esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results of Data Training and Discussion
### Logistic Regression Modeling
#### 3.1.4 F1-Score  
F1-Score is a metric that combines accuracy and recall into a single measure. It is especially useful in situations where both metrics are important and a balance between them is sought. It is calculated using Equation (7).  
\[\text{F1}-\text{Score}=2\times\frac{\text{Accuracy}\times\text{Recall}}{ \text{Accuracy}+\text{Recall}} \tag{7}\]  
In our case:  
\[\text{F1}-\text{Score}=2\times\frac{0.90\times 0.9916}{0.90+0.9916}\approx 0.9447 \tag{8}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **No Injury (Predicted)** & **Injury (Predicted)** \\ \hline No injury (Actual) & 6 & 37 \\ \hline Injury (Actual) & 3 & 354 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Logistic regression confusion matrix.  
As shown in Table 6, the high accuracy suggests that the model generally performs well in classifying cases. However, upon examining the recall, we observe that while it is high, there are some instances of false negatives (FNs). This means there are situations where the model did not correctly identify the presence of injuries, which is within the range in the context of athletes' health. The recall is approximately 0.9916, meaning the model correctly identified 99.16% of injury cases among all actual injury cases in athletes. This metric is crucial in contexts where identifying positive cases is of particular importance, such as in preventing injuries to athletes. The precision, with a value of approximately 0.9054, indicates that the model has a fairly high ability to correctly classify positive cases (athletes with injuries). In other words, when the model predicts that an athlete has an injury, there is a 90.54% chance that they have an injury. The F1-Score is approximately 0.9447. This score is relatively high and suggests a reasonable balance between the model's ability to correctly predict positive and negative cases.  
It is important to note that although the accuracy is high, a detailed analysis of other metrics like recall and precision provides a more complete view of the model's performance, especially in situations where identifying positive cases is critical. That is why it is essential to consider these metrics together to obtain a comprehensive evaluation of the model's performance. In medical and sports applications, where accurate identification of injuries is crucial, this detailed analysis allows for informed decisions about the practical utility of the model in the specific research domain.  
It is worth mentioning that 50 independent athletes were considered as a training sample. By using this, we emphasize that the model works correctly for this study.
## 6 Experiment Evaluation
### Evaluation Results
#### 6.2.2 EEG Sleep Relate Features
According to data analysis, we found that the third sleep cycles are usually between 1: 00 a.m. and 3: 00 a.m. Therefore, we preliminary concluded that the theta waveform in sleep stage 0 of the third sleep cycle was a potential risk factor that might have been previously unexplored. As aforementioned, we also extracted several other frequency domain features, including BSI, DAR, TBR, DTABR, TDABR, \(\text{BSI}_{(s_{j};\alpha\psi_{k},C_{x})}\), \(\text{DAR}_{(F_{1};s_{j};\alpha\psi_{k},C_{x})}\), \(\text{TBR}_{(F_{1};s_{j};\alpha\psi_{k},C_{x})}\), \(\text{DTABR}_{(F_{1};s_{j};\alpha\psi_{k},C_{x})}\), and \(\text{TDABR}_{(F_{1};s_{j};\alpha\psi_{k},C_{x})}\). However, none of them had significant difference between subjects of Type II and Type IV. For example, the \(p\)-value of BSI is 0.902, indicating that subjects of Type II and Type IV have almost equal CBF between the left and right hemispheres. Table 9 shows the comparison of the Sample Entropy of the EEG on channel \(C_{x}\) during the \(j\)-th sleep stage of the \(k\)-th sleep cycle for ischemic stroke patients and control subjects, where \(x\in\{3,4\},j\in\{0,1,2,3,5\}\), and \(k\in\{1,2,3,4,5\}\). Similarly, we also calculated the sample entropy of frequency bands delta, theta, alpha and beta on channel \(C_{x}\) during the \(j\)-th sleep stage of the \(k\)-th sleep cycle for ischemic stroke patients and control subjects, as well as the LZC and DFA. For the same reason described above, we did not list them one by one. According to the results of nonlinear features, we found that the most significant differences between types II and IV are concentrated on sleep cycles 1 and 2. The reason might be that subjects of Type II might have difficulty in falling into a stable state during the first sleep cycle. In order to acquire a better performance of classification and reduce the computing cost and the time delay in successive steps, we preserved the feature that made a significant contribution to the prediction. Specifically, we leverage information gain to measure the effectiveness of candidate features, i.e., a feature will be discarded if its information gain is smaller than 0.05. The input of the classifier is a vector consisting of the remaining features. To sum up, we mainly used \(RP_{(F_{1},F_{0},C_{0},C_{0},C_{0})}\), \(RP_{(F_{0},C_{0},C_{0},C_{0})}\), \(RP_{(F_{1},F_{0},C_{0},C_{0},C_{0})}\), \(\text{SampEn}_{(s_{0},C_{0},C_{0},C_{0})}\), \(\text{SampEn}_{(s_{0},C_{0},C_{0},C_{0})}\), \(\text{SampEn}_{(s_{0},C_{0},C_{0},C_{0})}\), \(\text{SampEn}_{(F_{0},C_{0},C_{0},C_Figure 6 shows a visualization in terms of several extracted features. With these views, we can find that the features of two types follow quite a different distribution. However, due to the overlap of feature distributions, we cannot distinguish a potential ischemic patient and a healthy person by using any single-feature. Therefore, we arrange all the useful attributes as a vector and then feed them into the prediction model.
