esa-sceva/satcom-chunk-collection huggingface dataset:
# New-Generation ASiR-V for Dose Reduction While Maintaining Image Quality in CT: A Phantom Study
## 3 Results
### Qualitative Analysis  
AsiR-V images for both NI22 and NI24 were graded as having significantly lower image noise scores than ASiR images. The best scores were obtained for ASiR-V NI22 in cluster 1 with respect to that of standard ASiR (67% vs. 0%, \(p\) = 0.001).  
For spatial resolution, ASiR-V NI22 cluster 3 had higher ratings than those of the standard ASiR, but there was no significant difference (78% vs. 50%, \(p\) = 0.37). Interestingly, ASiR-V NI30 cluster 2 had a higher rating than the standard ASiR (73% vs. 33%, \(p\) = 0.01).  
As expected, ASiR and ASiR-V introduced a number of artifacts in the reconstructed images in comparison to FBP. The worst scores were found for ASiR-V NI30 in cluster 3, while the best results were obtained for ASiR-V images with NI24.  
The images showed no statistically significant differences in terms of detectability.  
ASiR-V NI24 in cluster 3 resulted in higher ratings with respect to those of the standard ASiR, although not reaching statistical significance (78% vs. 33%, \(p\) = 0.08).  
The overall image quality was better for ASiR-V NI24 images that obtained the same FBP image scores. Moreover, the scores of all the images reconstructed with ASiR-V NI24 were significantly higher than those acquired with ASiR cluster 2 (89% vs. 33%, \(p\) = 0.02) and cluster 3 (83% vs. 33%, \(p\) = 0.02). Similar scores were obtained for ASiR-V NI24 cluster 1, although it was not statistically significant (78% vs. 33%, \(p\) = 0.08).  
The physicians' comments recorded during the qualitative analysis session about the reconstructed images included a \"plastic-like\" appearance of the ASiR-V image texture. The ASiR-V NI30 image series was also often described as having an overly smoothed appearance that might affect the diagnosis. As expected, all the physicians independently noted FBP images as the best ones compared with the images of other reconstruction procedures.
# DELER: Distributed Clustering with Local Direction Centrality and Density Measure
## 3 The DEALER Algorithm
### Computation of DCM and \(\rho\) for Local Clustering  
To facilitate a clearer introduction of the DEALER algorithm, we first present the efficient computation of \(\rho\) and _DCM_ values within each partition.  
Unlike the CDC algorithm, which relies solely on the _DCM_ metric to distinguish between boundary points and internal points within clusters, the proposed DEALER algorithm incorporates \(\rho\), a metric reflecting the sparsity of the region where a data point resides, into the _DCM_ calculation. As illustrated in Figure 3 in Section 1, this hybrid metric not only maintains the ability to identify sparse clusters but also alleviates issues related to weak connectivity, thereby enhancing the effectiveness of the clustering algorithm.  
Figure 8: Overall framework of the algorithm.  
Specifically, the algorithm operates in two steps: First, it computes the _DCM_ value and \(\rho\) value for each data point. Using the _DCM_ values, it performs an initial classification, dividing the points into core point candidates and boundary point candidates. Then, the core candidate set is further refined by leveraging the \(\rho\) values. Specifically, if a point within the candidate set for internal points has a relatively low \(\rho\) value, it is determined not to be an internal point. This screening process is applied to the candidate set, effectively distinguishing internal points from boundary points. Compared to traditional directional-center clustering methods, the incorporation of \(\rho\) effectively mitigates weak connectivity issues. Meanwhile, in contrast to conventional density-based clustering, the directional-center assessment based on _DCM_ values demonstrates superior performance in identifying sparse clusters. Under our proposed distributed computing framework, once the data partitioning is complete, the \(\rho\) and _DCM_ values for data points within each partition can be computed independently, without requiring inter-node communication.
