esa-sceva/satcom-chunk-collection huggingface dataset:
# Echocardiography in the Diagnosis and Management of Tricuspid Atresia
## 4 Echo-Doppler Features of TA on the Basis of Current Technology
Studies from the suprasternal notch may show aortic coarctation (Figure 16), which is common in patients with Type II anatomy. A large number of other cardiac defects are known to be associated with tricuspid atresia (Table III--Reference [22]). Consequently, echo-Doppler studies should be scrutinized for these defects during echo evaluation. Contrast study following the venous injection of agitated saline during 2D imaging clearly demonstrates the successive opacification of the RA, LA, LV and then the RV, in that order, but such a study is not necessary for diagnosis and is not routinely performed. Figure 16: Selected video frames from suprasternal notch views of the aortic (Ao) arch in 2D (**A**) and color flow (**B**) images of a neonate with tricuspid atresia and transposition of the great arteries, demonstrating coarctation of the aorta (CoA) and a hypoplastic transverse aortic arch (TAA) and isthmus. The association of CoA with tricuspid atresia, plus transposition of the great arteries, is well known. DAo, descending aorta. Reproduced from Reference [29]. Figure 7:0 Selected video frame of continuous wave Doppler across the ventricular septal defect of the same patient, as is shown in Figure 7:1 The low velocity flow across the ventricular septal defect suggests that the defect is nonobstructive. Reproduced from Reference [29]. In summary, the delineation of the majority of anatomic and physiologic issues related to TA is feasible by M-mode, 2D, and Doppler (pulsed, continuous wave, and color) echocardiography, and, consequently, magnetic resonance imaging (MRI), computed tomography (CT), and cardiac catheterization with selective cineangiography are not necessary for the confirming of the diagnosis. In addition, the echo-Doppler studies can help define pathophysiologic features relevant in the management of TA patients.
## 3 Results
### Influence of Temperature and ER on the Lower Heating Value of the Gas and the Gas Flow  
Table 2 reports the lower heating value of the raw gas as well as the gas flow obtained (measured under normal conditions), depending on temperature and ER. It should be noted that in the calculation of the LHV, only the energy contents of carbon monoxide, hydrogen, and methane were considered since the rest of the fuel compounds, such as light hydrocarbons with two or more carbon atoms, were not quantified.  
Figure 3: Effect of temperature on the molar composition of the gas for the three levels of ER studied (blue is presented in **(a)** (800 \({}^{\circ}\)C and 900 \({}^{\circ}\)C) and also in **(c)** (900 \({}^{\circ}\)C)).  
Furthermore, the LHV includes the effect of nitrogen dilution because the gas was generated by the operation of the gasifier using air. This made the \(\mathrm{LHV_{gas}}\) low, between 2.21 and 5.11 MJ/Nm\({}^{3}\), compared to other gasification studies with other agents, in which \(\mathrm{LHV_{gas}}\) reached values of up to 11.3 MJ/m\({}^{3}\), as in [PERSON] et al. [30]. Thus, for example, [PERSON] et al. [31], [PERSON] [32], and [PERSON] et al. [33] found LHV values of 3 to 6 MJ/m\({}^{3}\) and explained the nitrogen dilution in their works. More recently, [PERSON] et al. [34] also obtained an LHV between 3.2 and 4.8 MJ/Nm\({}^{3}\) in their experimental investigation of the gasification of macadamia nutshells.  
It can be observed that, in general, by increasing the operating temperature, there is no significant increase in the LHV of the gas. However, Table 2 shows that the ER affects the LHV of the product gas because LHV is dependent on the concentrations of CO, H\({}_{2}\), and CH\({}_{4}\), and H\({}_{2}\) concentrations decrease greatly as the ER becomes higher. At a higher ER, oxidation reactions are more favorable due to the increased availability of oxygen. This resulted in a higher production of CO\({}_{2}\) and a reduced concentration of combustible gases, ultimately causing a negative effect on the syngas higher heating value [34]. Instead, the gas flow was practically constant with the temperature, reaching values in the range of 3.44-14.89 L/min (825.6-3573.6 L/kg feed), and it varied substantially with the ER, with the highest value observed at 0.5 and the lowest value at 0.1.
