esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, DEFORM-3D software was used to establish the simulation model of the warm forging of the spiral bevel gear, and the numerical simulation of a warm-forging-finishing process of spiral bevel gear was carried out. The main conclusions are as follows:  
1. In the warm-forging process of spiral bevel gear, the maximum deforming force is at the dedendum of the gear big end, and the deforming force increases with the increase in the friction factor in the forging process and decreases with the increase in the forging speed and workpiece heating temperature.
2. Based on the orthogonal experiment method, the combination of four process parameters was designed, and the combination of process parameters with the minimum warm-forging force of spiral bevel gear was obtained as follows: a forging speed of 30 mm/s, a die preheating temperature of 523 K, the friction factor of 0.1, and a workpiece heating temperature of 1073 K.
3. The combination of a numerical simulation and orthogonal test can help to work out the warm-forging process scheme of spiral bevel gear quickly, accurately and reasonably.  
**Author Contributions:** Conceptualization, Z.W. and C.J.; methodology, Z.W.; software, Z.W.; validation, B.W., Y.W. and C.J.; formal analysis, Y.W.; investigation, Z.W.; resources, C.J.; data curation, Z.W.; writing--original draft preparation, Z.W.; writing--review and editing, Z.W. and C.J.; visualization, Y.W.; supervision, B.W.; project administration, B.W.; funding acquisition, C.J. and B.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Key Project (J2019-VII-0017-0159).  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(A\) **(mm/s)** & \(B\) **(K)** & \(C\) & \(D\) **(K)** \\ \hline
30 & 523 & 0.1 & 1073 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimal process parameter combinations.  
Figure 15: Trend diagram of influence of process parameters on gear forming load.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the requirements of the project party.  
**Acknowledgments:** The authors would like to thank all the authors of the references that gave us inspiration and help. The authors are grateful to the editors and anonymous reviewers for their valuable comments that improved the quality of this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Chlorophylls and Total Carotenoids  
Chlorophylls, carotenoids (oxygenated and non-oxygenated), and phycobilins are the three major groups of photosynthetic pigments in microalgae. Extraction of pigments from microalgae can be done either by using a solvent or supercritical CO\({}_{2}\) method [54]. In this study, solvent extraction was employed to extract the pigments followed by a spectrophotometric method to determine the concentrations of chlorophyll a and b, as well as total carotenoids in the lipid extract of the studied species (Figure 3).  
The results obtained show that _C. reinhardtii_ was a superior source of chlorophyll a and b as well as for total carotenoids when compared with both _Chlorella_ and _Spirulina_. The two main chlorophylls (a and b) were also present at high concentrations in _Chlorella_, although extra steps of cell wall disruption using a mini bead beater were needed to allow the full extraction of the green pigments (this was assessed visually). This step was not necessary for _C. reinhardtii_ and _Spirulina_ because _Chlorella_ has cellulosic cell wall which is more robust and needed mechanical disruption to be permeable. These results are supported by the literature that chlorophyll a and b are the most dominant in green microalgae [55]. However, the ratio of these pigments is highly affected by the growing conditions.  
_Spirulina_ contained the lowest amount of Chlorophyll b and total carotenoids. _Spirulina_, however, contains a high amount of the blue pigment phycocyanin which has important application in food colouring and as potent antioxidant, immune system enhancer and having anti-viral properties [6].  
The importance of these pigments comes from their many biological values, such as pro-vitamins and antioxidant, and is a natural alternative to the synthesised colouring agents [7]. Total chlorophyll and carotenoid levels for various leafy vegetables were assessed within our group [56]. The study found that spinach leaves from a local supermarket contained 7.8 mg/g DW of chlorophyll (a and b)and 2.2 mg/g DW of total carotenoids. Comparing to spinach, _C. reinhardtii_ could be considered a super-rich source for both chlorophyll (28. 6 mg/g DW) and carotenoids (6.8 mg/g DW).
