esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
An improved method for calibrating the misalignment error is proposed in this paper. Firstly, the misalignment error is modeled. Then, we set a constraint to transform the calibration of misalignment error into estimating calibration parameters to minimize the value of the objective function. Finally, the MATLAB's NLS function is used to estimate the calibration parameters. The feasibility and performance of the improved method are verified by simulation and the field experiment.  
Compared to the preceding methods, this improved method has the following advantages: (1) The first advantage is that the method has high operability. Because the normal vector and tangent vector of data trajectory are used simultaneously, the calibration experiment only needs to rotate about an axis once and the axis of rotation can be arbitrarily chosen. In addition, the DPI method and [PERSON]'s method cannot uniquely estimate the calibration matrix \(R\) with the same data. These two methods need at least two sets of dates rotated about different axes to calibrate the misalignment error. (2) The second advantage is that the method is insensitive to the selection of the virtual vector. The virtual vector can be arbitrarily chosen, and different virtual vectors do not affect the accuracy of calibration.  
Future research should focus on developing a method to accurately fit data trajectories. The error of a non-magnetic turntable could lead to the data not in the same plane. The discretization of data could lead to a decrease in calibration accuracy.  
[PERSON] proposed the research ideas; [PERSON], [PERSON] and [PERSON] conceived the experiments; [PERSON], [PERSON] and [PERSON] conducted the experiments; [PERSON], [PERSON] and [PERSON] analyzed the results. All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
Figure 8: The calibration results. (**a**) The \(x\) component; (**b**) The \(y\) component; (**c**) The \(z\) component.  
**Funding:** This work was funded in part by the National Natural Science Foundation of China under grant 42076196, 41704172, in part by the National Key Research and Development Project grant 2016 YFC0303000, and, in part, by the National Key Research and Development Project grant 2017 YFC0602000.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Mechanical Performance of Gilsonite Modified Asphalt Mixture Containing Recycled Concrete Aggregate
## 1 Introduction
### Problem Statement and Objective  
In Colombia, average annual temperatures above 24 \({}^{\circ}\)C cover almost 70% of the total area in the country. Moisture, in terms of average annual relative humidity, is above 60% [73]. Because of this, within a large part of the Colombian territory, the most common pavement distress type is rutting. This motivated the use of gilsonite in this study, with the purpose of stiffening the asphalt binder, thus attempting to increase rutting resistance for the two types of HMAs that are mostly used throughout Colombia (named MDC-19 and MDC-25, according to the authors of [74]). Additionally, for the case of Colombia, the use of RCAs as replacements for NAs in HMAs could be an interesting alternative, taking into account that projections for 2019-2024 indicate the use of 75 million tons of NAs for the construction of roadways [75]. In addition, a great portion of the national roadway network is comprised of unpavel roadways [76].  
Few studies have combined the effects of modifying the asphalt binder with gilsonite and using RCAs as replacement for NAs in HMAs. The authors of [77; 78] conducted limited experimental studies combining both effects, but in porous mixes. The authors of [79] conducted a study on an HMA; however, the scope of the experimental phase was also limited (only conducted using Marshall, indirect tensile strength (ITS), and Cantabro tests; used one gradation; and the substitution of NA with RCA was carried out according to mass). In addition, few studies have replaced the NA with RCA dosing according to volume; despite this, several researchers recommend doing so because it is more appropriate for these types of porous materials [59; 80]. This is because RCA particles generally have a lower specific gravity than NAs, and when replacing according to mass, a greater amount of RCA particles that are to be coated with asphalt are introduced into the mix [81].  
Given all of the above reasons, in contrast to other studies conducted on the topic, this study evaluated the combined effects of modifying asphalt cement (AC) with gilsonite through a wet process, and replaced (dosing by mass and by volume) part of the coarse fraction of NA with RCA in mixes of MDC-19 and MDC-25. Marshall, resilient modulus, and permanent deformation tests were carried out in order to evaluate the rutting resistance. Additionally, ITS, fatigue, and Cantabro tests were carried out to evaluate the durability related aspects. In order to know if changes in properties were statistically significant,an ANOVA test was carried out with test F and a reliability level of 95%. The values of F \(>\) F\({}_{0.05}\) represented statistically significant variations.
