esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Governing Equation and Calculation Setting
### Mesh Partition and Simulation Setting  
Figure 1 shows a schematic diagram of the internal structure of the Venturi injector, consisting of a nozzle, suction chamber, mixing section, and diffuser. The specific structural parameters are detailed in Table 1.  
In this study, ICEM CFD 2022 R1 was employed to create a three-dimensional, non-uniformly distributed structured hexahedral grid. Figure 2 depicts a schematic representation of the 3D computational grid for the Venturi injector. The quality of the grid is assessed through the minimum orthogonal value, yielding a value of 0.64, affirming the rationality of the grid division strategy.  
In this study, water was utilized as the primary phase fluid, while air served as the secondary phase fluid. The boundary conditions of the model are defined as follows: The liquid inlet is designated as a velocity inlet, with the velocity determined by the velocity of the nozzle inlet; the gas inlet is specified as the atmospheric pressure inlet; the outlet is also set as the atmospheric pressure outlet.  
The computational software Fluent 2022 R1 was employed to solve the governing equations, utilizing a solver based on the pressure method. The pressure-velocity coupling method was implemented using the SIMPLEC scheme; the PRESTO scheme was employed for pressure term discretization; the momentum was solved using a second-order upwind scheme; turbulent kinetic energy and dissipation rate were computed using a first-order  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Structure Parameter** & **Value** \\ \hline Liquid/gas inlet diameter & 25 mm \\ Nozzle outlet diameter (\(D_{N}\)) & 12 mm \\ Nozzle contraction angle & 20\({}^{\circ}\) \\ Suction chamber inlet diameter & 65 mm \\ Suction chamber contraction angle & 30\({}^{\circ}\) \\ Throat nozzle distance & 10 mm \\ Mixing section diameter (\(D_{M}\)) & 20 mm \\ Mixing section length (\(L_{M}\)) & 200 mm \\ Diffuser contraction angle & 6\({}^{\circ}\) \\ Diffuser outlet diameter & 40 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure parameter values of the injector.  
Figure 1: Diagram of Venturi injector structure.  
Figure 2: Computational grid diagram of the Venturi injector.  
upwind scheme; and the volume fraction was handled using the Geo-Reconstruct scheme. Default relaxation factors were applied for pressure, density, and momentum.  
Due to the significant fluctuations inherent in turbulent jet flows, achieving a steady-state solution was difficult, particularly as the VOF model is generally not well-suited for steady-state solutions. Consequently, transient simulations were employed in place of steady-state approaches. The time step was set at \(5\times 10^{-5}\) s. Convergence criteria for mass, momentum, and k-\(\epsilon\) residuals were established at \(10^{-4}\), with a maximum of 20 iterations per time step. The simulation reached a stable state when variable values fluctuated within 5% of the stable value. To mitigate errors associated with single-time-point solutions, time-averaged sampling was employed, with data being continuously collected over a period of 0.2 s.
# Estimation of Synaptic Activity during Neuronal Oscillations
## 2 Methods
### Estimation Procedures  
In order to estimate the synaptic conductances from the membrane potential trace we use similar ideas to those presented in [26]. In that paper, which was uniquely devoted to a piecewise linear system (the McKean model), the knowledge of an analytical approximation of the period \(T\) as a function of the synaptic conductance \(g_{syn}\) was essential. However, as exposed in the Introduction, in general this analytical approximation cannot be obtained, and therefore we next propose a computational adaptation.  
We first fit the in silico data, namely, the membrane potential, to a base model; here, we use the exponential (EIF) model, already described in Section 2.1.1.  
Once we have determined the parameters of the base model, we integrate it for different constant synaptic conductances \(g_{syn}\) and compute the period of oscillation, \(T\). This results in a table of corresponding \(T-g_{syn}\) pairs, which can further be interpolated, using for instance the PCHIP interpolation, to obtain a continuous function \(G_{syn}(T)\).  
Finally, given any membrane potential trace, we compute, for each interspike time interval (ISI), the ISI duration, \(T^{*}\), and its corresponding \(G_{syn}(T^{*})\). Repeating the procedure for all the ISIs, we obtain a discrete trace of synaptic conductances, which can finally be interpolated using also the PCHIP interpolation.  
