esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of PolyJet 3D Printing in Production of Flexographic Printing Plates
## 2 Materials and Methods
### Testing of Printting Plates  
In order to check the properties of the fabricated printing plates, chemical resistance and contact angle measurements were acquired.  
The chemical resistance test was carried out by swelling based on PN-EN ISO 175:2010 [38]. Solvents widely used in the printing process and flexographic inks, namely distilled water, ethyl acetate, ethyl alcohol, acetone, isopropyl alcohol, propyl alcohol, a mixture of ethyl acetate and ethanol (chesol), water-based flexographic ink, solvent-based flexographic ink, and UV flexographic ink, were selected for the study. A 3D-printed plate sample and a conventional plate sample with dimensions 10 mm \(\times\) 20 mm \(\times\) 1.7 mm were used. Before starting the test, the width, length, thickness of the samples were measured using a caliper. The samples were then placed in beakers filled with either solvent or printing ink. Then, after 1, 2, or 4 h respectively, the samples were removed, dried with blotting paper, and the same dimensions were measured. Chemical resistance was determined as the percentage increase in sample volume calculated based on the change in sample dimensions. The volume value of the samples was calculated by multiplying the linear dimensions of the samples by themselves. The increment was calculated from Equation (2),  
\[x_{n}=\frac{x-x_{0}}{x_{0}}*100 \tag{2}\]  
where the following variables are presented:  
x\({}_{n}\)--percentage increase,  
x--value of volume at a given time,  
x\({}_{0}\)--value of volume before immersion of samples.  
Surface contact angle measurements of both conventional and 3D-printed plates were carried out on a DSA30E (Kruss, Hamburg, Germany) using distilled water and diiodomethane 99% CH\({}_{2}\)I\({}_{2}\) (Sigma-Aldrich, Taufkirchen, Germany). The Tangent method was used for the study. Drops of the substance with a constant volume of about 4 mL were applied to the surface of the printing plates and the contact angles were measured 15 s after the liquid drop was applied. The tests were carried out in constant environmental conditions and temperature 23 \(\pm\) 1 \({}^{\circ}\)C. The results presented are the arithmetic averages of 5 measurements for each of the plates and measuring liquids. The determined values of the contact angle were used to calculate the values of the surface free energy of printing plates and their polar and dispersion components. The Owens-Wendt method [39] was used for the calculations.
# A Novel QoS Provisioning Algorithm for Optimal Multicast Routing in WMNs
## 3 The Proposed NMHA Algorithm
### The Procedure of the NMHA Algorithm  
The detailed procedure of NMHA is presented as shown in Algorithm 1.  
```
Input:\(\mathbf{G}(\mathbf{V},\mathbf{E},\widetilde{\mathbf{W}},\widetilde{\mathbf{L}})\), the source node \(s\) and the set of destination nodes \(\mathbf{D}\), and the approximation ratio \(\varepsilon\) Output:\(\mathbf{T}^{\mathrm{opt}}\)
1. For each \(e\in\mathbf{E}\) in \(\mathbf{G}(\mathbf{V},\mathbf{E},\widetilde{\mathbf{W}},\widetilde{\mathbf{L}})\)
