esa-sceva/satcom-chunk-collection huggingface dataset:
# Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality
## 2 Materials and Methods
### Rotor in a 2D Model of Human Atrial Tissue Under Atrial Fibrillation Conditions  
A 2D model of human atrial tissue was designed as a 6 x 6 cm\({}^{2}\) surface, discretized into 150 x 150 grid points with a spatial resolution of 400 \(\upmu\)m. The cellular electrophysiology was simulated using the Courtemanche human atrial action potential model [35]. To reproduce the electrical conditions of isolated myocytes from patients with AF [36; 37; 38], the conductances of different ionic channels were adjusted as follows: the maximum conductances of the ultra-rapid outward potassium current and the L-type calcium current were both reduced by 35%, the maximum conductance of the ultra-rapid outward potassium current was reduced by 50%, and the maximum conductance of the inward rectifier potassium current was increased by 100%. These conductance adjustments aimed to simulate AF-induced electrical remodeling. Additionally, the cholinergic effect was simulated by incorporating the acetylcholine-dependent potassium current with an acetylcholine concentration of 5 nM.  
The atrial cell model was integrated into the 2D virtual tissue. In a previous work [17], the action potential propagation over a 2D domain was modeled using a fractional diffusion equation. Isotropy and standard diffusion conditions were simulated to obtain a real conduction velocity of 67 cm/s. Discretization and the numerical solution of the propagation equation were accomplished using a semi-spectral approach previously reported [39], using a time step of 0.01 ms.  
A rotor propagating pattern was generated by applying the S1-S2 cross-field stimulation protocol. S1 is a train of stimuli applied to the left border of the tissue, a region containing 150 \(\times\) 1 nodes. Once the wave generated by the last S1 stimulus had passed the middle of the domain, a single S2 extra stimulus was applied to the first quarter of the domain (75 x 75 nodes), generating a single rotor (Figure 3). Each stimulus consisted of a rectangular pulse lasting 2 ms with a current of 4200 pA.  
Figure 3: (**a**) Stable rotor in a 2D atrial model. The arrows indicate the points where two EGMs are recorded, one at the rotor tip and the other far from it. (**b**) Non-CFAE (top) and CFAE (bottom) signals, and (**c**) their corresponding PSDs.  
Figure 2: Flowchart illustrating the building blocks of the methodological sequence of the study and the integration of different phases in the development of the data fusion model.
# A Predictive Model of Chlorophyll a in Western Lake Erie Based on Artificial Neural Network
## 2 Materials and Methods
### Modeling Methodology
#### 2.2.2 ANN Description  
The architecture of an artificial neural network (ANN) is typically composed of the input layer, the hidden layer(s), and the output layer [43]. The nodes in the input layer transmit the information to the nodes in the hidden layer by applying an activation function, each value of each node in the input layer is multiplied by its weight to obtain a new value, and then the new value of each node in the hidden layer is multiplied by its weight and pass to the node in the output layer by applying an activation function to get the final output [44]. The weights are assigned during training the ANN to minimize errors between the outputs and the targets [45]. The architecture of the ANN is shown in Figure 2.  
In this study, a multilayer perceptron (MLP) with a backpropagation algorithm was used since it was the most commonly used training methodology [46] and this kind of network has already been widely used for describing non-linear relationships in previous studies [28; 29; 47]. The inputs in the ANN model were the same as GLM-AED, including meteorological forcing, as well as flow rates, temperature, and 15 state variables of the Detroit and Maumee Rivers, so the number of nodes in the input layer is 40. Particularly, because the salinity of Lake Erie is constant with a value of 0 in GLM-AED, the salinity was not regarded as input in the ANN model. Due to the lack of continuous data for Chl-a concentrations, the simulated Chl-a concentrations from GLM-AED were regarded as the targets during the training process in the ANN model. The number of nodes in the output layer is 1 since there was only one output of Chl-a. The number of nodes in the hidden layers typically satisfies Equation (1) according to the previous study [48]:  
\[2n_{i}^{1/2}+n_{o}<n_{h}<2n_{i}+n_{o} \tag{1}\]  
where \(n_{i}\), \(n_{h}\), and \(n_{o}\) represent the number of nodes in the input, hidden, and output layers, respectively. In this study, the number of nodes in the hidden layers was in the range of 14-81. Between the input layer and the hidden layers, as well as the hidden layers and the output layer, the sigmoid activation function was used [49]. The training ranges of the  
Figure 2: Schematic view of ANN.  
learning rate for weight updates, momentum applied to the weight updates, and batch size representing the preferred number of instances to process were 0.1-0.5, 0.1-0.8, and 10-1000, with steps of 0.1, 0.1, and 5. The database from 2002 to 2015 was divided into two parts, including training and testing sections.
