esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Simulation and Analysis
### SLP-MACGT Comparison with Other SLP Schemes  
#### 5.3.1 Network Safety Time  
In the evaluation of network safety time depicted in Figure 8a, the influence of network side length on algorithm performance is striking. With the integration of multiple source location privacy protection strategies and the attainment of Nash equilibrium via evolutionary games, SLP-MACGT emerges as the frontruner among the algorithms considered. Conversely, the safety times of SSLP and PP-SLPP exhibit dependence on AUV movement. When the network scale increases, the safety times of these three algorithms will suddenly increase.  
Moreover, Figure 8b offers results into safety time dynamics concerning varying node counts. Despite fluctuations in the curves, a consistent trend emerges: both the SLP-MACGT algorithm and its counterparts exhibit an increme  
Figure 8: Safety time: (**a**) network side length vs. safety time; (**b**) number of nodes vs. safety time.  
Figure 7: Safety time for a single attacker and multiple attackers: (**a**) network side length vs. safety time; (**b**) number of nodes vs. safety time.  
with increasing sensor nodes. This trend stems from the heightened node density, which augments the number of available transmission paths, thereby enhancing path diversity and bolstering the overall network safety time. Notably, the performance of MRGSLP initially falters, but it gradually improves, which could be chiefly attributed to communication range adjustments.
## 2 Soil-Borne Plant Pathogens and Microbiota Determine Disease Suppression
### Soil Microbiome Influences Disease Suppression  
Microbiota disturbance can limit detrimental effects due to severe diseases in the field, preserving the natural soil microbial biodiversity. For example, deciphering wheat endosphere-rhizosphere microbiomes in _R. solant_-infested soils is a challenge in developing new _Streptomyces_ strains employed as BCAs [123] and in reducing the incidence and severity of plant diseases [124; 125]. In this regard, the main functions, dynamics and roles of the rhizosphere microbiome in plant disease protection were reviewed by [PERSON] et al. (2020) [126]. A multitude of studies revealed that the enrichment of specific microbial populations is related to the composition and amount of root exudates released from the crop into the rhizosphere [127; 128; 129; 130]. On the other hand, microbiota disturbances have consequences for macro- and micronutrients and SOC, pH, origin and localization of the topsoil, and microbial diversity and functions [131]. The microbial interactions caused by disturbance of the microbiomes are the main drivers to shape bacterial abundance, alpha-diversity, richness, and functional diversity in the rhizosphere from undisturbed to disturbed soils, with consequences for functional redundancy in the soil ecosystems [132; 133; 134; 135; 136; 137; 138; 139].  
Authors have reported that the beneficial microbiota act as a protective defense layer generally in the rhizosphere and endophyte root microbiome [140]. How specific bacterial taxa are enriched in the rhizosphere, giving important plant defense mechanisms operating as a true \"second microbial barrier of plant defense\" has been reported in literature. The perception of the complexity and structure of the rhizosphere microbiota has been highlighted in the last 10 years [141]. Plant protection against pathogens by bacterial and fungal communities related to beneficial taxa inhabiting the rhizosphere is generally displayed for DSS [82; 142]. Modification and selection of the rhizosphere microbiome represent a suitable strategy to improve crop health, thanks to the rhizosphere and endophyte root microbiomes that act in synergy as \"the first and second lines of defense against pathogens,\" respectively [143; 144; 145]. From this perspective, the use and exploration of beneficial microbial consortia provide a challenge for farmers to significantly increase productivity in agricultural production systems in a sustainable way [146].
## 2 Related Work
### Context-Based Routing in Opportunistic Networks  
OppNets are prone to long delay paths, disconnections, and segmentation, hindering end-to-end connectivity. Hence, traditional routing based on contemporaneous end-to-end connectivity is not feasible. Therefore, in OppNets, data forwarding is driven hop-to-hop using the store-carry-and-forward (SCF) paradigm originally designed for DTNs [14]. This forwarding paradigm consists of the node _storing_ the data and _carrying_ the data along the network according to the node's mobility until a contact opportunity occurs and then _forwarding_ the data to the contacted node.  
Data forwarding is principal in OppNets as application deployment relies on the forwarding as a guarantee of their particular QoS requirements [14]. Seeking forwarding efficiency, [PERSON] et al. [14] state that context information helps to make a more efficient forwarding. [PERSON] et al. [15] classified data forwarding algorithms into two main categories depending on the context information used to make routing decisions: social-based routing and pure opportunistic routing. The latter mainly considers device context information.  
