esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Validation of the Numerical Simulation  
The grid independence analysis was carried out taking into account three different meshes (coarse, mid, and fine) described in Table 6. This procedure assures the accuracy of the numerical results, considering its computational cost. As shown in Figure 11, the simulated torque results in the bucket located in the middle using the \"course\", \"mid\", and \"fine\" meshes converge during the transient analysis. The simulations performed using the denominated \"mid\" mesh lasted approximately eight hours and displayed a relative error between the real torque (16,695.92 [Nm]) and simulated torque (16,095 [Nm]) of 3.59%. Thus, CFD analyses were performed using the \"mid\" mesh parameters. Is it worth mentioning that the y+ on the simulation for the three grids tested was lower than 300, which is common in this kind of application [28].  
In Figure 12, which depicts the behavior of the three buckets over time, a periodic pattern indicative of the operation of a Pelton turbine can be seen. According to [28], only the middle bucket was examined, because it was the only one affected by the prior and following buckets in the flow during turbine operation. At t = 0.033 [s], the maximum simulated torque value was 16,095 [Nm], with a calculated hydraulic efficiency of 79.64%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Id** & **Element Max. Spacing** & **Elements** & **Layers** & **Layer Expansion Ratio** \\ \hline Coarse & 0.01 [m] & 668,342 & & \\ Mid & 0.009 [m] & 970,199 & 10 & \(1.1\) \\ Fine & 0.008 [m] & 1,225,341 & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Characteristics of the meshes obtained for the mesh independence study.  
Figure 11: Mesh independence study performed using three different meshes: coarse, mid and fine. (**a**) Full-scale torque curves for the mesh independence. (**b**) Magnified torque curves.  
The pressure distribution on the bucket surface is depicted in Figure 13 at various pressure levels, with a maximum pressure of \(1.7\times 10^{6}\) [kPa] at t = 0.033 [s], or the moment the water jet fully strikes the bucket. The maximum pressure region is as expected at the bottom of the bucket, and as the bucket advances, its area grows. A major contributor to cavitation, according to [24], is the Pelton bucket cutout, which has a minimum pressure of \(-1.5\times 10^{6}\) [kPa] at t = 0.033 [s].
# Modeling the Formation of Urea-Water Sprays from an Air-Assisted Nozzle
## 2 Experimental and Numerical Methods
### Computational Method
#### 2.2.1 Mesh Generation  
The computational mesh of the hot gas test bench contains the inflow section starting downstream of a turbulence grid and has a total length of 1060 mm from inlet to outlet in order to capture all four measurement positions chosen for the experiment. This also provides the opportunity to investigate the experimentally observed evaporation behavior of the urea-water droplets in the hot gas flow. The axial dimension of the mesh requires a local adaption of the cell size to keep the total cell count and the resulting computational effort manageable. Starting from a base cell size of 2\({}^{\circ}\)mm in the inflow and outflow area, the cells are step-by-step refined down to 125 \(\upmu\)m close to the nozzle in order to ensure an appropriate resolution of the momentum transport of the expanding gas jet and the momentum exchange with the liquid droplets. On the other hand, the cells are sufficiently big compared to the droplet size to comply with the numerical recommendations of the Euler-Lagrange approach. A spray image from the experiment of the near nozzle area is shown in Figure 3a. A plane cut along the center axis of the computational mesh at this near nozzle area is depicted in Figure 3b. Thereby, the refinements are denoted with their corresponding size, and the radial range of the first measurement positions downstream of the atomizer are illustrated.  
Figure 3: (**a**) Spray image of the near nozzle area taken from the experiment. (**b**) Local mesh refinements of the computational mesh in the near nozzle area with corresponding cell sizes and identification of the radial range of the first measurement position.  
The outflow channel of the nozzle is depicted by the model to provide defined boundary conditions for the simulation of the gas jet. With a bore diameter of 2 mm and a length of 2 mm, the channel is resolved with around 8200 cells. Using a cell size of 125 \(\upmu\)m at the nozzle exit is necessary to capture the high velocity gradients in the radial direction occurring during the expansion of the gas jet. This is of particular importance in the case of the present atomizer, since the gas jet reaches Mach numbers greater than one. The diameter of the nozzle orifice is resolved with 16 cells, which depicts a fine enough resolution. [PERSON] et. al. [21] found a resolution of 10 cells appropriate to reduce the grid dependence of the solution of an expanding gas jet, while [PERSON] et al. [22] recommended at least 8 cells. The mesh is coarsened 25 mm downstream of the nozzle exit to 250 \(\upmu\)m, which still provides a fine resolution of the jet. This refinement level covers the first measurement position 50 mm downstream of the atomizer (see Figure 3) and enables an evaluation of the radial velocity profile of the gas jet and the spray characteristics. An overview of all implemented mesh refinements and their dimensions related to the nozzle exit is given in Table 1.
