esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 3 Materials and Methods
### 3D CNN for Automatic Pulse Rate Estimation
#### 3.3.2 Learning the Model  
Backpropagation algorithm is currently the standard training method [79]. It is based on gradient descent to update the learnable parameters. Adam optimizer [80] was selected as an optimization function with an initial learning rate of 10\({}^{-3}\). All weights were randomly initialized using the method proposed by [PERSON] and [PERSON] [81]. Biases were initialized to zero.  
Each video was centered around zero by removing the mean value. Training was carried out by successively launching batches of 15,200 in size (200 video patches in each of 76 classes). Thus, each batch updated the weights of the networks according to an input tensor of a 15,200 \(\times\) 25 \(\times\) 25 \(\times\) 60 size. New synthetic video patches were generated before passing a new batch to the network. We chose to pass a given batch of data to the network a single time (1 iteration). The number of epochs (which is the same as the number of batches because iteration = 1) was set to 5000.  
We reserved a full batch of data for validation. We used this set to monitor the training process by stopping the procedure with an early-stopping criterion based on overfitting detection. We used categorical cross-entropy as a loss function. In practice, we observed that the training procedure converged well before the 5000 th epoch, the decrease in validation loss becoming negligible (Figure 7a). Validation accuracy was greater than training accuracy, both being greater than 0.9 (Figure 7b). Dropout regularization presumably caused this particularity. These metrics are, of course, completely virtual since the model learned only synthetic data. We next present pulse rate estimations that were computed on real PPG videos using the 3D CNN model trained on synthetic data.
# Plant-Growth-Promoting Bacteria Mitigating Soil Salinity Stress in Plants
## 3 Plant Response to Salinity Stress  
Many plants are susceptible to soil salinization, the result of which their turgor, quality, and quantity of harvest decreases and the rate of growth and development slows down. Some of them, however, tolerate certain concentrations of soluble salts in the soil solution throughout their life cycle, which is the tolerance of plants to salinization. It depends on many factors, but the main one is the biology of the specific plant species and variety. There is a huge diversity in salinity tolerance among species. Some of the species are glycopytes and are sensitive to salinity, while others are halophytes and \"need\" salt [33]. In this sense, they have both built different strategies to exist in such an environment. The first type of plant tries to exclude the salt from their root cells in order to survive and ameliorate the stress effect, while the second accumulate the salt in their tissues, mainly in the leaves via xylem flux. While sensitive plant species do not have many mechanisms of their own to adapt to this abiotic stress, some halophytes tolerate up to 1 M of NaCl in the soil due to genes that are activated in response to salinity [34], which lead to the formation of specific proteins [11].  
Halophytes use different mechanisms to adapt to salty environments [5; 35; 36; 37; 11; 33]. The basic mechanisms include sophisticated changes at physiological, molecular, and biochemical levels: (i) modulation of hormone levels together with associate enzymes [11; 38]; (ii) production of solutes and osmoprotectants [11; 37; 39]; (iii) modulating the K\({}^{+}\)/Na\({}^{+}\) relationship at high values, as potassium is vital for stomata and enzyme functioning, regulating toxic ion accumulation and nutrient status [5; 37; 40]; (iv) selective intake or extrusion of ions [41]; (v) synthetizing polyamides that take part in ROS modulation [11; 42]; (vi) antioxidant compound production [11; 43]; (vii) regulation of salt overly sensitive (SOS) genes as a response to salinity-produced stress [37; 44]; (viii) nitric oxide generation, which activates diverse gene expression and antioxidant enzymes [11; 45]; (ix) changing photosynthetic activity [12; 34; 46]; (x) regulation of salinity tolerance gene expression [13]. The halophytes can be classified into three major groups: obligate, facultative, and habitat-indifferent. The _oligate halophytes_ (_Chenopodiaceae_) require salt to live and complete their biological cycle, and some of them are assigned as extremophiles [11], whereas the _facultative halophytes_ tolerate certain salt concentrations and can grow in salty soils, achieving maximum yield and growth in salt-free soils. Some authors have established another called _habitat-indifferent halophytes_ that are not affected by the salinity in their own habitats [5; 33; 35].