## 5 Results and Discussion
### Comparison of Moment of Inertia  
A genetic algorithm-based optimisation technique was used for the turbine design optimisation [23]. Figure 33a,b shows the 3D model of the optimized turbine blade wheel and the turbine, respectively. Once the 3D model was generated, finite element analysis was performed to determine the moment of inertia and the factor of safety. The comparison of the moment of inertia and the factor of safety between the radial, initial axial, and optimised axial turbine designs is shown in Table 11. The table shows that the use of axial turbine reduced the mass moment of inertia by around 35% without compromising much on the safety factor.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Radial Turbine** & **Axial Turbine (Initial Design)** & **Axial Turbine (Optimized Design)** \\ \hline Mass (g) & 226.76 & 112.93 & 85.41 \\ Mass moment of inertia (kg/m\({}^{2}\)) & \(1.82\times 10^{-5}\) & \(1.59\times 10^{-5}\) & \(1.16\times 10^{-5}\) \\ Safety Factor & NA & 2.38 & 2.16 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of baseline radial, axial turbine, and optimised axial turbine design.  
Figure 31: Comparison between radial (**left**) and axial (**right**) turbines time to maximum shaft speed at different engine operating conditions.  
Figure 33: (**a**) Optimized blade design and (**b**) 3D model of the axial turbine.  
Figure 32: Transient response profiles for turbocharger shaft speed and manifold pressure at engine speeds of 5000 rpm (**a**,**b**), 4000 rpm (**c**,**d**), and 3000 rpm (**e**,**f**) at 100% (left) and 60% (right) engine loads.
## 3 Model and Background
### Network Assumptions  
In this paper, we consider a large-scale sensor network with densely deployed sensor nodes. We assume a general multi-hop network with a set \(S=\{s_{1},\,...,\,s_{d}\}\) of \(d\) sensor nodes and a single trusted BS which has a long-lasting power. Some sensor nodes are dynamically assigned as aggregators based on their remainder energy levels to aggregate data from their neighboring nodes. The sensor network is mostly static with a topology known to BS. Typical aggregation functions include SUM, AVERAGE, COUNT, MAX, and MIN, and all of them can be reduced to the additive aggregation function SUM [26].
# EduTVA: Metadata Schema for Educational Audiovisual Contents in Digital Television Environments
## 5 Results and Discussion
### Functionality Assessment  
Table 6 shows the results when executing the search queries on Crayon for looking for educational content (see Table 3) that has been described using both TV-Anytime and EduTVA metadata schemas. This table includes the identifier of the search query (see Table 4), the number of Expected Results (ER) per search query (i.e., see Table 5), the number of Obtained Results (OR) from the search query, the number of Inconsistent Results (IR), and the value of the OPC metric (see Table 2). IR refers to the number of obtained results that were not expected plus the number of expected results that were not obtained.  
The results show a poor performance of Crayon when looking for the educational content described with TV-Anytime. The search queries S2, S3, S5, S8, and S9 obtained no content (OR = 0); therefore, IR = ER and OPC = 0. The search queries S1 and S10 missed three pieces of content each (IR = 3), achieving an OPC of 0.896 and 0.727, respectively. The search query S4 obtained a non-expected piece of content besides all the expected ones (IR = 1), for an OPC of 0.833. Only the two remaining search queries (S6 and S7) correctly obtained the expected results (OPC = 1).  
Conversely, the performance of Crayon improved when looking for the educational content described with EduTVA. The search queries S1 and S2 missed only one piece of content each (IR = 1), achieving an OPC of 0.965 and 0.96, respectively. The search query S4 obtained two non-expected pieces of content in addition to all the expected ones (IR = 2), for an OPC of 0.666. The remaining seven search queries (S3, S5, S6, S7, S8, S9, S10) correctly obtained the expected results (OPC = 1). Note that only the OPC of S4 performed worse with EduTVA than with TV-Anytime. For the rest of the search queries, the OPC with EduTVA was either better than or equal to the OPC with TV-Anytime.  
To summarize, Crayon correctly obtained the expected results (OPC = 1) from 70% of the search queries with EduTVA, in contrast to 20% of the search queries with TV-Anytime. Moreover, 50% of the search queries with TV-Anytime obtained no content (OPC = 0) while all the search queries with EduTVA obtained expected content ((OPC > 0). Lastly, 80% of the search queries with TV-Anytime obtained inconsistent content (IR > 0; OPC < 1) versus 30% of the search queries with EduTVA. Only the search query S4 performed worse with EduTVA (OPC = 0.666) than with TV-Anytime (OPC = 0.833). Therefore, EduTVA provides a more functional metadata schema than TV-Anytime for implementing searching services of educational content in digital television, such as Crayon.