2. \(w_{i}^{\mathrm{N}}(e)=\left\lfloor\frac{w_{i}(e)}{\mathbf{L}_{i}}\frac{n-1}{ \varepsilon}\right\rfloor\);
3. set \(\tau=\mathbf{L}_{1}^{\mathrm{N}}=...=\mathbf{L}_{k}^{\mathrm{N}}=\left\lceil \frac{n-1}{\varepsilon}\right\rceil\);
4. end for
5. \(\rightarrow\mathbf{G}_{\mathrm{K}}^{\mathrm{N}}(\mathbf{V}_{\mathrm{K}}^{ \mathrm{N}},\mathbf{E}_{\mathrm{K}}^{\mathrm{N}},\mathbf{W}_{\mathrm{K}}^{ \mathrm{N}},\tau)\);
6. Choose any node \(d_{1}\) from the set \(\mathbf{D}\) of multicast destination nodes, initialize generating tree \(\mathbf{T}_{0}^{\mathrm{opt}}=\{s\}\), \(\mathbf{V}_{\mathbf{T}_{0}}=\{s\}\);
7. for every node \(v\in\mathbf{V}\) and \(\mathrm{C}_{i}=0\) to \(\tau\), \(2\leq i\leq\mathrm{K}\), do
8. \(\mathbf{P}(v,\mathrm{C}_{2},\mathrm{C}_{i},...,\mathrm{C}_{\mathrm{K}}) \leftarrow\infty\); \(\mathbf{P}(s,\mathrm{C}_{2},\mathrm{C}_{i},...,\mathrm{C}_{\mathrm{K}}) \gets 0\);
9. end for
10. for \(j=1:q\) do
11. for every adjacent node \(v(v\
otin\mathbf{T}_{j-1}^{\mathrm{opt}})\) of node \(d_{j}\), where \(d_{j}\in\mathbf{T}_{j-1}^{\mathrm{opt}},v\
otin\mathbf{T}_{j-1}^{\mathrm{opt}}\) and \(\mathrm{C}_{i}\leq\tau\) do
12. if \(\mathbf{P}(v,\mathrm{D}_{2},\mathrm{D}_{i},...,\mathrm{D}_{\mathrm{K}})> \mathbf{P}(d_{j},\mathrm{C}_{2},\mathrm{C}_{i},...,\mathrm{C}_{\mathrm{K}})+ w_{1}(u,v)\) where \(\mathrm{D}_{i}=\mathrm{C}_{i}+w_{i}(d_{j},v),2\leq i\leq\mathrm{K}\)
13. then \(\mathbf{P}(v,\mathrm{D}_{2},\mathrm{D}_{i},...,\mathrm{D}_{\mathrm{K}}) \leftarrow\mathbf{P}(d_{j},\mathrm{C}_{2},\mathrm{C}_{i},...,\mathrm{C}_{ \mathrm{K}})+w_{1}(u,v)\)
14. \(v\gets d_{j}\);
15. \(\mathbf{T}_{j}^{\mathrm{opt}}\leftarrow\) node \(v\) and edge \((d_{j},v)\);
16. end if
17. end for
18. \(w_{1}(d_{j},\mathbf{T}_{j-1}^{\mathrm{opt}})=\min\{w_{1}(d_{k},\mathbf{T}_{j-1 }^{\mathrm{opt}})\Big{|}d_{k}\in\overline{\mathbf{D}\cap\mathbf{T}_{j-1}^{ \mathrm{opt}}}\}\); \(\mathbf{T}_{j}^{\mathrm{opt}}=\) PATH\(\left(d_{j},\mathbf{T}_{j-1}^{\mathrm{opt}}\right)\cup \mathbf{T}_{j-1}^{\mathrm{opt}}\)
19. end for
20. if \(w_{1}^{\mathrm{N}}(\mathbf{T}_{j-1}^{\mathrm{opt}})>\tau\)
21. then return. No feasible multicast routing tree, exit;
22. else, \(\mathbf{T}_{j}^{\mathrm{opt}}=\) PATH\((d_{j},\mathbf{T}_{j-1}^{\mathrm{opt}})\cup\mathbf{T}_{j-1}^{\mathrm{opt}}\); \(j=j+1\);
23. end if
24. end for
25. \(\mathbf{OUTPUT}\)\(\mathbf{T}^{\mathrm{opt}}\);
```  
**Algorithm 1**NMHA algorithm
## 4 Model Simulations with FEM-3D
### Simulation Results in Standstill Conditions
#### 4.3.2 Effect of Adding Two Slots of Dielectric into the Secondary Part  
Model 2 incorporated two slots into the aluminum layer (see Figure 6) with a width of 1 mm. These two new regions of air allowed for a decrease in the negative thrust force generated over the lateral teeth. With these new three regions of aluminum electrically insulated by the slots, the generated eddy currents into the aluminum layer had a different behavior with respect to model 1. Figure 15 shows the principal forces in this model. Considering their average values, the increment in the thrust force with respect to model 1 was about 20 N (an increment around 23.7%). However, the levitation force decreased to 200 N (a decrement around 49.46%).  
