esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The achievement of the success in endodontic treatment results from precision during both diagnostic and therapeutic procedures. Mechanical root preparation is one of the most important stages in root canal treatment. That particular stage influences the following procedures, such as chemical decontamination and three dimensional obturation, determining the final effect. The purpose of the mechanical canal preparation is to remove the vital or necrotic pulp from canals and shape to obtain conical shape with maintained original curvature and apical foramen location. The complete procedure should not be iatrogenic; perforations, instruments' fracture and ledge formation should be avoided [1; 2].  
During canal shaping, regardless of the used technique, procedural mistakes may occur, such as deformations or changes of the primary canal shape, in the form of over-preparation of external canal wall in its apical part and not enough negotiation of coronal part. Removal of too much dentin in one canal region results in not enough mechanic preparation of the opposite part. The real thickness of the dentin must be taken into account while canal preparation in order to prevent overpreparation and root fracture. Not properly instrumented canal's walls may result in the unsuccessful outcome of the root canal treatment [3; 4].  
Intense development of materials science in the second half of the 20 th century has led to the invention of shape-memory alloys, since [PERSON] in 1962 invented the most known shape-memory alloy nickel-titanium (NiTi) known as NITINOL. NiTi alloys used in the manufacturing of endodontic instruments are characterized by superelasticity which means that they can be distorted and return to their pre-defined shape. This unique feature enables the preparation of curved canals with maintaining their natural shape [5; 6; 7]. The assessment of canal shaping and mechanical instrumentation using different endodontic instruments [8; 9; 10], in vast majority of studies was made on the basics of images taken before and after the preparation of training blocks or with the usage of volumetric tomography (3D) [11]. The magnetic resonance microscopy/imaging enables precise imaging and during the following years there appeared publications presenting attempts at using magnetic resonance microscopy to determine the geometry of the tooth surface, root canals, the location of cavities, and to compare tooth structure of younger and older patients. The techniques used for this type of imaging are generally referred to as spin echo and gradient echo methods and they facilitate the imaging of soft tissue or a hydrated medium [12; 13; 14; 15]. Authors decided to use that method in the assessment of training blocks' volumetric topography before and after their mechanical preparation.  
Here, our study aimed to assess, in a comparative way, the preparation mode of simulated root canals with type L curvature negotiated with the rotary NiTi Mtwo and ProTaper Next instruments reciprocating WaveOne instruments vs. standard hand stainless steel K-files using magnetic resonance imaging based on spin echo.
## 2 Virtual Array Model  
Each element of the FDVA consists of an elliptic patch antenna assembled on a quadcopter drone, as shown in Figure 1. The maximum size of the drone is 131 mm. The parts of the drone consist of metal and carbon fiber. The antenna is strategically located on the side of the drone to focus the radiation on the front.  
The radiation pattern for a VAA of N elements is formulated by the next formula [21],  
\[P(\theta,d)=\sum_{n=1}^{N}g(\theta)e^{i(kx_{n}sin\theta+2\pi(f_{0}+f_{n})(d-d_ {0})/c)} \tag{1}\]  
where \(\theta\) is the elevation angle, and the wave number is defined as \(k=2\pi/\lambda\) with \(\lambda\) as the wavelength in the initial frequency \(f_{0}\). The variable \(x_{n}\) is the element position in space. The term \(f_{n}\) is the frequency offset concerning \(f_{0}\). The difference between frequency offsets is \(\Delta f_{n}=(f_{n}-f_{n-1})\). The variable d is the distance variable. The term \(d_{0}\) is the distance between the array and the maximum radiation. The constant c is the velocity of light in a vacuum. The function \(g(\theta)\) is the element pattern of the nth antenna in the frequency \(f_{0}\). This function considers the pattern distortion due to the aircraft structure depicted in Figure 1. The elliptical patch antenna is shown in Figure 2. The antenna material is an FR4  
Figure 1: Element model of FDVA.  
substrate of 1.6 mm thickness, with a permittivity of \(\varepsilon r\) = 4.3, a tangential loss of \(\delta\) = 0.0025, and a copper layer of 0.04 mm. The physical dimensions are W = 37.5 mm, L = 25 mm, G = 37.5 mm, R = 9 mm, r = 6.5 mm, h = 17.6 mm, x = 1.9 mm, and lg = 9 mm. The antenna parameters were calculated based on the theory reported in [36]. It should be noted that the radiation pattern of the FDVA was simulated in the CST microwave studio. The reflection coefficient is shown in Figure 3; this antenna operates from 3.25 GHz to 3.79 GHz. We selected this element because it is low profile, which is an important characteristic when the antenna is mounted on a drone. Nevertheless, the drone can use a different antenna element. The selection of the best antenna for a frequency diversity virtual array is an open research topic.