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 3 SON Applications
### The Key SON Applications Leading to Full Automation
#### 3.2.5 Scenarios and Impairments That Trigger MLB Operation  
Depending on the optimization scenario that must be deployed, load balancing and traffic steering aim to resolve the following impairments [8; 22; 23; 64; 73]:  
* Offload loaded cells towards non-loaded ones, achieve a better UE throughput and QoS (Quality of Service), and ensure optimum cell capacity while users are moving within an area of interest, as a scenario assumes that an operator uses three carriers for one radio access technology. When a high load is observed in one of the three carriers, load balancing is achieved by forwarding traffic from one of the three carriers to the other through a reselection or handover.
* Offload higher power/capacity macro cells towards low-power/capacity micro/pico or femto cells. This scenario is deployed through the decreasing or increasing cell coverage by adjusting cell power. The same stands in a case where cells operate at high power, but traffic is low.
* Ping pong effects limitation (cases where handovers take place constantly). This phenomenon is seen especially in cases such as highways with fast moving UEs trying to camp on lower cells.
## 4 Hybrid Control Based on Extension Theory
### Structure of the Extension Control System  
The extension control system consists of upper and lower controllers, as depicted in Figure 6. The signal \(e\), which is the deviation between output signal \(y\) and desired signal \(r\), is taken as the input of the controller. The damping coefficient \(u\), which meets the performance requirements of the suspension and the driving requirements of the vehicle, is calculated by the suspension extension switching controller. Therefore, the suspension with the extension control system forms a closed-loop control system.  
Figure 5: Extension set diagram: (a) \(\text{T}(x)>0\); (b) \(\text{T}(x)<0\).  
The upper part is an extension controller, which divides the control area and decides the control strategy for the lower controller. In addition, control weight \(\alpha\) of the lower controller is also calculated in the upper part. According to the change of control weight \(\alpha\), control methods are changed to expand the control range of the suspension control system. The lower controller is a damping controller. Through the control weight \(\alpha\) calculated by the upper part, the control damping force is obtained. There are three control modes in the lower controller, which correspond to separately sky-hook control, ground-hook control, and hybrid control, as shown in Figure 7.
# Grassmann Manifold Based State Analysis Method of Traffic Surveillance Video
## 4 Experiment Results and Discussion  
In this section, to test the effectiveness of our proposed method, we conduct several unsupervised experiments for TSV clips classification. The two datasets used in our experiments are listed below:  
* UCSD dataset ([[http://www.svcl.ucsd.edu/projects/traffic/](http://www.svcl.ucsd.edu/projects/traffic/)]([http://www.svcl.ucsd.edu/projects/traffic/](http://www.svcl.ucsd.edu/projects/traffic/))).
* Capital Airport Expressway dataset (We establish the dataset by ourselves).  
To demonstrate the performance of Grassmann Neural Networks (GNN) method, we compare GNN method with several state-of-the-art clustering methods. Since our method is related to Grassmann Sparse Representation (GSR) models, we select GSR based methods as baselines, which are listed below:  
GDA: A transform over the Grassmann manifold is learned to simultaneously maximize interclass distance and minimize intra-class distance in GDA.  
Graph-embedding Grassmann Discriminant Analysis (GGDA): It is considered as an extension of GDA, where a local discriminant transform over Grassmann manifolds is learned. This is achieved by incorporating local similarities/dissimilarities through within-class and between-class similarity graphs.  
Kernel Affine Hull method (KAHM): Images are considered as points in a linear or affine feature space, while image sets are characterized by a convex geometric region (affine or convex hull) spanned by their feature points in KAHM.  
Our proposed methods are also compared against Linear Dynamical System (LDS), Compressive Sensing Linear Dynamical System (CS-LDS) and Grassmann Sparse Representation (GSR).  
In our experiments, the performances of different algorithms are measured by the following clustering accuracy \(Accuracy=\frac{M}{N}\times 100\%\), where \(M\) means the number of correctly classified points, and \(N\) means the total number of points. All algorithms are run in matlab 2014a environment, and workstation is configured for Intel Core i7-4770K 3.5 GHz CPU and 16G RAM. We first introduce the database and experimental set-up, then report and analyze our results.
