esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental and Discussion
### Performance of the Simplification Algorithm
After the processing of Algorithm 2, all the meshes can be transformed into the Delaunay meshes and the number of vertices is reduced. To demonstrate the advantage of the simplification algorithm, the methods of the classical QEM [3] and the Delaunay mesh simplification [13] were conducted and illustrated as the contrast. All these two methods and the proposed method in this paper are simplified to the same number of vertices. The QEM method [3] is considered as a general control experiment for the simplification algorithm because of its good performance in the aspect of geometric feature preservation of the model. However, it is noteworthy that the results obtained with this method do not have the local and global properties of Delaunay mesh. There are still many narrow triangles among the simplified meshes, which are easy to be visually detected by its surface meshes. Although [PERSON]'s method [13] can preserve the local and global properties of the Delaunay meshes, it is visually noticeable that the meshes obtained by our method are more uniform with the same level of simplification. Figure 12: The comparison of the processed effects of the Delaunay mesh, (**a**) the original mesh, (**b**) our method, (**c**) QEM method, and (**d**) [PERSON]’s method. To better illustrate the preservation level of the geometric features after the simplification, the ratios surface-area-to-volume \(r\) of these four models are calculated and listed in Table 1. The algorithm proposed in this paper is based on the principle of minimal volume destruction. It can be seen that the vertices of mesh gradually decreases when the quantity of the meshes is improved. As the illustration factor of geometric features, the quantification criterions \(r\) of all these methods differ slightly. The values of \(\Delta r\) are defined as the geometric feature variations of the meshes compared to the original mesh. The small \(\Delta r\) value means that the changes to the geometric feature of the simplified mesh is little. The results show that the geometric features of all these meshes can be preserved after the simplification algorithm. In particular, for the original mesh with a large number of vertices, such as \"rocker\", \"elephant\", and \"horse\", the simplified mesh of the proposed method has smaller \(\Delta r\) compared to the simplified meshes of other methods. However, since the proposed method is based on minimal volume destruction, when the original model of \"head\" with fewer vertices is simplified, the vertex collapsing may result in the undesired volume destruction. Therefore, the proposed method in this paper is not applicable to process meshes with few vertices. All in all, it is demonstrated that the method proposed in this paper can protect the local and global properties of the Delaunay mesh and preserve the geometric features of the original mesh in most cases.
# Minimum Audible Angle in 3 rd-Order Ambisonics in Horizontal Plane for Different Ambsonic Decoders
###### Abstract
The spatial resolution, however, is understudied. The MAA was determined for the Vector Base Amplitude Panning (VBAP) method [15]. A ninth-order ambisonic reproduction system has been shown to produce spatial blurring that remains below the threshold of human spatial auditory acuity [16]. Ref. [17] compared mode matching and pseudoinverse methods on a horizontal layer with AllRAD rendering, proving that there is no statistical difference in the MAA between horizontal and periphonic rendering methods on that layer. However, to the authors' best knowledge, no study has looked into the differences in the MAA due to sound field reproduction discrepancies caused by ambisonic decoders while looking at both objective metrics and subjective evaluation. This work focuses on studying the MAA in the horizontal plane for three commonly used ambisonic decoders: the SAD, EPAD, and AllRAD. The rationale behind selecting these three decoders was a comparison between the simplest approach without perceptual consideration and the two most popular perceptual approaches. The objective evaluation of the decoder is relatively easy to perform; however, further evaluation is needed to fully determine its perceptual performance. The localisation accuracy across the decoders is evaluated in a listening test using third-order Ambisonics, focusing on the MAA and its dependency on a specific decoder design. The structure of this paper is as follows. Section 2 presents the motivation behind investigating the MAA in ambisonic technology and the context of existing studies on the MAA. Section 3 describes the investigated ambisonic decoders, including their objective metrics. Section 4 elaborates on the methodology and experimental design, followed by a comprehensive description of the results in Section 5. In the discussion in Section 6, some additional factors are raised that are beyond the scope of this study but are worth considering in future research. Section 7 offers concluding remarks.
