esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
## 5 Conclusions  
In this article, our goal is to propose a deep learning-based approach to classify pneumonia from chest X-ray images using transfer learning. In this framework, we adopted the transfer learning approach and used the pretrained architectures, AlexNet, DenseNet121, Inception V3, GoogLeNet and ResNet18 trained on the ImageNet dataset, to extract features. These features were passed to the classifiers of respective models, and the output was collected from individual architectures. Finally, we employed an ensemble model that used all five pretrained models and outperformed all other models. We observed that performance could be improved further, by increasing dataset size, using a data augmentation approach, and by using hand-crafted features, in future.  
Our findings support the notion that deep learning methods can be used to simplify the diagnostic process and improve disease management. While pneumonia diagnoses are commonly confirmed by a single doctor, allowing for the possibility of error, deep learning methods can be regarded as a two-way confirmation system. In this case, the decision support system provides a diagnosis based on chest X-ray images, which can then be confirmed by the attending physician, drastically minimizing both human and computer error. Our results suggest that deep learning methods can be used to improve diagnosis relative to traditional methods, which may improve the quality of treatment. When compared with the previous state-of-the-art methods, our approach can effectively detect the inflammatory region in chest X-ray images of children.  
Conceptualization, P.T., C.M., and V.H.C.d.A.; methodology, C.M., and V.H.C.d.A.; software, V.C., S.K.S., A.K., D.G., P.T., and C.M.; validation, V.C., S.K.S., A.K., D.G., P.T., C.M., and V.H.C.d.A.; formal analysis, C.M., R.D. and V.H.C.d.A.; investigation, V.C., S.K.S., A.K., D.G., P.T., and C.M.; resources, V.C., S.K.S., A.K., and D.G.; data curation, V.C., S.K.S., and A.K.; writing--original draft preparation, V.C., S.K.S., A.K., D.G., P.T., and C.M.; writing--review and editing, R.D. and V.H.C.d.d.A.; visualization, V.C., S.K.S., A.K., D.G., and P.T.; supervision, C.M. and V.H.C.d.A. All authors have read and agree to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Cross-Layer Protocol Based on Directional Reception in Underwater Acoustic Wireless Sensor Networks
## 1 Introduction
Based on the fact that the relays face higher packet collisions with more interfering neighbors, a relay selection scheme based on neighbor interference and congestion level is proposed, which provides high PDR and low latency routes with lower overhead by weighing the key factors affecting network performance. 2. A MAC protocol with a channel reservation mechanism is also presented. Nodes compete for channels by Contend packets (CT). The degree-of-congestion in the transmitting node and the degree of interference in the receiving node determine the priority of the CT. According to the characteristics of single vector hydrophone directional reception, the backoff of nodes depends on the degree of interference caused by data transmission, which reduces network congestion and packet collisions and improves bandwidth and energy efficiency. 3. The CLIC with a directional reception mode is proposed on the basis of the spatial multiplexing property of directional reception using vector hydrophones. In addition, an integrated routing-MAC is used in a route to adaptively bypass nodes with high collision probability and high congestion level. The remainder of this paper is organized as follows: In Section 2, we review the related literature. The network model is presented in Section 3. Section 4 describes the proposed DM protocol, and its performance evaluation is presented in Section 5 via comparative simulations. Section 6 is a short conclusion.
## 4 Discussion
### Comparisons between This Study and Previous Studies  
In this study, the booting stage was shown to be the optimal stage for rice yield estimation with vegetation indices at a single stage. This was because leaf area index reached its maximum at the booting stage. Some studies also found that the booting stage was the most important stage for yield estimation [23; 38]. As for the spectral bands for the three types of vegetation indexes, bands in red-edge and near-infrared regions were identified to be the optimal combinations for rice yield estimation. This result was consistent with that of [PERSON] et al. [23]. Although the effects of rice florets on canopy spectra were seldom studied before, there were a few studies on the effects of rice panicles on rice spectra. For example, [PERSON] et al. [46] also found that a vegetation index constructed by a combination of red-edge and near-infrared bands could best predict panicles-affected LAI, which proved effectiveness of red-edge and near-infrared bands for monitoring of rice florescence spectral information.  
From the aspect of rice yield estimation based on UAV Remote Sensing, [PERSON] et al. [23] found the best estimation of rice yield was obtained at the booting and heading stages and the corresponding RMSE was 926.46 kg/ha; [PERSON] et al. [47] combined vegetation indices and textural information from UAV hyperspectral images to estimate rice yield, and a RMSE of 521 kg/ha was obtained. In this study, after some florescence spectral indices were introduced, the relatively smaller RMSE values were obtained. For the two-growth stage and three-growth stage models the RMSE values were 376.16 kg/ha and 373.31 kg/ha, respectively. This showed the importance of florescence information in yield estimation.
