esa-sceva/satcom-chunk-collection huggingface dataset:
# Systematic Literature Review of Challenges in Blockchain Scalability
## 7 RQ2: What Vital Root Factors Are Causing Scalability Issue in Blockchain?
### Storage  
Public blockchain storage is another essential factor to be seriously considered in the context of scalability. Public blockchain storage requirement grows in tandem with the increasing number of nodes and transactions [5]. As such, full nodes that store complete block data demand high storage capacity. Moreover, high storage requirements can be relieved by increasing partial nodes in public blockchains [35]. Although partial nodes do not store the whole blockchain, they may considerably increase the workload. Throughput would therefore be affected, and addressing high storage requirements is non-trivial.
## 3 Elastographic Methods
### Two-Dimensional Shear Wave Elastography (2D-SWE)
#### 3.3.5 Pediatric Patients with NAFLD  
2D-SWE is an accurate and reproducible non-invasive technique that efficiently depicts the presence of significant liver fibrosis and, less accurately, mild liver fibrosis in pediatric patients with NAFLD. 2D-SWE showed a very high correlation with liver fibrosis (\(p\) < 0.001) at univariate and multivariate analyses. The AUROCs for the association of any and significant fibrosis were 0.92 and 0.97, respectively [12].
## 2 Materials and Methods  
Figure 1 presents an overview of the implemented methodology. First, data were collected from Hospital Santa Clara in Bogota, Colombia. Next, a preprocessing and cleaning phase was conducted to prepare the data before extracting the features used as input for the algorithms. For this, four different schemes were designed, each employing five ML models. To assess the model performance, the stratified \(k\)-fold cross-validation technique was established. The following sections provide a detailed explanation of each step illustrated in Figure 1.
###### Abstract  
Significant intensification in livestock farming has become prevalent to meet the increasing meat production demand, resulting in a higher density of pigs in relatively small areas in a commercial swine building. The subsequent challenges of maintaining the quality of both routine management and environmental comfort of pigs to minimize the loss of both pigs' health and welfare can be attained by implementing autonomous monitoring and intelligent management decisions based on precision livestock farming (PLF). A three-layer wireless sensor network (WSN) based on ZigBee technology has been devised to monitor four environmental parameters in real-time, namely: temperature, relative humidity, concentrations of carbon dioxide and ammonia in a commercial gestating sow house. The overall packet loss rate of the WSN system which reported 16,371 records from its 41 indoor slave nodes in a 10-min interval for three consecutive days was 4%. The carbon dioxide sensors had an average outlier rate of 6.5% after a series of preprocessing procedures. The spatial and temporal characteristics showed that the carbon dioxide level exceeded the limit of 2700 mg/m\({}^{3}\) twice during both 07:00-08:00 and 14:00-15:00. Besides, the overall NH\({}_{3}\) concentration in the swine building was maintained in a relatively low-level range with a maximum of less than 8 mg/m\({}^{3}\). In sum, the real-time monitoring and timely intervention of microclimate in this commercial gestating sow house can be achieved by deploying this WSN system, thereby making it possible to provide an intelligent decision on precise management of livestock automatically.  
environmental monitoring; pig house; wireless sensor network; ZigBee +
Footnote †: journal: Applied Sciences
# Fast Coherent Video Style Transfer via Flow Errors Reduction
## 5 Experiments
### Qualitative Evaluation
#### 5.1.2 Analysis of Loss Functions  
**Sharpness Losses.** Figure 12 shows the effect of Sharpness Losses in our approach. Without the Sharpness Losses, the straightforward idea that copying and pasting pixels from the per-frame stylized result \(f_{s}^{t}\) and the warped image \(w^{t}\) into corresponding regions through the mask \(M^{t}\) causes the pixel loss, and this loss accumulates along the entire video process, which results in a significant number of image blurriness artefacts. By adding the Sharpness Losses, our approach ensures that the pixel values are compensated for from the beginning to the end which prevents image blurriness artefacts in the video outputs.  
**Coherent Losses.** Figure 13 shows the effect of Coherent Losses in our method. Per-frame processing methods like [10; 13] produce flickering artefacts in adjacent frames (see the zoom-ins) without any consideration of temporal consistency. The proposed method takes the per-frame stylized frames as inputs and produces the texture consistent consecutive outputs; for example, the rectangle areas.  
