esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Method Validation
#### 3.2.1 Linearity, Sensitivity and Matrix Effect  
The linear regression coefficients (R\({}^{2}\)) were higher than 0.99 for all the studied NCAs, indicating good linearity of the calibration curves [19; 21]. LODs and LOQs for the four NCAs in food simulant D1 are given in Table 3. LODs ranged from 1.1 ng mL\({}^{-1}\) for FCM no. 752 to 4.9 ng mL\({}^{-1}\) for FCM no. 674, while LOQs ranged from 3.6 ng mL\({}^{-1}\) to 16.2 ng mL\({}^{-1}\), respectively.  
Regarding the ME, results ranged from \(-2.1\%\) to \(1.2\%\), a non-relevant variation that indicates that no signal suppression or enhancement affects the analysis for quantifying the target NCAs in food simulants D1 [19; 21].  
[PERSON] et al. reported two analytical methods for analyzing NCAs, namely (1,3: The reported LODs and LOQs for the LC-MS were in line with the ones of the present study [2]. In another work, where derivatization and GC-MS analysis were used, reported LODs and LOQs were higher than the ones of this work (0.05 to 0.10 mg L\({}^{-1}\) and LOQs of 0.17 up to 0.33 mg L\({}^{-1}\), respectively) [4]. Additionally, the necessary derivatization steps for GC analysis translates into additional sample preparation steps and increased total analysis time.
## 5 Conclusions  
The use of contrast agents aims to produce higher quality CT images and improve the delineation of target volumes and OARs. We have evaluated the effect of contrast agents on the CT images used for radiotherapy treatment planning. Although this effect is noticeable and the associated changes in HU values may be statistically significant in many cases, these changes are clinically insignificant in terms of dose calculation. About 6% of all plans failed 3D gamma-evaluation using a tight 2% and 2 mm criteria mainly due to registration inaccuracies. Thus, we conclude that CCT could be acquired for VMAT radiotherapy planning purposes instead of NCCT to improve targets and OAR's delineation without compromising dose calculation accuracy.  
Conceptualization, A.A.E., S.A., M.A., R.A.M. and H.A. ([PERSON]); methodology, A.A.E., S.A. and M.A.; formal analysis, A.A.E. and S.A.; validation, S.A., R.A. and H.A. ([PERSON]); data curation, S.A., R.A. and H.A. ([PERSON]); writing--original draft preparation, A.A.E.; writing--review and editing, M.A.; supervision, A.N.; funding acquisition, A.A.E. and S.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Research Center at King Fahad Medical City, grant number 018-070.  
The study was approved by the Institutional Review Board of King Fahad Medical City, IRB log no. 18-350.  
Not applicable.  
The authors would like to thank [PERSON] for her contribution in data collection.  
The authors declare no conflict of interest.  
Figure 7: Reduced gamma pass rates in some patients’ plans are the result of patients geometrical differences between NCCT and CCT scans. Subset A shows the difference in geometry due to penile movement, (**A1**) NCCT, and (**A2**) CCT. Subset B shows respiratory induced oral cavity movement between NCCT (**B1**) and CCT (**B2**).
# Optimal Transport in Multilayer Networks for Traffic Flow Optimization
## 2 Materials and Methods
### Multilayer Transportation Networks
This auxiliary structure allows the model to allocate in an optimal way the passengers along the inter-super edges when they enter from a station with connections to more than one layer, thereby avoiding the selection of arbitrary entrances a priori. This becomes relevant in applications where the cost to travel along inter-layer edges is non trivial, for instance, in situations where changing connection impacts the comfort of the passengers. Moreover, the introduction of super nodes and edges facilitates how we represent the multilayer network. In fact, by adding these auxiliary super nodes and inter-super edges, we only need to consider an individual network adjacency matrix \(A\), instead of two separate tensors. This matrix has entries \(A_{uv}=1\) if an edge exists between nodes \(u\) and \(v\) and 0 otherwise, where a node \(u\) can be a node \(u_{\alpha}\) in layer \(\alpha\) or a super node \(u\). The set of nodes is then \(\mathcal{V}=\mathcal{V}_{0}\cup\mathcal{V}_{super}\), where \(\mathcal{V}_{super}\) is the set of super nodes, and \(|\mathcal{V}_{super}|=N_{super}\) is their number, which corresponds to the number of nodes that belong to more than one layer. Similarly, the new set of edges is \(\mathcal{E}=(\cup_{\alpha}\mathcal{E}_{\alpha})\cup\mathcal{E}_{super}\), where \(\mathcal{E}_{super}\) is the set of inter-super edges. The final numbers of nodes and edges are \(N=|\mathcal{V}|=N_{0}+N_{super}\) and \(E=|\mathcal{E}|\geq E_{0}\). Notice that this construction is equivalent to assume that the network has \(L+1\) layers, where the extra layer is made of inter-super edges \(\mathcal{E}_{super}\) and all nodes incident to them (without loss of generality, we assume that all the inter-super edges are treated equally). We denote it as the _super_ layer and this corresponds to \(\alpha=L+1\), so that \(\mathcal{E}_{L+1}\equiv\mathcal{E}_{super}\). We show an example of this structure in Figure 1.  