Under the pure opportunistic routing category, sound forwarding strategies have been proposed in the literature. Those proposals fit well under determined network conditions and application requirements. In this regard, flooding-based strategies, consisting of message replication, have proven to maximize the delivery ratio with a low latency when the OppNet is characterized by the unpredictable nodes' movement [9]. Under the aeg of multi-copy forwarding, the epidemic flooding approach proposed by [PERSON] et al. [16] is a context-oblivious strategy prone to suffer from the congestion derived from the replication overhead. [PERSON] et al. [17] addressed the congestion overhead by establishing a static configured replication quota. Context-aware strategies aim to reduce the effects of a naive replication by calculating the utility of a relay based on historical information. [PERSON] et al. [15] highlighted the most relevant routing proposals in this category.  
Finally, [PERSON] et al. [18] pointed out the relevance of a contextual middleware to manage context information in an OppNet. In this regard, but in the context of wired networks, SDNs [10] decouple the control from the network devices in a control plane where a software named controller gathers network information from the data plane tobuild an overview of the network. The controller uses this information to orchestrate the network data flows and resources optimally. To our knowledge, SDN and OppNets have been converged by [PERSON] et al. [19]. The authors applied the SDN paradigm in OppNets to implement a mobile crowdsensing system. Nevertheless, southbound communication relies on a cellular network.
# Graphene-to-Phoneme Conversion with Convolutional Neural Networks
## 3 Convolutional Neural Networks for Grapheme to Phoneme Conversion
### Models
Figure 2: G2P conversion model based on encoder-decoder (**a**) LSTMs (first model); (**b**) Bi-LSTMs (second model); (**c**) encoder CNN, decoder Bi-LSTM (third model). **f, d, s** are the number of the filters, length of the filters and stride, respectively, in the convolutional layer. First, we apply one convolutional layer with 64 filters to the input layer, followed by a stack of residual blocks. Through hyperparameter optimization, the best result was achieved by 4 residual blocks, as shown in Figure 3a, and the number of filters in each residual block is 64, 128, 256, 512, respectively. Each residual block contains a sequence of two convolutional layers followed by a batch normalization [41] layer and ReLU activation. The filter size of all convolutional layers is three. After these blocks, one more batch normalization layer and ReLU activation are applied. The architecture ends with a fully connected layer, which uses the softmax activation function. We carried out experiments with the same fully convolutional models without residual connections; however, the phoneme and word error rates were worse than with residual connections, as expected. The fifth model combines Models 3 and 4: the encoder has the same convolutional neural network architecture with residual connections and batch normalization that was introduced in model four. The decoder is a Bi-LSTM, as in model three. The structure of this model is presented in Figure 3b. In all models except Model 4, we used stateless LSTM (or Bi-LSTM) configurations; the internal state is reset after each batch for predictions.
# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 2 Modern ECG Signal Processing Techniques for Detection and Prediction of Ventricular Fibrillation
### Machine Learning Algorithms  
Machine learning algorithms are essential in ECG signal analysis due to their ability to model and recognize complex patterns in high-dimensional data.
## 5 Results and Analyses
### Prediction Models
#### 5.3.5 Fourth Scenario: Datasets with Both FS and Sampling  
Table 15 provides the results of AUC for prediction models across seven datasets in the fourth scenario. This scenario lists the prediction accuracy using both the FS and sampling techniques. The main difference between the fourth scenario compared with the third scenario is using different metrics, but they used the same sampling method. Overall, the results of the fourth scenario indicate that the performance of machine learning models in most cases performed well. However, the results in this scenario are worse than the  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Models** & \multicolumn{3}{c}{**Change-Proneness Dataset**} & & & & \\ \hline  & **antr4** & **junit** & **MapDB** & **mcMMO** & **mct** & **oryx** & **itin** \\ \hline \multicolumn{6}{l}{**Second Scenario**} & & & & \\ \hline ZeroR & 0.45 & 0.44 & 0.19 & 0.19 & 0.41 & 0.41 & 0.41 \\ \hline NB & 0.77 & **0.77** & 0.65 & **0.7** & **0.92** & 0.7 & **0.8** \\ \hline SVM & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 & 0.5 \\ \hline KNN & 0.66 & 0.73 & 0.62 & 0.66 & 0.72 & 0.74 & 0.71 \\ \hline RF & **0.81** & 0.75 & **0.7** & **0.7** & 0.89 & **0.8** & **0.8** \\ \hline \hline \end{tabular}
\end{table}
Table 13: AUC values for performance evaluation of prediction models across seven datasets in the second scenario.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Models** & \multicolumn{3}{c}{**Change-Proneness Dataset**} & & & \\ \hline  & **antr4** & **junit** & **MapDB** & **mcMMO** & **mct** & **oryx** & **itin** \\ \hline \multicolumn{6}{l}{**Third Scenario**} & & & & \\ \hline ZeroR & 0.46 & 0.44 & 0.46 & 0.38 & 0.49 & 0.48 & 0.48 \\ \hline NB & 0.73 & 0.80 & 0.73 & **0.99** & 0.92 & 0.82 & 0.81 \\ \hline SVM & 0.64 & 0.74 & 0.64 & 0.98 & 0.93 & 0.83 & 0.72 \\ \hline KNN & 0.81 & 0.88 & 0.81 & 0.91 & 0.98 & 0.9 & 0.93 \\ \hline RF & **0.89** & **0.92** & **0.89** & 0.98 & **0.99** & **0.92** & **0.97** \\ \hline \hline \end{tabular}
\end{table}
Table 14: AUC values for performance evaluation of prediction models across seven datasets in the third scenario.  