Figure 11: Qualitative ablation study on proposed mask techniques of Alley_2 scene from MPI Sintel dataset [66]. The naive method using the flow mask produced by [24] causes ghosting artefacts (see unexpected grids and curves in red and orange rectangles). The multi-scale scheme causes worse ghosting artefacts. By gradually adding the incremental mask and multi-frame mask fusion techniques, the unexpected grids and curves are effectively mitigated which produces better visual quality without ghosting artefacts.
# Multi-Fidelity Gradient-Based Strategy for Robust Optimization in Computational Fluid Dynamics
## Appendix A Calculation of the Gradient from CFD Codes  
As GEK and the MoM both require as input also the gradient of the QoIs with respect to the uncertain parameters, it is worth providing a quick overview of the methodologies used to calculate the derivatives of the quantities from the CFD code employed.  
In general, the gradient evaluation problem may be stated as follows: \(J\) is a vector of objective functions and constraints of dimension \(N_{j}\) (Equation (11)) that must comply with a set \(R\) of governing equations (Equation (12)).  
\[J=J(\omega(\alpha),\alpha) \tag{13}\]  
\[R(\omega(\alpha),\alpha)=0 \tag{14}\]  
where:  
* \(\alpha\) is a vector of the control/design variables of dimension \(N_{\alpha}\), which parametrizes the problem.  
\(\bullet\)\(\omega\) is a vector of state variables depending on \(\alpha\) with dimension \(N_{\omega}\).  
In this work, \(J\) is the objective function defined in Equation (3); the governing equations \(R\) correspond to Euler equations for quasi-1D flow Equation (4); \(\omega\) are the conservative variables \([\rho,\rho v,\rho e_{t}]\); and \(\alpha\) is the vector of the design parameters parametrizing the geometry \([a,b,c,d]\) that should be optimized.  
The final aim of the problem is to evaluate the gradient of the objective functions and constraints \(J\) expressed in Equation (11) with respect to the design variables \(\alpha\), as defined in Equation (13).  
\[\frac{dJ}{d\alpha}=\left[\frac{\partial J^{T}}{\partial\omega}\right]\frac{d \omega}{d\alpha}+\left[\frac{\partial J^{T}}{\partial\alpha}\right] \tag{13}\]  
In the present work, the continuous adjoint and discrete adjoint were used to compute the gradient in Equation (13); hereafter, in the following sections, the adopted formulation of both of these techniques is provided.
## 3 Mathematical Model of Formation Operation Control
### Convergence Proof  
In the field of automatic control, the Lyapunov stability can be used to describe the stability of the dynamic system. According to Sections 3.1-3.3, the Lyapunov function selected in this article is shown in Equation (29). Then  
\[\begin{array}{ll}V(x_{ie},y_{ie},\theta_{ie})&=\big{(}1+e^{-t}\big{)}\times \frac{1}{2}(x_{ie}-d(1-\cos\theta_{ie}))^{2}+\big{(}1+e^{-t}\big{)}\times\frac{ 1}{2}(y_{ie}-d\sin\theta_{ie}+k_{\theta}\theta_{ie})^{2}\\ &\quad+\big{(}1+e^{-t}\big{)}\times\frac{1}{k_{a}}(1-\cos\theta_{ie})\end{array} \tag{29}\]Obviously, \(\big{(}1+e^{-t}\big{)}\big{(}x_{ie}-d(1-\cos\theta_{ie})\big{)}^{2}/2\) and \((1+e^{-t})(y_{ie}-d\sin\theta_{ie}+k_{\theta}\theta_{ie})^{2}/2\) are greater than or equal to 0 respectively. \(\big{(}1+e^{-t}\big{)}(1-\cos\theta_{ie})/k_{y}\) is also greater than or equal to 0. So, the function \(V(x_{ie},y_{ie},\theta_{ie},t)\) positives definite.  
According to Equation (29), for function \(V(x_{ie},y_{ie},\theta_{ie},t)\) after derivation, the following Equation (30) is obtained.  