Figure 16 presents a new distribution of eddy currents. The new slots generated new paths into the aluminum layer. Now, there were three loops of eddy currents along the x-axis. With this configuration, it was possible that over each tooth (central and lateral), the vector product between \(\overrightarrow{J}\) and \(\overrightarrow{B}\) generated a force along the direction of the movement, but at the same time, the levitation force decreased considerably.  
Figure 16: Spatial distribution of eddy currents in model 2.  
Figure 15: Forces in model 2.  
Figure 17 represents the consumed electric currents in model 2. Every phase decreased approximately 2 A, compared to model 1. This effect supposes a little raise in the efficiency of the LIM.
# Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
## 4 The PingBasedDown Algorithm
### Link Evaluation Model
#### 4.1.5 Judgement Module  
Recall the very first of this section, we define 3 states of link quality:  
* Healthy, which stands for normally working link without packet loss;
* Unhealthy, it stands for abnormal link with packet losses;
* Pending, which stands for fuzzy link which may need further detection.  
To determine which state the link should be, we compare the \(\hat{\mathcal{A}}\) with two thresholds, namely safe threshold and alert threshold, denoted by \(T_{safe}\) and \(T_{alert}\), respectively. In addition, the link status is determined by the following equation:  
\[I_{n}(v_{i},v_{j})=\begin{cases}Unhealthy,&\hat{\mathcal{A}}\geqslant T_{ alert},\\ Pending,&T_{alert}>\hat{\mathcal{A}}>T_{safe},\\ Health,&\hat{\mathcal{A}}\leqslant T_{safe}.\end{cases} \tag{7}\]  
Two parameters of \(T_{safe}\) and \(T_{alert}\) determine the sensitivity of Link Evaluation Model on link failures. A higher \(T_{alert}\) makes the model more stable and decreases the false alarm rate when working on noisy networks. However, an exorbitant \(T_{alert}\) also makes the model hard to detect a link failure. Moreover, when the normalized accumulated jitter \(\hat{\mathcal{A}}\) is lower but very closed to \(T_{alert}\), it indicates that the link quality is fuzzy. To make our model more robust, we must prevent giving a Healthy mark on fuzzy links. Therefore, we propose the \(T_{safe}\) threshold. A lower \(T_{safe}\) makes the model give a Healthy mark of a link more strictly. In the datacenter environment where the nodes in the cluster are physically closed to each other, we believe that the jitter rate of latency there is low and therefore a low \(T_{safe}\) shall be set. As for other scenarios such as cloud services, \(T_{safe}\) shall be set to a higher value to make the model properly working. These two thresholds can be determined either statically by empirical values or dynamically by adaptive algorithms. In the simulation, we set the thresholds statically based on a long statistical data.
# Recognition of Customers' Impulsivity from Behavioral Patterns in Virtual Reality
## 2 Materials and Methods
### Data Preprocessing
#### Feature Extraction  
Eye-tracking (ET) features extracted using simple kinematic definitions and data points were classified into fixation and saccades using the algorithm presented in [71]. Moreover, the following signals were defined. Navigation (NAV) is the projection of the head position in the 2D environment on the floor plan. Posture (POS) was 3D head and hand positions. All the interactions with the products such as picking, dropping, and purchasing were considered to be interaction (INT). Since the INT set had a low number of features, POS and INT features were considered in a single set, which was called POS & INT. To investigate the whole customer behavior, a feature set was created by putting all the ET, NAV, and POS & INT features together. This new feature set was named Human Behavior Tracking (HBT). After feature extraction, 33, 68, 157, and 258 features were extracted for ET, NAV, POS & INT, and HBT, respectively.  