## 4 Simulation Results and Analysis  
A simulation model is built in MATLAB2022a/Simulink to verify the effectiveness of the method. The relevant motor parameters are shown in Table 2.  
The existing loop employs a PI controller with the parameters set as follows: \(K_{p}=100\), \(K_{i}=100\). The position loop responses under ADRC and RBF-ADRC are illustrated in Figures 8-12, and the simulation analysis is categorized into four main sections, as described in Sections 4.1-4.4.
# Research on Automatic Generation of Park Road Network Based on Skeleton Algorithm
## 1 Introduction  
A road network constitutes the framework of a park [1], being accountable for interconnecting various scenic spots within a park. It serves as the \"artery\", linking diverse nodes within a park, and determines the degree of connection among them. The \"Park Design Code\" (GB 51192-2016) [2] puts forward the following stipulations with respect to the design of a park road network: The park road ought to be integrated with other facilities like terrain, water bodies, plants, and buildings to fulfill the requirements of transportation and sightseeing and form a complete scenic composition; the park road should establish a route that presents the landscape space of the park in an orderly manner. The road is required to cover the park to the greatest extent possible and possess strong accessibility.  
The configuration of a park road network is correlated with environmental elements such as the topography and vegetation within the park, and it exhibits a certain degree of subjectivity and uncertainty in its evolution. How to promptly generate a rational park road network while integrating it with the environment constitutes the primary issue that designers must confront. Particularly when encountering complex environmental textures, traditional road design demands a comprehensive analysis of numerous objective factors, and it is inevitable that problems like omissions and inadequate considerations will exist. This design process is not only time-consuming but also lacks reasonable and scientific quantitative criteria.  
This research put forward a sustainable approach for the automatic generation of park road networks with the aim of assisting designers in achieving their design tasks efficiently and precisely. The research employed skeleton algorithms derived from computer graphics for the automatic generation of road networks, taking into consideration environmental factors. The research utilized an enhanced Douglas-Peucker algorithm and convex hull operations to optimize the skeleton diagram and subsequently validated the efficacy of this method by adopting a park environment encompassing roads, water, and vegetation.
# A 32-GHz Eight-Way Power Amplifier MMIC in 150 nm GaN HEMT Technology
## 2 32 GHz High-Power Amplifier Design
### Device Selection and Stabilization
To improve the stability of the HPA, we added a de-Qing resistor in series with the bypass capacitor to suppress the ringing of the series RLC network from the parasitic routing inductance (\(L_{para}\)). It is important to select a proper series resistance since the de-Qing resistor trades off with the performance of the filtering capacitance. To keep the series RLC network in the over-damped oscillation, the series de-Qing resistor \(R_{deQ}\) should meet the condition provided by:  
\[R_{deQ}>2\sqrt{\frac{L_{para}}{C_{bypass}}}\]  
where \(L_{para}\) is the routing inductance and \(C_{bypass}\) is the bypass capacitance. It is noted that \(L_{para}\) should be minimized to keep \(R_{deQ}\) small enough not to degrade the filtering performance. The values of \(L_{para1\_3}\) were estimated from the ADS momentum simulation and the value of \(C_{bypass}\) was determined to meet the over-damped oscillation condition as a function of the de-Qing resistor in time domain simulations. The parameters of the series RLC network of the bypass capacitor with de-Qing resistors are presented in Figure 3.  
Figure 3: (**a**) Schematic of the bypass capacitor with the de-Qing resistor’s series RLC network and (**b**) parameters of the series RLC network components. A simplified block diagram of the implemented stabilization circuits is illustrated in Figure 4.
## 5 Prediction Model of Slope Flow Value and Test Time
### Slope Flow Value and Specim Prediction Model with Different Filler Content  
From Figure 4, the relationship between the slope flow value and the time of different filler content at the oil/stone ratio of 6.6% was obtained. According to the change rule of slope flow value and test time, the exponential function BoxLucas1 model was used to describe the change rule of slope flow value (\(U_{F}\)) with time (\(T\)):  
\[U_{F}=a_{2}[1-exp(-b_{2}T)] \tag{9}\]  
In the formula: \(a_{2}\) and \(b_{2}\) are material parameters.  