## 5 Conclusions  
This paper proposed an MSFOA for cooperative path planning of multi-UAVs over three-dimensional rugged terrain. In order to ensure the convergence speed and avoid local optimization, we have improved this in many ways. The multi-swarm strategy is used to increase the searching ability of the algorithm. In order to make use of any searching information effectively, we proposed the competition strategy of offspring, which ensures the efficient use of searching information and avoids falling into local optimum. In order to plan the flight path for multi-UAVs and avoid the collision between UAVs, we proposed a method to detect the collision between UAVs. Several simulations have shown the effective performance of the proposed approach. This paper proposed a method which can be used for multi-UAV path planning and can solve the high-dimensional function optimization problem. It can be widely used in the field of robotics. In addition, the parameters  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**First Scenario**} & \multicolumn{4}{c}{**Second Scenario**} & \multicolumn{4}{c}{**Third Scenario**} \\ \cline{2-10}  & **Best** & **Average** & **Median** & **Best** & **Average** & **Median** & **Best** & **Average** & **Median** \\ \hline MSFOA & **404.37** & **490.23** & **466.56** & **382.95** & 485.46 & 505.18 & **440.71** & **504.20** & **492.95** \\ FOA & 507.15 & 708.73 & 698.53 & 520.27 & 755.35 & 693.81 & 730.46 & 1001.69 & 993.81 \\ PSO & 485.14 & 797.76 & 754.04 & 520.27 & 755.35 & 693.81 & 628.71 & 953.93 & 954.92 \\ DE & 561.53 & 674.52 & 665.62 & 655.81 & 761.37 & 768.37 & 664.59 & 809.74 & 815.48 \\ ABC & 411.16 & 503.64 & 497.22 & 401.02 & 516.94 & 520.93 & 457.45 & 589.69 & 591.39 \\ IFFO & 497.29 & 859.11 & 860.97 & 547.44 & 928.37 & 908.26 & 589.85 & 953.56 & 934.38 \\ MFOA & 417.07 & 495.04 & 493.33 & 388.33 & **482.81** & **475.26** & 465.87 & 566.83 & 564.83 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The statistical results of cost of the path in different scenarios.  
Figure 6: Convergence curves of the average best values for the second scenario.  
Figure 7: Convergence curves of the average best values for the third scenario.  
and sub-swarm amount usually affect the searching performance, and this may be improved in further work.  
[PERSON] and [PERSON] proposed the algorithm and wrote the paper, as well as analyzed the data; [PERSON] and [PERSON] conceived and performed the experiments. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Beijing Municipal Education Commission, the National Natural Science Foundation of China (No. 61703012), and the Beijing Natural Science Foundation (No. 4182010).  
The authors declare no conflict of interest.
## 4 Hyperparameter Tuning
### Encoding Variant Selection  
The aim of this experiment is to select the best encoding variant for the representation of the medical state vector from the two possibilities from Section 3.3.5: abnormality-encoded or dimensionality-reduced.  
In this experiment, we evaluate the strategies abnormal-hot-accumulated (AHA) and abnormal-umap-accumulated (AUA), which differ only in their uses of encoding variant,  
Figure 6: The treatment percentiles are based on the rarity of the treatments for all non-sequestered data. The cumulative F1-score is computed as described in the “Accuracy with respect to treatments” paragraph of Section 4. The graphs show that using abnormals as the selection criterion for finding similar patients greatly contributes to the F1-score of rare treatments. (Of all the treatments in the non-sequestered data, only 82 are in the evaluation subset of non-sequestered data.)  
Figure 7: The treatment percentiles are based on the rarity of the treatments for all non-sequestered data. (The cumulative F1-score is computed as described in the “Accuracy with respect to treatments” paragraph of Section 4.) The graphs show the F1-score performance of DataToCare on the sequestered data with respect to treatment rarity. DataToCare using Abnormal-UMAP-Accumulated selection dominates Random and Proportional throughout.  
while the other control parameters of time horizon \(h=2\), and the use of abnorms for relevant patient evaluation (described below in Section 4.3) are the same.  