# S2p: Provenance Research for Stream Processing System
## 1 Introduction
We consider the semantics of each operator (e.g., map, filter, etc.) when deciding the relationship among data, but we leave the user-defined logic in UDFs as backboxes. We augment each operator to propagate provenance metadata (e.g., UUID of data) with each stream data. We implement s2p by modifying the DSP framework and extending its runtime to propagate provenance data. Our s2p is transparent to users without any additional modification to the original program. Using s2p, we can exploit the detailed provenance information of the interested stream data only. In addition, s2p can achieve high performance by: (1) only replying from some specific point that is close to the computation process for target data, (2) parallelizing the replay process on a cluster, (3) caching the most frequent queried data sets for fast further access, and (4) building provenance graph asynchronously and stopping the replaying process once necessary provenance information for target data is obtained. In our study, we have built a prototype to materialize the main features of s2p and designed experiments to quantify its side effect on normal DSP system execution. We also carried out one case study to show the feasibility of s2p. In summary, the main contributions of this paper are:  
* s2p, a novel provenance solution for stream processing systems that combines offline and online parts and provides fine- and coarse-grained provenance at a level of precision supported by few existing DSP provenance systems. * Previous approaches aim for all input data oriented provenance analysis; however, it is hard to support detailed provenance analysis at the tuple level. On the contrary, our solution targets detailed provenance for a small amount of stream data by replaying the computation process and merely monitoring the interested data. * Our solution considers state transformation of stateful operators together with the data transformation process in provenance analysis, whereas few existing DSP provenance systems take operator states into account. * To minimize the data transformation burden to the network, we manage our provenance data locally and only aggregate some chosen data when a provenance query happens. * One prototype s2p-Flink is built on Apache Flink [28] to demonstrate the solution of s2p. * We conduct experimental evaluations with three subject applications. Results show the s2p scaling to large data sets with acceptable overhead. * One case study is to show its feasibility.
## 4 Numerical Modelling and Calculation Methodology
### Calculation Results and Analysis of GRCs
#### 4.2.2 GRCs Under Different Burial Depths with Initial Support  
The GRCs of the tunnel surrounding rock under different burial depths for each grade of surrounding rock in the drill-and-blast condition are shown in Figure 7. Among them, Grade III surrounding rock has a higher self-stabilizing capacity. The application of initial support can ensure the stability of the surrounding rock (Figure 7a). The final deformation at the corresponding burial depth is taken as the excavation allowance. After the deformation is fully developed, the required support stress for the tunnels at burial depths of 200 m, 400 m, 600 m, 800 m, 1000 m, 1200 m, 1400 m, 1600 m, and 2000 m all tends to zero. The maximum deformation is approximately 0.52 cm, 1.20 cm, 1.94 cm, 2.72 cm, 3.48 cm, 4.24 cm, 5.05 cm, 5.82 cm, and 7.50 cm, respectively.  
For Grade IV surrounding rock (Figure 7b), the final deformation of the tunnels at burial depths of 200 m, 400 m, 600 m, 800 m, 1000 m, 1200 m, 1400 m, 1600 m, and 2000 m is approximately 2.18 cm, 4.69 cm, 7.67 cm, 10.60 cm, 14.20 cm, 17.70 cm, 21.30 cm, 25.30 cm, and 33.40 cm, respectively. Under this deformation condition, the required support stress for the surrounding rock is close to zero. At this point, the installation of a secondary lining  
Figure 7: GRCs of different levels of surrounding rock under different buried depths under the drilling and blasting method. (**a**) Grade III; (**b**) Grade IV; and (**c**) Grade V.  
can ensure the stability of the surrounding rock, and the secondary lining structure does not bear or bears only a small amount of the surrounding rock pressure.  