# Recent Advances in Electrochemical Immunosensors
## 3 Discussion
### Unconventionnal Substrates: Nanochannels for Electrochemical Immunodetection  
Even before the period considered in this review, unconventional porous substrates have been reported for electrochemical immunodetection [183; 184; 185]. These works were continued; in 2012 then 2013, [PERSON] et al. [186] and [PERSON] et al. [187] developed an electrochemical sensor for detection of dengue virus, based on a porous alumina membrane on a platinum electrode, functionalized with antibodies specific for dengue. Upon recognition of the virus by the antibodies, blocking of the pores were characterized by DPV (example below) and impedance spectroscopy. The detection limit was found at ca. 0.5 PFU mL\({}^{-1}\) (Figure 36). More recently, [188; 189] [PERSON] et al. and [PERSON] et al. described nanochannel arrays made from nanoparticles assembled on a solid surface, for immunodetection through a principle like that described above. The blocking in the nanochannel was characterized by a decrease in the voltammetric signal of a redox indicator, or an increase in impedance. Very recently, [PERSON] et al. [190] described again the use of anodized alumina membranes, to measure ionic conductivity through pores decorated by capture probes. MS2 bacteriophage was chosen as an example. A LoD of ca. 7 pfu mL\({}^{-1}\) was found. Examples of EIS spectra are given in Figure 37. These approaches have been recently reviewed [191].  
Figure 36: (**A**) Biosensor’s structure and detection procedure. DENV-2 corresponds to the dengue virus; (**B**) Differential pulse voltammograms of alumina-modified electrode obtained in 1.0 mM ferrocenemethanol, 0.1 M phosphate buffered saline, pH 7.4 after each step of the biosensor construction procedure, DENV-2 concentration = 10\({}^{2}\) pfu mL\({}^{-1}\); Adapted from [186], Copyright 2012, with permission from Elsevier.
## 3 Numerical Simulation Analysis  
### Model Construction  
To better understand the impact of shield tunnel crossing on the embankment, some effective measures may be taken in advance to curb the deformation of the embankment [16]. Finite element software [17] is used to establish a three-dimensional model of the super-large-diameter shield tunnel crossing the north embankment under the upper-soft and lower-hard strata according to actual engineering and stratigraphic information (Figure 3). There are two working situations with and without reinforcement measures that will be simulated. To minimize the influence of the boundary effect of excavation, the left and right boundaries and the lower boundary of the model are set to four times and three times the hole diameter, respectively, while the upper boundary is set to 18.1 m according to the actual buried depth of the shield tunnel. The result is that the entire model with a length of 142.2 m, a width of 123.98 m, and a height of 81.3 m includes a total of 91,104 elements and 62,617 nodes. The three directions (X, Y, and Z) of the bottom surface of the model are all set as displacement constraints, and the top surface (including the top surface of the embankment) is set as a free boundary. The left and right sides of the model are set as the displacement constraints in the X direction, while the front and back are set as the displacement constraints in the Y direction.  