Then  
\[\begin{array}{rl}\dot{V}(x_{ie},y_{ie},\theta_{ie})&=\big{(}1+e^{-t}\big{)} \times\frac{1}{2}(x_{ie}-d(1-\cos\theta_{ie}))\times\Big{(}\dot{x}_{ie}-d\sin \theta_{ie}\dot{\theta}_{ie}\Big{)}\\ &+\big{(}1+e^{-t}\big{)}\times\frac{1}{2}\big{(}y_{ie}+d\sin\theta_{ie}+k_{ \theta}\theta_{ie}\big{)}\times\Big{(}\dot{y}_{ie}+d\cos\theta_{ie}\dot{ \theta}_{ie}+k_{\theta}\dot{\theta}_{ie}\Big{)}\\ &+\big{(}1+e^{-t}\big{)}\times\frac{1}{k_{y}}\sin\theta_{ie}\dot{\theta}_{ie} -e^{-t}\times\frac{1}{2}(x_{ie}-d(1-\cos\theta_{ie}))^{2}\\ &+e^{-t}\times\frac{1}{2}(y_{ie}-d\sin\theta_{ie}+k_{\theta}\theta_{ie})^{2}+ e^{-t}\times\frac{1}{k_{y}}(1-\cos\theta_{ie})\end{array} \tag{30}\]  
By introducing \((k_{a}+k_{b}=1)\) and Equation (26) into Equation (30), Equation (31) can be obtained. Then  
\[\begin{array}{rl}\dot{V}(x_{ie},y_{ie},\theta_{ie})&=\big{(}1+e^{-t}\big{)} \Big{\{}-k_{x}(x_{ie}-d(1-\cos\theta_{ie}))^{2}-v_{v}k_{y}k_{\theta}k_{a}(y_{ ie}+d\sin\theta_{ie}+k_{\theta}\theta_{ie})^{2}-\frac{k_{b}v_{\theta}}{k_{y}k_{ \theta}}\sin\theta\Big{\}}\\ &-e^{-t}\Big{\{}\frac{1}{2}(x_{ie}-d(1-\cos\theta_{ie}))^{2}+\frac{1}{2}(y_{ ie}-d\sin\theta_{ie}+k_{\theta}\theta_{ie})^{2}+\frac{1}{k_{y}}(1-\cos\theta_{ie}) \Big{\}}\leq 0\\ \end{array} \tag{31}\]  
The function \(\dot{V}(x_{ie},y_{ie},\theta_{ie},t)\) is known from Equation (31). It negatives definite.  
Lyapunov stability main criterion is often used to prove the stability of the system. Let the state equation of the system be: \(\dot{x}=f(x)\), if there is a scalar function \(V(x)\) with continuous first derivative and it satisfies that \(V(x)\) is positive definite and \(\dot{V}(x)\) is negative definite. Then, the system is asymptotically stable in the equilibrium state [20].  
According to Lyapunov stability main criterion, the function \(V(x_{ie},y_{ie},\theta_{ie},t)\) positive definite, and \(\dot{V}(x_{ie},y_{ie},\theta_{ie},t)\) is negative definite, so the above control law makes the error system in the equilibrium state asymptotic stability.
# Springback Behavior of AA 7075-T6 Alloy in V-Shaped Bending
## 1 Introduction
He validated his findings through corresponding experimental results [25]. Similarly, [PERSON] et al. used the Minitab statistical analysis software to study how bending parameters impact springback, constructing both numerical and mathematical models to interpret their data [26; 27]. [PERSON] et al. explored the springback effects in V-bending operations using both experimental procedures and statistical analysis. Their findings indicated that variables such as punch dwell time, material type, and lubrication considerably impacted the springback behavior of aluminum sheets [27]. In another investigation, [PERSON] et al. studied how varying the die radius and sheet thickness influences the amount of springback, finding a good correlation between simulation and experimental outcomes [28]. Another researcher carried out an elastoplastic finite element analysis to predict springback, focusing on geometric factors and the mechanical properties of the material involved [29]. [PERSON] et al. analyzed the springback behavior of 6063 aluminum profiles during the forming process, combining finite element simulations with experimental verification. Their study identified an optimal process route for manufacturing bent aluminum parts [30]. [PERSON] et al. introduced a machine learning-based predictive model, supported by finite element simulations, to determine the springback angle of 7075-T6 high-strength aluminum alloy pipes. Their method provided a reliable and efficient means for estimating springback in large-diameter, thin-walled tubes and significantly enhanced the production quality of bent fittings [31]. This research focuses on analyzing the bending behavior of aluminum sheets at elevated temperatures through finite element simulations. The study explores the influence of key parameters including forming temperature, sheet thickness, bending angle, and bending radius on the V-shaped bending process. To achieve minimal springback, these parameters are optimized, and their individual and combined effects on springback are thoroughly examined. The findings are intended to serve as a reference for future applications and developments in industrial forming processes.