Table 2 and the line graphs (black for strategy AHA and blue for strategy AUA) of Figure 6 show that dimensionality reduction leads to the best performance. Also, from Figure 6, it is evident that on rare treatments with percentiles lower than 50, strategy AHA has a cumulative F1-score of zero.  
Intuitively, dimensionality reduction performs well because many patient dimensions co-vary. This allows UMAP to embed more than 1000 dimensions into 20. An additional reason is that random forests work best for a modest number of dimensions. We use the dimensionality-reduced encoding variant for the remainder of the training experiments.
# A Dual-Channel End-to-End Speech Enhancement Method Using Complex Operations in the Time Domain
###### Abstract  
This study investigates the utilization of complex operations to perform multichannel speech enhancement in the time domain using a neural network. Previous studies have demonstrated the advantages of incorporating complex operations when designing neural networks; however, they have solely focused on frequency-domain enhancement techniques. In contrast, our research study presents an end-to-end approach to perform speech enhancement in the time domain. We used the Hilbert transform to intelligently generate complex time-domain waveforms as inputs to the network. This allowed us to create an end-to-end approach that explores spatial information. To handle the complexity of the inputs, we developed a complex neural adaptive beamformer (CNAB). We utilized complex shared long short-term memory (LSTM), split-LSTM, and complex convolutions to generate the beamforming output. Following this, we developed a complex full convolutional network (CFCN) to enhance the beamforming output. We leveraged complex dilated convolutions to model the long-term temporal dependencies of speech. By cascading the CNAB and CFCN, we created the final end-to-end time-domain enhancement network, named CNABCFCN. We trained and tested CNABCFCN using the deep noise suppression (DNS) challenge dataset. Our results demonstrate the advantages of using complex operations over the baseline model. Furthermore, the proposed CNABCFCN performed better in terms of both objective and subjective measures compared with other networks.  
speech enhancement; dual channel; complex network; end to end
## 5 Dependent Shrink Algorithm and Case Study  
In this section, we propose the dependent shrink algorithm based on the discussion in the last section and apply this algorithm to the IL-3 signaling pathway Petri net model (shown in Figure 15), which is transformed from the IL-3 phenomenon model (shown in Figure 14) obtained from the website [27], as a case study. Note that IL-3 is a glycoprotein and is known to be involved in the immune response [28; 29; 30].
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 1 Introduction
In Figure 1, for 360\({}^{\circ}\) azimuthal coverage, we hold the smartphone still and rotate our body around 360\({}^{\circ}\) at an approximate constant velocity during the sampling period to imitate a Virtual Uniform Circular Array (VUCA). However, in practice, the uses' rotation velocity cannot sometimes be maintained approximately constant. Hence, we leverage the gyroscope of our smartphone to correct the sampled virtual nonuniform circular array to virtual uniform circular array under this particular circumstance. Then, we implement the MUSIC algorithm to calculate the direction of the target signals  
Figure 1: Virtual Uniform Linear Array and Virtual Uniform Circular Array system motion patterns of direction finding using a smartphone. and solve the multipath effect problem. We designed, deployed, and evaluated our system of direction finding in diverse circumstances. Our extensive experiment results show that our scheme supports high accuracy of direction finding. For phone to speaker direction finding, the mean error and the standard deviation of the measured angle are 3.4\({}^{\circ}\) and 3.9\({}^{\circ}\), respectively, within the range of 20 m.  
The rest of the paper is organized as follows. Section 2 contains the work related to our research. Then, we present the preliminary approach in Section 3. After that, we introduce the design of acoustic signals direction finding system using VUCA in Section 4 and then report our experiment design and results in Section 5. Finally, we conclude the paper in Section 6.
## 3 Materials and Methods
### Classification Methods
#### 3.4.3 Extra Trees Classifier  
The extra trees classifier follows a conventional top-down approach to generate a set of unpruned decision trees. In essence, it involves splitting a tree node while severely randomizing the cut-point and attribute selection. In the worst case scenario, it produces fully randomized trees with structures that are unrelated to the output values of the training samples. It has two main differences from other tree-based ensemble methods. These differences are that it grows the trees using the entire training sample (rather than a bootstrap replica) and splits nodes by selecting cut-points fully at random. By means of a majority vote, the combined predictions of all the trees determines the ultimate prediction. The theory behind the extra trees classifier is that ensemble averaging, combined with the complete randomization of both the cut-point and attributes, will reduce variance more effectively than the weaker randomization strategies used by other methods. Additionally, by using all original training samples instead of bootstrap replicas, bias is minimized. One of the main advantages of this algorithm is its computational efficiency [57].