The stratum in the whole model is divided into nine layers: the upper six layers are soil layers, and the lower three layers are granite layers. Both the soil and rock layers are arranged to obey the Mohr-Coulomb (M-C) elastoplastic constitutive model. The surrounding rock, segment, grouting layer, and the existing north embankment in the model are all simulated by solid elements. The grouting filling body on the back of the segment is regarded as a homogeneous material with an equal thickness. The support pressure of the tunnel face and the grouting pressure of the grouting layer are performed by means of node-concentrated force in the process of shield tunneling. Table 1 lists the numerical calculation parameters of each layer. Additionally, the segment and grouting layers are given elastic models, and the values of the density, elastic modulus, and Poisson's ratio of the two materials are shown in Table 2.
## 2 The Normal-Mode Helical Antenna Design Considerations
### Testing the NMHA versus a Rohde & Schwarz Dual-Polarization (HE016) Active Antenna  
The active helical antenna was validated against a dual-polarization active antenna (HE016), at a validation site as shown in Figure 11a, using a Rohde & Schwarz (R&S) EM510 HF digital broadband receiver (Columbia, MD, USA) shown in Figure 11b [26,27].  
The helical antenna at the validation site, tested vertically and horizontally polarized, is shown in Figures 12a and 12b, respectively.  
Figure 11: (**a**) The HE016 antenna under test and (**b**) EM510 HF digital receiver.  
Figure 10: Spectrum analyzer results of the helical antenna versus the half-wave dipole.  
Although the signals received during the test fluctuate by more than 10 dB, instantaneous measurements of the helical antenna against the active antenna (HE016) are shown in the screenshots below.  
As shown from the measured spectrum of the HE016 in Figure 13a compared to that of Figure 13b, where the helical antenna was tested vertically polarized, a multitude of signals were received that could not be detected by the HE016 vertical component. This verifies that the helical antenna is capable of receiving horizontally polarized signals as well. Furthermore, despite the fluctuations in signal strength, the test results indicated that the gain of the HE016's horizontal component in Figure 14a is similar to the gain of the helical antenna in a horizontally polarized configuration, as depicted in Figure 14b. In the following section, we discuss the helical phased array design, implementation, and evaluation in greater detail.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 6 Simulation Results
### Importance of Implementing the Control-Parameters Sets on PDTR
#### 6.3.3 On the Network Lifetime  
Prolonging the network lifetime can be achieved by selecting the nodes with higher residual energy. To maximize the network lifetime, we should maximize the value of the residual energy control parameter \(\gamma\).  
For this, we consider two scenarios. The first scenario selected 11 parameters set such that the value of \(\alpha=3,\ \beta=2,\ \text{while}\ \gamma=[0,0.2,0.4,0.6,0.8,1,1.2,1.4,1.6,1.8,2]\). Each source node sends one packet every 0.1 s for the parameter values given by Table 2, the communication range is fixed \(R=50\ \text{m}\), and the number of nodes is fixed N = 100. Figure 7, first scenario shows the simulation results after the round when the first node is declared dead. It shows that the maximum lifetime is reached when \(\gamma=1.2\).  
The second scenario selected 11 parameters set such that the value of \(\alpha=3,\ \beta=2.6,\ \text{while}\ \gamma=\{0,0.2,0.4,0.6,0.8,1,1.2,1.4,1.6,1.8,2\}\). Each source node sends one packet every 0.1 s for the parameter values given by Table 2, the communication range is fixed \(R=50\ \text{m}\), and the number of nodes is fixed N = 100. Figure 7, second scenario shows the simulation results after the round when the first node is declared dead. It shows that the maximum lifetime is reached when \(\gamma=1.4\).  
From the above two scenarios, we can conclude that the maximum network lifetime in the proposed PDTR protocol is reached when: \(\frac{3\beta}{4}>\gamma>\frac{\beta}{2}\); for \(\alpha+\gamma+\beta\leq U_{b}\), and \(\frac{3\alpha}{4}>\beta>\frac{\alpha}{2}\).  
Figure 6: Impact of control parameters sets on total energy consumption for first scenario, second scenario and third scenario.  
Figure 7: Impact of control parameters sets on the network lifetime for first scenario and second scenario.
# Constructing of 3D Fluvial Reservoir Model Based on 2D Training Images
## 1 Introduction
### Introduction to the s2 Dcd+DS Algorithm  
In order to overcome the difficulties in acquiring 3D training images, [PERSON] et al. [15] proposed a sequential simulation method based on two-dimensional slices (s2 Dcd for short) in 2012, with the flow chart shown in Figure 1. The key to this method is to define the sequence of the interfaces of the simulated layers and to include as many condition data points as possible. Sequential 2D conditional simulation is suitable when the condition data are sparse, and when the condition data are dense, the simulation quality slowly decreases due to the constraint of too much condition data. [PERSON] also suggests that different techniques can be mixed and a layered approach can be used, but the simulation framework may become complex, and its complexity mainly comes from the simulation of observations and taking into account non-smoothness.  