For all the features except INT, two main categories were defined. The first category, called general features, considered the whole experiment, and the second category, called zonal features, applied to areas of interest (AOI) and zones of interest (ZOI). In both categories, three sub-categories were defined: temporal, spatial, and kinematic, which are related to time, space, and movement, respectively. Hence, each set of features comprises  
Figure 6: (a) The virtual shelf containing the sneakers, (b) the position of the target shelf in the virtual store.  
six sub-categories: general-temporal, general-spatial, general-kinematic, zonal-temporal, zonal-spatial, and zonal-kinematic.  
In terms of ZOIs, the floor plan was segmented into four ZOIs: Shelf, Adjacent, Near, and Far, based on the proximity to the shelves. As shown in Figure 7a-c, all the zones with the same color are in one ZOI and the rest of the areas fall into the Far zone. Besides, as it is presented in Figure 7d, in all the ZOIs except Far, the vertical dimension of the shop was divided into three levels, namely Up, Middle, and Down based on the levels of the shelves. By combining the ZOIs and levels, the space in the virtual shop was divided into AOIs, which are called Shelf_Down, Adjacent_Up, etc. Note that, in the Far zone, the whole vertical dimension is considered to be one level. This means that, in that ZOI, the entire space is considered to be one AOI, which is called \"Far\".  
Using a genetic algorithm (GA) with a modified Fisher criterion as a cost function, the optimal widths of Adjacent and Near were set at 18 and 13 cm, respectively [19]. Note that the lengths of these two zones are fixed and equal to the lengths of the shelves. As an exception, in the planned tasks, the Near zone covers all the front space of the shelf after the Adjacent zone; only the width of Adjacent had to be determined. This width was also considered to be 18 cm, as it is found in the exploration task.
# STOD: Towards Scalable Task-Oriented Dialogue System on MultiWOZ-API
## 2 Related Work  
### Task-oriented Dialogue System  
The **task-oriented dialogue system** aims to assist the user in completing certain tasks in a specific domain, such as restaurant booking and flight booking [3]. Existing studies can be broadly classified into two categories: pipeline and end-to-end methods. The former is typically divided into several modules, including natural language understanding (NLU) [4; 5; 6], dialogue state tracking (DST) [7], dialogue policy (Policy) [8; 9], and natural language generation (NLG) [10]. In contrast, end-to-end methods [11; 12; 13; 14; 15] build the system using a single model, which directly takes a natural language context as input and outputs a natural language response as well. Recent advances in large-scale pre-trained language models (PLMs) [16; 17] boost the development of end-to-end dialogue models. [PERSON] et al. [11], [PERSON] et al. [12], and [PERSON] et al. [13] generally follow a similar multi-task learning  
Figure 1: The comparison of our framework with the existing framework. The upper part indicates the interactions between the user, system, and database in the existing framework.  
paradigm: build a unified model by simultaneously learning multiple pipeline subtasks based on PLMs. [PERSON] et al. [14] and [PERSON] et al. [15] further mitigate the exposure bias of cascade generation. However, these methods highly rely on a pre-defined domain schema, e.g., the slots of the restaurant domain contain address area, cuisine type, price range, etc. They do not consider the pre-defined ontology in the model design and, therefore, suffer from poor generalization and scalability ability to new domains. They are dependent on heuristic rules to transfer dialogue states (belief spans) to database queries, which are not totally end-to-end.
## 3 Formation Controller Design
### Reference Trajectory Replanning  
The reference velocities can be obtained as  
\[\begin{split}\
u_{ri}=&\
u_{di}+\
u_{ai}\\ =&\sqrt{\frac{\partial x_{di}\left(s\right)^{2}}{ \partial s}+\frac{\partial y_{di}\left(s\right)^{2}}{\partial s}}\dot{s}-c_{3 i}\Psi_{vi}\\ \omega_{ri}=&\omega_{di}+\omega_{ai}\\ =&\frac{\frac{\partial x_{di}\left(s\right)}{ \partial s}\frac{\partial^{2}y_{di}\left(s\right)}{\partial s^{2}}-\frac{ \partial^{2}x_{di}\left(s\right)}{\partial s^{2}}\frac{\partial y_{di}\left(s \right)}{\partial s}\frac{\partial x_{di}\left(s\right)}{\partial s}}\dot{s}- c_{4i}\Psi_{vi}\\ \end{split} \tag{10}\]  
It should be noted that the adjusted component \(\left[\begin{array}{cc}\Psi_{vi}&\Psi_{vi}\end{array}\right]^{T}\) is equal to 0 when the robot is outside the detection region.  