###### Abstract  
During the exploration and visualization of big spatio-temporal data, massive volume poses a number of challenges to the achievement of interactive visualization, including large memory consumption, high rendering delay, and poor visual effects. Research has shown that the development of distributed computing frameworks provides a feasible solution for big spatio-temporal data management and visualization. Accordingly, to address these challenges, this paper adopts a proprietary pre-processing visualization scheme and designs and implements a highly scalable distributed visual analysis framework, especially targeted at massive point-type datasets. Firstly, we propose a generic multi-dimensional aggregation pyramid (MAP) model based on two well-known graphics concepts, namely the Spatio-temporal Cube and 2D Tile Pyramid. The proposed MAP model can support the simultaneous hierarchical aggregation of time, space, and attributes, and also later transformation of the derived aggregates into discrete key-value pairs for scalable storage and efficient retrieval. Using the generated MAP datasets, we develop an open-source distributed visualization framework (MAP-Vis). In MAP-Vis, a high-performance Spark cluster is used as a parallel preprocessing platform, while distributed HBase is used as the massive storage for the generated MAP data. The client of MAP-Vis provides a variety of correlated visualization views, including heat map, time series, and attribute histogram. Four open datasets, with record numbers ranging from the millions to the tens of billions, are chosen for system demonstration and performance evaluation. The experimental results demonstrate that MAP-Vis can achieve millisecond-level query response and support efficient interactive visualization under different queries on the space, time, and attribute dimensions.  
Keywords:spatial-temporal data; multi-dimensional; hierarchical aggregation; distributed database; visual analysis +
Footnote †: journal: Applied Sciences
# Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks
## 2 Related Work
In YEAST algorithm, those nodes detecting the same event are grouped in a cluster and the cluster head is the node closest to the sink [20]. The cluster is divided into spatially correlated cells, and only one node within each cell transfers its data to its cluster head, which stand for all the sensing data in this cell. Cells can be resized dynamically according to the application requirements. However, the bigger cells become, the less the entropy is. Since a representative node's sensing area could not cover the whole cell, YEAST causes low accuracy to some extent. For several synchronous events, DRINA [21] tends to maximize the number of aggregation nodes and decrease the overhead of control packets. Nodes sensing the same event form a cluster, and a route for a new event is connected with an already established route which has the shortest path between them. Experiment results show that DRINA has a high aggregation rate, and reliable data aggregation and transmission. Nevertheless, it increases the overload of nodes in existing routes, and thus leads to unbalanced energy consumption and even the energy-hole problem. In the above aggregation schemes, those nodes located in a particular area usually compose a cluster. If the area is too large, the similarity of data gathered by member nodes is small; if the area is too small, the advantage of data aggregation is degraded. In order to balance aggregation efficiency and data accuracy, we explore the relation of sensing areas of nodes to deal with redundant data, and utilize a threshold of overlapping sensing area to guarantee high accuracy after data aggregation.
## 2 Materials and Methods
### Ion Exchange Capacity (IEC) and Water Uptake  
The ion exchange capacity (_IEC_) of the membranes was estimated using the standard titration method [34]. Samples in acid form were immersed in a 2M NaCl solution for 24 h at room temperature to completely release H\({}^{+}\) via exchanging with Na\({}^{+}\). The amount of released H\({}^{+}\) was titrated with a standard NaOH solution (0.1 M), using phenolphthalein as an indicator. The _IEC_ values (meq g\({}^{-1}\)) were calculated according to Equation (1) and reported as an average of at least three independent measurements:  
\[IEC=\frac{C_{(NaOH)}\ V_{(NaOH)}}{W_{dry}};\text{meq g}^{-1} \tag{1}\]  
where \(C_{(NaOH)}\) is the concentration and \(V_{(NaOH)}\) is the volume of NaOH solution consumed during titration.  