A schematic representation of this estimation procedure is presented in Algorithm 1 and displayed in Figure 1.  
Note that, in step 3b of Algorithm 1, several different synaptic conductances can provide the same period \(\hat{T}\). This lack of uniqueness is a trouble for the following steps in the algorithm. Even though in all cases we have treated, this issue had not happened, a control condition needs to be considered in this step to ensure the monotonic behavior in the \(T-g_{syn}\) curve. Therefore, we have overcome this drawback by choosing the closest value to the last computed conductance.
## 2 Materials and Method  
### Remotely Sensed Data  
The 8-day GPP [51; 52] and ET [53; 54] data with 1-km spatial resolution are acquired from the MODIS instrument onboard Terra satellite during 2000 to 2014, from the Numerical Terradynamic Simulation Group ([[http://www.ntsg.umt.edu](http://www.ntsg.umt.edu)]([http://www.ntsg.umt.edu](http://www.ntsg.umt.edu))). The MODIS GPP product (MOD17A2) was developed based on a light-use efficiency model [55]. GPP is the largest contributor of carbon flux and the largest carbon uptake by terrestrial ecosystems.  
Many studies have confirmed the accuracy and validity of MODIS GPP [56; 57; 58; 59; 60; 61] and it is compared with station observations in many regions and biomes [52; 56; 62]. The MODIS GPP product has been widely used in studies with various spatial scales and domains (regional to global) in different ecosystems [63; 64].  
The Penman-Monteith model was adopted to estimate the global MODIS ET product (MOD16A2), which uses meteorological reanalysis data and vegetation property dynamics (e.g., land cover, leaf area index and albedo). The forcing data for the model are retrieved from the MODIS data [54; 65]. The validation of MODIS ET product using station flux tower data showed reasonable accuracy over the Contiguous United States (CONUS) [65; 66].  
In addition to GPP and ET, the biome types over the CONUS are determined according the MODIS global land cover product (MCD12Q1) acquired from the global land cover facility of the University of Maryland ([[http://glcf.umd.edu/data/lc/](http://glcf.umd.edu/data/lc/)]([http://glcf.umd.edu/data/lc/](http://glcf.umd.edu/data/lc/))). In this study, the biomes are classified into 10 types as follows: Evergreen Needleleaf Forest (ENF), Evergreen Broadleaf Forest (EBF), deciduous Needleleaf Forest (DNF), Deciduous Broadleaf Forest (DBF), Mixed forest, Shrublands, Savannas, Grasslands, Croplands/natural vegetation and Wetlands. The original spatial resolution of biomes are 500 m, which are aggregated to 1-km in order to be consistent with the GPP and ET datasets.
# Tunnel Geology Prediction Using a Neural Network Based on Instrumented Drilling Test
## 5 Geology Prediction: Results from Borehole Testing Data  
The trained basic and deep neural network models were used to predict geological conditions from 3611 data points of testing data obtained from a borehole into the tunnel face. The predictive accuracy of the basic neural network was 90.55%, while that of the deep neural network was 91.68%. Figure 11 shows a comparison of the predicted geological profile and the actual geological profile. The geological profiles predicted by the basic and deep neural networks both matched the actual geological profile quite well. The deep neural network model outperformed the basic neural network in predicting occurrences of clay and soft interlayers. For both neural network models, most of the incorrect predictions occurred at rock interfaces and weak interlayers. Some incorrect predictions occurred at interfaces between fractured limestone and non-fractured limestone units since fractured limestone and limestone ultimately represent the same rock compositional type under different structural conditions. The phenomenon is the same as that observed in the neural network training confusion matrix (Figure 10).  
Figure 11: _Cont_.  
Figure 11: Comparison of the neural network prediction results and the actual geological profile: (**a**) 0–14.44 m, (**b**) 14.44–28.88 m, (**c**) 28.88–43.32 m, (**d**) 43.32–57.76 m, (**e**) 57.76–72.20 m.
