esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study: UBC's BRDF Expansion Project
### Understanding Specific Project Constraints and Challenges That Led to the Use of BIM
#### 4.2.1 Complex Spatial Constraints  
Figure 2 provides an insight regarding the complex spatial conditions within the existing facility. The limited space and the size of the entrance posed significant challenges from the project's outset.  
Some of the largest pieces of equipment had been installed before the building was fully enclosed, making it especially difficult to move and maneuver this equipment for the expansion project.  
\"Having major limits on the amount of space that we have in order to maneuver some of these massive pieces of equipment like the boiler and the ESP (Electrostatic Precipitator), and they've been basically shoehorned into this building. That was a huge challenge that we faced on this project\" --Owner's Project Manager  
The spatial constraints were a driving factor in the decision to use BIM as a planning and coordination tool to ensure the equipment could fit through door openings and be properly installed.
## 6 Conclusions  
This paper proposes a transitional connection method for the optimization design of FGMs. Based on the parametric level set method, the configuration of every cellular based cell is represented by a specific level set function. For every two gradient base cells in a FGM, a special transitional cell is constructed using the two level set functions of two candidate base cells. Since the transitional cell has the geometric features of two gradient base cells, the shape of the transitional cell fits perfectly with its connected gradient cells on both sides. Thus, the FGM design can have smooth connectivity with  
Figure 21: Different perspectives of the 3D transitional cellular structure: (**a**) perspective 1, top surface connects perfectly with the maximum shear modulus CBS; (**b**) perspective 2, top surface connects perfectly with the maximum bulk modulus CBS.  
Figure 22: Sectional view of the connection between different cells.  
C\({}^{1}\) continuity. In particular, the interpolation of the transitional cell independent of the optimization avoids added computing time.  
To validate the proposed method for devising the FGMs, several 2D and 3D examples are provided. In addition, a series of comparative simulation analyses are carried out. Firstly, a number of FGM designs obtained by using the proposed connection method exhibit smooth interfaces between different layers. Secondly, simulation results demonstrate the advantages of the proposed method over the direct connection method to alleviate the stress concentration in the design. Thirdly, compared to the conventional porous structure with single type CBS, when the structural strain energy is taken as a reference, the devised gradient structure can exhibit better structural stiffness. Finally, for the FGM with different types of CBSs, the proposed method can be effectively used to solve the boundary mismatches. The optimization design process and the comparative simulation analysis indicate the easiness and effectiveness of the proposed method. Note that the proposed method is developed under the assumption that the material microstructures are with the plane symmetry. The more general elastic symmetries (e.g., tetragonal or monoclinic symmetries) are also quite important and worth a comprehensive investigation. In that case, the approach based on tensor invariants can be adopted [55; 56]. The consideration of general elastic symmetries can be a future extension of the proposed method.  
Conceptualization, S.L. and H.L.; methodology, S.L. and L.G.; data analyses, Y.Z.; supervision, L.G. and H.L. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the support of the National Natural-Science-Foundation of China (51705166 and 51825502) and the Fundamental Research Funds for the Central Universities through Program no. 2172019 kfyXJS078 and 2019 kfyXKJC042.  
The authors declare no conflict of interest.
## 4 Case Study of Interworking of QKD Networks and the Role of SDN Orchestrator
### Korea Research Environment Network (KOREN) Overview  
KOREN is a national research and education network that provides advanced network services for the research and education community in South Korea. KOREN connects researchers, educators, and students from universities, research institutions, and government agencies to each other and to international research networks and resources. It provides high-speed internet connectivity, cloud services, and other advanced network servicesto support research, education, and collaboration. Additionally, KOREN also operates a testbed network for experimentation and innovation in advanced networking technologies.  
KOREN is a research backbone network that connects 10 regional access points across South Korea at speeds ranging from 10 Gbps to 260 Gbps. Subscriber circuits have been provided at speeds ranging from 1 G/10 G to a maximum of 25 G since 2020. The KOREN network in South Korea is also connected directly to international networks, such as those in Hong Kong and Singapore, with a 100 Gbps bandwidth and supports international collaborative research with the USA and Europe.