###### Abstract
The model is assessed against DNS data covering a wide range of flow conditions in terms of the friction Weissenberg number, \(Wi_{\tau_{0}}\), maximum polymer extension, \(L^{2}\), viscosity ratio, \(\beta\), and friction Reynolds number, \(Re_{\tau_{0}}\); along with comparisons against other turbulent FENE-P models within the literature. The paper is organized as follows: Section 2 introduces the instantaneous and time-averaged governing equations and identifies the viscoelastic terms that will require modeling; Section 3 explains in detail the development of the viscoelastic turbulent closures; Section 4 summarises the model; Section 5 gives the numerical procedure applied; Section 6 presents the results of the flow fields in fully developed channel flow, covering all range of DR and flow conditions; and finally in Section 7, the main conclusions are presented.
## 3 Material and Numerical Models
### Material Models
#### 3.2.4 Model of Reinforcement  
The steel reinforcement was modelled with the Huber-Mises-Hencky yield surface, illustrated by Haigh-Westergaard coordinates as an infinitely long cylindrical surface (Figure 14) expressed by the following equation:  
\[F(\rho)=\rho-\sqrt{\frac{2}{3}}f_{t}, \tag{22}\]  
where \(f_{t}\) is the yield strength of steel.  
The criterion, based on a bilinear relationship between stress and deformation, was used with the associated flow law and kinematic hardening law (Figure 14) that is usually adequate for metals and is known as the Prandtl-Reuss model (Figure 15). The applied law of kinematic hardening takes into account the change in orientation of the yield surface under compression due to tensile yield strength, that is, the Baushinger effect. Parameters of concrete steel in lintels, confining elements and tie beams are shown in Table 7.  
Figure 14: The Huber-Mises–Hencky yield surface: (**a**) the space of principal stresses, (**b**) axiatoric section, (**c**) deviatoric section.  
Figure 15: The Prandtl-Reuss model: (**a**) bilinear relationship \(\sigma\)-\(\varepsilon\), (**b**) law of kinematic hardening: 1—initial location of yield surface, 2—subsequent location of yield surface.  
The models of materials and contact elements used were calibrated and verified by conducting numerical analysis of the wall models to determine the wall strength. The obtained results are described in [9; 53; 54; 55]. The results, in the form of the relationship between the normal stress \(\sigma_{\rm y}\) and horizontal \(\varepsilon_{\rm x}\) and vertical deformations \(\varepsilon_{\rm x}\), were compared with experimental results. Initially assumed parameters of materials indicated that the results for maximum compressive stress were lower by 30% than the test results, and that the moduli of longitudinal elasticity were consistent. Such a big discrepancy in the failure stress led us to assume some material parameters corresponding to masonry units and replacement of bed joints with the contact elements. The calibration of material parameters consisted in using the reduction coefficient, equal to the ratio of the compressive strength of wall to the compressive strength of the masonry unit. For compressive strength values obtained from the tests, the reduction coefficient was equal to \(f_{\rm cm}\)/\(f_{\rm b}\) = 2.83/4.04 = 0.70. The results of the numerical calculations based on the corrected material parameters differed by no more than 2% from the experimental results. The wall analysis, with reference to deformability and crack morphology, also provided satisfactory results. The calibrated elastic-plastic model of the material with the failure surface was used for analysis in the subsequent phase.