It can be seen from Table 8 that the variation range of \(b\) value was small and there was no obvious rule. Therefore, the \(b\) value can be replaced by the mean value \(b\) in the calculation. The results show that the relationship between \(a\) and \(F\) was an exponential function, as shown in Figure 10.  
After fitting, it was found that the relationship between \(a\) value and \(F\) function was as follows:  
\[a=exp\Big{(}a_{2}+\beta_{2}F+\gamma F^{2}\Big{)} \tag{10}\]  
In the formula: \(a_{2}\), \(\beta_{2}\), \(\gamma\) are fitting parameters.  
Figure 9: Different gradation index slope flow value and time calculation curve.  
By substituting Formula (10) into (9), we obtain:  
\[U_{F}=exp\Big{(}\alpha_{2}+\beta_{2}F+\gamma F^{2}\Big{)}[1-exp(-b_{2}T)] \tag{11}\]  
Formula (11) was the BoxLucas1 correction model, and the calculation results were in good agreement with the experimental data. The formula can be used as a slope flow value and test time correction curve model for different filler contents. The correction model had a total of four parameters. After calculation, \(\alpha_{2}=-0.432\), \(\beta_{2}=-0.156\), \(\gamma=0.013\), \(b_{2}=0.393\).  
According to the experimental calculation parameters, according to the calculation model of Formula (11), the prediction model of slope flow value with different filler content was obtained. As shown in Figure 11, it was found that the calculation results of this formula were in good agreement with the experimental data.
# A Game-Theoretic Rent-Seeking Framework for Improving Multipath TCP Performance +
## 6 Potential Usage and Applications
### Potential for Impact in 6G Specifications  
The 3 GPP specification put forward ATSSS for multi-connectivity of WiFi and cellular over the 6G core [2; 3]. This is also reflected in the ongoing development by Google of BBR (bottleneck bandwidth and roundtrip time) and the redesign of QUIC (a clean-sheet design overlaid on UDP, standardized by IETF in 2021) at the transport layer. Important trends include: (i) multi-connectivity with ATSSS, especially the concurrent use of both 3 GPP and non-3 GPP interfaces on mobile devices [34], (ii) use of forward error correction (FEC) to reduce latency, (iii) improving operation over lossy wireless links, and (iv) support for incremental rollout by avoiding the need for changes to the network fabric. However, many knowledge gaps need to be resolved for these to occur and for them to be applied for emerging technologies, which range from semantic communication [35] to federated learning over wireless vehicle-to-grid networks [36].
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 4 Slot Bonding Implementation  
This section provides an overview of our proof-of-concept TSCH slot bonding implementation in the Contiki-NG firmware. The implementation is publicly available ([[https://github.com/imec-idlab/tsch-slotbonding](https://github.com/imec-idlab/tsch-slotbonding)]([https://github.com/imec-idlab/tsch-slotbonding](https://github.com/imec-idlab/tsch-slotbonding)), accessed on 12 May 2021). First, the development platform is discussed. Second, the different PHYs that were considered and the possible channel allocations are explained. Finally, the TSCH timing values used in the implementation are discussed.
## 2 GRU-HMM Fault Prediction Model
### GRU-HMM Fault Prediction Model Algorithm Flow  
The algorithm flow of the GRU-HMM fault prediction modeling algorithm for the EMU gearbox is shown in Figure 1.  
Figure 1: GRU-HMM fault prediction model flowchart.  
(1) Perform wavelet de-noising and normalization on the operation monitoring data samples \(O=\{o_{1},\,o_{2},\,\ldots,o_{N}\}\) and the degradation feature sets \(F=\{f_{1},f_{2},\,\ldots,f_{N}\}\) of each component to determine the training and testing sets.  
(2) Define the HMM hidden state \(n=4\) and initialize the parameters \(\pi_{0},\,a_{0},\,\) and \(b_{0}(v_{0})\).  
(3) Construct the GRU network model, set the number of hidden layers, \(M=4\). The number of GRU units in each layer, \(u=128\).  
(4) Input the training set, optimize the network weights and parameters through iterative training, and find the optimal prediction step size and each parameter.  
(5) Output the GRU training results to update the HMM parameters \(\pi_{i},\,a_{ij}\).  