## 4 Methods and Devices for Assessing Erythrocyte (RBC) Aggregation
### Laser Optical Technique  
Parameters characterizing RBC aggregation and deformability can be measured by various optical techniques. These include diffractometry, optical trapping and manipulation (e.g., stretching), diffuse light scattering, and various types of microscopies, including vital microscopy and capillaroscopy. While most of these techniques are implemented in vitro, the two latter ones are implemented in vivo. [PERSON] and co-authors [26; 27] use an RBC aggregometry method based on the diffuse light scattering from whole-blood samples in microfluidic flow chambers, a laser diffractometry technique used to analyze dilute suspensions of RBC in controlled shear flow, laser trapping and manipulation of single RBCs (all in vitro), and digital capillaroscopy of blood flows in nail bed capillaries in vivo. [PERSON] et al. [27] used laser optical techniques and flow cytometry to investigate how macromolecule adsorption (specifically human serum albumin (HSA) and fibrinogen) onto the RBC membrane affects cell biomechanics and aggregation.  
RBC aggregation force (Fagg) was measured using laser tweezers in autologous plasma. The results obtained show that the Fagg increased linearly with fibrinogen concentration, showing fibrinogen's strong role in promoting RBC aggregation. Fagg was almost unaffected by HSA, indicating that HSA does not significantly influence RBC aggregation. Fibrinogen strongly promotes RBC aggregation, while HSA has a minimal effect, and laser-based methods combined with flow cytometry offer powerful tools for assessing the mechanical and aggregation properties of RBCs. The results showed that SSC values significantly increased for RBC incubated in glutaraldehyde solution and the Fagg of such cells was nearly absent. The authors hypothesized that serum albumin may influence the biomechanical properties of RBC due to non-specific adsorption, leading to the alterations of membrane-bounded hemoglobin that result in (controversial) effects on RBC aggregation [27].
# Finite Differences on Sparse Grids for Continuous-Time Heterogeneous Agent Models
## 3 Sparse Grids
### Overcoming the Non-Monotonicity of Sparse Grid Interpolation  
There are several possible approaches to obtain monotone interpolation on sparse grids. The most trivial way is to go to a higher sparse grid level; this is visualized in Figure 5, where the interpolation of \(f_{4}\) is presented for sparse grid levels \(l=5,7,9\) instead of \(l=3\). The approach is simple, but one cannot go to arbitrarily high levels in higher dimensions; moreover, for higher levels, corresponding counter examples can be constructed as well, therefore a guarantee of monotonicity cannot be expected.  
Alternatively, one can identify the areas where non-monotonicities arise and insert points only in these areas. For that, one can adapt the sparse grid using the hierarchical coefficients as error indicators, as is performed in standard adaptive approaches. Another approach is to go to a \"full\" grid in the critical area. Here, one determines the maximal one-dimensional level used in the critical area and then simply uses the full grid level. Thereby non-monotonicity cannot arise in this area. For investigations on these approaches for monotone functions see [33]. Being of heuristic nature, we do not expect that monotonicity can be guaranteed. Specific to our situation, the most promising alternative refinement is to use the computed derivatives. That is, one can use the approximations of the derivative and check if these are non-negative since that indicates a non-monotone interpolation. By marking such points for adaption, one can iterate until all derivative approximations  
Figure 4: For the backward difference at \((0.5,0.25)\), one uses the values drawn as the green and the red points.  
Figure 5: Plots of contour plots for the sparse grid interpolation of \(f_{4}\) for different levels. (**a**) Contour plot for the interpolation of \(f_{4}\) for level \(l=5\). (**b**) Contour plot for the interpolation of \(f_{4}\) for level \(l=7\). (**c**) Contour plot for the interpolation of \(f_{4}\) for level \(l=9\).  
are non-negative or below a certain error threshold. With this approach, a guarantee of monotonicity might be achievable.  