Finally, we consider a coupling between layers as in [42] that controls how the layers are linked. Specifically, we multiply the lengths of each edge by a factor \(w_{\alpha}\in[0,1]\) that depends on what layer the edge belongs to. For convenience, we introduce \(q_{e}\equiv q_{e}(\alpha)\) taking values \(q_{e}=\alpha\) for each \(e\in\mathcal{E}_{\alpha}\) and with \(\alpha=1,\ldots,L+1\). Using this, we define the resulting length as \(\ell_{e}:=w_{q_{e}}l_{e}\). This ensures that edges in different layers can be navigated differently. If we interpret \(w_{\alpha}\) as the inverse of a velocity, then \(\ell_{e}\) is proportional to the time needed to travel along edge \(e\), which can be seen as an \"effective\" length. When \(w_{\alpha}<1\) and \(w_{\gamma}=1\), a passenger takes less time to travel along an edge of length \(l_{e}\) in \(\alpha\) than one in \(\gamma\). Typically, \(\ell_{e}\) are small for inter-super edges. Nevertheless, one can tune the cost to travel along them by tuning \(w_{L+1}\).
## 5 Digital Twin AR in Chemical Industry
### Implementation Aspects  
Adopting a systematic approach that considers practical, technical, and cultural aspects is essential to overcome the limitations of adopting augmented reality (AR) in the chemical industry and ensure that it is implemented effectively. Some strategies to consider follow:  
1. Start with small-scale pilot projects to demonstrate the value of AR, collect feedback, and refine applications before expanding them. In the context of AR, starting with small-scale projects allows the company to validate the applicability of the technology in its specific environment without the financial and operational risks of full implementation from the beginning. In addition, by starting with a scaled-down version,it is possible to collect genuine feedback from employees and stakeholders, identify potential improvements in the developed solutions, and adjust the functionalities to better meet the needs of the team and operational challenges.
2. Partner with AR technology providers and experts in the chemical field to create tailored solutions that address specific challenges and regulatory requirements. Collaboration with experts in the chemical industry is essential for AR solutions to be appropriate and effective. Working with experts makes it possible to develop tailored solutions that meet these specific requirements and ensures that AR implementation is aligned with industry regulations.
3. Focus on developing AR solutions that integrate seamlessly with existing digital systems and processes to minimize disruption. Integrating AR into existing digital systems and processes prevents the implementation of the technology from causing operational disruption. Like any other industrial sector, the chemical industry already has well-established processes and digital systems that manage data, operations, and production logistics. The introduction of AR should be done in a way that does not interfere with these processes but rather complements and optimizes existing operations. The transition to AR use should be smooth, with an implementation process that minimizes the impact on day-to-day operations and ensures that employees can continue their activities without interruption.
4. Implement training initiatives that emphasize the benefits of AR and ensure that employees feel confident in using the technology. One of the main barriers to adopting new technologies is employee resistance. To overcome this resistance, it is essential to implement training programs that clearly explain the benefits of AR and show employees how it can improve their daily work. Additionally, employees will feel more confident about adopting the technology by promoting an understanding of the tangible benefits of AR, such as increased safety, reduced errors, and increased efficiency.
# Planning of High-Power Charging Stations for Electric Vehicles: A Review
## 2 Target Identification
### Candidate Sites  
Most of the investigated articles pre-select candidate sites that can be suitable for hosting charging stations. A large share of authors individuate traffic network nodes, such as road intersections or major transport hubs, as recommended options, wherein the allocation optimization or algorithm can evaluate the best set of sites [23; 40; 42; 53; 54]. Electric networks can also be considered. For example, in [22], the traffic system is superimposed on the the electrical network topology, and intersections were used as possible FCS locations. Models that include a power flow analysis of the electric network usually site a load representing the charging station directly at low voltage buses ([82]). Other approaches are more complex than the ones described, as they involve spatially analysing the charging demand [21] or dwelling times [46; 55]. For example, [56] applies a trajectory reduction procedure in order to reduce the traffic network to a set of candidate location points and finally weights the locations based on the mobility behavior of EV drivers. In [106], the authors use heat equations through a finite element analysis to produce heat maps for the cities of Boston and Milan. These analyses include parameters such as population density, routes, and electricity consumption. These approaches aim to individuate hot spots, which are places where it is more effective to locate a charging station (CS). In [48], the authors apply a clustering method, filtering only commercial urban zones and using origin charging demands points to create a set of contiguous clusters. Once these areas are obtained, the candidate locations are the centroids of the clusters. Instead, the authors of [28] do not indicate a specific location where to install a parking lot, but produce the optimal zones (cluster of buses) or the specific buses that correspond to which sitting should be implemented. In [38], the authors point out that commercial retailers can be good candidates for siting FCSs, mainly because they likely have both the physical space and electrical infrastructure to install them. This thesis is also confirmed in [107]. CS candidate sites in literature usually include different points of interests (shopping centers, supermarkets, etc.) and public parking lots [40]. Finally, urban major transport hubs, bus depots, terminals, or bus stations are used as candidate sites in [32; 42; 44; 57].