For Grade V surrounding rock (Figure 7c), for tunnels with burial depths of 200 m and 400 m, the required support stress tends to zero after a deformation of 17.71 cm and 60.00 cm, respectively (0.06 MPa and \(<\)1 MPa, 0.4 MPa, 1.25 MPa, and 2.33 MPa). At this point, the use of a secondary lining with an appropriate thickness can ensure the stability of the surrounding rock. However, for tunnels with burial depths \(\geq\) 1000 m, the initial support conditions still cannot ensure the stability of the surrounding rock, and strong secondary lining support measures are not economically rational. Further reinforcement measures are required under the conditions of drill-and-blast construction to ensure the stability of Grade V surrounding rock.
# Automatic Generation of Guidance for Indoor Navigation at Metro Stations
## 3 Materials and Methods
### Landmarks and Turn-by-Turn Directions  
As studies [4; 52; 53] show, turn-by-turn navigation does not work well indoors. Pedestrians describe the route differently--they do not use manoeuvres to be executed at decision points but use more general cues specifying directions of movement. In addition, a direction such as 'turn left/right' can be difficult to interpret due to the inaccuracy of position and the ease with which the body can be reoriented in space.  
A more intuitive approach is based on the use of landmarks [54; 55; 56]. Such landmarks are defined as easily recognisable objects, e.g., mountain peaks or characteristic buildings. In indoor navigation, these can be unchanging, easily identifiable elements of the building facade, e.g., ornaments, or also paintings hanging on the wall. The guidance provided during navigation refers to such points so that the information message is unambiguous, and the user understands how to perform subsequent manoeuvres. The main problem with landmark-based guidance is the cost of acquiring and updating data. Some landmarks are permanent fixtures in buildings, such as a fire extinguisher; others are slightly more variable, such as a painting that can be taken down or rehung in another location; and others are dynamic, such as tables or chairs.
# An Adaptive Launch Control for Balloon-Borne UAVs with Large Wingspans
## 3 Flight Control Analysis of Pull-Up Process
### Low Reynolds Number  
High-altitude balloons and solar-powered UAVs have obvious low Reynolds number characteristics in near-space areas. There are many special flow phenomena [24; 25; 26; 27; 28; 29]. For asymmetric airfoils such as wings, its aerodynamic efficiency drops sharply and the lift-drag ratio decreases with a decrease in Reynolds number. When the Reynolds number decreases to \(10^{5}\), the lift-drag ratio is only 10% of the conventional Reynolds number [30]. These features affect the lift and drag of the aircraft, its propeller efficiency, and its control surface efficiency. For symmetrical airfoils such as tje horizontal stabilizer and the vertical fin, there is a small platform or even \"s\" bending near small attack angles, and the platform will gradually be eased and disappear with an increase in the Reynolds number [31]. The reasons for these two special phenomena are related to the triggering and evolution mechanism of laminar-induced separation that are formulated during its bubble variation with the Reynolds number and attack angle.
# Concurrent Transmission Based on Distributed Scheduling for Underwater Acoustic Networks
## 3 Concurrent Transmission Based on Distributed Scheduling
### Protocol Description
#### 3.2.2 Cts Stage  
When the RTS stage ends, all nodes in the cluster enter the CTS stage. In the CTS stage, the nodes that receive the RTS requests and are ready for receiving data will send out the CTS packets to their corresponding source nodes. The proposed method also allows multiple CTS packets to be sent in this stage. The CTS packets are sent out randomly within a time interval to reduce collisions. The CTS stage ends after a fixed delay when the last CTS packet is sent out.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 3 Background
### System Model  
In proposed schemes, the 3D multi-sink network architecture is assumed [4], which is composed of the anchor, relay and sink nodes, as shown in Figure 2. The anchor nodes are fixed at the bottom of the water. These nodes are used to sense and to gather data, while the relay nodes are placed at different depths, which receive and forward the data towards the sink by collecting data from the anchor nodes. The sink node is housed with acoustic and radio modems to communicate with nodes deployed inside the water and in the terrestrial environment, respectively. Additionally, the burden of overhead is reduced by forming a cluster. It helps in local data gathering where only the head node of the cluster transmits data to the sink node. Additionally, it enables the network nodes to reduce the probability of interference via restricting the number of nodes involved in data transmission over the wireless acoustic channel. Furthermore, the selection of the Cluster Head (CH) is based on the residual energy, which allows continuous rotation of the head node. The sink nodes are placed at the surface of the water to direct data packets to the control center. An assumption is made that each sink is connected with the others through radio links. Furthermore, if a packet is received at one sink, it is assumed that data are successfully transmitted to the base station. The underwater settings affect the consumption of energy and the delay propagation of sound waves. To explain the underwater communication, [PERSON]'s propagation model is used [29].