In our numerical experiments, we do not investigate these strategies and consider the enforcing of monotone interpolation on adaptive sparse grids a topic of future research.
# Enhancing Healthcare Security: A Unified RBAC and ABAC Risk-Aware Access Control Approach
## 4 Implementation of Proposed Model in Healthcare Scenario
### Attribute-Based Access Control Implementation
#### 4.4.2 Policy Information Point (PIP)  
The PIP supports the ABAC evaluation process by conveying attributes to crucial components like the PDP and risk engine. It collects contextual and risk factors according to the four categories, which include environment, resource, user and risk attributes, to support the PDP and the risk engine. The PIP was implemented based on a set of JPA repositories from various databases into one class. This allows the seamless retrieval of attributes required for access control decisions.
## 3 Methodology
### Shadow Direction Acquisition  
Shadow direction indicates the location relationship between buildings and their shadows. As high-level, high-resolution image products are already calibrated, the parameters that indicate the solar altitude and sun height are the default. This means that shadow direction cannot be calculated with parameters taken directly from the image. This step obtains the shadow direction by the area and shape of the shadows. Since an image covers a certain area at similar times, the direction of the shadows in a single high-resolution image can be regarded as the same.
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## 1 Introduction
However, there are distinct differences between irrigated and rainfed wheat in growth processes, the length of the growing season and the periods of peak NDVI. Moreover, the NDVI threshold used in the supervised classification method usually has to be modified for local conditions, which may reduce the accuracy of the classification [5; 9]. To overcome the aforementioned disadvantages in mapping irrigated and rainfed areas, we propose an improved classification method based on the following three aspects. First, we used data from two of China's small environmental satellites, the HJ-1A/B (HuanJing(HJ)) means environment in Chinese) satellites, launched on 6 September 2008. A spatial resolution of 30 \(\times\) 30 m and a two-day repeat cycle make them suitable for dynamic monitoring of irrigated and rainfed areas on a regional scale [23]. Second, the support vector machine (SVM) algorithm was used as a classification method to avoid subjectivity in the definition of NDVI thresholds. The SVM is a pattern recognition method developed on the basis of statistical learning theory with high flexibility, global optimization, high efficiency and robustness [24; 25; 26]. It has been widely used in the remote sensing classification [27; 28; 29]. Third, considering the differences between irrigated and rainfed wheat, peak NDVI and TIN were selected as feature vectors for classification. During this process, sowing and maturity dates derived from NDVI thresholds were set as the start and end points of the growing period for TIN calculations. In this study, we mapped irrigated and rainfed land in the semi-arid hilly areas of Shanxi Province, China. Due to the high fragmentation of arable land, smallholder management, long growth period of winter wheat and dispersed irrigation times, it is difficult to estimate irrigated areas in this region. Until now, there are no reliable maps of irrigated winter wheat in Shanxi Province. Our major research objectives were to: (1) develop a novel classification method for irrigated and rainfed areas based on high resolution satellite data, the SVM algorithm, and site-level observations; (2) map irrigated and rainfed winter wheat in the south central part of Shanxi Province; and (3) quantify the spatial patterns of sowing and maturation dates of winter wheat under different water supply conditions.
## 4 3D Laser Terrestrial Scanning: Knowledge Base  
Utilization of 3D laser scanners is nowadays one of the most accurate, fastest and most efficient ways of obtaining 3D data (models) of existing objects. Obtained data are the source of information for real objects' transformation into an accurate 3D digital copy, which can capture spatial geometry and can be used for various types of analysis and further processing on a computer [19; 20; 21].  
Terrestrial laser scanning, also known as terrestrial LiDAR (light detection and ranging) or topographic LiDAR, works by the XYZ coordinate system for many points on the ground. Laser pulses are projected to these points and the device-target distance is computed.  
Laser scanning provides high accuracy when recording real-world objects. The scan result is a point cloud that represents a 3D image of the scanned objects. This technology is used in many areas of industry, but also in architecture [22].  