## 3 System Model
#### User Allocation by SINR Ranking  
In our research, the system model is based on [43], where we use an SINR-based user classification method to perform the allocation of macrocells or radio resources named UAV-BS. Users are assigned the best available radio resources according to their SINR, taking into account the available backhaul capacity during the association process. If the \(SINRi,j\) of a user \(i\) is above a certain threshold and the macrocell or UAV-BS \(j\) meets the following requirements, the user is assigned the radio resource in that time interval: (1) the radio resources have enough resource blocks (RBs) to meet the user's throughput requirements; (2) the sum of the radio resources' backhaul throughputs does not exceed their capacity in this action. If the user or radio resource does not meet some of these requirements, the next macrocell or UAV-BS is attempted based on the highest SINR. If a user still cannot be assigned a macrocell or UAV-BS after attempting all radio resources, the user is considered as outage (out of service coverage) for that time interval. A resource block is the smallest unit of resources that can be assigned to a user. The required resource blocks are calculated based on the required throughput of user i and the \(SINRi,j\), which is related to the macrocell or UAV-BS \(j\).
## 3 Results and Discussion
### Fracture Surface Morphology
During cyclic loading, the dislocation and shearing between mineral grains within the rock intensify, leading to the rapid development of the original secondary cracks, which eventually merge with the main crack [69; 70]. As a result, there is a notable increase in the number and depth of cracks on the fracture surface compared to those in the MTB tests. Meanwhile, the alternating stress from cyclic loading intensifies the extrusion and friction between mineral grains on either side of the intergranular crack [71]. This effect  
Figure 11: Photos of SCB samples under different upper load limits and increased temperature settings. Figure 12: Crack path of SCB samples under 85% and 90% upper load limits. separates certain mineral grains from the rock matrix, causing grain decohesion and their scattering on the fracture surface, as illustrated in Figure 13b,d. With an increase in the number of loading cycles, the number of scattered grains and crystal fracture zones also increases, triggering significant changes in the topography of the granite fracture surface. This research demonstrates that granite failure under cyclic loading occurs due to the coalescence of numerous microcracks and grain decohesion rather than the growth of a single macrocrack. Based on the fracture morphology, the presence of numerous microcracks and crystal fracture zones suggests a transition towards ductile fracture. However, the mechanical test results still demonstrate a predominantly abrupt fracture trend, indicating that the granite is currently undergoing a brittle-to-ductile transformation stage [22; 72]. [PERSON] et al. 's experimental results demonstrated that as the temperature increases up to 500 \({}^{\circ}\)C, the fracture behavior of granite transitions towards ductile fracture [73]. Figure 13: _Cont._  
Interestingly, when the upper load limit is set at 85% at 400 \({}^{\circ}\)C, the significant reduction in the required number of cycles results in fewer exfoliated particles, ultimately leading to minimal disparity in the microstructures of the fracture surfaces after MTB and CTB tests. It should be noted that during cyclic loading, the heterogeneity of the granite microstructure causes a localized deformation and slip surfaces, introducing new shell-like cracks similar to those observed by [PERSON] et al. [74] and [PERSON] et al. [75]. At the microscopic level, these findings demonstrate the significant impact of high temperature and cyclic loading on the damage to the granite microstructure.
# A Framework for Mobile-Assisted Formative Assessment to Promote Students' Self-Determination
## 1 Introduction
### Self-Determination Theory of Motivation  
Our proposed framework is based on the Self-Determination Theory (SDT) of motivation [14]. Motivation is the process that initiates and sustains human behavior and it is a fundamental issue in learning and in online learning environments as well [25]. Self-Determination Theory of motivation distinguishes between two basic types of motivation: intrinsic and extrinsic motivation [26]. Intrinsic motivation refers to doing something because it is interesting and enjoyable, while extrinsic motivation refers to doing something due to an externally imposed control, e.g., a reward or punishment. SDT argues that intrinsic motivation is sustained when the three basic and universal human psychological needs of autonomy, competency, and relatedness are satisfied. Autonomy refers to the desire of people to regulate and self-control their own behavior. Competence refers to the desire of being effective and sufficient when performing an activity. Relatedness refers to the desire of people to feel connected and associated with others. When students' perceptions of autonomy, competence, and relatedness are high enough, students feel self-determined and autonomously motivated [27]. Autonomous motivation in turn, in contrast to controlled motivation, fosters positive change [28] and leads to better learning [29]. The Self-Determination Theory of motivation has been already successfully applied in online [30; 31; 32] and mobile [33] learning. Moreover, since assessment for learning has the potential to motivate learners [2], SDT can be an appropriate foundation for our proposed framework.
# Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data
## 1 Introduction
Previous studies have shown that the large fluctuations of WTI (West Texas Intermediate) crude oil market have great instability in both the short and long term, that is, the crude oil market is ineffective in the weak form [20], while the addition of macroeconomic uncertainties can improve the accuracy of oil price volatility assessment. The realized volatility model was to extract forecast information from historical volatility or prices [21]. Macro variables were not included in this model, and previous and current oil price fluctuations could not reflect and contain all available information [20]. The form of mixed data contained in the influencing factor data needs to be emphasized and studied in risk modeling. Due to many factors that affect crude oil price fluctuations, the complicated time series data and the various data sampling frequencies, as well as the factors involved in the data of supply and demand fundamentals, the macroeconomy, and the financial market, will change because of varying efficiency and difficulty of statistical work. Research must consider the form of mixed data [20]. However, the current research mostly uses the same frequency data modeling, which is a method of adding up or replacing. The high-frequency data are processed as low-frequency data, which causes the loss of sample information in the high-frequency data, and will artificially reduce the fluctuation of high-frequency financial data to a certain extent, and cannot meet the timeliness requirements. The high-frequency data are obtained by interpolating low-frequency data using mathematical methods that often lack economic theoretical support and cannot ensure accuracy. At the same time, in the current mixed modeling process, the statistical properties of the financial data, such as the peak and thick tail characteristics, are often been ignored, which may lead to the use of inappropriate models to analyze financial phenomena. Therefore, in the context of the complex characteristics of macroeconomic and financial market data, different data characteristics of different types of influencing factors need to be paid full attention in the research. This article will use the GARCH-MIDAS method,considering the form of mixed data contained in crude oil futures data and the peak and thick tail characteristics of returns, to construct a mixed measurement model and to explore the influence of various factors with different data frequencies on crude oil futures price volatility.
###### Abstract  
This paper presents high definition (HD) map-based localization using advanced driver assistance system (ADAS) environment sensors for application to automated driving vehicles. A variety of autonomous driving technologies are being developed using expensive and high-performance sensors, but limitations exist due to several practical issues. In respect of the application of autonomous driving cars in the near future, it is necessary to ensure autonomous driving performance by effectively utilizing sensors that are already installed for ADAS purposes. Additionally, the most common localization algorithm, which is usually used lane information only, has a highly unstable disadvantage in the absence of that information. Therefore, it is essential to ensure localization performance with other road features such as guardrails when there are no lane markings. In this study, we would like to propose a localization algorithm that could be implemented in the near future by using low-cost sensors and HD maps. The proposed localization algorithm consists of several sections: environment feature representation with low-cost sensors, digital map analysis and application, position correction based on map-matching, designated validation gates, and extended Kalman filter (EKF)-based localization filtering and fusion. Lane information is detected by monocular vision in front of the vehicle. A guardrail is perceived by radar by distinguishing low-speed object measurements and by accumulating several steps to extract wall features. These lane and guardrail information are able to correct the host vehicle position by using the iterative closest point (ICP) algorithm. The rigid transformation between the digital high definition map (HD map) and environment features is calculated through ICP matching. Each corrected vehicle position by map-matching is selected and merged based on EKF with double updating. The proposed algorithm was verified through simulation based on actual driving log data.  
high definition(HD) map; advanced driver assistance systems (ADASs); localization; iterative closest point (ICP); automated driving vehicle
## 4 Discussion
### Limitations  
There are some limitations in the current study. One of them is the limited population sample, which limits the generalizability of the findings. For future studies, a more diverse sample could enhance the applicability of the findings.  
Furthermore, the lack of bilateral testing on the C-station could be a limitation. The addition of a comparison between bilateral and unilateral tests across different devices could provide additional insights into the consistency of the C-station for a wider range of strength assessments.  
Another limitation of the current study is the lack of familiarization sessions. By implementing one familiarization session or more, it would be possible to eliminate a potential learning effect and obtain more representative data.  
The difference in equipment used for the C-station and the AMTI force plates could be seen as a limitation, as it could influence the participants' ability to make the same movement on both systems. To account for this, future studies should make use of the same setup on both measuring systems.  
The lack of longitudinal data could be seen as a limitation. Future studies should investigate long-term reliability to ensure the device's consistency over extended periods, particularly in a clinical or rehabilitative setting.