Then, define a switch coefficient  
\[\eta=\left\{\begin{array}{cc}1,&r\leq d\leq R\\ 0,&R<d\end{array}\right. \tag{11}\]  
Figure 4: Trajectory adjustment for collision avoidance via the potential function method.  
Therefore, the reference trajectories can be replanned as  
\[R_{ri}(t)=(1-\eta)R_{di}(t)+\eta R_{ai}(t) \tag{12}\]  
where \(R_{di}(t)\) are basic trajectories and \(R_{ai}(t)\) are adjusted trajectories.  
The adjusted trajectories \(R_{ai}(t)\) are generated by the reference velocities when the robot gets close to the obstacle. It can be obtained as  
\[\begin{split} R_{ai}(t)&=\left[\begin{array}{c}x_ {ai}(t)\\ y_{ai}(t)\end{array}\right]\\ &=\left[\begin{array}{c}\int_{t_{0}}^{t}v_{ri}(\tau)\cos\theta_{ai}(\tau)d \tau+x_{ri}(t_{0})\\ \int_{t_{0}}^{t}v_{ri}(\tau)\sin\theta_{ai}(\tau)d\tau+y_{ri}(t_{0})\end{array}\right] \end{split} \tag{13}\]  
where \(\theta_{ai}(t)=\int_{t_{0}}^{t}\omega_{ri}(\tau)d\tau+\theta_{ri}(t_{0})\) and \(t_{0}\) is the moment when \(\eta\) switches from \(0\) to \(1\).  
**Remark 1**.: _When the ith robot is out of the detection region--i.e., no collision occurs--the reference trajectories \(\left[\begin{array}{cc}x_{ri}&y_{ri}\end{array}\right]^{T}\) are equal to the desired \(\left[\begin{array}{cc}x_{di}&y_{di}\end{array}\right]^{T}\). However, when the ith robot is inside the detection region and the potential function \(\Gamma_{i}\) blows up, the reference trajectories will have already been adjusted to deviate from the desired ones to avoid the collision. In this case, the \(\Gamma_{i}\) can asymptotically converge to \(0\) if the robots track the reference trajectories._
# Experimental Study of the Thermal and Dynamic Behaviors of Polypropylene Fiber-Reinforced Concrete
## 3 Test Results
### SHPB Test Results  
Three kinds of ordinary concrete and high-performance concrete with different strength grades, C30, C40, and C50, were used as samples for the dynamic tests using SHPB. The normal concrete was only tested at room temperature, while the high-performance concrete was divided into nine temperature gradients: room temperature (25 \({}^{\circ}\)C), 100 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, 500 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, 700 \({}^{\circ}\)C, and 800 \({}^{\circ}\)C. There were also at four pressures of 0.4 MPa, 0.6 MPa, 0.8 MPa, and 1.2 MPa for the gas gun. V1, V2, V3, and V4 are used to indicate four speeds of the striker corresponding to the four gas gun pressure settings.  
Figure 7 shows the test results of the HPC specimens at the same pressure (1.2 MPa) with various high-temperature treatments--i.e., 100 \({}^{\circ}\)C, 200 \({}^{\circ}\)C, 300 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, 500 \({}^{\circ}\)C, 600 \({}^{\circ}\)C, 700 \({}^{\circ}\)C, and 800 \({}^{\circ}\)C--from the first picture to the last one.  
Figure 6: Insulation measures for the HPC during SHPB tests.  
It can be seen from the first picture--i.e., the specimen that received a 100 \({}^{\circ}\)C temperature treatment--that many fractures were produced. However, it was not smashed into fragments. For the rest of the test results, the size of the fragments decreases as the temperature increases.  