# Topology Control of Low-Connection UAV Laser Network with Virtual Nodes
## 3 Model
Given the conditions from Figure 2, where \(\max\{\|mm^{\prime}\|_{2}\}=r^{*}\), \(\max\{\|mu\|_{2}\}=\max\{\|mv\|_{2}\}=r/2\), and the paired node corresponding to \(m^{\prime}\) is located within a circle of radius \(r/2\) centered at \(m^{\prime}\), we can derive the following inequality:  
\[\frac{r}{2}+r^{*}+\frac{r}{2}\leq 1\]  
This simplifies to  
\[r^{*}\leq 1-r \tag{1}\]  
Figure 1: After removing \(v\), the number of connected branches is 5. This inequality ensures that the communication range of the virtual node \(r^{*}\), when combined with the half-distance to the paired nodes \(r/2\), does not exceed the overall communication radius of \(1\). This is a necessary condition to maintain the connectivity of the virtual graph \(G^{*}\) within the constraints of the original graph \(G\). In the subgraph \(G^{\prime}_{r}\), for all \(u,v\in V\), if there exists a path \(\{u,v_{1},v_{2},\cdots,v\}\) between \(u\) and \(v\), where any two adjacent nodes on this path are neighbors and the corresponding virtual nodes of these nodes are also neighbors, then there exists a corresponding path \(\{u^{*},v_{1}^{*},v_{2}^{*},\cdots,v^{*}\}\) in the virtual graph \(G^{*}\), that is, a path between the virtual nodes corresponding to \(u\) and \(v\).Therefore, if graph \(G\) is connected, then, for all \(u,v\in V\), there exists a path between them, and the corresponding virtual nodes \(u^{*}\) and \(v^{*}\) also have a path between them, implying that the virtual graph \(G^{*}\) is connected. Considering two adjacent pairs of paired nodes, the maximum distance between the nodes in these pairs is \(3r\). Since the virtual node is located at the center of the line connecting the paired nodes, the communication distance of the virtual node is  
\[r^{*}\geq 3r-2\times\frac{r}{2}=2r \tag{2}\]  
From Equations (1) and (2), we obtain \(2r\leq r^{*}\leq 1-r\), which yields \(r\leq 1/3\), and \(r^{*}=2/3\). Therefore, if the subgraph \(G^{\prime}_{r}\) remains connected when \(r=1/3\), then a connected subgraph with a maximum node degree of \(4\) can be constructed using the aforementioned method. The previous discussion has established the feasibility of node pairing. Clearly, a node can only be paired once, which means that, in the subgraph \(G^{\prime}_{r}\), a pairing method is needed to ensure that as many nodes as possible can be successfully paired, which is essentially the maximum matching problem in general graphs. For this particular challenge, a well-established solution exists, namely the blossom algorithm.
## 4 Calculations  
The stepsize tuning was implemented into the MC program MMC [16] for the translation and rotation of non-solvent molecules and for the torsion angle sampling, both for regular torsion moves and for local torsion moves. Regular torsion changes a single torsion angle. In local torsion moves [17; 18; 19] a backbone angle change (called the 'driver torsion') is combined with complementary changes in the six successive torsion angles to keep the remaining part of the backbone unchanged. The choice of the stepsize only affects the range of the driver torsion -- the rest of the torsion angles are determined by the geometrical constraints imposed by the fixed bond lengths and bond angles. Note, that there is no guarantee to find a solution after a driver torsion change (in that case, of course, the move attempt is rejected). The larger the driver torsion change, the higher is the probability that no solution will be found.  
Tuning of the torsion stepsizes poses by far the most serious problem since the acceptance of a given torsion change depends strongly on the environment and relative topology of each torsion. Thus the test of the tuning method introduced will focus on tuning torsion angle stepsizes.  
For each degree of freedom a separate controller was implemented. However, we used the same controller tuning parameters \(K_{C}\) and \(\tau_{I}\) (see Eq. 3) and same averaging weight \(w_{\rm a}\) (see Eq. 4) for all simple torsions, and a different set for all local torsions.  
The system chosen for the test was the octapeptide (RHK(Ac-K)LMFK), the segment of the protein p53 that binds to PCAF Bromodomain. It was simulated with a ca 6 A layer of water, stabilized with the Primary Hydration Shell technique [20], the MC variant of the original version developed for MD simulations [21]. The water potential used was TIP3P [22] and both the solute-solute and the solute-solvent interactions were described by the CHARMM-27 force field [23]. The \(\phi\) and \(\psi\) backbone torsion angles were sampled also with local moves [17; 18; 19] enhanced with the reverse proximity criterion [24]. The peptide bond \(\omega\) was kept fixed and the iterative algorithm described in Ref.[24] was used to find the proximal solution to the chain closure problem.  
This system has a total of 52 torsion angles and 294 water molecules. Eight of the 52 angles were sampled by local moves (resulting in changing six additional backbone angles). Since every 20 th step involved the attempted change of a torsion angle every 1040 steps attempted to move each torsion angle once.