## 2 Materials and Methods
### The Variance of the Maximal Overlap Discrete Wavelet Transform  
The absence of a typical P-wave characterizes an ECG signal that contains episodes of AF. This fact affects the energy distribution in that signal. For this reason, maximal overlap discrete wavelet transformation is applied to the atrial activity signal to identify the absence of a common P-wave component in the ECG signal. MODWT preserves energy; it is well suited to analyzing the dependence of variation on scale in ANOVA studies. The examination of wavelet variance permits the identification of the scales that are most significant in the general variability of the data. In addition, MODWT can be used as a variance estimator [54]. This transformation resolves the issue of the time variance property, promoting no downsampling. That is, all MODWT decomposition layers maintain the exact time resolution without phase distortion [55]. MODWT enhances the alignment of the decomposed wavelet and scaling coefficients at an individual level with the original time series. This transform aligns the wavelet coefficients at each time range with the original signal. The calculated variances \(\hat{v}_{j}^{2}\) allow the analysis of the localized signal variation with respect to scale and time [56]. To apply MODWT to a finite time-series signal \(x(n)\) and decompose it into \(J\) levels, \(J\) pairs of wavelet (high-pass) \(\tilde{h}_{j,l}\) and scaling (low-pass) filters \(\tilde{g}_{j,l}\) must be used. At the \(j\)-th level, a filtering operation is performed to obtain a set of wavelet and scaling coefficients, as described in ref. [54]:  
\[\tilde{W}_{j,n}=\sum_{l=1}^{L_{j}-1}\tilde{h}_{j,l}x(n-l), \tag{7}\]  
\[\tilde{V}_{j,n}=\sum_{l=1}^{L_{j}-1}\tilde{g}_{j,l}x(n-l), \tag{8}\]  
where \(n=0,1,\ldots,N-1\), and \(\tilde{h}_{j,l}\) and \(\tilde{g}_{j,l}\) are the \(j\)-th level MODWT wavelet and scaling filters, respectively. The variance estimate is defined as  
\[\hat{v}_{j}^{2}=\frac{1}{L_{j}}\sum_{l=1}^{L_{j}-1}\tilde{W}_{j,n}^{2}. \tag{9}\]  
In this work, the Matlab procedures _modwt_ and _modwtvar_ are used, and \(\hat{v}_{j}^{2}\) denotes the variances obtained. Variances \(\hat{v}_{j}^{2}\) (\(j=1,\ldots,9\)) are calculated for the first nine levels of decomposition.  
Figure 4: Different energy distributions within sub-bands in wavelet coefficient domain for normal sinus rhythm (NSR) (**a**,**b**) and for atrial fibrillation (**c**,**d**).
# Effect of Fibers on Mixture Design of Stone Matrix Asphalt
## 1 Introduction
The success in SMA mixtures spurred the adoption of the fiber for many major highway projects. Then, lignin fiber, glass fiber, and mineral fiber have been studied in asphalt mixtures [25; 26]. From then on, other types of fibers, such as carpet fiber, polyester fiber, waste tires, cellulose oil palm fiber, waste glass fiber, and coconut fiber, were used to study the service properties of the HMA mixture and SMA mixture [26; 27; 28; 29; 30]. The studies focused on the mixture to obtain a better performance, such as strength, stability, durability, reduction of noise, rutting resistance, fatigue life, and water sensitivity. However, fiber types may influence oil absorption and fiber content will affect the VV. Then it can affect the VMA directly. Limited studies were conducted to analyze the influence of fibers on the VMA during the mixture design. Therefore, this study investigated the effects of four different fibers on the mixture volume parameter during the SMA mixture design, with the goal of identifying the adaptability of polyester fiber and mineral fiber for satisfactory binder performance. The mineral fiber has a similar density with aggregates and smaller oil absorption and specific surface area, which means it cannot absorb much asphalt binder to fill the mineral outside space, and is less sensitive to the content change. Polyester fiber has better asphalt absorption and higher ductility; therefore, it can form much more space in the SMA mixture [31]. High content of polyester fiber means low asphalt content, which potentially reduces the adhesion between the aggregate and asphalt binder.
## 4 Tree-Based Protocols
### Smart Trend Traversal Protocol  
The Smart Trend Traversal protocol (STT) is a deterministic and memoryless protocol that was created with the aim of reducing the number of collisions in the QT protocol [46].  
This protocol has the ability to dynamically issue queries according to an online, learned tag density and distribution. It proposes a combination of the QT protocol and the shortcutting method in order to skip a query that results in a collision. When the protocol detects the potential possibility of a collision, it will avoid it and move to the bottom level of the binary query tree. STT provides trend recognition.  
The reader keeps track of the tag density and distribution in order to issue subsequent queries, and consequently, minimizes the number of empty slots and collision slots.  