## 4 Parametric Study  
The strengthening effect of STPU with varying thicknesses was evaluated through a parametric study. Using the previously developed finite element analysis (FEA) model, the STPU thickness was varied from 1 mm to 8 mm in 1 mm increments and applied to the  
Figure 13: Comparison of time–displacement graph between simulation results and experimental data: (a) MWN (0.3 g); (b) MWP2 (0.3 g); (c) MWPF5 (0.3 g).  
masonry wall specimen. The same numerical modeling approach and boundary conditions described in Sections 3.2 and 3.3 for the MWP2 specimen were used, and a seismic load corresponding to a PGA of 0.4 g was applied to all specimens. The results of the parametric study are summarized in Table 6. In thickness of 6 mm, the strain and stress results show the lowest value, and in thickness of 2 mm, displacement results show the lowest value. Although the seismic performance improved slightly with increasing STPU thickness, the difference in maximum displacement and strain reduction beyond 2 mm was marginal. Considering the cost, ease of application, curing time, and material efficiency, the 2 mm thickness was determined to be the most practical and best choice for retrofitting masonry walls. Therefore, it is recommended as an optimal thickness for field application.  
The strengthening effect weakens after the thickness exceeds 6 mm. These results are similar to those of [PERSON] et al. (2011) [44]. In [PERSON] et al.'s study (2011), they retrofitted the RC slab with different thicknesses (3, 5, 7 mm) of PU, and when the PU thickness of 7 mm was applied, the strengthening effect was lower than for other thicknesses [44]. These findings imply that in structures where PU is excessively applied, the high elongation rate, a characteristic of PU, cannot be accurately exhibited. While the parametric study focused on the effect of STPU thickness, other mechanical properties--such as elastic modulus, tensile strength, and elongation--of STPU may also influence the structural response. Future studies will consider expanding the parametric analysis to include variations in these material properties to better understand their sensitivity and optimize the retrofitting design.
## 3 Finite Element Modeling and Damage Criteria  
### Finite Element Model of Three-Point Bending of CARALL  
To accurately model the behavior of the FMLs in finite element analysis, the radius of the loading nose and supports, and the size of FMLs were the same as those in the experiment. Face-to-face contact was adopted between the laminates, loading nose, and supports, and the normal contact attribute was set to hard contact, tangential contact attribute to penalty function, and friction coefficient to 0.15. The boundary condition of the supports was completely fixed, and symmetric constraints were set for the two surfaces of the laminates to avoid rigid body displacement. A reference point was set for the loading nose and all degrees of freedom except the loading direction were constrained. The loading displacement of the reference point was set, and the support reaction and displacement of the reference point were output to compare with the load-deflection curve obtained from testing. The aluminum alloy sheets, loading nose, and supports were modeled using C3D8I and R3D4 elements, respectively. The CFRP layer was modeled using the C3D8R element [26]. The material parameters used were consistent with those given in the chapter. Additionally, zero-thickness cohesive elements (COH3D8) were used to simulate the mechanical behavior between aluminum/CFRP layer and CFRP/CFRP layers [23; 27]. The finite element model established according to the above material parameters and constraints is shown in Figure 4.  
Figure 3: Details of three-point bending experiment and specimen size.
# Optimization of Additive and Current Conditions for Void-Free Filled Through-Silicon Via
## 4 Conclusions  
In this study, the effects of additives (PEG, SPS, and JGB) on copper electroplating were confirmed using CV analysis. As the concentration of PEG and JGB increased, the suppressing effect on copper plating also increased. As SPS concentration increased, the accelerating effect on copper plating also increased.  
Based on the results of the CV analysis, copper electroplating was performed by varying the current and additive conditions, and the filling ratio was observed through cross-sectional analysis. The highest filling ratio was obtained at concentrations of 500 ppm PEG, 5 ppm SPS, and 30 ppm JGB. Thereafter, pulse electroplating was performed with fixed on-current of 10 mA/cm\({}^{2}\) and varying reverse current.  