# Enhancing Robustness of Per-Packet Load-Balancing for Fat-Tree
## 4 Simulation Results
### Long-lived flows in a 16-ary fat-tree with oversubscription ratios  
We simulated one link failure in a 16-ary fat-tree. We partitioned shortest paths into the same number of path groups as in an 8-ary fat-tree. Consequently, since each path group has more paths, the probability of packet loss due to congestion decreases. We disconnected a link connecting a ToR switch and an aggregation switch as in the tests for an 8-ary fat-tree. We generated five flows going to one pod: four flows coming from the pod containing the disconnected link and one flow from another pod. Figure 7a shows the result. For oversubscription ratio of 1:1, a link failure or a node failure does not significantly affect TCP flow completion time of the original scatter scheme. This was also the case when we simulated oversubscription ratio of 4:1 by reducing the bandwidth of the links connecting core switches and aggregation switches to 250 Mbps. However, when the oversubscription ratio was set to 8:1, the flow completion time of the original packet scatter scheme drastically increases, even when only one link is disconnected. In this case, there was no packet drop. However, it was observed that queue occupancy irregularly increased in many links connecting core switches and aggregation switches. It implies that the destination host suffered a lot of packet reordering. In contrast, the flow completion time of LBSP is not affected.  
Figure 6: Flow completion time in tests for an 8-ary fat-tree: (**a**) result of all-in-all short-lived flow tests; and (**b**) result of long-lived flow tests.
## 3 State-of-the-Art Solutions
### 12-Pulse Thyristor Rectifier with Active Shunt Power Filter (12-TRASPF)  
Considering the advantages and disadvantages of the above two topologies, a hybrid architecture, a 12-pulse thyristor rectifier with active shunt power filter (12-TRASPF), is proposed [16], as is depicted in Figure 6. The electrolyzer is supplied by the 12-TR, since it features lower costs and high-current capability. The power quality issues of the 12-TR  
Figure 4: Circuit diagram of a 12-pulse thyristor rectifier with passive trap filter (12-TR).  
are compensated by a low-power shunt active power filter. Although the loss and cost of the 12-TRASPF system can be more significant compared with the 12-TR, promising power quality can be expected.
# Research on Medical Text Parsing Method Based on BiGRU-BiLSTM Multi-Task Learning
## 3 Materials and Methods
### Multi-Task Learning Implementation  
#### 3.4.1 Model Construction  
The text parsing model based on multi-task learning includes the input layer, BERT coding layer, BiGRU layer, BiLSTM layer, and task-specific layer. The model schematic diagram is shown in Figure 9.  
For the hypothesis of the input sequence for \(X=(x_{1},x_{2},...,x_{t})\), first, extract the context representation of the input sentence through BERT:  
\[H=BERT(x)=[h_{1},h_{2},...,h_{t}]\]  
The data processed by BERT are used as the input of the BiGRU layer, and based on the output of BiGRU, the BiLSTM structure is used to enhance the capture of sequence features, and the output of BiGRU is used as the input of BiLSTM.  
\[\text{Forward GRU output: }\overset{\rightarrow}{h_{t}}=GRU_{f}(\text{BERT}(x_{t}), \overset{\rightarrow}{h_{t-1}})\] \[\text{Backward GRU output: }\overset{\leftarrow}{h_{t}}=GRU_{b}(\text{BERT}(x_{t}), \overset{\leftarrow}{h_{t-1}})\] \[\text{Combine hidden states in both directions: }h_{t}=\begin{bmatrix} \overset{\rightarrow}{h_{t}};\overset{\leftarrow}{h_{t}}\end{bmatrix}\] \[\text{Forward LSTM output: }\overset{\rightarrow}{h_{t}}=\text{LSTM} \begin{pmatrix} h_{t},\overset{\rightarrow}{h_{t-1}}^{\prime}\end{pmatrix}\] \[\text{Backward LSTM output: }\overset{\leftarrow}{h_{t}}^{\prime}=\text{LSTM} \begin{pmatrix} h_{t},\overset{\rightarrow}{h_{t+1}}^{\prime}\end{pmatrix}\] \[\text{Combine hidden states in both directions: }h_{t}=\begin{bmatrix} \overset{\rightarrow}{h_{t}}^{\prime};\overset{\leftarrow}{h_{t}}^{\prime} \end{bmatrix}\]  
The output from the shared layer is passed to a dedicated layer for each task.  