In this protocol, it is not necessary to have any prior knowledge of the network, and it outperforms the existing protocols. The ideal number of queries can be the total number of single nodes. The ideal queries group, referred to as the query traversal path (QTP), is denoted by \(Q=q_{1},q_{2},q_{3},\ldots,q_{n}\), where \(q_{n}\) is the last query used in the identification process [47].  
It is difficult to achieve, but is desirable to get close to its value. The reader can calculate the subsequent queries depending on the tag response, which can be classified into three types:  
* A collision occurs when the QTP is at too high a certain level and should be moved down by adding a longer prefix to the query. Consequently, the reader appends t bits of 0's to the last query, where \(t=s+n_{col}-1\). Let \(s\) denote the minimum increase, and \(n_{col}\) be the number of consecutive colliding slots.
* An idle slot occurs when no tag responds to a reader query. QTP needs to traverse up just one level, which can lead to a new collision. This rule will be applied only to the right side. If the empty response comes from the left side of the tree, QTP must move horizontally to the right. The reader will decrease the query length by \(m\) bits, where \(m=s+n_{emp}-1\) and \(n_{emp}\) is the number of consecutive idle slots.
* Upon a successful response, a single node is visited, indicating that the tag has been successfully identified by the reader. Then QTP moves to the symmetric node if the query finishes with a 0, but it returns one level if the query finishes with a 1.  
The identification process of the STT protocol, which was explained above, is depicted in Figure 13 with 4 tags.  
Figure 12: Example of the QT protocol.  
In conclusion, STT significantly reduces the number of collisions, the identification time, and the energy consumption as compared to the existing Aloha-based and tree-based protocols.
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 6 Recent Advancements in Clustering Optimization Algorithms
### Hybrid Techniques
#### 6.3.2 Hybrid Fuzzy
during the neighbor finding phase, the details of each node are transmitted using the non-persistent CSMA MAC protocol. The activities in the steady-state phase comprise CH selection, clustering, and data delivery. The selection of the CHs is implemented using fuzzy logic and ACO. The fuzzy logic is implemented using three input parameters for CH selection: residual energy, communication link quality, and number of neighboring nodes. The energy hole problem is solved by constructing unequal clustering structures in the protocol, forming smaller-sized clusters nearer to the BS. The inclusion of link quality in the CH selection process improved the reliability of the system. * LEACH-SF: Optimized Sugeno fuzzy clustering algorithm  
The LEACH-SF algorithm [91] is an adaptive fuzzy clustering technique that clusters sensor nodes into balanced clusters using the fuzzy c-means algorithm. At the same time, the appropriate CHs are selected using the Sugeno fuzzy inference system. It is an energy-efficient routing protocol that extends network service time through routing via the CHs of the WSN. Since tuning the fuzzy rules is the most crucial issue in this framework, the Sugeno fuzzy system relies on local sensor information for this task and, as such, plays a major role in the activity of LEACH-SF. The ACO is also used to improve the Sugeno fuzzy rules; this must be done once before implementing the LEACH-SF. This protocol relies on the ABC algorithm for adjusting the fuzzy rules in the LEACH-SF.
# An Effective Tangent Stiffness of Train-Track-Bridge Systems Based on Artificial Neural Network
## 3 Application Examples  
### The Model of Train-Rail-Bridge System  
A seven-span simply supported beam bridge [20] is analyzed in this study, the model of which is shown in Figure 5a. The single-span length of the bridge is 32.6 m, and the total length is 228.2 m. Both ends are supported on the foundation, and the middle spans are supported by 8-m-high piers. The model in OpenSees is shown in Figure 5b. The track, girder, and piers are simulated by the elasticBeamColumn elements in OpenSees, the parameters of which can be found in Table 1. The simulation of the cross-section of the bridge in OpenSees can be referred to in Figure 6. The tolerance of the convergence is selected as 0.1 N. The analysis type in this paper is algorithm Newton.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(A\) (\(\mathbf{m^{2}}\)) & \(\mathbf{I_{x}}\) (\(\mathbf{m^{4}}\)) & \(\mathbf{I_{y}}\) (\(\mathbf{m^{4}}\)) & \(\mathbf{I_{z}}\) (\(\mathbf{m^{4}}\)) & \(\mathbf{E}\) (N/\(\mathbf{m^{2}}\)) & \(\mathbf{\rho}\) (kg/m) & \(\mathbf{\mu_{r}}\) \\ \hline rail beam & \(7.67\times 10^{-3}\) & \(2.104\times 10^{-6}\) & \(3.038\times 10^{-5}\) & \(5.123\times 10^{-6}\) & \(2.059\times 10^{11}\) & 60.21 & 0.3 \\ girder beam & 8.343 & 16.815 & 9.0 & 69.282 & \(4.4\times 10^{10}\) & 20,857.5 & 0.2 \\ pier beam & 15.86 & 97.85 & 29.24 & 68.61 & \(3.45\times 10^{10}\) & 39,650 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the elasticBeamColumn beams in OpenSees.  