(6) Define the forward variable \(\alpha_{M}(i)\),  
\[\alpha_{t}(i)=P(o_{1},o_{2},\ldots,o_{t},q_{t}=s_{i}|\lambda\,),1\leq i\leq N \tag{9}\]  
(7) Define the backward variable \(\beta_{M}(i)\),  
\[\beta_{M}(i)=P(o_{t+1},o_{t+2},\ldots,o_{T},q_{t}=s_{i}|\lambda\,),1\leq i\leq N \tag{10}\]  
(8) Perform training on the parameters of the Baum-Welch algorithm. Define the variable \(\gamma_{t}(i)\), denoting the probability of being in state \(q_{i}\) at moment \(t\). Define the variable \(\xi_{m}(i)\), denoting the probability of being in state \(q_{i}\) at moment \(t\) and in state \(q_{j}\) at \(t+1\),  
\[\gamma_{t}(i)=\frac{\alpha_{t}(i)\beta_{t}(i)}{\sum\limits_{i=1}^{N}\alpha_{t }(i)\beta_{t}(i)} \tag{11}\]  
\[\xi_{t}(i)(i,j)=\frac{\alpha_{t}(i)\alpha_{ij}(o_{t+1})\beta_{t+1}(j)}{\sum \limits_{i=1}^{N}\sum\limits_{j=1}^{N}\alpha_{t}(i)\alpha_{ij}(o_{t+1})\beta_{ t+1}(j)} \tag{12}\]  
(9) Update the HMM model parameters \(\pi_{i},\,a_{ij}\), and \(b_{j}(v_{i})\) again. If the values of \(\pi_{i},\,a_{ij}\), and \(b_{j}(v_{i})\) have converged, the algorithm ends; otherwise, go back to (5) to continue the iteration as follows:  
\[\pi_{i}=\frac{\sum\limits_{n=1}^{N}\gamma_{t}(n)(i)}{N} \tag{13}\]  
\[a_{ij}=\frac{\sum\limits_{n=1}^{N}\sum\limits_{t=1}^{T-1}\xi_{t}(i)(i,j)}{ \sum\limits_{n=1}^{N}\sum\limits_{t=1}^{T-1}\gamma_{t}(n)(i)} \tag{14}\]  
\[b_{j}(v_{i})=\frac{\sum\limits_{n=1}^{N}\sum\limits_{t=1,o_{n-1}^{(n)}}^{T-1} \gamma_{t}(n)(i)}{\sum\limits_{n=1}^{N}\sum\limits_{t=1}^{T-1}\gamma_{t}(n)(i)} \tag{15}\]  
(10) Output model \(\overline{\lambda}=(\overline{\pi},\overline{A},\overline{B})\), using the test set to output the failure probabilities \(P=P(O|\overline{\lambda}\,)\).
## 3 Results and Discussion
### Self-Aggregation, i.e., Critical Micelle Concentration (CMC)  
The molecules of taurine and tyramine with D-glucose aggregate at the surface like micelles. The assembly of self-aggregated molecules above a concentration is called critical micelle concentration. The value of CMC is estimated from the intersection points of two lines above and below CMC for each temperature. These values of CMC are utilized to evaluate many thermodynamic and transport parameters, which helps to understand the hydrophilic and hydrophobic characteristics of molecules. The surface tension of the five different concentrations of glucose, i.e., 0.01, 0.02, 0.03, 0.04, and 0.05 mol kg\({}^{-1}\) in aqueous taurine and tyramine solutions at 298.15, 303.15, 308.15, 313.15, and 318.15 K temperatures, have been measured experimentally. The observed data were essential parameters for the estimation of the Gibbs adsorption isotherm equation and the thermodynamic parameters of surface formation.
## 3 Simulation Examples
### Residual STFT-Based Mode Shape Extraction
#### 3.2.3 Effect of Road Roughness  
Even if the bridge surface roughness in the contact point's response could be eliminated by using the residual response between the contact points, it is assumed that the magnitude of roughness (\(u_{r}\)) in the term \([\omega_{v,i}^{2}u_{r}(V_{e}(t-t_{11}))+g]\) in Equation (5) is much smaller than gravity (\(g\)) and could be ignored. However, when the roughness is increasing, its contribution should be explored to validate the above assumption. So, in addition to the Class A road profile in Figure 4, Class B and Class C road profiles are used to explore the effect of road roughness. For the velocity of 5 m/s, the resulting mode shapes are plotted in Figure 8. For all three road classes, the first three modes could match the analytical ones well. All the MAC values larger than 0.96 could indicate that the roughness level has no obvious effect on the mode shape extraction.
