accurate following and slow down convergence speed. The following strategy of the LOS method is depicted in Figure 4:  
Figure 3: Schematic diagram of 3D path following.  
In Figure 4, \(P_{i-1}P_{i}\) is the current following path segment; \(V\) is the flight speed; \(P_{LOS}\) is the position of the reference point of LOS; \(P\) is the position of the robot; \(P^{\prime}\) is the projected position of the robot on the path segment;\(\Delta\) is the forward-looking distance; \(R_{0,i}\) is the radius of the acceptance circle of the trajectory point; and \(\gamma_{LOS}\) and \(\chi_{LOS}\) are the demand heading angle and the demand trajectory inclination, respectively.\(P=(P_{1},P_{2},\ldots,P_{i},\ldots P_{n})\) is a set of planning path reference points, where each path point has a serial number in the lower right corner, and the points are connected by straight lines.  
On the path segment \(P_{i-1}P_{i}\), the position of the reference point, \(P_{LOS}\), can be found using the following relationship:  
\[\left\{\begin{array}{l}\left(x_{\text{LOS}}-x_{p}\right)^{2}+\left(y_{\text{ LOS}}-y_{p}\right)^{2}+\left(h_{\text{LOS}}-z_{p}\right)^{2}=\Delta^{2}\\ \frac{x_{\text{LOS}}-x_{i-1}}{x_{i}-x_{i-1}}=\frac{y_{\text{LOS}}-y_{i-1}}{y_ {i}-y_{i-1}}=\frac{h_{\text{LOS}}-h_{i-1}}{h_{i-1}-h_{i-1}}\end{array}\right. \tag{2}\]  
Switch the reference path segment from \(P_{i-1}P_{i}\) to \(P_{i}P_{i+1}\) as the robot converges to path point \(P_{i}\). This should be done when the distance between the robot and path point \(P_{i}\) satisfies the following condition:  
\[\left|P\right.-P_{i}\right|\leqslant R_{0,i} \tag{3}\]  
The robot switches to following the next segment of the path as the reference path segment changes. To achieve the fast following of the reference path, the traditional LOS method employs a fixed acceptance circle. However, if the reference path segment changes, the fixed acceptance circle will affect the convergence speed and the following error for different path angles and flight speeds.
## 2 Materials and Methods
### GC-MS Data Preprocessing  
The raw GC/MS data obtained from the analysis were converted into mzML format using the ProteoWizard 3.0.21260 64-bit program. This format conversion allowed for further processing and analysis of the data. The mzML data were then imported into the XCMS Online platform (Version 3.7.1), which is the online version of the XCMS software.  
In XCMS Online, various data processing steps were performed. This included feature detection, where all the detectable characteristics in the data were identified. Additionally, retention time correction, statistical analysis, alignment, and normalization were conducted to ensure data quality and consistency. The processed data were then visualized for analysis purposes. The output of this process was a table in Excel format. The table contained information on all the detected characteristics, including their mass, retention time, \(p\)-value, and other relevant data. Any missing values in the table were corrected using the MetaboAnalyst 5.0 software.  
Subsequently, the corrected table was imported into the SIMCA program (Sartorius Stedim Biotech GmbH, Gottingen, Germany) for further analysis and interpretation of the data. SIMCA is a multivariate analysis software commonly used for analyzing complex data sets in various fields, including metabolomics. In summary, the GC/MS data were converted into mzML format, processed, and analyzed using XCMS Online, corrected for missing values with MetaboAnalyst 5.0, and further analyzed in the SIMCA program.
## 3 Results and Discussion
### Theoretical Assay
#### 3.7.3 Molecular Dynamic (MD) Profile  
\"MD\" simulation was performed to provide a better understanding of the \"Inhibitor-Fe\" interaction (Figure 11). The \"Adsorption Locator Model\" was utilized to recognize the ideal adsorption site for the mild steel surface against AO and HERA inhibitors. Thus, the lowest inhibition energy is listed in Table 8 as follows: total energy for the substrate, rigid adsorption energy (unrelaxed AO and HERA components which adsorbed on Fe metal), adsorption energy (rigid adsorption and deformation energies), and (\(\mathrm{d_{Ead}/d_{Ni}}\)) (Fe and AO and HERA inhibitors energy; where one of the inhibitors was removed), binding energy (the negative value of adsorption energy).  