When the reverse current was set to 16 mA/cm\({}^{2}\), a void-free filled TSV was obtained under optimized plating conditions.  
**Author Contributions:** [PERSON] designed the experiments, performed the experiments, analyzed the result of the research, and wrote the manuscript; [PERSON] and [PERSON] designed the experiments, and analyzed the result of the research; [PERSON] reviewed the manuscript, and administered project. All authors approved the final version of the manuscript.  
**Funding:** This work was partly supported by the GRRC program of Gyeonggi province [GRRC Sungkyunkwan] grant number [2017-B01].  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: SEM image of void-free filled TSV.
## 7 Nanomedicine to Enhance the Therapeutic Effectiveness of BC  
In recent years, nanomedicine has shown significant potential in addressing the limitations of traditional BC treatments. Nanotechnology focuses on the fabrication of nanomaterials/nanoparticles with sizes between 1 and 100 nm in at least one dimension. For instance, nanoparticles (NPs) can be engineered for selective medications and are utilized as drug delivery systems, commonly known as nanomedicines. These NPs offer substantial advantages by enhancing the biological distribution of drugs, directing active molecules precisely to affected tissues, and safeguarding healthy tissues against unnecessary drug exposure [94; 95]. The distinctive characteristics of NPs encompass their minuscule size, extensive surface-to-volume ratio, modifiable physicochemical properties, capability to carry substantial drug quantities, prolonged circulation time, elevated absorption and retention, efficient tumor targeting, extended drug release, biocompatibility, improved bioavailability, and capability to counteract multidrug resistance [94; 95]. Additionally, the minute size of nanoparticles enables them to penetrate biological barriers, offering a treatment avenue for BC patients with brain metastases [94; 95]. Moreover, these nanoparticle-based medicines possess all the significant properties of a conventional breast cancer drug along with enhanced abilities to overcome some of the major limitations discussed earlier.  
Drug delivery systems based on nanocarriers have been identified to possess enhanced effectiveness for targeting multiple cancer sites. The predominant methods of nanocarrier-based drug delivery utilize both organic and inorganic particles. Common organic particles employed in drug delivery include micelles, liposomes, polymers, dendrimers, and nanogels, known for their adaptable surface structures that aid in efficient drug loading and cellular uptake. Further, nanoparticles as carriers possess surfaces that can be tailored to direct drugs specifically to tumor blood vessels. Recently, the utilization of nanodevices to encapsulate chemotherapeutic drugs has proven beneficial in reducing side effects and enhancing the drugs' bioavailability, especially for breast cancer [94; 95]. Hence, nanocarrier platforms offer the most effective method for targeting drug-resistant cells in breast tumors.
# Delving into Causal Discovery in Health-Related Quality of Life Questionnaires
## 3 Results
### Resource Description Framework Knowledge Graph
#### 3.2.2 Representing Cause-Effect Relationships  
Still remaining within the RDF realm, the cause-effect relationships among the questions were also represented in the same manner. Figure 6 illustrates a subset from synthetic DAG #6 (see Figure 1(6)) where the cause-effect relationship between questions (red nodes) was represented via the property \"isCauseOf\". The order of questions in the questionnaire via the predicate \":isAfter\" was not displayed in Figure 6, since in this example it was trivial and corresponded to the alphabetical order of the questions (e.g., T is after S). Figure 6 also includes hypothetical facets corresponding to each question along with respective hypothetical second-level domains (both facets and domains are illustrated as cyan nodes). For instance, question Q is the cause of question R. Moreover, question Q has a facet of \"Activities of daily living\", question R has a facet of \"Work capacity\", and both of the facets belong to the same second-level domain \"Physical health\".  
Figure 5: Serialization of a sample hypothetical patient and their responses to two questions as a Resource Description Framework Turtle.  
Figure 6: A sample cause–effect graph in Resource Description Framework Turtle format, corresponding to DAG #6 (see Figure 1). The red nodes represent specific questions in DAG #6 (namely, the questionsQ. R, S, T, U, V, W, X, Y, and Z), while the cyan nodes represent the respective hypothetical facets and second-level domains of the questions. The labels over the edges reflect the respective semantics including the cause-effect relationships between questions. For instance, over the edge that connects questions S and T, the label \"is cause of\" shows that question S is the cause of question T. Moreover, question S has a facet of \"Self-esteem\" that belongs to the second-level domain \"Psychological\".  