# In Vivo Biosynthesis of Au and Ag NPs Using Two _Medicago sativa_ L. Genotypes
## 3 Results
### Obtaining Plant Suspension from Seedings for Spectrophotometry
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Genotype** & **Precursor** & **Wavelength (nm)** & **Concentration (mg/L)** & **Average Absorbance** & **Standard Deviation** \\ \hline _’Kometa’_ & None & 450 & Control & 0.132 & - \\ \hline _’Kometa’_ & HAuCl4 & 450 & 50 & 0.295\(\uparrow\) & \(\pm\)0.116 \\ \hline _’Kometa’_ & HAuCl4 & 450 & 100 & 0.281\(\uparrow\) & \(\pm\)0.106 \\ \hline _’Kometa’_ & HAuCl4 & 450 & 200 & 0.391\(\uparrow\) & \(\pm\)0.183 \\ \hline _’Kometa’_ & None & 650 & Control & 0.045 & - \\ \hline _’Kometa’_ & HAuCl4 & 650 & 50 & 0.099 \(\uparrow\) & \(\pm\)0.039 \\ \hline _’Kometa’_ & HAuCl4 & 650 & 100 & 0.096 \(\uparrow\) & \(\pm\)0.036 \\ \hline _’Kometa’_ & HAuCl4 & 650 & 200 & 0.129 \(\uparrow\) & \(\pm\)0.059 \\ \hline _’Kometa’_ & None & 470 & Control & 0.132 & - \\ \hline _’Kometa’_ & AgNO3 & 470 & 250 & 0.186\(\uparrow\) & \(\pm\)0.168 \\ \hline _’Kometa’_ & AgNO3 & 470 & 500 & 0.167\(\uparrow\) & \(\pm\)0.195 \\ \hline _’Kometa’_ & AgNO3 & 470 & 1000 & 0.195\(\uparrow\) & \(\pm\)0.205 \\ \hline _’Kometa’_ & None & 350 & Control & 0.045 & - \\ \hline _’Kometa’_ & AgNO3 & 350 & 250 & 0.283\(\uparrow\) & \(\pm\)0.168 \\ \hline _’Kometa’_ & AgNO3 & 350 & 500 & 0.320\(\uparrow\) & \(\pm\)0.195 \\ \hline _’Kometa’_ & AgNO3 & 350 & 1000 & 0.335\(\uparrow\) & \(\pm\)0.195 \\ \hline _’la Bella’_ & None & 450 & Control & 0.045 & - \\ \hline _’la Bella’_ & HAuCl4 & 450 & 50 & 0.283 \(\uparrow\) & \(\pm\)0.010 \\ \hline _’la Bella’_ & HAuCl4 & 450 & 100 & 0.320 \(\uparrow\) & \(\pm\)0.023 \\ \hline _’la Bella’_ & HAuCl4 & 450 & 200 & 0.234 \(\uparrow\) & \(\pm\)0.023 \\ \hline _’la Bella’_ & None & 650 & Control & 0.106 & - \\ \hline _’la Bella’_ & HAuCl4 & 650 & 50 & 0.091\(\downarrow\) & \(\pm\)0.010 \\ \hline _’la Bella’_ & HAuCl4 & 650 & 100 & 0.074 \(\uparrow\) & \(\pm\)0.023 \\ \hline _’la Bella’_ & HAuCl4 & 650 & 200 & 0.073 \(\uparrow\) & \(\pm\)0.023 \\ \hline _’la Bella’_ & None & 470 & Control & 0.293 & - \\ \hline _’la Bella’_ & AgNO3 & 470 & 250 & 0.157 \(\uparrow\) & \(\pm\)0.096 \\ \hline _’la Bella’_ & AgNO3 & 470 & 500 & 0.142\(\downarrow\) & \(\pm\)0.107 \\ \hline _’la Bella’_ & AgNO3 & 470 & 1000 & 0.171 \(\uparrow\) & \(\pm\)0.087 \\ \hline _’la Bella’_ & None & 350 & Control & 0.106 & - \\ \hline _’la Bella’_ & AgNO3 & 350 & 250 & 0.271\(\uparrow\) & \(\pm\)0.117 \\ \hline _’la Bella’_ & AgNO3 & 350 & 500 & 0.256\(\uparrow\) & \(\pm\)0.106 \\ \hline _’la Bella’_ & AgNO3 & 350 & 1000 & 0.341\(\uparrow\) & \(\pm\)0.167 \\ \hline \hline \end{tabular}  
* indicates a statistically significant difference compared to the control (\(p<0.05\)); \(\uparrow\downarrow\) indicate an increase, decrease, in data compared to the control.  