## 2 Results
### Large-Signal Investigation for PA Design  
In this section LS performance including efficiency, linearity, output power, and power gain of the configurations are investigated. Here, efficiency is evaluated in terms of power added efficiency (PAE), while two-tone linearity is evaluated in term of third-order inter-modulation (IMD3). These are critical parameters when designing the PA. They are evaluated by using a Harmonic Balance analysis in the Keysight ADS simulator. The simulation setup implemented in the Keysight ADS for LS simulation of the configurations is similar to the one shown in the Figure 2 as for the SS performance investigation. The difference between the SS and LS simulation setup is that while port impedance in the SS simulation setup is set to 50 \(\Omega\), for the LS simulation it is set to \(Z_{\text{Sopt}}\) and \(Z_{\text{Lopt}}\) at the input and output, respectively. Here, \(Z_{\text{Sopt}}\) and \(Z_{\text{Lopt}}\) are optimum source and load impedance, respectively, and they are found by employing a source/load pull technique. In the Harmonic Balance analysis in the ADS simulator, a Krylov matrix solver type with a Krylov Restart length of 1000 is used. The convergence mode is set to Advanced with a maximum iterations of Robust. For the one-tone simulation of efficiency, power gain, and output power, 15 harmonics settings, which is high enough for the accuracy of the non-linear system, are used. For the two-tone simulation of the linearity (IMD3), 15 harmonics settings for each tone are also used.
# An Intelligent Fuzzy-Based Routing Protocol for Vehicular Opportunistic Networks
## 2 Literature Review of Opportunistic Networks
### Routing Protocols in Opportunistic Networks  
In OppNets, routing protocols define the rules and mechanisms for forwarding messages from a source to a destination in environments with intermittent connectivity and dynamically changing paths [21; 22; 23; 24; 25; 26]. These protocols rely on the \"store, carry, and forward\" paradigm, where nodes temporarily store messages, carry them while in motion, and forward them when a suitable opportunity arises, as illustrated in Figure 1. This approach makes OppNets highly resilient to disruptions, making them ideal for scenarios lacking reliable conventional network infrastructure.  
While all OppNet routing protocols leverage this mechanism, they differ in how message copies are deployed and can be broadly classified into three categories: epidemic, deterministic, and probabilistic routing.  
* Epidemic Routing: Epidemic routing protocols, such as Epidemic and Spray and Wait, flood the network with copies of the message to ensure that at least one copy reaches the destination. While this approach maximizes delivery probability, it incurs high overhead due to the large number of message transmissions, leading to network congestion and resource depletion. The Epidemic protocol is straightforward and effective in ensuring message delivery but is highly inefficient in terms of resource usage.
* Deterministic Routing: Deterministic routing protocols, such as Direct Delivery and Two-Hop, use predefined rules and knowledge about the network topology to make routing decisions. These protocols are less flexible and may not perform well in highly dynamic environments where the network topology changes frequently [27]. For instance, Direct Delivery only forwards messages when the destination is within direct contact, minimizing overhead but potentially increasing delivery delay. Two-Hop allows one intermediate node to relay messages, offering a slight improvement in flexibility, but is still constrained by its deterministic nature.  
Figure 1: Store, carry and, forward mechanism in opportunistic networks.  
* Probabilistic Routing: Probabilistic routing protocols make forwarding decisions based on the likelihood of successful delivery, considering factors such as encounter history, node mobility patterns, and contact duration. Examples of probabilistic routing protocols include PROPHET and MaxProp. These protocols aim to balance delivery probability and overhead, making them more suitable for OppNets.
# Temperature Regulation of Hot Vapor Preservation Treatment of Litch Based on PSO-Fuzzy PID
## 3 Principle of Particle Swarm Optimization Fuzzy PID Algorithm and Controller Design
### PSO-Fuzzy PID Controller Design  
In this paper, the PSO method optimizes the input and output of the fuzzy controller. This means the quantization factors \(G_{e}\) and \(G_{ec}\) are added to the input end of the fuzzy controller, and the scale factors \(G_{p}\), \(G_{i}\), and \(G_{d}\) are added to the output end. The optimal values of these parameters are found through the optimization process so that the fuzzy controller can output the most suitable parameters to the PID controller.  
In MATLAB, the update and iteration of particles were divided into two parts, as shown in the dashed box in Figure 7. The first part (1) is the control system model built in the Simulink platform, and the performance index is output once per sampling time. In the second part (2), the PSO optimization part is programmed in the executable code file (.m file) of MATLAB, and the speed and position of the particles are updated according to the fitness value. Among them, the historical best position of the particle group _gBest_ is the value of five quantitative and proportional factors. The optimization results are transmitted back to the corresponding parameters of the Simulink model, and the model is run again so that the iteration is repeated. When the number of iterations exceeds the specified value or meets the fitness requirement, the particle swarm stops updating the iteration.  