# Metamaterial Based Design of Compact UWB/MIMO Monopoles Antenna with Characteristic Mode Analysis
## 3 Topology and Analysis
### Design Procedure for Single Element  
An itemized evolution technique for the proposed set up is presented in Figure 4. In step 1, a round molded single component with an opened and partial ground plane is designed. The ground plane covers the region between the lower edge and the feed line. In step-2, two radiating patches with a common ground is made. In step three,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Size (mm)** & **Parameter** & **Size (mm)** & **Parameter** & **Size (mm)** \\ \hline \(L\) & 30 & \(W\) & 60 & \(W_{1}\) & 0.4 \\ \(A\) & 3 & \(B\) & 4.8 & \(C\) & 4.2 \\ \(L_{f}\) & \(11.05\) & \(L_{S}\) & 10 & \(L_{s}\) & 20 \\ \(R_{p}\) & 9.35 & \(R_{s}\) & 2.6 & \(W_{2}\) & 5 \\ \(W_{f}\) & 2.5 & \(G_{w}\) & 3 & \(G_{p}\) & 2.5 \\ \(D\) & 1 & \(E\) & 0.2 & \(F\) & 1.75 \\ \(G\) & 2.4 & \(H\) & 1.6 & \(D\) & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different parameters and size of the antenna.  
Figure 3: Equivalent circuit model of the proposed UWB/multiple-input-multiple-output (MIMO) antenna.  
Figure 2: System model; (**a**) Top view, (**b**) bottom view, (**c**) unit cell.  
six metamaterial segments were set around every radiator at the upper plane with two perturbed stubs at the ground plane to lessen the electromagnetic interaction between the MIMO components. The metamaterial structure and perturbed stubs suppress the surface waves reducing the electromagnetic interaction between the antenna components keeping it below \(-20\) dB. The parametric analysis was also carried out to have an optimal value for the radius of the slots (\(R_{S}\)) as shown in Figure 5.
# Intelligent Classification of Japonica Rice Growth Duration (GD) Based on CapsNets
## 1 Introduction
Machine learning methods, such as principal component analysis (PCA), support vector machines (SVM) [26; 27], random forests [28], and classification and regression trees (CART) [29], are often used in classification problems based on Raman spectral data [30; 31; 32; 33; 34; 35]. However, they are limited by the shallow architecture of their own models, their performance is far inferior to machine deep learning methods, and the analysis of spectral data cannot be further improved [36]. In 2006, a paper by [PERSON] et al. [37] in _Science_ led to the study of machine deep learning. Machine deep learning has been successfully applied to multiple pattern classification problems [38; 39], including agricultural applications [40]. Convolutional neural networks (CNNs) [41; 42] have been widely used as a classic kind of deep neural network. However, CNNs typically perform poorly with small datasets [43], which is the case for most of the plant database. In 2017, [PERSON] et al. [44] proposed a vector capsule network and a dynamic routing algorithm between capsules. Capsule networks establish the location relationship of features so that they can achieve better results than CNNs with similar structures in many small datasets [45]. In view of this, this paper was based on the structural advantages of CapsNets and its excellent performance. We adopted CapsNets architecture to solve the japonica rice classification problem. At present, the application of Raman spectroscopy combined with CapsNets in the identification and classification of GD of japonica rice has not been reported. In this study, an intelligent classification model of japonica rice GD is proposed for the first time by combining Raman spectral data with CapsNets. The main purpose of this study was to provide an efficient and accurate intelligent classification method for japonica rice GD for breeding experts.
# Designing an Adaptive Underwater Visible Light Communication System +
## 5 Transmitter Design
### Signal Generation  
Signals were generated in MATLAB. A 16-bit pseudo noise (PN) sequence and 4-PPM signal were chosen as the preamble and payload, respectively. The generated signal was modulated using a 2.4 MHz tone signal. While designing the transmitter process in MATLAB, \"underrun\" was encountered because the writing speed of the host computer was slower than the reading speed of the USRP. Underrun depends on the working station processor speed and the interpolation factor of the radio device. Since the working station had a sufficient processing speed (2.8 GHz), the problem was solved by carefully setting the interpolation factor to 16, corresponding to the tone signal, optical transmitter, and PC. This adjustment allowed for smooth data transmission without underrun.