Next, Table 1 indicates the respective intensities of the inter-domain associations based on sample DAG #6. Associations of a specific domain to itself were not taken into account. For instance, \"Environment\" was observed to be the cause domain of \"Physical Health\" three times; namely, a cause-effect relationship with the cause variable belonging to the domain of \"Environment\" and the effect variable belonging to the domain of \"Physical Health\" was observed three times in total. On the other hand, a cause-effect relationship with the cause variable belonging to the domain of \"Social relationships\" and the effect variable belonging to the domain of \"Environment\" was observed only once.
# Recognition Algorithm Based on Improved FCM and Rough Sets for Meibomian Gland Morphology
## 1 Introduction
### Fcm
#### 2.2.1 Traditional FCM Algorithm  
Let \(X=\{x_{1},x_{2},\cdots,x_{n}\}\) be a dataset containing \(n\) samples, \(v_{i}(i=1,2,\cdots,c)\) is the center of each cluster, \(c\) is the number of clusters, and \(\mu_{ik}\) is the membership of the sample \(k\) belonging to the class \(i\). [PERSON] [19] defined the objective function as follows:  
\[J(X;U,V)=\sum_{i=1}^{c}\sum_{k=1}^{n}{(\mu_{ik})^{m}}\Big{|}\Big{|}x_{k}-v_{i} \Big{|}\Big{|}_{A}^{2}\]  
where \(U\) is the membership matrix, \(V\) is the matrix of cluster centers; \(D_{ikA}^{2}=\)\(\Big{|}\Big{|}x_{k}-v_{i}\Big{|}\Big{|}_{A}^{2}=(x_{k}-v_{i})^{T}A(x_{k}-v_{i})\) is the Euclidean distance from samples to the cluster centers; \(A\) is a positive definite matrix; and \(m\) is a weighted index affecting the degree of fuzzy membership matrix. Generally, \(m\) is 2. The FCM clustering requires that membership meets the following condition:  
\[\sum_{i=1}^{c}\mu_{ik}=1,1\leq k\leq n\]Using the Lagrange multiplier method [20; 21], the condition of the objective function achieving the minimum is as follows:  
\[\mu_{ik}=\frac{1}{\sum_{j=1}^{c}{(D_{ikA}/D_{jkA})^{2/(m-1)}}},1\leq i\leq c,1 \leq k\leq n\]  
\[v_{i}=\frac{\sum\limits_{k=1}^{N}{\mu_{ik}^{m}x_{k}}}{\sum\limits_{k=1}^{N}{ \mu_{ik}^{m}}}\]  
It can be seen that the FCM clustering algorithm obtains the cluster centers through the iteration of \(\mu_{ik}\) and \(v_{i}\).
## 4 Conclusions  
The MCT beamline constructed as part of the BRIGHT project at the ANSTO Australian Synchrotron is suitable for high-resolution computed tomographic imaging both in absorption-contrast and propagation-based phase-contrast imaging modalities. The resolution study for the white-beam detector showed good agreement between theoretical and experimental values. \"First light\", first phase-contrast images, and first CT data were produced successfully at the MCT beamline. The CT result showed that the phase-retrieval TIE-Hom algorithm can be used to enhance image contrast of low Z materials and improve SNR. The MCT users can rapidly process the collected data during their beam time and/or access the data after the beam time using the synchrotron's ASCI desktop facility. This new MCT beamline provides complementary capability to the IMBL beamline. This extends the capability of imaging for a broader range of object sizes and resolutions.  
All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We acknowledge the contributions from the extended MCT project team that contributed to the planning, delivery, building and commissioning of this new beamline. We also acknowledge the support of the Beamline Advisory Panel who are providing guidance on the science applications for MCT and were also informing the design of MCT for the benefit of the user community.  
The authors declare no conflict of interest.  
Figure 8: Slice reconstruction of wood structure (toothpick) without TIE-Hom phase-retrieval in (**a**) and with TIE-Hom phase-retrieval in (**b**), with the 3D visualization of the data such as in (**b**) presented in (**c**). Data were collected using white beam, 20\(\times\) objective lens, exposure time of 0.7 s, and sample-detector distance of 160 mm.