In the above control scheme, the parameters to be optimized are five coefficients of quantization factors \(G_{e}\) and \(G_{ec}\), and scale factors \(G_{p}\), \(G_{i}\), and \(G_{d}\), so the dimension of the particle swarm was set to 5. The particle optimization range is \([l_{d},u_{d}]=[-20,\,20]\); the larger inertia weight can strengthen the global search ability of PSO, and the smaller inertia weight can strengthen the local search ability. Therefore, the inertia weight was set to decrease linearly with the increase in the number of iterations between 0.9 and 0.4 [28]. The learning factor \(c_{1}=c_{2}=2\); the maximum particle velocity \(v_{max}\) was set to 0.15 times the particle search range, setting 30 initial particles; the number of iterations was set to 50. ITAE (Integral Time-weighted Absolute Error ) was used as an indicator of the performance of the control system; that is, the fitness value of the particle swarm. The smaller the output value, the shorter the adjustment time of the system and the better the performance. The specific expression is:  
\[ITAE=\int_{0}^{\infty}t|e(t)|dt \tag{3}\]  
where \(t\) is the current time, in seconds; \(e(t)\) is the current system error.
# Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis
## 4 Results
### Threshold Determination  
Then, the pattern of eye blinks activity was investigated by employing Markov chains [27], described in Section 3.5. In Figure 4 the histogram of the calculated IBIs, for each experiment from all tasks (neutral, stressed and relaxed) is shown. The identification of the number of groups into which the IBIs can be differentiated is performed by visual inspection, but also by using the kernel density function on the histogram of all IBIs, as described in [28]. Two groups are finally selected for each of the three experiments. The time-point threshold for the two groups for the 1 st, 2 nd and 3 rd experiment was 1.975 s, 2.325 s and 1.975 s respectively. The highest value of these three thresholds was used, namely 2.325 s. Accordingly, two states can be selected for the Markov Analysis, the \"long\" IBI state and the \"short\" IBI state.
# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 5 Experiments
### Evaluation Metrics  
In this work, we consider the following metrics to perform a comparative analysis among the forecasting models presented, the root mean squared error (RMSE), the mean absolute percentage error (MAPE), the mean absolute error (MAE) the average relative variance (ARV), and the index of agreement (IA) [67, 68], which are given by Equations (3)-(7), respectively:  
\[\text{RMSE}=\frac{1}{N}\sum\
olimits_{t=1}^{N}\biggl{(}\sqrt{\left(\hat{x}_{t }-x_{t}\right)^{2}}\biggr{)}, \tag{3}\]  
\[\text{MAPE}\ =\frac{100}{N}\sum\
olimits_{t=1}^{N}\biggl{|}\frac{\hat{x}-x_{t}}{x_ {t}}\biggr{|}, \tag{4}\]  
\[\text{MAE}\ =\frac{1}{N}\sum\
olimits_{t=1}^{N}|\hat{x}_{t}-x_{t}|, \tag{5}\]  
\[\text{ARV}\ =\frac{\sum\
olimits_{t=1}^{N}\left(\hat{x}_{t}-x_{t}\right)^{2}}{ \sum\
olimits_{t=1}^{N}\left(\hat{x}_{t}-\overline{x}\right)^{2}}, \tag{6}\]  
\[\text{IA}\ =1-\frac{\sum\
olimits_{t=1}^{N}\left(\hat{x}_{t}-x_{t}\right)^{2}}{ \sum\
olimits_{t=1}^{N}\left(\ |\ \hat{x}_{t}-\overline{x}|-|x_{t}-\overline{x}|\right)^{2}}, \tag{7}\]  
where \(N\) is the number of samples, \(x_{t}\) is the observed value in time \(t\), \(x_{t}\) is the predicted value, and \(\overline{x}\) is the mean of the series. The percentage difference (PD), given by Equation (8), is calculated to compare the performance of HetDS with the other approaches:  
\[PD=\frac{Metric_{model}-Metric_{HetDS}}{Metric_{model}}\cdot 100, \tag{8}\]  
where \(Metric_{HetDS}\) and \(Metric_{model}\) are the RMSE values attained by HetDS and the comparative models used in the experimental evaluation, respectively. The higher the \(PD\) value, the better the RMSE value obtained by HetDS with respect to the model under comparison.