## 3 Results and Discussion  
### Analysis of Cladding-Thickness Uniformity  
Figure 8 shows an image of the end face and shaft face of the clad rebar. As can be seen, the composite interface of the clad rebar is tightly bonded, with no holes or cracks visible to the naked eye, and the thickness of the cladding is relatively evenly distributed; the minimum thickness is 0.5 mm, far exceeding the value of 0.18 mm specified by the Chinese National Standard GB/T 36707-2018.
# Predicting the Aggregate Mobility of a Vehicle Fleet within a City Graph
## 3 Problem Statement  
This paper addresses the comparative assessment of different schemes for the prediction of the aggregated mobility of a fleet of vehicles that move along a graph describing a city. In this type of graph, the nodes represent crossroads in the city, and the directed edges represent the roads (and their directions) between the crossings. Simple simulation-controlled scenarios will be considered for focusing on the main objective of the comparative assessment.  
The aggregated mobility will be defined by the distribution of the assets along the graph nodes represented by a vector \(y_{t}\in(\mathbb{N}\cup\{0\})^{N}\) for a set of discrete time stamps \(t=0,1,\ldots\) Assuming available a sequence of occupation values \(y_{t},\ t=0,1,\ldots,T-1\), the objective is to predict the next value \(y_{\tau}\).  
Three approaches are considered to perform such predictions. On the one hand, motivated by the underlying graph structure, a Markov chain (MC) model, defined by a transition matrix \(\Pi\), is assumed to model the mobility of each vehicle; then, such a matrix (denoted as \(\widehat{\Pi}\)) is estimated using past mobility data \(y_{t},\ t=0,1,\ldots,T-1\). Ultimately, the prediction is performed using \(\widehat{\Pi}\):  
\[y_{\tau}^{p}=\widehat{\Pi}\cdot y_{\tau-1}. \tag{1}\]  
The second approach considers an MLP architecture trained in a supervised manner using the past mobility data \(y_{t},\ t=0,1,\ldots,T-1\), so that:  
\[y_{\tau}^{p}=\mathrm{MLP}_{\mathbf{\Theta}}(y_{\tau-1},\ldots,y_{\tau-4}) \tag{2}\]  
where \(\mathbf{\Theta}\) is the set of adjustable parameters and \(d\geq 1\) defines the size of the time window of previously selected values as predictors.  
Finally, the third approach proposes a GNN that takes advantage of both the underlying graph structure and the proven effectiveness of graph neural networks (GNNs) for time series prediction [15]. Such GNN is also trained in a supervised manner with the past mobility data \(y_{t},\ t=0,1,\ldots,T-1\), so that in general:  
\[y_{\tau}^{p}=\mathrm{GNN}(y_{0},\ldots,y_{\tau-1}). \tag{3}\]  
The details of the MLP and GNN implementations and the use of the available data are provided in Section 5.2.  
The three proposed approaches are comparatively assessed together with other simpler prediction schemes employed as baselines.
# Priority of Emergency Vehicle Dynamic Right-Of-Way Control Method in Networked Environment
### PDR-EVs Control Strategy  
We set a non-intrusive interval of length at the front end of the EV to ensure driving safety and to make the EV prioritize the right-of-way space. The interval value is related to the EV's speed and the front CV's speed and should be determined based on the speed of both [16]. In addition, a buffer interval of length was appropriately set before the no-intrusion interval. When the CVs ahead of the EVs enter the buffer, it is necessary to request them ahead to change lanes in time for them to pass quickly. As depicted in Figure 1, since the EV's speed exceeds the CVs', the front CVs should complete a lane change before entering the non-intrusion zone. Among them, CV vehicles are equipped with sensors, controllers, actuators and other devices, and incorporate modern communication technologies that are capable of exchanging information between vehicles and X (vehicles, roads, people, etc.) through sensing, decision-making, and collaborative control functions. Subsequently, the physical meaning of the non-intrusive zone is the safe-following distance that CV vehicles are not allowed to enter, which is adjusted according to the vehicle's speed in front and behind. The physical meaning of the buffer zone is the perceived space of the EV, for a buffered lane change for CV vehicles, thereby reflecting its right-of-way priority privilege, which is adjusted according to the traffic state. The lengths of the non-intrusive and buffer zones could be flexibly and dynamically adjusted to suit various traffic scenarios. Obviously, EVs can obtain the priority right-of-way space in this process.