Figure 11: _Cont._In the stable configuration (Figure 11), palmitoic, linolenic, and stearic acids were stabilized in parallel to the mild steel surface (1 0 0) plane, while myristic and palmitic acids arranged in plane to the mild steel (1 0 0). In addition, the adsorption energy arranged as **Linoleic >****Stearic >****Palmitic**>****Palmitic**>******Myristic** acids (Table 8). This theoretical vision is in line with the experimental findings. The located electron lone pair over O atoms of AO and HERA inhibitors caused a high stability for coordination interaction (inhibitor \(\rightarrow\) Fe). From Figure 11, we can observe that AO and HERA inhibitors are able to adsorb into the mild-steel by the carboxyl groups of AO and HERA inhibitors. Thus, this will cause a mild steel surface to not interact with the acid solution.
## 4 Description of the Proposed Transport System  
Figure 2 illustrates the proposed EFBNs architecture to transport the multiple eCPRI traffic streams simultaneously between the RRHs and the BBU pool. A traffic stream is defined as a continuous bit rate transmission of the IQ packets at different eCPRI line rates that reserved the MFH bandwidth throughout the communications. However, with the improvement of bandwidth utilization in Ethernet-bridged networks, the E2E latencies for all the RRHs could not be minimized within the eCPRI limits [18]. This is because under high-load conditions and due to the rigorous requirements of the eCPRI, a few RRHs streams would experience the intolerable E2E latencies. Therefore, an optimized path selection algorithm with a low-latency packet forwarding procedure was highly desirable to retain the QoS parameters of EFBNs within the limits, and thus is proposed in this study.
## 4 Challenges and Considerations
### Product Quality  
While metal AM offers great opportunities at the design stage, it lacks precision due to variations in mechanical properties [123], distorted geometry of the part [124]. Some researchers have found that metal AM products may have anisotropy and heterogeneity in microstructure and mechanical properties [123]. Indeed, the material properties do not remain the same after production; and material behaviour changes under cyclic thermal  
Figure 11: AM standardisation activities.  
Figure 12: Current and under development metal AM 40 identified standards.  
loading conditions [125]. [PERSON] et al. [123] reviewed the mechanical properties of several metal AM products and believed that post processing operations such as heat treatment may provide the required mechanical properties. Clearly, inspections are required after production to identify defects such as voids, porosity, and undesirable grain characteristics. Another problem is the deviation of the printed part from its actual geometry and size. The precision and accuracy of metal AM technologies have been seriously restricted by geometric deviations and tolerances [126]. Besides these problems, distortions may be induced from residual stresses; and also the accuracy of the part can be affected by the slicing software causing the staircase effect [127]. Furthermore, transferring 3D CAD models into a 3D-printer format such as StereoLithogArphy (STL) can result in some unexpected inaccuracies and defects, particularly in inclined and curved profiles [128]. Moreover, the variations in series of a specific printer and quality of calibration and setup may substantially change the product quality.  
The above-mentioned problems cause challenges at the metrology and quality control stage. Quality is \"the use of a standard against which other things of similar kind are compared\" [129]. As such, the quality control is required to compare a company's metal AM products against the benchmark criteria. This is a key step in AM's development and commercialization pathways, to operating versions to show that a metal AM component has met required criteria. The challenge is the lack of widely accepted quality control methods, measurement techniques, and the need of greater data processing capabilities for organic and freeform external geometries fabricated with metal AM techniques. The fact is that the current measurement systems defined in the ISO standard did not consider complex freeform geometries. This becomes more challenging for internal shapes such as conformal cooling channels [130] and lattice structures [131], and it requires advanced imaging techniques such as computed tomography [132, 133]. Furthermore, designers face difficulties to define \"tolerance zone for these geometries [115]. However, some research has been done on geometric communicating techniques for complex and freeform profiles [134]. Clearly, to ensure more extensive adoption of metal AM in industry applications, the product quality and metrology techniques should be considered at the early design stage.