\end{table} 
Table 6: Light absorbance data at different wavelengths after 48 h of precursor exposure.
# Vibration Energy Harvester Based on Bilateral Periodic One-Dimensional Acoustic Black Hole
## 3 Simulation and Analysis
### Simulation of VEH with ABHs
#### 3.2.2 Transient Response under Gaussian Pulse  
The characteristic of a Gaussian pulse is energy concentration, which is beneficial to compress the signal spectrum and reduce the adjacent channel interference. The excitation in the actual environment can be better simulated by Gaussian pulses. The broadband Gaussian pulse is used as the excitation signal during the transient simulation of the VEH with ABHs. The formula of the broadband Gaussian pulse is as below:  
\[\cos\left[6000\tau\left(t-5\times 10^{-4}\right)\right]e^{-\frac{t-5\times 10^{- 4}}{3\times 10^{-8}}} \tag{17}\]  
The Gaussian pulse has a wide frequency band and short excitation time, and its center frequency is set to 3000 Hz. Time domain and frequency domain images of Gaussian pulses are shown in Figures 9 and 10, respectively. In the simulation, a given displacement of \(5\times 10^{-5}\) m is loaded in the rightmost boundary of the beam to obtain an ideal excitation effect. The energy focus effect here of the ABH is reflected by the energy density of the ABH region, which can be validated by calculating the focused energy of the ABH zone. The Gaussian impulse response in the time domain is shown in Figure 11. Simulation results show that the energy focus effect of the ABH increased with the number of ABH arrays.  
Figure 8: First four-order natural frequency.  
Figure 9: Normalized time domain for Gaussian pulse.  
Figure 11: Integral energy density of ABH region under Gaussian pulse.  
Figure 12: _Cont._  
Figure 10: Normalized frequency domain for Gaussian pulse.  
The response of the VEH with ABHs under Gaussian pulse is transformed by FFT, as shown in Figure 13. The VEH with more ABH periodic arrays can store more vibration energy in a wide frequency range of around 3000 Hz.
## 2 Materials and Methods
### Exploratory Data Analysis (EDA) and Preprocessing  
The exploratory data analysis (EDA) and preprocessing stages are essential in any Machine Learning workflow, as they ensure the consistency, quality, and usability of the data. Data preparation tasks, such as cleaning, transformation, and structuring, typically account for over 60% of the total effort in ML projects [34], highlighting the foundational importance of this step.  
Once the target variable and input features were defined, an initial data cleaning phase was conducted to remove entries that, based on the nature of the phenomenon under study, were not relevant for the modeling task. The following cleaning steps were applied:  
* Removal of regeneration periods, which do not reflect natural soot accumulation behavior.
* Elimination of null, inconsistent, or physically implausible values.
* Isolation of complete soot zone transitions, ensuring that only uninterrupted operational cycles were retained.  
Given the limited dataset size, the analysis followed a visualization-first approach, where patterns, anomalies, and structural inconsistencies were primarily explored through graphical methods. This was carried out by using ydata-profiling (an open-source Python library), which played a central role in this process by providing an automated and comprehensive EDA report. This tool facilitated the generation of multiple statistical diagnostics, such as univariate summaries, histograms, and more, which are crucial for assessing data quality without requiring extensive manual scripting. For this specific study, heatmaps, scatter plots, and boxplots, alongside univariate statistics, were used to uncover underlying patterns and identify outliers. Additionally, expert domain knowledge was a key factor in validating any data filtering decisions and ensuring the operational plausibility of the remaining records.  
The study was conducted separately for Zones 3 and 4 to better capture the distinct accumulation patterns and operating conditions of each zone. Although the full dataset included soot zone transitions from Zones 0 to 5, the scope of this study is operationally centered on Zones 3 and 4, as they represent critical thresholds for intervention and power limitation.