## 5 Conclusions  
By comparing GPP estimates with parameter adjustment in the MODIS algorithm across nine PFTs at half-hourly, daily, monthly and seasonal scales, our multisite study illustrates as follows:  
1. Large bias was observed in the MODIS GPP product, especially in deciduous forests and shrubs and grasslands. Its uncertainties were affected by both input data and the look-up table values of \(\varepsilon_{max}\) for individual PFTs. It is necessary to optimize the parameters in the look-up table used by the MODIS algorithm, but the optimized parameters should correspond to specific input data for applications, _i.e._, the optimized parameters cannot be applied to a simulation with changed driver data because errors from parameters and input data can accumulate.
2. Optimizing the key parameter \(\varepsilon_{max}\) in the MODIS GPP algorithm can compensate the errors caused by ignoring soil water factor at the site level, but the \(\varepsilon_{max}\) values would have large uncertainties among sites within the same PFT and among the PFTs, especially for sites with low yearly average soil water factors. This result casts doubt on the accuracy of simulated spatial distribution of GPP yielded by the MODIS algorithm. Moreover, GPP was underestimated by the one-leaf models in summer, regardless of whether the soil water factor was considered, but could be improved by separating the canopy structure into sunlit and shaded parts. This result indicates that improving model structure is a better choice than only adjusting parameters. Photosynthetic dynamics in spring and fall for forests and shrubs and seasonal GPP change for grasslands could not be captured by both one-leaf and two-leaf models. Therefore, there is a need to improve seasonal and phenology variations of key parameters and variables in carbon assimilation calculation to reduce uncertainties in GPP simulation.
## 3 Materials and Test Program
### Test methods and Master Curve Generation Method
#### Fourier-Transform Infrared Spectroscopy  
The Fourier-transform infrared spectroscopy (FTIR) is a machine used to determine the spectroscopy; it is widely used to analyze the chemical functional groups of asphalt materials. The TENSOR27, produced by the Bruker Optics Company, was used to research the asphalts and bio-asphalts in this study. The resolution was 4 cm\({}^{-1}\), the scanning speed was 32 sheets per second, and the selected scanning range was 4000-600 cm\({}^{-1}\). The functional groups were identified and compared for further analysis.
## 3 Results and Discussion
### Passive Cooling of the New System and Base Case
#### 3.2.2 Cooling Time and Thermal Equilibrium Temperature of the New System with and without WHR  
When considering the WHR, the cooling time of the new system and temperature of the heat balance point were affected to a certain extent. As shown in Figure 13, during the first 4 min, the heat transfer conditions with and without WHR were roughly the same. However, when the air in the cold alley entered in the new system, part of the air was affected by the solar radiation and was heated by the envelope structure. At 50 min, the air in both rooms on the side of the cold alley reached the thermal equilibrium temperature and began to flow to the first floor and the room on the side away from the cold alley; the thermal equilibrium temperature changed. In the absence of WHR, the thermal equilibrium temperature of the room near the cold alley was approximately 30 \({}^{\circ}\)C, whereas the temperature of the room with WHR was approximately 32 \({}^{\circ}\)C. The new system was also affected by the solar radiation. As cold air flowed into the room far from the cold alley, the air storage space in the wall was heated. Therefore, the purpose of cooling the far room could not be achieved within a certain period of time. At this time, the air ducts connecting  
Figure 12: Comparison of passive cooling of the new system with the base case (without WHR).  
the rooms on both sides played an important role. They directly introduced cold air into the room on one side of the cold alley and into the room on the other side, reducing the cooling time and improving the cooling efficiency. All of the rooms without the influence of WHR reached the thermal equilibrium point after 1.66 h. The average indoor temperature was 28 \({}^{\circ}\)C, and the cooling efficiency was 3.61 \({}^{\circ}\)C/h. In the case with WHR, the temperature of all rooms reached the equilibrium point after 1.94 h. The temperature was 32 \({}^{\circ}\)C, and the cooling efficiency was 1.03 \({}^{\circ}\)C/h. Although the cooling efficiency of the new system was reduced when considering the WHR of the wall, it was still closer to reality. Nevertheless, this shows that, in the indoor heat transfer simulation, the influence of the WHR on the indoor temperature cannot be ignored.  
Figure 13: \(Cont.\)