## 3 Results
### Adaptability Assessment Tools and Models
#### 3.1.2 The Adaptable Building Design (ABD) Framework, 2010  
The Adaptable Building Design (ABD) Framework [45] introduces a design-support tool relying on predictive uncertainty modeling to deliver buildings with adaptive capacity to change and therefore increased longevity. The framework aims to integrate multiple flexibility options in the design phase that address a variety of future uncertainties. Then, the value of these options is assessed for decision-making. The framework consists of four main phases: in phase 1, possible future uncertainties are identified, including market conditions, performance and technology, policies and standards, and climate conditions. In phase 2, based on the uncertainties identified in phase 1, flexibility options are analyzed, and necessary ones are embedded in the design of several building systems, e.g., spatial, structural, and service systems. In phase 3, a mathematical logic decision-making mechanism is applied to formulate design rules that identify when to use each embedded flexibility option. Establishing design rules relies on multiple parameters that assess the building's real estate performance at a particular time. In phase 4, value analysis and calculation are conducted using the Real Option Analysis (ROA) methodology, which allows evaluating the capacity of accommodating future uncertainties of the project as well as its economic value by carrying out probabilistic simulations of future scenarios. The simulation outcomes suggest lifecycle planning, including the initial construction and the later incremental adaptations to achieve the maximum value of the project. Although adaptability options are embedded in the early design stage of the project, the ABD methodology allows for re-evaluation during the redesign phases to refine these options [46].  
The authors used an illustrative case study project to demonstrate their approach. However, due to the commercial nature of the studied project (multi-use building with office and retail spaces), only market uncertainties were addressed and evaluated for integration of flexibility options. Further cases of buildings with varying uses should be considered to validate the practical implementation of the method.
# Digital Technologies' Risks and Opportunities: Case Study of an RFID System
## 3 X.0 Technologies
### Blockchain  
Since it was first identified by [PERSON] in his PhD thesis in 1982 [47], blockchain has increasingly evolved over the years and gained in terms of performance. Moreover, this evolution led to the enlargement of its applications to the whole social level [48; 49]. Indeed, blockchain was defined as the fifth breaking innovation within the computing paradigm, preceded by the mainframe, personal computer, internet, mobile and social network [50], and Gartner's recently predicted that blockchain technology will contribute $3.1 trillion of new business value by 2030 [51].  
Applied in all trustworthy fields with strong certifications, blockchain is the enabler of the digital currency known as Bitcoin [52]. From Blockchain 1.0 (Bitcoin and the like) to Blockchain 2.0 (Ethereum and SCs), to Blockchain 3.0 (IOTA, Cardano, Tezos, etc.), and the ongoing Blockchain 4.0 (use of A.I., Blockchain as a Service, etc.), This technology has many advantages [53; 54]:  
* distribution: a resilient and secured system through storage of information on multiple computers;
* traceability: all transactions are traceable;
* anonymity: addresses' owners' name is only used to prove the ownership of a private key. Transactions can be done anonymously;decentralization: transactions are managed without a central authority;
* immutability: once accepted, it is not possible to modify data;
* cost friendly: open-source software with low maintenance costs;
* autonomy: data exchange, record, and update are performed in a trustworthy environment using consensus-based specifications and protocols. Therefore, it ensures the authenticity of the recorded transaction on the blockchain without human intervention.  
Despite the promising features of blockchain, the technology is still not highly adopted [55] due to its several drawbacks. Indeed, blockchain still has performance and scalability issues, their energy consumption is high, their cost is not predictable and there is no control of written and read information, especially regarding public blockchains [53]. In addition to that, security is still a huge concern for this technology. On 22 May 2018, hackers attacked the blockchain Verge, stealing nearly 35 million anonymous coins [56]. On 5 January 2019, hackers caused a loss of $1.1 million US dollars by attacking the blockchain Ethereum Classic (ETC) [54, 57].  
Another constraint to consider is that successful blockchain implementation goes with integrating several technologies, such as big data and cloud computing, making the process much more difficult [57]. Finally, from an organizational aspect, barriers to be expected are a lack of skills, tools (hardware and software) and standards/business models for both implementation and maintenance [55].