1. Implementing sequence tagging with CRF  
Figure 9: Multi-task learning model framework.  
A CRF layer is added on top of the BiGRU-BiLSTM layer for labeling sequences, enabling entity recognition and slot filling tasks. The CRF layer can capture the dependencies between labels in the sequence, further improving the accuracy of the annotation task.  
The CRF layer is designed for the NER and slot filling tasks, and the output is as follows:  
\[y_{ner}=CRF_{ner}\big{(}H_{seq}\big{)} \tag{25}\]  
\[y_{slot}=CRF_{slot}\big{(}H_{seq}\big{)} \tag{26}\]  
2. DPCNN implementation intention recognition  
For the intention recognition task, DPCNN is introduced for classification to help capture key short words and lexical patterns in intention recognition, improve the accuracy of intention recognition, and output the result through the SoftMax activation function, whose output result is as follows:  
\[y_{intent}=softmax(Wh_{DPCNN}+b) \tag{27}\]  
where \(y_{intent}\) is the output probability distribution; \(W\) is the weight matrix of all connected layers; \(h_{DPCNN}\) is the feature representation obtained after convolution and pooling of input data by DPCNN; and \(b\) is the offset of the fully connected layer.
## 4 The Considered Nodes' Synchronization and Data Transmission Protocol
### Optimization of the Activation Times in SYNCH Mode
To this aim, it is straightforward to derive:  
\[f_{t_{s+1}}\left(x\right)=f_{t_{s}}\left(x-1\right)\text{Prob} \left\{r_{s+1}\leq x-1\right\}+\left(\sum\limits_{n=2}^{\infty}f_{t_{s}}\left( x-n\right)\right)\text{Prob}\left\{x-2\leq r_{s+1}\leq x-1\right\} \tag{12}\] \[\qquad\qquad\qquad=f_{t_{s}}\left(x-1\right)\int\limits_{-\infty}^ {x-1}f_{r_{s+1}}\left(y|R_{s+1}\right)dy+\sum\limits_{n=2}^{\infty}f_{t_{s}} \left(x-n\right)\int\limits_{x-2}^{x-1}f_{r_{s+1}}\left(y|R_{s+1}\right)dy\]  
where the two terms in (12) correspond to the situations where \(r_{s+1}\leq t_{s}\) and \(r_{s+1}>t_{s}\), respectively. Note that in the second case, \(n\) represents the number of SYNCH packets transmitted by node \(s\) before node \(s+1\) may transmit its own packet. We are now in the position of evaluating the total average battery capacity depletion for each transmission cycle due to SYNCH and DATA transmission operations. To this aim, let us assume that, at each transmission cycle, each node produces a data packet with a given transmission probability \(p_{tx}\). Hence, we observe that according to the protocol depicted in Figure 7, the capacity depletion at node \(s\) for transmitting data after synchronization is due (on average) to three terms: (_i_) idle listening of \(T_{A}\) seconds (only if \(s<S\)); (_ii_) reception of \((s-1)p_{tx}\) data packets from node \(s-1\) (only if \(s>1\)); (_iii_) transmission of \(sp_{tx}\) data packets towards node \(s+1\). Accordingly, the total average capacity depletion can be derived as \(Q_{tot,s}=Q_{synch,s}+Q_{data,s}\), where:  
\[ \begin{split} Q_{synch,s}=\left\{\begin{array}{ll}Q_{txs}T_{P}+ C_{tx}&\text{if $s=1$}\\ Q_{txs}T_{P}+C_{tx}+Q_{lst,s}T_{P}+C_{lst}&\text{if $1<s<S$}\\ C_{tx}+Q_{lst,s}T_{P}+C_{lst}&\text{if $s=S$}\end{array}\right.\\ Q_{data,s}=\left\{\begin{array}{ll}p_{tx}C_{tx}&\text{if $s=1$}\\ sp_{tx}C_{tx}+(s-1)p_{tx}C_{lst}+T_{A}C_{lst}&\text{if $1<s<S$}\\ sp_{tx}C_{tx}+(s-1)p_{tx}C_{lst}&\text{if $s=S$}\end{array}\right.\end{split}  \tag{13}\]
## 2 Multi-Material Additive Manufacturing and Adhesion Phenomena
### Multi Material Additive Manufacturing in Science and Technology  
Material Extrusion (MEX) is an AM process that utilizes extrusion of thermoplastic polymers to fabricate a part layer by layer. For this purpose, pre-product in the form of a wire, called filament, is conveyed into an extruder, where it is heated until plastification and applied onto the previous layer. Other names for this process include fused deposition modeling (FDM, trademark of Stratasys) and fused filament fabrication (FFF) [9; 10]. As can be deduced from Figure 1, combination of materials even in the same layer is easily possible with MEX by use of several extrusion nozzles. Therefore, MEX is predestiined for multi-material additive manufacturing (MMAM). Hence, manufacturing of specimens is confined to MEX in this publication. Nevertheless, most results can be transferred to other AM processes.  