# Impact of Dataset Size on Classification Performance:
###### Abstract
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Purpose/Goal** & **No. of Datasets** & **Dataset Size Range** \\ \hline
[8] & Enhance the performance of models on limited datasets & 1 & 176 \\ \hline
[11] & Enhance the performance of models on limited datasets & 2 & NA \\ \hline
[12] & Augment training set instances & 2 & (19–30) \\ \hline
[13] & Augment training set instances & 3 & (66–90) \\ \hline
[14] & Extend training set features & 1 & 30 \\ \hline
[15] & Extend training set features & 7 & (18–768) \\ \hline
[17] & Extend training set features & 8 & (18–768) \\ \hline
[18] & Extend training set features & 4 & (19–1030) \\ \hline
[20] & Study the impact of dataset size in sentiment classification domain & 4 & (4000–10,000) \\ \hline
[2] & Study the impact of dataset size in sentiment classification domain & 7 & (1000–243,000) \\ \hline
[21] & Study the impact of dataset size in object detection domain & 2 & (1218–81,075) \\ \hline
[22] & Study the impact of dataset size in plant disease classification domain & 1 & 1383 \\ \hline
[23] & Study the impact of dataset size in information retrieval domain & 2 & (857–8651) \\ \hline This work & Study the impact of dataset size in medical domain & 20 & (80–245,057) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of related works. This work aims to investigate the impact of dataset size on the performance of six widely-used supervised machine learning models in the medical domain. For this purpose, we carried out extensive experiments on six classification models including support vector machine (SVM), neural networks (NN), C4.5 decision tree (DT), random forest (RF), ad-aboost (AB), and naive Bayes (NB) using twenty medical UCI datasets [24]. We further implemented three dataset size reduction scenarios on two large datasets, resulting in three small subsets. We then analyzed the change in performance of the models as a response to the reduction of dataset size with respect to accuracy, precision, recall, f-score, specificity, and area under the ROC curve (AUC). Statistical tests are used to assess the statistical significance of the differences in performances in different scenarios. The rest of the paper is organized as follows. In Section 2, we describe the methodology, including the datasets, the classification models, and performance evaluation. In Section 3, we present and discuss the results. Finally, Section 4 concludes our work.
# Nutrient Sensing and Biofilm Modulation: The Example of L-arginine in _Pseudomonas_
## 1 Introduction
QS is based on small diffusible molecules called autoinducers produced by bacteria and released to the environment at a low basal rate. When a critical threshold concentration of these molecules in the local environment is reached, QS mechanisms are activated, leading to coordinated expression of specific genes in a cell density-dependent manner [17]. This process allows bacteria to synchronise particular behaviors and thus act like a multicellular organism, as well as to control a wide range of functions, some of which influence biofilm development [18]. Environmental, intercellular and intracellular signals do not lead to independent regulatory cascades but are interconnected, resulting in a complex network that determines the expression and activity of transcriptional and post-transcriptional regulators controlling different aspects of biofilm formation and dispersal. In the following sections, we focus on the role of nutrient signals, and most specifically amino acid L-arginine, in the sessile lifestyle of _Pseudomonas_ by presenting the state of the art on its relevance as a nutrient, a biofilm modulator and an ATP source.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 3 UWB Positioning System
### Optimization Filters and Fusion-Based Algorithms for IPSs
#### 3.3.3 Particle Filter (PF)  
Particle filter (PF) is another application of Bayesian filters that is used for position estimation or to solve state estimation problems. PF generates particles to represent the state of the system, where each particle represents a particular state of the position and direction of the tracked object (tag). When the object to be tracked starts moving, the filter generates particles with a normal distribution, where there is no prior information about the location of the tag. The coordinates and orientations of the tag are generated and distributed according to the geometry of the location. This process is called sampling, where each particle has its own weight parameter (number). This number represents the probability of the system being in a particular location or state, and the larger the number (weight parameter), the greater the probability of finding the tag in the relevant location. Then, as the tag continues to move, the measurement data captured by the sensors provide more accurate information about the location of the tag being tracked [27; 28].
# Blob Detection and Deep Learning for Leukemic Blood Image Analysis
## 5 Experimental Results
### Dataset Description: SMC-IDB  
SMC-IDB was proposed in [20] and presented at IEEE's 2012 SMC conference; it is available at SMC-IDB (www.mathworks.com/matlabcentral/fileexchange/36634). It is a public image dataset of peripheral blood samples, acquired from Giemsa-stained slides, as in ALL-IDB. Nevertheless, the images are different, since they were acquired with a different combination of microscopes and cameras. This dataset provides a total of 367 peripheral blood images of size \(640\times 480\).