## 2 Results
### JA Treatment Did Not Influence Ethylene Formation in Tea Flowers  
It has been reported that JA can enhance ethylene formation in _dendrobium_ and _petunia_ flowers [33]. Therefore, we also investigated whether JA in tea flowers has a similar effect on ethylene formation. \(S\)-Adenosyl-l-methionine synthetase (SAM) converts methionine into \(S\)-adenosyl-l-methionine (\(S\)-AdoMet), the precursor of ethylene. This is followed by ethylene biosynthesis. In the first step, \(S\)-AdoMet is converted into 1-aminocyclopropane-1-carboxylic acid (ACC) by ACC synthase (ACS). ACC oxidase (ACO) then oxidizes ACC to ethylene [34]. The key genes involved in ethylene formation, including a structural gene \(ACS\) and an ethylene receptor _ethylene-insensitive_ (_EIN_) gene, found in \(C\). _sinensis_ were analyzed. In flowers of \(C\). _sinensis_ cv. Jinxuan (middle and small leaf species), the expression levels of these ethylene synthesis-related genes were not significantly affected by JA treatment (Figure 6A). To further confirm the relationship between ethylene and JA we measured the ethylene levels released from flowers (a big leaf species cv. Yinghong NO. 9) after JA treatment to exclude the effects of different tea cultivars (Figure 6B). Like ethylene synthesis-related gene expression levels, ethylene content was not significantly affected by JA treatment (Figure 6B). These results suggest that JA in tea flowers may have different functions than in the flowers of _dendrobium_ and _petunia_[33].  
Figure 5: Effect of JA treatment on metabolite profiles of tea leaves. (**A**) JA treatment: 2.5 mM JA dissolved in 0.5% ethanol; control: distilled water dissolved in 0.5% ethanol. Principal component analysis (PCA) of emitted volatiles and internal volatiles, which were analyzed by GC-MS (\(n=5\)). (**B**) PCA analysis of nonvolatile metabolites, which were analyzed by UPLC-QTOF-MS (\(n=5\)).
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S/No.** & **Scheduling Algorithms** & **Parameters** & **Values** \\ \hline  & & Number of ants in colony & 1000 \\ \cline{3-4}
1 & HWACO &
\begin{tabular}{c} Makespan constant (\(\alpha\)) \\ \end{tabular} & 0.3 \\ \cline{3-4}  & & Cost constant (\(\beta\)) & 0.6 \\ \cline{3-4}  & & Heuristic constant (n) & 0.498 \\ \hline  & & Number of ants in colony & 1000 \\ \cline{3-4}
2 & ACO &
\begin{tabular}{c} Evaporation factor (p) \\ \end{tabular} & 0.398 \\ \cline{3-4}
2 & ACO & \
\begin{tabular}{c} Pheromone tracking weight (\(\alpha\)) \\ \end{tabular} & 0.3 \\ \cline{3-4}  & & Heuristic information weight (\(\beta\)) & 0.6 \\ \cline{3-4}  & &
\begin{tabular}{c} Pheromone updating constant (q) \\ \end{tabular} & 10 \\ \hline  & & Makespan constant (\(\alpha\)) & 0.3 \\ \cline{3-4}
3 & MIN-MIN &
\begin{tabular}{c} Cost constant (\(\beta\)) \\ \end{tabular} & 0.6 \\ \cline{3-4}  & & Population size & 1000 \\ \hline  & & Makespan constant (\(\alpha\)) & 0.3 \\ \cline{3-4}
4 & FCFS &
\begin{tabular}{c} Cost constant (\(\beta\)) \\ \end{tabular} & 0.6 \\ \cline{3-4}  & & Population size & 1000 \\ \hline  & & The number of flocks (k) & 10 \\ \cline{3-4}
5 & QANA &
\begin{tabular}{c} Memory size (K\({}^{\prime}\)) \\ \end{tabular} & 8 \\ \cline{3-4}  & & Short-term memory with memory size (K\({}^{\prime\prime}\)) & 48 \\ \hline  & & Decision variable \(\xi\) & \(1\) or 0 depending on task size \\ \cline{3-4}
6 & MTF-BPSO &
\begin{tabular}{c} Social learning factor c1 \\ \end{tabular} & 0.5 \\ \cline{3-4}  & & Personal learning factor c2 & 2.4 \\ \cline{3-4}  & & Inertia factor & 0.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter initialization.
## 3 Results
### Materials Characterization
#### 3.4.3 Sem-Edx  
SEM-EDX analysis has been performed on the commercial P25 powder and on both the deposited and impregnated P25. All the samples comprised a uniform array of titania nanoparticles at ca. 20 nm in size. The composition was checked via EDX, but as a result of the low metal loading, it was necessary to set the acquisition time to 12 min.  
Figure S2a-c (see Supporting Information File) illustrate the growth of titania NPs that were thermally treated after deposition over glass and after the second impregnation cycle, which aimed to metallize the surface with silver. In addition, Ag loading was checked qualitatively via EDX, which confirmed the presence of the metal (2.2% wt) along with traces of Si that are probably due to glass debris. Results from the EDX analysis are reported in Figure S2f. Impregnation over a relatively high surface oxide as P25 usually results in good metal dispersion. An example of a uniform Ag dispersion, obtained for a Ag-P25 impregnated sample, is reported in Figure S3.