Figure 1: Schematic diagram of material extrusion principle and relevant adhesion mechanisms [11].  
MMAM can be categorized into discrete multiple materials, composite materials and porous materials. It is an effective way to increase performance of parts by utilizing material complexity offered by additive manufacturing. Especially combination of functional materials with conventional build materials has high potential for future applications. Strategic attachment of materials with different properties like thermal or electrical conductivity, hardness, temperature resistance, etc., allows for integration functions and reduction of parts [12].  
Acoustic functions via combination of elastic and stiff materials by material extrusion have been proposed in literature [1]. Furthermore, a way to realize functions by combining conventional materials with shape memory polymers via vat polymerization has been shown as well [2; 5]. Moreover, integration of conductive structures via MEX has been shown to be possible and can be utilized to produce heat radiant surfaces [3; 4; 6; 13].  
Since MMAM offers a multitude of new design potentials, designers need to be aided in their decision-making regarding material choice, machine operation and part design [14; 15; 16; 17]. Hence, this publication aims to give recommendations for this decision making process.
## 2 Literature Review
They used a class weight method to assign appropriate weights to imbalanced data and obtain accurate prediction results. [PERSON] et al. [22] demonstrated that datasets based on adverse events such as accidents, aircraft damage, or deaths have the problem of data imbalance. They embedded class weights in a long short-term memory model to create a classifier and effectively address the problem of imbalanced data. [PERSON] et al. [23] examined datasets recording traffic accidents in the United States by using a DNN with class weights to adjust the weights of specific data. They tested the model with data from major US cities and reported a considerable improvement in the prediction of abnormal traffic events. [PERSON] et al. [6] explored the problem of imbalanced data in the expressway system of Shanghai, which prevented the comprehensive capturing of spatiotemporal traffic flow characteristics. They used binary cross-entropy, \(\alpha\)-weighted cross-entropy, and focus loss to optimize the loss function of a convolutional neural network (CNN). They also used datasets with different ratios of crash and non-crash data to test the model. Their results indicated that CNN models with focal loss functions exhibited high classification performance with imbalanced data. This study makes the following contributions. First, time-series datasets for traffic accidents were established using the traffic flow data and geographical locations of ETC and VD devices and records of highway traffic accidents, thereby verifying the ratio of speed difference-related traffic accidents to all traffic accidents. Second, the identified association was used to label the periods and locations of traffic accidents. Third, data balancing techniques were used to address problems pertaining to imbalanced data. Finally, a DNN was established using the ETC and VD datasets to predict highway traffic accidents. The goal was to send accurate warnings to road users in advance and effectively prevent the occurrence of traffic accidents. The rest of this paper is organized as follows. First, crash prediction problems are examined and evaluated, followed by a description of the sources of immense multidimensional temporal data used in this study and their data processing procedures, data analysis methods, and research rationales. Subsequently, the use of data analysis and mining techniques to further explore the associations between traffic accidents, traffic speed differences, and shock wave propagation on national highways is presented. The aforementioned methods for processing imbalanced data are also discussed to establish a DNN model and predict the occurrence of traffic accidents. Finally, conclusions and recommendations for future applications of the proposed method and promotion of traffic safety are presented.
## 5 Limitations and Future Work  
In this study, the GDUI significantly improves the fidelity and diversity of generated images on target unlabeled datasets, and even outperforms the diffusion model based on ground-truth labels in most cases. However, both the clustering method we used and existing clustering methods assume prior knowledge of the number of clusters \(K\), and in order to match with the ground-truth labels, we also need to know the names of the true classes, which may not always be available in some cases. In the future, our method could be combined with image-to-text approaches to obtain the true textual information, not just the class information, for unlabeled datasets. Furthermore, our method can also be applied to tasks such as graph property prediction [57] and improving the adversarial robustness in classification tasks [58]. In future personalized photo customization [59], our approach is poised to produce higher-quality photos more effectively, while also better aligning with user preferences and specific needs. It provides assistance for the issue of annotating medical images, which requires domain experts [60; 61]. In addition, the diffusion model in this paper is based on a pixel-wise diffusion model, which incurs high training costs. However, the GDUI framework is a generic diffusion model framework designed for unlabeled datasets, and it can be adapted to be compatible with latent-based diffusion models in the future to achieve the goal of reducing training costs.