From the _IEC_ value, the sulfonation degree (\(DS\%\)) of the polymer was determined by Equation (2):  
\[DS[\%]=\frac{M_{(p)}\times IEC}{1000-\left(IEC-M_{(f)}\right)}\times 100;\ \% \tag{2}\]  
where \(M(p)\) is the molecular weight of the PEEK repeat unit without the functional group, and \(M(f)\) is the molecular weight of the sulfonic group (-SO\({}_{3}\)H). From the difference between the wet and dry mass of each membrane, the water uptake (_w.u._) was measured. In detail, a rectangular shaped sample was directly cut from the membrane and dried in a conventional oven @ 120 \({}^{\circ}\)C for 18 h to obtain the dry mass (\(m_{dry}\)). The sample was then swelled by immersing it in distilled water for 24 h at 20 \({}^{\circ}\)C and at higher temperatures, in particular from 30 to 80 \({}^{\circ}\)C each 10 \({}^{\circ}\)C, for 2 h. After removing the sample from the distilled water, the surface was rapidly wiped with blotting paper to eliminate water droplets and was quickly weighted (\(m_{wet}\)). Accordingly, the water uptake was then calculated by Equation (3):  
\[w.u.=\frac{m_{wet}-m_{dry}}{m_{dry}}\times 100;\ \% \tag{3}\]  
Therefore, the \(\lambda\) value, i.e., the number of water molecules per -SO\({}_{3}\)H group, was calculated by Equation (4):  
\[\lambda=\frac{w.u.}{IEC\times M_{w}} \tag{4}\]  
where _Mw_ is the molecular weight of water (18.01 g mol\({}^{-1}\)).  
Figure 1: Pictures of pristine sPEEK and sPEEK_PSLM composites (at 1, 3, and 5% of filler loading) membranes.
## 3 Research Design
### Forecasting Performance Evaluation Criteria  
In order to verify the validity of the proposed model in this paper, we select three generally adopted error indexes to evaluate the performance of proposed model compared with other models, including mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The paper comprehensively evaluates the forecasting performance by these three methods. The computational equations of these three error methods are given as follows:  
\[MAE=\frac{1}{N}{\sum\limits_{t=1}^{N}}|\hat{y}(t)-y(t)| \tag{19}\]  
\[RMSE=\sqrt{\frac{1}{N}{\sum\limits_{t=1}^{N}}(\hat{y}(t)-y(t))}^{2} \tag{20}\]  
\[MAPE=\frac{1}{N}{\sum\limits_{t=1}^{N}}|\frac{\hat{y}(t)-y(t)}{y(t)}| \tag{21}\]  
where \(N\) is the number of the testing sample, \(y(t)\) is the actual data of each dataset, and \(\hat{y}(t)\) is the forecasting value of the corresponding futures prices data.
# DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering
## 4 Methodology
### Tag-Aware GCN
#### 4.1.2 Graph Convolution  
The core of graph convolution is to design appropriate aggregation function for integrating the features of neighbors effectively. Many researchers have proposed different aggregation methods, including the weighted sum aggregator [48], LSTM aggregator [49] and attention-based aggregator [50]. Moreover, most of the related studies have also introduced feature transformation or nonlinear activation into the aggregator. Although achieving good performance on node or graph classification with rich semantic input features, they may be cumbersome for the recommendation task, where each node is only represented by the ID embeddings.  
Based on these considerations above, we adapt the similar graph convolution operators on tag-aware graphs used in LightGCN [26], where only the most essential component of GCN, namely neighbor aggregation, is retained. Suppose that \(e_{u}^{(k)}\) and \(e_{ut}^{(k)}\) are the embeddings of user \(u\) and tag \(t\) at the \(k\)th layer, respectively. The aggregation function on the user-tag graph can be defined as:  
\[\begin{array}{l}e_{u}^{(k+1)}=\sum_{t\in N_{u}}\frac{1}{\sqrt{|N_{u}|}\sqrt{| N_{ut}|}}e_{ut}^{(k)},\\ e_{ut}^{(k+1)}=\sum_{t\in N_{ut}}\frac{1}{\sqrt{|N_{u}|}\sqrt{|N_{ut}|}}e_{ut}^ {(k)},\end{array} \tag{4}\]  
where \(N_{u}\) and \(N_{ut}\) represent the neighbor set of users and tags in the user-tag graph \(G_{u}\), respectively. Following the standard GCN [51], we utilize the normalization term \(\frac{1}{\sqrt{|N_{u}|}\sqrt{|N_{ut}|}}\) to prevent the increase scale of embeddings caused by the graph convolution operator. A similar aggregation operator is performed on the item-tag graph at the \(k\)th layer to generate item representation \(e_{i}^{(k+1)}\) and tag representation \(e_{it}^{(k+1)}\). It is worth noting that, except at the 0 th layer, the obtained tag representations from two graphs at \(k\)th layer are obviously different. By applying GCN on the user-tag graph (item-tag graph), node embeddings can propagate back and forth between users (items) and tags. Consequently, the learned tag representations \(e_{ut}\) and \(e_{it}\) can describe the user interests and item characteristics, respectively.  