The DS (direct sampling) algorithm is a multipoint modeling method based on the style proposed by [PERSON] in 2010, that is, assign values in the simulated grid according to the information of conditional points around each grid point until all points have values. The advantages of this method are: (1) it uses multipoint statistics information, so the whole pattern can be propagated; (2) because the neighborhood is flexible, it is possible to use patterns composed of irregular location data; (3) it can accommodate both categorical variables and continuous variables; (4) there is no problem with data relocation because there is no need for multiple grids. The disadvantage is that the simulation effect is poor when the conditional data are small [16; 17].  
The s2 Dcd+DS algorithm combines the advantages of sequential two-dimensional conditional simulation and direct sampling simulation, weakens the influence of conditional data, reduces the simulation error, and achieves the ideal effect. Through algorithm fusion, multiple directional slices are used, and the advantages of the two methods are taken into account in the transverse and longitudinal morphological characteristics of the fluvial facies so as to ensure that the fluvial geological spatial geometric characteristics can be reproduced under reasonable constraints. The pseudo-code of the s2 Dcd+DS algorithm is shown below, Algorithm 1.  
Figure 1: s2 Dcd condition simulation flowchart.  
The method simplifies the modeling process to a series of two-dimensional multipoint simulations by defining a two-dimensional simulation surface sequence. The operation steps of this method are as follows: (1) select the required two-dimensional training image, corresponding to step 10 of the pseudo-code; (2) define the two-dimensional random simulation surface sequence, and simulate the slices that pass through the most conditional data first, corresponding to step 13 of the pseudo-code; (3) perform DS simulation along the defined s2 Dcd two-dimensional sequence, corresponding to step 14 of the pseudo-code; (4) until all grid nodes are simulated, corresponding to step 19 of the pseudo-code.
# A Sparse Bayesian Approach for Forward-Looking Superresolution Radar Imaging
## 3 Sparse Bayesian Algorithm for High Cross-Range Resolution
### Simulation of Point Targets
With the decrease of SNR, the phenomenon of noise amplification will seriously influence the estimation of the target number and position. The proposed sparse Bayesian algorithm has superior superresolution performance in the low SNR condition. The noise is suppressed because the penalty term was  
Figure 2: Simulation results of point target when the signal-to-noise ratio (SNR) is 25 dB. The top subfigure is the cross-range received signal after motion compensation; the middle subfigure is the superresolution result processed by the Poisson-based maximum a posteriori (MAP) algorithm; and the bottom subfigure is result processed by the proposed sparse Bayesian algorithm. (**a**) Platform velocity is 0 m/s; (**b**) platform velocity is 100 m/s; (**c**) platform velocity is 200 m/s; (**d**) platform velocity is 300 m/s. considered in the proposed sparse Bayesian algorithm. In addition, the proposed sparse Bayesian algorithm still compressed the target about four-times more than the Poisson-based MAP algorithm in the low SNR condition. The point target simulations demonstrate the performance of the proposed sparse Bayesian algorithm. Figure 4 shows the curves of the Poisson-based MAP algorithm and the proposed sparse Bayesian algorithm under different SNRs. Compared to the Poisson-based algorithm, the proposed algorithm obviously has a smaller RMSE and faster convergence rate. It has almost converged when the SNR is higher than 15 dB. The simulation results verify that the proposed sparse Bayesian algorithm is suitable for the practical condition of forward-looking imaging. Figure 3: Simulation results of the point target when the SNR is 15 dB. The top subfigure is the cross-range received signal after motion compensation; the middle subfigure is the superresolution result processed by the Poisson-based MAP algorithm; and the bottom subfigure is the result processed by the proposed sparse Bayesian algorithm. (**a**) Platform velocity is 0 m/s; (**b**) platform velocity is 100 m/s; (**c**) platform velocity is 200 m/s; (**d**) platform velocity is 300 m/s.