## 2 Results
### Detailed Analysis of Putative ORFs
#### 2.5.2 DNA Metabolism, Replication, Repair and Recombination  
The XaC1 and XbC2 phages encode several proteins involved in DNA replication, including DNA polymerases (XaC1, ORF342 and ORF411; XbC2, ORF259 and ORF276), which showed a high similarity to other phage polymerases. Moreover, the replication machinery of these Xanthomonas phages includes DNA ligases, ssDNA binding proteins, DNA primases, helicases, clamp loaders, topoisomerases and RNases. Meanwhile, for DNA metabolism, both phages encode for several genes involved in biosynthetic pathways, such as thymidylate synthase, dihydrofolate reductase and CMP/dCMP deaminase, and other enzymes such as cytidyltransferases and nucleotidyl transferase (Tables S9 and S10). In addition, several nudix and Mut/NUDIX hydrolases were identified in both phages; these proteins have been associated to nucleotide metabolism and DNA oxidative damage repair  
Figure 3: Comparison of codon usage between _Xanthomonas_ phages and _X. vesicatoria_. (**a**) XaC1; (**b**) XbC2. Blue dots represent codons where the tRNAs have a higher codon usage in the host; in red, codons where the tRNAs has a similar usage in phage and host; in yellow, codons where the tRNAs showed a higher usage in the phage.  
in bacteria [25]. Other repair genes are 2 OG-Fe(II) oxygenase (ORF458 in XaC1), pyrimidine dimer DNA glycosylase/endonuclease V (ORF528 in Xbc2) and phosphoglycolate phosphatase (ORF329 in Xbc2), which may be involved in DNA activation repair pathways due to alkylation, UV-induce lesions and the formation of 2'phosphoglycolate [26]. In the case of recombination, XaC1 and XbC2 encode one RecA-like protein and recombination-related endonucleases, which can be important during replication and repair (Tables S9 and S10).
# Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey
## 3 The Microsoft Kinect
### Technical Characteristics  
The Kinect is composed of different sensors that allow visual and sound data to be collected, consolidated, and analyzed; in other words, it offers restitution of what it films and hears within its operating range. It can capture three types of images: RGB, depth, and infrared. We collected the following technical information from Microsoft's official documentation [32] and books [33; 34; 35]. Table 1 presents the technical characteristics of each model. The Kinect V1 includes an RGB camera, a 3D depth sensor (infrared camera and CMOS detector), a three-axis accelerometer, a four-microphone network, and a tilt motor. The Israeli company PrimeSense developed its 3D vision technology. The data are continuously interpreted from the projected structured infrared light, which maps the infrared lightroom and creates a thermal map that identifies the distance between the lens and the scene for each pixel. The RGB camera allows the video stream to be recorded in 640 \(\times\) 480 at 30 fps. The depth sensor generates depth data in 320 \(\times\) 240 format at 30 fps. However, microphones allow for the spatial location of a sound source and the elimination of background noise. They collect sound data in 16 Khz. The motorized foot makes it possible to move the change of vision up and down by a value of 27\({}^{\circ}\). A USB 2.0 port and USB power adapter are required to communicate with a PC. Microsoft's minimum recommendations are a \(\times\)32 or \(\times\)64 processor of at least a dual 2.66 GHz type, 2 GB RAM, a Direct\(\times\) 9.0c-compatible graphics card, and a Windows 7 operating system or newer. The Kinect V2 contains an RGB camera with high definition of 1920 \(\times\) 1080 and a wide capture angle. It is possible to collect data at 30 fps under high light conditions or 15 fps under low light conditions. The 3D information is obtained by the depth sensor and the infrared emitter. Combining these two technologies makes it possible to obtain accurate data in 3D space even under dark lighting conditions. Version 2 uses the TOF method to generate a complete 3D image of the measured object. This principle makes it possible to measure the distance of a scene from the time required for light to reach the object and return. The depth sensor operates at a resolution of 512 \(\times\) 424 in 30 fps, and the IR sensor can be used at the same time as the color.