The 3D model obtained by this process helps architects in the renovation planning of buildings and structures and brings the possibility of testing various design variants. These options are very effective in civil engineering too [23]. Designers can access data from different parts of the world and work simultaneously on new projects without the need for traveling and manual measurement in the case of potential changes evaluation.  
Three-dimensional laser scanning is one of the reverse engineering techniques, which is also used in the digitization of cultural heritage objects. Reverse engineering (RE) is a way by which is possible to obtain the shape and the geometry of a given object and thus create its 3D digital model based on a physical model. The scientific and technical development of these methods brings many advantages applicable in almost the entire field of protection and visualization of cultural heritage. The possibilities of using the methods are, for example, digital twin creation, computer-aided repair, new educational tools (virtual tours and virtual museums) and much more [24].  
According to the authors of [25], research and new technologies in the field of image processing, computer graphics and virtual reality have significantly progressed in creation of 3D computer images of real objects.
## 5 Evaluation Results
### Feature Engineering  
As a result of feature selection, it was found that the most selected feature is mean value of traffic time-interval and the least selected feature is entropy of IMSI. In particular, the most frequent top features were in order of _t_mean_time_ (mean value of time), _imsi_gnb_entropy_ (single IMSI entropy on gnb IP), _t_imsi_count_ (number of single IMSIs for 1 min), and _imsi_upf_entropy_ (single IMSI entropy on UPF IP).  
In the case of selecting fourr features, as shown in Table 9, the feature selected commonly in three statistical models was the mean value of time (_t_mean_time_) of the session create interval.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Dataset** & **Creation Criteria** & **Description** \\ \hline
1 & XDR & XDR create time & Training data constructed, based on XDR create time \\ \hline
2 & Bearer & Bearer create time & Training data constructed, based on Bearer create time \\ \hline
3 & Total & XDR create time + Bearer create time & Combine XDR dataset and Bearer dataset \\ \hline \hline \end{tabular}
\end{table}
Table 7: Three dataset types of training data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Total Number of Data** & **Number of Normal Data** & **Description** \\ \hline XDR & 9689 & 3580 & 6109 \\ \hline Bearer & 9938 & 3582 & 6356 \\ \hline Total & 19,627 & 7162 & 12,465 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Number of normal datasets and abnormal datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **Feature** \\ **Selection** \\ \end{tabular} & _t_mean_time_ & _t_var_time_ & _t_imsi_count_ & _imsi_snsf_entropy_ & _imsi_ant_entropy_ & _imsi_gnb_entropy_ & _total_count_ \\ \hline XDR & Chi-square & O & O & O & X & X & X & O \\ \hline XDR & ANOVA & O & X & X & O & O & O & X \\ \hline XDR & \
\begin{tabular}{c} Mutual \\ Information \\ \end{tabular} & O & X & O & X & O & O & X & X \\ \hline BEARE & Chi-square & O & O & O & X & X & X & O \\ \hline BEARE & ANOVA & O & X & X & O & O & O & X \\ \hline BEARE & Mutual & O & X & O & X & O & X & O \\ \hline TOTAL & Chi-square & O & O & O & X & X & X & O \\ \hline TOTAL & ANOVA & O & X & X & X & O & O & O & X \\ \hline TOTAL &
\begin{tabular}{c} Mutual \\ Information \\ \end{tabular} & O & X & X & O & O & O & X & X \\ \hline Grand Total & 9 & 3 & 5 & 1 & 6 & 5 & 4 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Result of feature selection by method of selecting training data construct and variable.  
As addressed in Section 5.5 below, the performance was found to be the best when a chi square test was used. In the case of selecting two features only, it can be known that _t_mean_time_ and _total_count_ have the highest correlation.
# Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review
## 3 Review Findings  
### Taxonomy  
Based on the current literature, the CE datasets comprised two types according to the domain analysis: image and video. The literature based on CE image and video datasets was included in this review. To answer the research question, a taxonomy was generated from the literature findings, which is shown in Figure 2. The image and video domains were further divided into three categories: classification, segmentation, and combined (classification + segmentation). Classification is a task that can be performed to classify bleeding images from non-bleeding images using classification algorithms. To detect a bleeding zone, segmentation is another important technique that can cluster a bleeding image into several coherent subregions. Both classification and segmentation algorithms are used in the development of advanced computer-aided diagnosis systems to identify bleeding images as well as recognize bleeding regions. Different types of feature extraction techniques, for example, color space, shape, texture attributes with region of interest (ROI), pixel- or block-wise contourlet transform, etc., featured extraction domains that were used to tune the machine learning algorithms for accurate identification of the bleeding images. Also, various convolutional neural networks, such as Visual Geometry Group (VGG), Residual Network (ResNet), Densely-Connected Convolutional Networks (DenseNet), etc., were used to extract features from CE images. Most of the literature used a color space for feature extraction, like RGB (Red, Green, and Blue), HSV (Hue Saturation Value), HIS (Hue, Saturation, Intensity), YCbCr (Luminance, Chrominance [chroma CB and chroma CR]), CMYK (Cyan, Magenta, Yellow, and Key [black]), CIE L*a*b* (Lightness [L], Red--Green [a], and Yellow--Blue [b]), etc. From the review findings, the state-of-the-art computer-aided bleeding detection algorithms were categorized into two types: conventional machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), K-Means Clustering, Naive Bayes, Random Tree, Random Forest, Artificial Neural Networks (ANN), Probabilistic Neural Networks (PNN), Multilayer Perceptron (MLP), etc., and deep learning algorithms, such as CNN, AlexNet, VGG, ResNet, SegNet, DenseNet, etc. The available bleeding detection algorithms in the literature covered the research question.
## 5 Conclusions  
The final evaporation temperature in NCS production influenced the volatile component composition, retronasal aroma release profile, and sensory characteristics of NCS. The quantity of volatile components, particularly those categorized as Maillard reaction products such as pyrazine, furan, furanone, pyridine, pyrrole, and pyranone, as well as the retronasal aroma releases from NCS, significantly increased with increasing evaporation temperature. The in-mouth retronasal release of the NCS solution was stronger than the in-nose release. Moreover, PTR-TOF-MS analysis identified ion _m/z_ 87.10 (derived from dihydro-2(3H)-furanone and 2,3-butanedione) as the primary top-note odor contributors to NCS, providing a buttery-caramel aroma, whereas other volatile compounds might be perceived as its base aroma. The variation in the concentration of these aroma compounds was possibly a key contributor to improving the acceptable sensory characteristics, particularly the roasted aroma, of the NCS produced at higher evaporation temperatures. Taken together, this study elucidated the dynamic changes in the volatile composition and retronasal aroma release of NCS produced at various evaporation temperatures. These insights are valuable for optimizing the NCS processing conditions to obtain the desired flavor quality. The considerable increase in the Maillard reaction products demonstrates the potential of controlled thermal processing to enhance the sensory attributes of NCS. These findings also serve as a solid basis for further research on how thermal processing affects flavor development during large-scale NCS production.  
Conceptualization, Y.A., H.K. and K.W.; methodology, Y.A., M.O., H.K., G.M. and K.W.; software, Y.A. and H.K.; validation, H.K., G.M., K.T. and K.W.; formal analysis, Y.N. and M.O.; investigation, Y.A., Y.N. and M.O.; resources, G.M. and K.W.; data curation, Y.A. and Y.N.; writing--original draft preparation, Y.A. and Y.N.; writing--review and editing, Y.A. and K.W.; visualization, Y.A. and Y.N.; supervision, Y.A., H.K., K.T. and K.W.; project administration, Y.A. and K.W.; funding acquisition, Y.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Urakami Foundation for Food and Food Culture Promotion.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee of Kyushu Sangyo University (approval number and date: No. 2022-0003 and 20 September 2022, respectively).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We thank [PERSON] (University of the Ryukyus) for technical assistance with GC-MS analysis.  
The authors declare no conflicts of interest.