previous scenario. This suggests that applying both ensemble FS and sampling techniques decreased the prediction accuracy and using only sampling techniques was adequate to achieve high prediction accuracy. Again, RF outperformed other prediction models in all datasets except NB in mcMMO dataset, where the values of AUC range from 0.85 to 0.98, which is recognized as a good result. As in the first and second scenarios, KNN also was the second-best prediction model and performed better than other individual models.
## 5 Experimental Verification
### Measurement of the BS Correlation  
In the first step of the investigation, the proposed method was evaluated via OTA testing with a uniform azimuthal angular power spectrum using a 4 \(\times\) 4 correlation matrix according to the desired BS correlation using a two-dimensional bidirectional fading emulator. To evaluate the antenna performance on the terminal side, the MIMO terminal was placed at the center of the bidirectional fading emulator.  
The arrangement of the MIMO terminal using a half-wavelength dipole antenna is a quasi-linear array with a half-wavelength spacing aligned along the \(x\)-axis, as presented in Figure 14. Table 2 lists the measurement and analytical conditions used to perform the OTA testing and Monte Carlo simulations. The frequency used in the measurement is 1.95 GHz, which is the center frequency of the uplink channel in the 2 GHz band in Japan. The polarization is assumed to be vertical because only vertically polarized dipole antennas are used in the bidirectional fading emulator.  
Figure 14: Configuration of the four-element MIMO terminal.  
Figure 13: Two-dimensional bidirectional MIMO-OTA apparatus.  
Figure 15 shows the BS correlation calculated using Equation (6) as a function of the incoming wave angle. The symbols are for OTA test results using the proposed method to realize the BS correlation, whereas the solid and broken curves indicate the analytical outcomes from the Monte Carlo simulations and the calculated correlation using Equation (5), respectively.  
\[\rho_{pq}=\frac{1}{4}\left(\frac{|h_{p1}h_{q1}{}^{*}|}{\sqrt{h_{p1}h_{p1}{}^{*} \sqrt{h_{q1}h_{q1}{}^{*}}}}+\frac{|h_{p2}h_{q2}{}^{*}|}{\sqrt{h_{p2}h_{p2}{}^{* }\sqrt{h_{q2}h_{q2}{}^{*}}}}+\frac{|h_{p3}h_{q3}{}^{*}|}{\sqrt{h_{p3}h_{p3}{}^{* }\sqrt{h_{q3}h_{q3}{}^{*}}}}+\frac{|h_{p4}h_{q4}{}^{*}|}{\sqrt{h_{p4}h_{p4}{}^ {*}\sqrt{h_{q4}h_{q4}{}^{*}}}}\right) \tag{6}\]  
where \(h_{pq}\) indicates the channel response between the BS antenna #\(p\) and the MIMO terminal antenna #\(r\), and the asterisk (*) denotes the complex conjugate.  
It can be observed in Figure 15 that the experimental results obtained using the bidirectional fading emulator are in good agreement with both the Monte Carlo analysis results and the theoretical BS correlations obtained from Equation (5). As described in Section 4.3, the error in the BS correlation at \(\phi=0^{\circ}\) in the linear array is owing to the error that occurs when creating the arrangement of the virtual point sources, as shown in Figure 12c. This confirms that the desired BS correlation in a 4 \(\times\) 4 MIMO system can be achieved using the initial phase matrix, depending on the virtual point source arrangement.