After performing the \(k\)th graph convolution operator, the proposed model can produce \(k\) embedding vectors for each node, which are from different receptive fields and thus have different semantic information. To combine those embeddings of different layers, we leverage the simple weighted sum function to generate the final representation for each node, which is verified to be effective and workable [26]. Specifically, we define the readout function of combining outputs of all \(K\) layers as:  
\[e_{u}=\frac{1}{K+1}\sum_{k=0}^{K}e_{u}^{(k)},\ \ \ e_{i}=\frac{1}{K+1}\sum_{k=0}^{K}e_{i}^ {(k)},\ \ \ e_{t}=\frac{1}{K+1}\sum_{k=0}^{K}e_{u}^{(k)}, \tag{5}\]  
where \(e_{u}\), \(e_{i}\), and \(e_{t}\) are the final representation of users, items, and tags, respectively.
## 5 Results and Discussion
### Seismic Resistance Analysis  
The strength-level analysis is essential to ensure that the platform exhibits adequate strength and stiffness, thereby preventing significant structural damage during seismic events. The proposed seismic design acceleration spectra correspond to the envelope of the expected mean values of the maximum accelerations in the zone of interest, rather than the envelope of the absolute maximum accelerations. This condition necessitates an ultimate strength analysis to verify sufficient safety factors (reliability indices). At the strength level, the seismic design acceleration spectra are defined for a return period of 20 years. In this study, the most common reinforcement solutions for structural strengthening are presented as illustrative recommendations rather than exhaustive prescriptions.  
Three identical models were developed, each differing in the configuration of master nodes (see Figure 4). The results indicate that the second model represents the most critical case for seismic design, with the following key findings:  
Figure 7: Summary of dynamic storm analysis results.  
* The maximum unity check (UC) occurs in element 2289-1B28, with a value of 1.001 > 1.00 (exceeding the allowable limit). To address this, the following reinforcement strategies are proposed: \(\circ\) Increasing the thickness of the web or flange. \(\circ\) Encasing the section with lateral plates to form secondary and tertiary webs at the extremities. \(\circ\) Enhancing the yield strength of the material.
* Regarding the assessment of tubular joints, joint 193 exhibits a U.C. of 1.048 > 1.00 (exceeding the allowable limit). The following strengthening solutions are recommended: \(\circ\) Increasing the tube thickness. \(\circ\) Reinforcing with stiffener plates. \(\circ\) Injecting cementitious material inside the tube for additional stiffness and load-bearing capacity.
* U.C. in Piles and Load Safety Factors in the Foundation. The piles were assessed to ensure compliance with design requirements. The maximum U.C. for piles above the seabed (mudline) was 0.530, whereas for those below the seabed, it was 0.504, with both values being within acceptable limits. Furthermore, the safety factor for axial load in the piles was 2.04 > 2.00 (meeting the minimum required threshold). These results confirm that the foundation adheres to the parameters established in the applicable design standards, as illustrated in Figure 8.
## 2 Road Network  
The city road network can be obtained by several methods: using Wireless Sensor Networks (WSN) [7], acoustic signal [8], etc. Like in our previous study about the city Duisburg inner ring area [9], the road network used in this work was extracted from OSM (Open Street Map) [10]. A rectangular area encompassing the area we need was extracted from OSM. We used JOSM [11] to edit the exported network from OSM and determined the boundaries of the road network. During the process of importing the road network into SUMO, the public transportations lines such as railway, subway, and bus-only lanes were ignored. This study focuses on the individual vehicles on the road, which are the main reason for the occurrence of traffic jams. What is more, there were no pedestrians or cyclists simulated.  