# Towards Robust Representations of Spatial Networks Using Graph Neural Networks
## 4 Methods
### Learning on Heterogeneous Graph Representations of Spatial Networks
#### 4.2.3 Neural Architecture
Furthermore, we use the dual graph representation (or line graph) for our experiments. That means in all cases, the nodes are the spatial objects and the edges denote relations. For the purpose of our experiments, we generate numeric features from a normal distribution for the graph representations. We limit the size of each feature to 5. Our problem is a node classification and the node labels refer to either street types or building types. For the homogeneous case, we have separate graph representations for the street and for the buildings, whereas, for the heterogeneous case, we have one graph representation for both the streets and the buildings. We visualise the network schema for the homogeneous and heterogeneous graph representations in Figure 3. The street types considered are residential, secondary and tertiary streets. The building types are residential buildings, parking and bicycle parking. For the experiments, we train on 20%, 40%, 50% and 70% splits of the graph nodes. We use 20% and 10% of the graph nodes for the validation and testing, respectively. The same validation and test nodes are used for all the training splits. We present the number of train, validation and test nodes used in Table 3. During training, we label only the training nodes in the input graph representation using a generated mask. Notice that in Table 3, there is a disparity between the number of nodes for the homogeneous and heterogeneous graph representation. This is because either graph representation is generated independently, albeit from the same domain and with different relational constraints imposed on them. The derived graph representations are provided as input to the neural network model. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Train**} & \multicolumn{2}{c}{**Validation**} & \multicolumn{2}{c}{**Test**} \\ \hline
**Dataset** & **Streets** & **Buildings** & **Streets** & **Buildings** & **Streets** & **Buildings** \\ \hline Dublin & 29,253 & 3847 & 8358 & 1099 & 4179 & 549 \\ Frankfurt & 6794 & 1862 & 1941 & 532 & 970 & 266 \\ Toronto & 26,749 & 3691 & 7642 & 1054 & 3821 & 527 \\ Manchester & 4688 & 121 & 1339 & 34 & 669 & 17 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Data Split for the homogeneous and heterogeneous models. The train split shown here is 70% of the entire dataset.
## 2 Background
### Computed Tomography Scanner  
The word tomography is derived from the Greek words tomos (slice or section) and graphen (to write or draw) [16]. Therefore, CT can be defined as the depiction of the cross-sectional images or slices of a patient's body [17]. The multiple slices can be stacked together to form a three-dimensional image or volume [17].  
As shown in Figure 1, the CT scanner consists of an X-ray tube or source, a gantry module, a detector system (DMS), collimators, a motorized patient's table, and an image reconstruction unit. For controlling and synchronizing all these components, different FPGAs are used in the CT scanner [18]. The X-ray tube system, collimators, and detector are fixed on the rotating disk, mounted on the gantry; the rest of the components are fixed on the stationary side. The communication between the rotating and stationary sides is done through slip-ring technology consisting of brushes that permit the electrical connection between the rotating and stationary sides.  
The CT scanner works by moving the patient table to the space inside the gantry module, and when the patient's body goes through it, the X-ray tube system and the DMS rotates around the object's body with a frequency of about 170 rpm [16]. In the meantime, the X-ray tube system shoots a narrow beam of photons through the object's body. The attenuated beam photons of the object's body are acquired by detector sensors on the opposite side of the X-ray tube system [16]. The data are acquired as pixels of 2D images, called projections. Usually, modern CT scanners collect over 1160 projections per round [18]. These data are transferred to the image reconstruction unit, where the volume of the object's body is reconstructed as volume.  
The image formation in CT involves pre-processing acquired detector data, reconstructing the volume from the processed projections, and post-processing the reconstructed volume. CT reconstruction from the processed projections (total attenuation values or line integrals) is an inverse problem [19]; it means that input values (3D volumes) are estimated from the output values (2D images). Numerous solutions can be found for this problem in the literature, including the filtered back-projection (FBP) and iterative reconstruction [20; 21; 22; 23]. Our article focuses on the X-ray pre-processing step to compute the total attenuation values from the digitized detector data. In interventional CT, it has to be executed in real time.
# A Stackelberg Trust-Based Human-Robot Collaboration Framework for Warehouse Picking
## 2 Related Works
### Online Trust Models  
For the real-time evaluation of trust states in human-robot collaboration, online trust models are necessary. These models incorporate parameters from the initial settings and use observed data during system operation to generate situational evidence-based predictions. [PERSON] et al. proposed an Online Probabilistic Trust Inference Model (OPTIMo) using a dynamic Bayesian network (DBN) to infer moment-to-moment trust states based on the observed interaction history [18]. [PERSON] et al. developed a hidden Markov model (HMM)-based algorithm for human intent estimation in flexible robotic warehouses, employing a Theory of Mind (ToM)-based approach [19]. [PERSON] constructed a POMDP-based trust evaluation model in autonomous driving, exploring the relationship between route planning and trust, where trust is treated as a partially observable state variable representing human psychological states [20]. [PERSON] et al. investigated cognitive reasoning between humans and robots in stochastic multi-agent (SMG) environments, proposing an Autonomous Stochastic Multi-Agent System (ASMAS) with a new probabilistic rational temporal logic, PRTL*, to capture traditional beliefs and intentions [21].  