## 3 Results and Discussion
### Texture Profile Analysis  
Texture profile analysis (TPA) comprising hardness and chewiness is used to determine the texture of foods by imitating repeated biting or chewing. Hardness is the force necessary to squeeze a substance between molar teeth or between tongue and palate; chewiness is the time required to grind the sample by applying steady pressure to reduce it to a texture appropriate for swallowing [65; 66]. The data on the meatload hardness and chewiness are provided in Table 1. The turmeric addition initially contributed to greater hardness (\(p<0.05\)). However, from day 14 onwards, no significant (\(p>0.05\)) difference was found. The hardness of the control changed over time. Until day seven, it remained lower, while later on, this parameter increased significantly (\(p<0.05\)). The hardness of T1 also varied, but not significantly (\(p>0.05\)). A similar hardness level was noted for the T3, except on the last day, when it increased significantly (\(p<0.05\)). In comparison, [PERSON] et al. [61] showed that hardness increased significantly after five days for chicken sausage prepared with turmeric powder. [PERSON] et al. [67] showed that hardness and chewiness increased with growing powder concentration (2%, 3%, and 4%). Initially, the turmeric impacted the chewiness of meatload, being higher than the control's (\(p<0.05\)). After seven days, although higher, an insignificant difference was found (\(p>0.05\)). No significant changes were demonstrated for T1 chewiness during storage (\(p>0.05\)). Up to day 14, the T3 chewiness was at a similar level. However, a significant increase was noted on the last day (\(p<0.05\)). [PERSON] et al. [57] observed a similar increasing tendency of hardness and chewiness when chia seeds were added to pork. We observed that the higher the turmeric addition, the less noticeable the meat flavor, with the T3 differing significantly from C throughout the storage (\(p<0.05\)). However, the meat flavor intensity for three meatloaves did not change significantly over time (\(p>0.05\)). The T3 was characterized by significantly lower flavor desirability, and T1 scored similarly to C. We did not see any effect of the storage time on this parameter.
## 5 Results and Discussion  
A sample three-bearing shaft system was investigated as shown in Figure 10, and the shaft was supported by three identical 7008C ACBBs. All ball bearings' inner rings were perfectly fixed to the shaft. The outer ring of front bearing was free to move axially in order to apply preload, and the outer ring of each rear bearing was fixed either ideally or with a small misalignment angle caused by mounting error. The three ball bearings were assembled 32.5 mm, 187.5 mm, and 237.5 mm away from the left end of the shaft, respectively, and named as the front bearing, Rear Bearing 1 and Rear Bearing 2. The geometrical and material parameters for the bearing and shaft are given in Tables 2 and 3, respectively. The external loads acting on the shaft (radial loads \(F_{\mathrm{x}}\), \(F_{\mathrm{y}}\) were 1500 N and 1000 N, respectively; axial load \(F_{\mathrm{z}}\) was \(-\)1000 N; and moments \(M_{\mathrm{x}}\), \(M_{\mathrm{y}}\) were 5000 N-mm and 6000 N-mm, respectively) were applied to the shaft node 110 mm away from the left end of the shaft. The shaft rotation speed \(n_{\mathrm{i}}\) was 10,000 r/min with a centrifugal force of around 11 N for each ball element. The two axial preload methods, including constant-displacement preload and constant-force preload, were considered, and the preload was applied on the outer ring of the front bearing. The outer ring of either Rear Bearing 1 or Rear Bearing 2 was subjected to angular misalignment caused by mounting error, and only the angular misalignment around the \(y\)-axis of the bearing local coordinate system was considered here. For brevity, Case I was used to indicate that Rear Bearing 1 was subjected to angular misalignment, and Case II implied that Rear Bearing 2 was subjected to angular misalignment.