Figure 5: (a) A seven-span simply supported beam bridge. (b) The model of the bridge in OpenSees.  
The connection between the track and the track plate is simplified by the spring-dampers, and the rail nodes are connected with the track plate nodes through the twoNoNodeLink element in OpenSees. The length of the twoNodeLink element is 0.787 m, and the spring and damping are three-dimensional. The bridge bearing is also simulated with the twoNodeLink element, and the element length is 0.2 m. However, since the bridge is simply supported, the bearings of each span are different, as can be seen in Figure 7, where \(k_{f}\), \(c_{f}\) represents the stiffness and damping of the fixed bearing, \(k_{m}\); \(c_{m}\) represents the stiffness and damping of the movable bearing. The parameters of the twoNodeLink element in OpenSees can be seen in Table 2. All the mass of the girder is concentrated on the girder nodes, and the girder node is connected with the track slab node and the upper node of the bearing through the rigidLink beam in OpenSees. The lower node of the bearing is also connected with the upper node of the pier through the rigidLink beam. The length of the rigidLink beam mentioned above is determined by the actual bridge size. The nodes at the bottom of each pier are fixed.  
Figure 6: Simulation of bridge cross-section in OpenSees.  
Figure 7: The model of bridge bearing (vertical view).
# Using Dynamo for Automatic Reconstruction of BIM Elements from Point Clouds
## 3 Case Study
### Walls  
The code developed for the automatic wall detection and modeling followed the same logic used for the cases of columns and structural beams but with some necessary adjustments due to this element's geometric and non-geometric characteristics.  
The algorithm works individually wall by wall, so the operator must select the points related to the wall that needs to be modeled in the point cloud. To optimize the code's operation, it is important to avoid selecting unwanted points from other elements or other walls and avoid including doors and windows in that wall. For this, it is necessary to carry  
Figure 14: (**a**) The algorithm developed with Dynamo for automatic detection and modeling of structural beams. (**b**) Point cloud used. (**c**) BIM elements created in Revit using the Dynamo algorithm.  
Figure 13: Diagram of the solution developed for the structural beam automation modeling algorithm.  
out a previous inspection in the point cloud to define the best place and height to make the selection, which in most cases is above the final height of the doors and windows. It is also essential to select points from both sides of the wall, inner and outer sides. Although more points bring more precision, the algorithm also works with a reduced number of points. After this selection, the code performs a cleaning-up procedure of unwanted points and artifacts and isolates the points related to the wall profile at their starting and ending insertion points. With these profiles defined, the central wall baseline is extracted, and the automatic measurement of the wall thickness occurs. With this information obtained, the code creates the wall type with the correct nomenclature and thickness using the wall family type previously chosen by the operator and automatically positions it in its correct axis with its final height linked to the base and top levels, which were also defined by the operator when choosing the wall family type to be used (Figure 15).  
The algorithm works with Revit wall families, whether they are simple or with their composite layers. The user selects the wall family to be used, and the algorithm automatically creates the type of wall family with the real thickness if no wall with the desired thickness is detected. The final thickness of the modeled element will always match the thickness detected in the point cloud. Thus, when composite walls are used, the thickness variation is made in the inner structural layer keeping the outer finishing and substrate layers intact. As in 3D laser scanning surveys, only the outer layer of the elements is captured; the choice of the family with the correct layers to be used depends on other information such as wall prospects or existing technical plans. Another characteristic of this code is that it was developed to model straight walls, so it does not model curved walls or existing vertical deviations (Figures 16, A8 and A9).