## 4 Results
### Evaluation Results  
We evaluate our models on all three presented datasets. We divide our datasets into train and test sets, so that about 20% of each class is included in the test set. Due to the small amount of data, we do not create a validation set. However, to show that our model generally performs better than the comparison models, we perform a stratified 5-fold cross-validation on all datasets. Stratified means that 20% of the data per class are selected as test data in each fold. In this way, each test set represents the action class distribution of the whole dataset. In Table 2, we show our evaluation results for the different models and datasets.  
MAROON outperforms the other models for all three datasets for the cross-validation. On average, all models perform best on the Rolandseck Daylight dataset and worst on the Bavarian Forest Nighttime dataset. This can be explained by the fact that the Rolandseck Daylight dataset contains the most sequences and can therefore create the most diversity of situations in training, whereas the Bavarian Forest Nighttime dataset contains the fewest sequences. In addition, the nighttime dataset is more difficult than daytime datasets due to the lack of color information as infrared flashes are used for recording. The accuracies of the two comparison models are approximately the same.  
In Figure 5, we show the top-1 accuracies for all action classes of the 5-fold cross-validation. In general, MAROON outperforms the other two models for all classes in all three datasets. There are only a few exceptions in the Bavarian Forest Daylight and Bavarian Forest Nighttime datasets for classes with few samples. MAROON achieves similar top-1 accuracies for all action classes, which is especially visible in the Rolandseck Daylight dataset. This ensures that the model does not focus on action classes with many samples but also considers smaller action classes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Model** & **Top-1** & **Top-5** \\ \hline \multirow{2}{*}{Rolandseck Daylight} & MAROON & **69.16** & **96.31** \\  & SlowFast & 42.05 & 89.66 \\  & MViT & 43.13 & 85.18 \\ \hline \multirow{2}{*}{Bavarian Forest Daylight} & MAROON & **46.39** & **97.24** \\  & SlowFast & 35.40 & 95.88 \\  & MViT & 35.05 & 94.17 \\ \hline \multirow{2}{*}{Bavarian Forest Daylight} & MAROON & **43.05** & **96.33** \\  & SlowFast & 35.49 & 95.68 \\  & MViT & 31.91 & 93.26 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results of our model MAROON compared for the different Rolandseck Daylight, Bavarian Forest Daylight and Bavarian Forest Nighttime datasets with 5-fold cross-validation. The top-1 and top-5 accuracies are depicted. The best value for each dataset is marked in bold.  
Figure 5: Action class top-1 accuracy of the cross-validation of the three datasets with the models MAROON, MViT and SlowFast.
## 3 Electrospinning Techniques  
Electrospinning is described in detail in many review papers and books, including the most recent developments [48; 49; 50]; thus, here, only a short overview of the principle is given and of different fiber and nonwoven structures that can be gained by it.  
Generally, electrospinning works by introducing polymer droplets into a strong electric field, mostly by a syringe (needle-based electrospinning, Figure 3a [51]) or by constantly coating a wire with the solution (wire-based electrospinning, Figure 3b [52]). Many other possibilities exist to introduce the polymer solution or melt into the electric field, e.g., by rotating spinnerets, often aiming at an improved spinning efficiency [53]. A special form of needle-based electrospinning is coaxial electrospinning, applying a needle with an inner and an outer nozzle through which two different polymer solutions or other fluids can be pressed to create, e.g., core-shell fibers [54; 55; 56].  
Inside the electric field, Taylor cones (Figure 3a) are formed at the tip of the needle, along the wire or at any other kind of spinneret, from which a jet of the polymer solution  
Figure 3: Electrospinning techniques: (**a**) Needle-based electrospinning. From [51], originally published under a CC-BY license; (**b**) wire-based electrospinning. From [52], originally published under a CC-BY license.  
spirals towards the counter electrode, while the solvent evaporates until nanofibers are placed on the collector. The collector especially defines the orientation of the nanofibers to each other--while homogenous, static collectors (Figure 3b) create arbitrarily oriented nanofibers, rotating collectors (Figure 3a) and partly conductive or otherwise-modified substrates can be used to reach a certain alignment of the fibers [57].