## 2 Methods Used
### Validation Methods
#### 2.6.2 Statistical Indices  
Numerous statistical indices have been used in different studies to evaluate the performance of predictive models [46; 73; 74]. In this study, we used positive (TP), true negative (TN), false positive (FP), false negative (FN), positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF), accuracy (ACC), root mean square error (RMSE), and Kappa for the comparison and validation of the models. The equation for each one of these indices is as follows [75; 76; 77; 78; 79; 80; 81; 82]:  
\[PPV=\frac{TP}{TP+FP} \tag{1}\]  
\[NPV=\frac{TN}{TN+FN} \tag{2}\]  
\[SST=\frac{TP}{TP+FN} \tag{3}\]  
\[SPF=\frac{TN}{TN+TP} \tag{4}\]  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
\[K=\frac{P_{0}-P_{est}}{1-P_{est}} \tag{6}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\bigl{(}V_{predicted}-V_{actual}\bigr{)}^{ 2}} \tag{7}\]  
where \(n\) is the total of the variable, \(V_{predicted}\) and \(V_{actual}\) are like the prediction and actual value of variable \(i\)-th, \(P_{a}\) is the relative observed agreement among raters, and \(P_{est}\) is the hypothetical probability of chance agreement.
# A Critical Review of SCWG in the Context of Available Gasification Technologies for Plastic Waste
## 2 Gasification Process
### Fundamentals of Gasification
Syngas is used also for fuel production, in particular, one of the commercially accessible methods of manufacturing clean synthetic liquid fuels from syngas is the Fischer-Tropsch process. Fischer-Tropsch synthesis is a surface polymerization reaction, in which hydrogen and carbon monoxide react on the surface of a catalyst in situ. The catalysts are based on iron or cobalt [37; 38; 39]. Syngas can also be used for chemicals production such as ammonia, ethylene glycol, DME, and methanol. One example is the Waste-to-Biofuels-and-Chemicals (WTFC) process developed by Enerkem, designed to gasify post-recycled residual from MSW into methanol and eventually ethanol. Enerkem operates a commercial-scale facility in Edmonton, Alberta, Canada, as well as an innovation center in Westbury, Quebec, Canada [40]. The company is developing another commercial facility in Canada (Varennes). The plant will use various types of municipal waste from the industrial, commercial, and institutional sectors as raw materials, as well as construction and demolition debris. As reported by the detailed Enerkem website [41], another facility planned by Enerkem in consortium with other entities is a waste-to-chemicals plant in the Botlek area of the Port of Rotterdam. The aim of this project is to provide a valid alternative to incineration for non-recyclable waste (coming from different sectors including industrial and municipal) by converting them into chemicals or biofuels. Making a comparison between gasification and pyrolysis, it is important to observe how the yields and compositions of the pyrolysis products (gas, oil, char) are strongly influenced by the nature of the feedstock used. Instead, in the gasification process, different types of raw materials influence only the composition of the syngas and modify the yields of the products considered of least interest Tar and char [20]. Therefore, the gasification process is characterized by greater versatility.
## 5 Results
### AUDAX Private Database

Table 7: Effectiveness of the compared methods on the AUDAX database. Considered are only landmarks from the ISBI database. The mean and standard deviation of the radial error are presented in pixels. A number and * in () denote the method’s rank and statistically significant difference. **Better results are marked in bold. **We gathered in Tables 8 and 9 the ten most accurately and the ten least accurately detected cephalometric landmarks by using our proposed SCN-EXT method. Among the ten best detected landmarks, there are as many as six landmarks (in Table 8 they are circled), which are also annotated in the ISBI database. The Th' point from the soft tissue of the throat was detected with the largest MRE error, which differs significantly from others (see Table 9). The reason is that the throat is not fully visible on all cephalograms and, therefore, an expert annotated the Th' point very inconsistently (i.e., Th' was annotated only approximately). If the Th' point was excluded from the metric calculation, the MRE for the SCN-EXT method decreased by about 0.7 pixels to \(10.57\pm 13.93\) pixels (see also Table 6). Similarly, the median radial error decreased to 6.44 pixels (previously 6.52) and the 90 th percentile of the radial error to 23.21 pixels (previously 24.13).
## 1 Introduction
### Research Literature Review
Upon evaluating the recent state-of-the-art studies, it can be observed that there is a potential lack of comprehensive consideration of dynamic power distribution network reconfiguration or other technical aspects crucial for optimal planning, potentially resulting in suboptimal solutions as evident from these references.