# A Heterogeneous Robotic Swarm for Long-Term Monitoring of Marine Environments
## 2 Distributed Marine Monitoring System  
In this section, the design and hardware systems of the aMussel and aPad robots as well as their roles within the swarm are described in detail. Table 1 gives an overview of the basic capabilities of the heterogeneous robotic swarm and their distribution over agent types, while also showing their dependencies on specific hardware modules. The general capabilities listed in the table can be used as a basis for developing more complex algorithms and elaborate behaviours, especially ones featuring collaboration between agents.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Perception** & **Information** & **Energy** & **Navigation and Movement** \\ \hline \multirow{6}{*}{Modules} & Acoustic modem & \(\bigotimes\) & & \\ \cline{2-5}  & Docking station & & \(\times\) & \\ \cline{2-5}  & Green Light & \(\bigcirc\) & & \\ \cline{2-5}  & GSM & \(\bigcirc\) & & \\ \cline{2-5}  & Inductive charging & & \(\bigotimes\) & \\ \cline{2-5}  & WiFi modem & \(\bigotimes\) & & \\ \hline \multirow{3}{*}{Actuators} & Buoyancy system & & & \(\bigcirc\) \\ \cline{2-5}  & Propulsion system & & & \(\times\) \\ \hline \multirow{6}{*}{Sensors} & Ambient light & \(\bigcirc\) & & \\ \cline{2-5}  & Battery state & \(\bigotimes\) & \(\bigotimes\) & \\ \cline{2-5}  & E-Sense & \(\bigcirc\) & & \\ \cline{2-5}  & GPS & \(\bigotimes\) & & \(\times\) \\ \cline{2-5}  & IMU & \(\bigotimes\) & & \(\bigotimes\) \\ \cline{2-5}  & IR camera & \(\times\) & & \\ \cline{2-5}  & Oxygen & \(\bigcirc\) & & \\ \cline{2-5}  & Pressure & \(\bigcirc\) & & \(\bigcirc\) \\ \cline{2-5}  & RGB camera & \(\bigotimes\) & & \\ \cline{2-5}  & RTC & \(\bigotimes\) & \(\bigotimes\) & \\ \cline{2-5}  & Temperature & \(\bigcirc\) & \\ \cline{2-5}  & Turbidity & \(\bigcirc\) & & \\ \hline \hline \end{tabular} aPad: \(\times\); aMussel: \(\bigcirc\); Both: \(\bigotimes\).  
\end{table}
Table 1: Swarm capabilities.  
Hardware present on the robots can be divided into three categories: sensors, actuators, and modules. While sensors and actuators are used as tools for robots perceiving and interacting with their environment, modules enable interactions between two or more robots, facilitating the exchange of information and energy.  
The more complex functionalities of the subCULTron multi-agent system involve multiple marine agents interacting in various ways. Table 2 shows how these higher-level interactions depend on the more basic capabilities of the swarm. Furthermore, the interactions are divided into intraspecies and interspecies interactions, and the involvement of specific agent species is noted.  
The robotic species--agent types that make up the heterogeneous swarm, as well as the development and realisation of mechanisms and algorithms related to specific capabilities of the system, are described in detail in the following sections.
# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
## 4 Results
### Social Media Text Classification  
#### 4.1.1 Experiment Details  
The experiments were conducted on a Dell workstation equipped with a 4 GHz, 16-core \(\times\) 32-thread AMD Ryzen Threadripper Pro 5995 WX processor, 80 GB of RAM, and two NVIDIA RTX A5000 GPUs. This computing setup provided the necessary com  
Figure 4: The architecture and implementation of the proposed website server.  
putational resources to handle large-scale training and reasoning tasks, particularly for processing extensive disaster-related social media datasets and fine-tuning transformer-based models. The substantial memory capacity and parallel processing capabilities of the GPUs enabled efficient model training, reducing computation time while ensuring optimal performance. To optimize model performance, cross-entropy loss was employed as the objective function for hyperparameter tuning. This loss function effectively measured the discrepancy between predicted and actual labels, guiding the model toward improved classification accuracy.  
The training process was configured with carefully selected hyperparameters to balance computational efficiency and model accuracy. A batch size of 32 was used to ensure stable gradient updates without excessive memory consumption. The learning rate was set to \(2\times 10^{-5}\) to facilitate gradual convergence while preventing divergence during optimization. The maximum sequence length was set to 150, allowing the model to process sufficiently long text inputs while maintaining efficiency. The model underwent 20 fine-tuning epochs to find meaningful patterns in the data without overfitting.