While these models focus on trust evaluation, the critical aspect of human-robot collaboration is not trust itself but leveraging trust to make better decisions, enabling stable and efficient collaboration. Additionally, these models are not directly applicable to human-robot collaborative picking scenarios, where humans cannot compute optimal strategies in real time and instead make decisions influenced by their trust in robots. Robots' predictions of human behavior should also be based on trust, which serves as a \"bridge\" in collaborative picking. Therefore, a new method is needed to evaluate trust and use it to facilitate better human-robot decision-making.
# Name Disambiguation Scheme Based on Heterogeneous Academic Sites
## 4 Performance Evaluation  
### Performance Evaluation Environment  
The performance of the proposed name disambiguation scheme was evaluated based on the termination criterion and linkage method of the HAC to validate its utility. In this study, the performance of the proposed and existing rule- and deep learning-based name disambiguation schemes was comparatively evaluated [7; 13]. Moreover, the performance of the multi-classifier, which selects a name disambiguation scheme based on attributes, was evaluated. The performance evaluation environment is summarized as follows. The performance evaluation was conducted on a system built with an Intel(R) Core(TM) i5-9600K CPU @ 3.70 GHz 64-bit processor, Santa Clara, CA, USA, and 32 GB of memory. The proposed scheme was implemented using Python 3.8.12 in the Python Anaconda environment, and machine-learning libraries, such as sklearn, keras, tensorflow, and the matplotlib library for data visualization, were used. The collected dataset is sum  
Figure 8: Overview of the multi-classifier structure.  
marized in Table 6. The dataset used for the performance evaluation includes all research results published in the last 1-10 years. It includes a total of 23,563 entries from academic and project databases, such as NTIS, DBPIA, KCI, and SCIENCEON. These entries are based on search keywords, like \"database indexing\", \"IoT applications\", \"cloud computing\", \"big data social network\", \"AI verification\", \"virtual reality\", and \"steering control\". Among all the collected research materials, 2460 name disambiguation candidate groups were created, targeting materials with the same author name appearing in two or more research outputs. The attributes of the collected data consisted of paper ID, co-authors, author ID, document title, academic journals and conferences, affiliation, publication year, etc. The performance evaluation of the proposed name disambiguation scheme consisted of its own performance evaluation, comparative performance evaluation with the existing name disambiguation schemes, and performance evaluation of the multi-classifier. To measure the accuracy of the proposed scheme, the precision, recall, and F1-measure were calculated. An intrinsic performance evaluation of the proposed scheme and a comparative evaluation with other name disambiguation schemes were conducted.
## 6 Discussion  
### Comparison of Test Results with Field Observations  
As mentioned above, the slide surfaces and buried depth in case 2 of model test are the same as these in the sections K1841 + 475-K1841 + 495 of the tunnel engineering in Section 2.3. Additionally, the buried depth and support parameters in the numerical simulation of lining loads also take from the same sections K1841 + 475-K1841 + 495. The lining cracks inspection results in sections K1841 + 475-K1841 + 495 could verify the reliability of the method and the model tests results. The difference between the four test cases is that the type of slide surface is different. By verifying the reliability of case 3, the reliability of other cases can be indirectly explained.  
The Figure 19 shows the comparison of inner surface cracks between test results of case 3 and lining inspection results in sections K1841 + 475-K1841 + 495. Because the field inspection of inner surface of secondary lining concerns the locations from right side wall to the left, so the corresponding inner surface cracks in model test are parts of cracks in case 3. The inspection results show that the cracks appeared in the vault and both side of side wall, and all cracks were longitudinal. The average length and width are 9.4 m and 0.9 mm for vault cracks, respectively, and 5.55 m and 0.6 mm for left sidewall cracks, respectively. The crack in right side wall is only 0.3 mm width and 2.3 m length, which is much narrower and shorter than the other cracks. It indicates that the vault cracks are more serious than on the left side wall, and that the cracking of left side wall is more serious than that of right side wall. The cracks distribution in case 2 of model test shows that the vault cracks much earlier than the left side wall. Because the vault of lining has the largest deformation (Figure 15c), the vault crack is wider than the crack in left side wall. The vault lining cracks also more serious than the left side wall. The right side wall has no cracks. Thus, the field inspection results of the tested section are basically consistent with the model test results, verifying the reliability of outcomes of the model test.