# Location Information-Assisted Robust Beamforming Design for Ultra-Wideband Communication Systems
## 2 System Model
### TDOA Correction Method
After obtaining the error vector of the \(j^{th}\) BS, this paper will correct the measured distance data between the \(k^{th}\) UE and the \(j^{th}\) BS in real time as follow:  
\[\frac{\Big{(}\tilde{d}_{j,k}(t)-\hat{B}_{j}\Big{)}c}{1+\hat{\epsilon}_{j}}= \left\|\mathbf{u}_{j}-\mathbf{p}_{k}(t)\right\|+\omega_{j,k}, \tag{10}\]  
Square the two sides of (10) and subtract the equation of a common RN from the corresponding equations of the other BS, then a set of linear equations of the UE location as follow:  
\[\mathbf{A}_{k}\mathbf{p}_{k}(t)=\mathbf{B}_{k}(t), \tag{11}\]  
where  
\[\mathbf{A}_{k}=-2\left[ \begin{array}{c}\big{(}\mathbf{u}_{2}^{\mathrm{T}}- \mathbf{u}_{1}^{\mathrm{T}}\big{)}\\ \vdots\\ \big{(}\mathbf{u}_{J}^{\mathrm{T}}-\mathbf{u}_{1}^{\mathrm{T}}\big{)}\end{array}  \right],\mathbf{B}_{k}(t)=\left[ \begin{array}{c}\left(\frac{\tilde{d}_{2,k}(t )-\hat{B}_{2}}{(1+\hat{\epsilon}_{2})}\right)^{2}-\left(\frac{\tilde{d}_{1,k}( t)-\hat{B}_{1}}{(1+\hat{\epsilon}_{1})}\right)^{2}-\mathbf{u}_{2}^{\mathrm{T}} \mathbf{u}_{2}+\mathbf{u}_{1}^{\mathrm{T}}\mathbf{u}_{1}\\ \vdots\\ \left(\frac{\tilde{d}_{1,k}(t)-\hat{B}_{j}}{(1+\hat{\epsilon}_{j})}\right)^{2}- \left(\frac{\tilde{d}_{1,k}(t)-\hat{B}_{1}}{(1+\hat{\epsilon}_{1})}\right)^{2} -\mathbf{u}_{j}^{\mathrm{T}}\mathbf{u}_{J}+\mathbf{u}_{1}^{\mathrm{T}} \mathbf{u}_{1}\end{array} \right], \tag{12}\]  
The classical LS localization method does not consider the effect of noise characteristics. To improve the localization accuracy, the WLS method using RN distance information will be designed, which is given by:  
\[\mathbf{\hat{p}}_{k}(t)=\Big{(}\mathbf{A}_{k}^{\mathrm{T}}\mathbf{Q}_{k} \mathbf{A}_{k}\Big{)}^{-1}\Big{(}\mathbf{A}_{k}^{\mathrm{T}}\mathbf{Q}_{k} \mathbf{B}_{k}(t)\Big{)}. \tag{13}\]  
where \(\mathbf{Q}_{k}\) denotes the weighting coefficient matrix.
## 5 The Finite Element Model
### Validation
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Specimen** & & \(M_{y}\) **(kN-m)** & \(\theta_{y}\) **(mrad)** & \(K_{i}\) **(kN-m/mrad)** & \(K_{\text{FE}}/K_{\text{test}}\) \\ \hline \multirow{2}{*}{ST-1} & Test & 57.00 & 9.0 & 10.83 & \multirow{2}{*}{1.10} \\  & Model & 65.87 & 10.5 & 11.96 & & \\  & Test & 63.97 & 6.2 & 15.42 & & \\ \multirow{2}{*}{ST-2} & Model & 71.26 & 7.9 & 16.70 & & \\  & Test & 80.12 & 10.5 & 15.21 & & \\ \multirow{2}{*}{ST-3} & Model & 82.53 & 9.9 & 12.56 & & \\  & Test & 118.57 & 10.4 & 19.18 & & \\ \multirow{2}{*}{ST-4} & Model & 119.89 & 9.8 & 20.60 & & \\  & Test & 90.38 & 9.2 & 17.77 & & \\ \multirow{2}{*}{ST-5} & Model & 99.89 & 10.7 & 19.62 & & \\  & Test & 95.19 & 12.5 & 17.57 & & \\ \multirow{2}{*}{ST-6} & Model & 96.02 & 10.8 & 19.20 & & \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of the FE analysis and test results.