# Trajectory-Tracking Control of Unmanned Vehicles Based on Adaptive Variable Parameter MPC
## 5 Adaptive Trajectory-Tracking Controller Design  
In this study, CarSim (2020.0) and MATLAB (2022b)/Simulink co-simulation were used as the system test platform to construct the classical MPC controller (MPC), tire cornering stiffness adaptive MPC controller (KF-MPC), and tire cornering stiffness and time domain adaptive MPC controller (KF-AG-MPC), respectively. The basic parameters of the three controllers are set the same: the prediction time domain \(N_{p}=30\), the control time domain \(N_{c}=20\), the upper limit of the pre-scanning distance \(L_{\text{max}}=4.5\) m, the lower limit \(L_{\text{min}}=0.5\) m, and the sampling time \(T=0.02\) s. In order to verify the adaptability of the trajectory-tracking controller in an all-terrain environment, a double-lane road with a variable lateral inclination angle and variable coefficient of road adhesion was set up to test the controller. The longitudinal speed of the unmanned vehicle was 72 km/h; it is assumed that the road surface attachment coefficient is 0.6 in the 0-70 m interval of the road and 0.4 in the 70-200 m interval. The controller framework diagram is shown in Figure 9 below:  
In order to verify the reasonableness of the Simulink model of the unmanned vehicle built in this paper, according to the steering transient response experiment (steering disc  
Figure 8: Influence of control time domain on trajectory-tracking control effectiveness. (a) \(N_{c}\) too big. (b) \(N_{c}\) too small.  
Figure 9: Controller architecture diagram.  
angle step input), the response characteristics are compared with those of the Carsim model under the working condition of a 50 km/h steering disc angle step, as shown in Figure 10.  
As can be seen in Figure 10, the Simulink model of the unmanned vehicle constructed in this study agrees with the trend of the yaw rate and the Beta output from the Carsim vehicle model, which proves that the present model is reasonable.  
The comparison of the control effects of the three controllers under double-shift conditions is given in Figures 11 and 12 and Table 1. In Figures 11 and 12, it can be seen that the KF-MPC and KF-AG-MPC have a greater improvement in trajectory-tracking accuracy and vehicle lateral stability compared
## 1 Introduction
The results showed that the predictions made by the neural network model were more accurate and efficient than those made by the other models. Although the neural network approach has been successfully used to model hot deformation while substantially compensating for the drawbacks of the traditional constitutive equations, the neural network approach is limited by inefficiencies. As a result, there is still a crucial need to improve the modeling method, and this occurs in the development process. The development process involves the complex work of optimizing the process parameters, including the number of hidden layers, neurons, epochs, etc. The developed neural network model is sometimes computationally expensive. Furthermore, the neural network model is often inaccurate due to overfitting. A literature review reveals the significance of new research adopting the new approach in modeling the flow stress of hot deformation. In other words, it is important to find a new highly efficient approach that makes accurate predictions. The research findings obtained using the new approach can be applied to research modeling the hot deformation of different types of metallic alloys. The present work proposes a new modeling method for the high-temperature tensile deformation of 5005 aluminum alloy. First, the deformation characteristics were studied by discussing the flow stress behavior. The variation in flow stress was first modeled by the Arrhenius constitutive equation and the neural network model. Then, a newly proposed multiple modeling methodology consisting of locally constrained regression models (LCRMs) that use logarithmic transformations through switching was used; the logarithmic transformation of variables in a regression model is one of the most common ways to establish a nonlinear relationship between independent and dependent variables, and using the logarithm of one or more variables can effectively capture a nonlinear property while maintaining the linearity of the model [16]. Log transformation is also an effective method of transforming heavily skewed variables close to a normal distribution. Therefore, the predictive regression model for each domain was optimized through a logarithmic transformation to better express the nonlinearity of the flow stress. The different models adopted in this research were compared to determine the comparative performance of locally constrained regression models (LCRMs) in terms of errors and reliability. This research aimed to contribute to the field of research searching for an efficient and accurate method for modeling the flow stress of hot deformation. Section 2 of this article explains the preparation of the test specimen and the process by which the hot tensile experiments were conducted. Section 3 describes the theory of the Arrhenius equation and LCRMs. In the Section 4, the measured stress and stress values predicted by the Arrhenius equation, neural network, and LCRMs are compared in terms of various aspects. Section 5 of this article provides the conclusion, which explains the limitations of the presented model and directions for future research.