Since the melting point of the polypropylene fiber in the admixture is 165 \({}^{\circ}\)C, the test sample at 100 \({}^{\circ}\)C becomes \"cracked\" without spreading, while the test samples at 200 \({}^{\circ}\)C and 300 \({}^{\circ}\)C appear to fracture into \"large blocks\" after impact. The distribution gradually becomes \"crushed\" as the temperature rises.
## 5 Results and Discussion  
### Characteristics of MAF According to the Type of Processing Oil  
Figure 5 shows the effects of the MAF process on the surface finish of the Ni-Ti stent wire with the different processing oils. The initial experiment was carried out at 500 rpm and a workpiece vibration frequency of 8 Hz, and the finishing time of each wire workpiece was a total of 350 s. The surface roughness was reduced with all three oils, which demonstrates the effect of the processing oil in the MAF process. With the castor oil, the Ra was reduced from 0.11 \(\upmu\)m to 0.07 \(\upmu\)m. With the olive oil, it was reduced from 0.12 \(\upmu\)m to 0.06 \(\upmu\)m, and with the grapeseed oil, it was reduced from 0.13 \(\upmu\)m to 0.05 \(\upmu\)m. The best result was obtained with the grapeseed oil, followed by the olive oil and the castor oil. This can be explained by grapeseed oil having the lowest viscosity. Here, the grapeseed oil's superior physical properties (low viscosity) made it feasible for ultra-finishing of orthopedic Ni-Ti stent wire. Table 1 shows that the grapeseed oil had the lowest viscosity when compared with the olive oil and castor oil. The viscosities of the grapeseed oil, olive oil, and castor oil were 0.0466 Pa.s, 0.0562 Pa.s, and 0.58 Pa.s, respectively.  
Figure 6 shows the results when the revolution speed of the magnetic field was changed to from 500 to 1000 rpm, the finishing time was reduced to 180 s, and the workpiece vibration frequency was still 8 Hz. The surface roughness was reduced with all three oils (from 0.13 \(\upmu\)m to 0.04 \(\upmu\)m with the grapeseed oil and to 0.05 \(\upmu\)m and 0.07 \(\upmu\)m with the olive oil and castor oil, respectively). When the revolution speed of the magnetic field was increased to 1000 rpm, the magnetic abrasive compound had more time to remove the unevenness from the Ni-Ti stent wire's surface, resulting in reducing the surface roughness. Figure 7 shows the results when the revolution speed of the magnetic field was increased to 2000 rpm while still at the 8-Hz frequency for 180 s. Similar to the 500 and 1000-rpm conditions, the surface roughness was reduced by all three processing oils (from 0.12 \(\upmu\)m to 0.06 \(\upmu\)m with the olive oil, from 0.13 \(\upmu\)m to 0.05 \(\upmu\)m with the grapeseed oil, and from 0.12 \(\upmu\)m to 0.06 \(\upmu\)m with the castor oil). When the revolution speed was increased to 2000 rpm, the centrifugal force acting on the abrasive particles was increased, which caused the displacement of the abrasive particles from the finished areas, resulting in a low surface roughness. Nevertheless, it was difficult to improve the surface roughness beyond 0.05 \(\upmu\)m, even at this higher speed. Therefore, it can be concluded that the optimal surface roughness was obtained when the revolution speed was 1000 rpm under the other given experimental conditions.  
Figure 5: Surface roughness (Ra) vs. finishing time according to the oil used (500 rpm).
## 2 Materials and Methods  
### Sample Preparation  
In this study, 95# Gasoline was chosen as the research object. Different brands of gasoline were purchased from Wenzhou gasoline stations (Wenzhou City, Zhejiang Province, China). Analytical-grade methanol reagent (Product No. M116122, purity \(>\) 99.9%) was purchased from Aladdin Reagent and used without any further treatments. To obtain the methanol gasoline, 95# gasoline samples were mixed with methanol according to a serial of volume ratios. A total of 16 gasoline samples varied six kinds of brands were collected. Volume ratios of methanol to gasoline were matched with the range of 0% to 30%.