# Radar Emitter Structure Inversion Method Based on Metric and Deep Learning
## 3 RES Modeling
### RES Model
#### 3.1.1 Signal Source Model  
Considering that direct digital synthesizer (DDS) technology has become the mainstream of signal sources in modern radar emitters, in the actual circuit, the DDS model increases the number of phase accumulator bits in order to improve frequency resolutions. In order to reduce the ROM's storage capacity, the higher \(A\) bits of the \(N\) bits are selected to address the amplitude information in the ROM, and the \(B=N-A\) bits are truncated, which introduces a phase truncation error [55].  
Therefore, the DDS model [55], widely used in the radar application field, was selected as the signal source to generate continuous wave (CW), linear frequency modulation (LFM), binary phase shift keying (BPSK), and quadrature phase shift keying (QPSK) signals using spurious information from the four DDS models of different modulation types, which were built according to the principle of phase truncation error: DDS_LFM model, DDS_BPSK model, and DDS_QPSK model.  
Figure 3: Different RES models.  
DDS_CW model: According to the CW signal simulation principle, the frequency of the DDS_CW signal generated by the DDS_CW model is as follows:  
\[f_{\text{CW}}=(K_{\text{CW}}/2^{N})f_{c}, \tag{1}\]  
where \(N\) represents the bits number of the phase accumulator, \(f_{c}\) denotes the frequency of the reference clock source, and \(K_{\text{CW}}\) is the frequency control word.  
DDS_LFM model: According to the LFM signal modulation principle, the DDS_LFM model is developed to generate the DDS_LFM signal. To perform the function of linear frequency modulation, the result of the frequency accumulator exhibits a linearly increasing characteristic, while the result of the phase accumulator exhibits the characteristic of a quadratic function. The initial frequency \(f_{\text{LFM}}\) and bandwidth \(B_{\text{LFM}}\) of the DDS_LFM signal are expressed as follows:  
\[f_{\text{LFM}}=(K_{\text{LFM}}/2^{N})f_{c} \tag{2}\] \[B_{\text{LFM}}=(K1_{\text{LFM}}/2^{N})f_{c}^{2}T, \tag{3}\]  
where \(K_{\text{LFM}}\) represents the frequency control word, \(K1_{\text{LFM}}\) denotes the chirp rate control word (i.e., frequency step word), and \(T\) denotes the pulse width.  
DDS_BPSK model: The DDS_BPSK model is developed according to the BPSK signal modulation principle. The DDS_CW signal is used as a carrier signal, and the DDS_BPSK signal is generated using a 7-bit Barker code (0001101).  
DDS_QPSK model: The DDS_QPSK model is developed according to the QPSK signal modulation principle. The DDS_CW signal is used as a carrier signal, and the DDS_QPSK signal is generated using the phase encoding (0213030303120).
## 4 Relationship Between LAA and Stroke
### Geometric Characteristics of Left Atrial Appendage
[69] introduced the fractal dimension (FD) to describe the surface complexity of the LAA, finding that incorporating FD into the CHA\({}_{2}\)DS\({}_{2}\)-VASc score significantly enhanced stroke risk assessment performance (AUC = 0.8479 vs. 0.6958). Likewise, [PERSON] et al. [70], [PERSON] and [PERSON] [71], [PERSON] et al. [72], and [PERSON] et al. [73] included the LA diameter in the CHA\({}_{2}\)DS\({}_{2}\)-VASc score, which notably improved its performance as well (AUC = [0.71, 0.88]). These studies demonstrate that quantitatively incorporating LAA dimensions into traditional risk stratification enhances objectivity and improves repeatability. This approach is expected to guide future advancements in stroke risk stratification.
## 2 Materials and Methods
### Data Preparation  
#### 2.2.1 Sentinel-2 Image Data  
The MultiSpectral Instrument (MSI) equipped on the Sentinel-2 satellite is a high-resolution multispectral imaging sensor. The Sentinel-2 mission comprises a constellation of two polar-orbiting satellites (2A and 2B). Sentinel-2 has an inter-resolution of 5d after the two satellites are networked, and carries a multispectral imager. The spatial resolution of the visible light bands and near-infrared band is 10 m [31]. The remote sensing data used in this study were the Sentinel-2 Level-2A products for the 2020 and 2021 plant growth season (from the beginning of June to the end of August). A total of 899 Sentinel-2 images were selected in QLM, and each image included 12 spectral bands and 3 quality assessment (QA) bands. The data acquisition and preprocessing were conducted through online code writing on the GEE cloud platform. The QA60 band of the images was used and set as a cloud mask to remove the clouds from the images in order to obtain a cloud-free image.