The calibration of the road network is a time-consuming task. However, it is important for the precision of the simulation system.  
To ensure that each street is correct, we compared the network from OSM with Google Street View [12]. Common defects of the road network are:  
* Divided intersections. Large intersections are always divided into smaller ones, especially those with a cover (e.g., viaducts and green belts) in the middle. The extra intersections are often accompanied by the extra unlinked traffic lights, which increase the possibility of unrealistic traffic jams.
* Occurrence of extra roads. Sometimes, the non-motorized roads or the dedicated roads are included in the normal motorways, however in street view, some isolation piles or stop signs can be found at the road entrance.
* Missing roads. There are no two-directional ways in SUMO. However, they are common in residential areas. Many people have a single-lane road leading to their garage, responsible for both incoming and outgoing directions. In SUMO, we had to add the reverse lanes for these kinds of roads. What is more, there were also other road-missing conditions. An example can be seen in Figure 1. In this case, a part of a ring road in the parking lots is missing. It causes a dead-end in Figure 1. After comparing with the satellite map in Figure 1 and the street view map in Figure 1, the missing sections were manually filled in. The modified road network of the parking lots is in Figure 1.
* Reverse directions. In some cases, some lanes on a road may be in the wrong direction, which affects their connection with other roads. This kind of mistake is less likely than the other three but is also a common error in road network imported from OSM.
## 3 Results
### Temperature Distribution within the Process Zone
#### 3.4.1 Spatial Temperature Profile  
After clarifying the geometrical properties of the cut, Figure 13 shows exemplarily sequences of the four following temperature distributions for each set. The temperature peak for static beam shaping (Figure 13a) remains at almost the same position with a medium temperature level. In contrast, the other sequences (Figure 13b-d) depict alternating temperature distributions regarding position and temperature level. This represents the additional beam movement due to beam oscillation.  
A closer consideration of the particular temperature measurements delivers the spatially resolved temperature profiles in Figure 14. The position 0 mm is forerunning to the cut front. Starting at 0 mm, the temperature increases from below melting temperature (1450 \({}^{\circ}\)C) until reaching the peak and finally decreases below melting temperature again. As indicated in Figure 13a, the four measurements of set #1 create almost coincident charts in Figure 14a. The peak temperature of set #1 differs between 1939 and 2248 \({}^{\circ}\)C during the four measurement moments. The position of the peak temperature changes in the range of 0.33 mm \(\pm\) 0.02 \(\upmu\)m. Approximately the position range above melting temperature (approximately 0.1 to 0.8 mm) belongs to the cut front.  
Figure 13: Sequence of four measurement moments for temperature distribution \(\text{T}_{\text{ij}}\)(t) calculated with e = 1 of 12 mm stainless steel cut with the following parameters: (**a**) Set #1: Static beam shaping with 488 fps; and different values of the longitudinal, linear beam oscillation parameters with 1360 fps; (**b**) Set #2: 300 \(\upmu\)m amplitude and 500 Hz frequency; (**c**) Set #3: 550 \(\upmu\)m amplitude and 500 Hz frequency; (**d**) Set #4: 300 \(\upmu\)m amplitude and 2000 Hz frequency.  
The spatially resolved temperature profiles of the beam oscillations behave quite different than static beam shaping. At first, the four measurement moments of one set are not coincident anymore. Second, beam oscillation achieves distinctly higher peak temperatures. Set #2 (Figure 14b) comprises e.g., temperatures between 2205 and 2543 \({}^{\circ}\)C and is comparable with Set #3 (Figure 14c), while set #4 (Figure 14d) reaches the highest temperature at 2797 \({}^{\circ}\)C. Third, the rise and fall of the temperature curves is either flatter or steeper compared to static beam shaping. Fourth, the position range above melting temperature is significantly enlarged, which will be further discussed in Section 3.4.4.