# Analysis and Optimization of the Stray Capacitance of Rogowski Coils
## 3 Stray Capacitance of Rogowski Coils
### Discussions of the Factors Affecting Stray Capacitance
#### 3.3.1 Investigations of Factors Affecting \(C_{tt}\)  
From Equation (10), it is obvious that \(C_{tt}\) is proportional to the length \(l_{t}\). The influences of \(h\), \(r_{c}\), \(\delta\), and \(\varepsilon_{r}\) on \(C_{tt}\) are shown by Figure 9. It can be seen that under fixed \(l_{t}\), \(C_{tt}\) can be reduced by increasing the distance \(p\) among the adjacent turns, or decreasing \(h\), \(r_{c}\), \(\delta\), and \(\varepsilon_{r}\)  
\begin{table}
\begin{tabular}{c c c c} \hline
**Stray Capacitance** & **FEM** & **Proposed Method** & **Relative Error** \\ \hline \(C_{ts}\) & 7.74 pF & 8.05 pF & 4.0\% \\ \(C_{tt}\) & 2.66 pF & 2.81 pF & 5.9\% \\ \(C_{AB}\) & 7.52 pF & 7.94 pF & 5.6\% \\ \hline \end{tabular}
\end{table}
Table 1: Validation of the stray capacitance model.  
Figure 8: Geometry and mesh of the finite element model of Rogowski coils. (The reference values are \(r_{c}=0.93\) mm, \(\delta=0.07\) mm, \(h=1.5\) mm).
## 2 Materials and Methods
### Electric Pulse Generation and Electrodes  
In this study, two different waveforms (electric pulse parameters) were used to achieve comparable effects with respect to membrane permeabilization and cell survival: monopolar and bipolar high-frequency irreversible electroporation (H-FIRE) pulses. Monopolar treatment of H9c2 cells comprised a total of eight 100 \(\upmu\)s pulses (1 Hz repetition frequency), while H-FIRE treatment involved delivery of 100 bursts of 32 bipolar pulses (1 Hz repetition frequency). In addition, H-FIRE signal consisted of pulses with a length of 2 \(\upmu\)s, alternating between positive and negative phases, 2 \(\upmu\)s pause between the positive and negative phases of each pulse, as well as a 2 \(\upmu\)s pause between bipolar pulses. Pulses were delivered using a laboratory prototype pulse generator (University of Ljubljana, Ljubljana, Slovenia), which is based on H-bridge digital amplifier with 1 kV MOSFETs (DE275-102N06A, IXYS, Milpitas, CA, USA) [42]. A high-voltage differential probe HVD3206A (Teledyne LeCroy, Chestnut Ridge, NY, USA), current probe, CP031 (Teledyne LeCroy, Chestnut Ridge, NY, USA), and oscilloscope WaveSurfer 3024z 200 MHz (Teledyne LeCroy, Chestnut Ridge, NY, USA) were used to measure the delivered pulses [43; 44; 45]. Cells were subjected to electric pulses by being positioned between two parallel stainless-steel electrodes. Electric field was determined by dividing the voltage applied with the distance between the electrodes (2 mm).
## 2 Materials and Methods
### Validation of the PST Technique  
To validate the PST approach, two segmentation techniques were tuned: the Canny edge detector [8] and a binarisation procedure. These techniques are usually used as steps in the analysis and identification of diatoms and the detection of other phytoplankton organisms [7]. Figure 5 shows nine images taken from the employed dataset [12]. A different kind of diatoms were chosen.  
1. Canny edge detection technique  
The Canny edge detector [8] is denoted as \(I_{B}=T_{C}(I_{,}[\ h_{min},h_{max},\sigma])\). It employs three parameters: \(\overrightarrow{p}=[h_{min},h_{max},\sigma]\), where \(\sigma\) is the standard deviation of a convolution mask given by the first derivate of the Gaussian function, and \(h_{min}\) and \(h_{max}\) are the thresholds used in the hysteresis process. The purpose of this process is to reduce the appearance of false contours and local maximum values produced by noise.  
2. Binarisation technique  
To test the PST technique in a thresholding method, we developed our own thresholding algorithm. This segmentation technique consists of separating pixels on the image into two classes, high- and low-intensity pixels. This technique transforms a greyscale image into a binary one \(I_{B}\). This makes it possible to differentiate objects in the background by identifying a threshold \(t\). In this case, the pixels labelled with 1 belong to the object, while the pixels labelled with 0 belong to the background.  
We propose the following method to find the optimal threshold \(t^{*}\) using similarity functions.  
\[I_{B}=T(I,t(\kappa))=\left\{\begin{array}{l}1\text{ if }I(x,y)>t\\ 0\text{ in other case}\end{array}\right.\]  
Figure 4: Results obtained with the Canny edge detector. (**A**) Original image. (**B**) Results obtained with three different set of parameters [\(\text{h}_{\min}\), \(\text{h}_{\max}\), \(\sigma\)]: [0.095, 1.0, 0.1], [0.095, 1.0, 0.3], [0.15, 1.0, 0.9]. (**C**) Original image. (**D**) Results obtained with [\(\text{h}_{\min}\), \(\text{h}_{\max}\), \(\sigma\)]: [0.15, 0.6, 0.7], [0.15, 0.5, 0.7], [0.15, 0.7, 0.9]. Only one result is reproduced given that they are almost identical.  
The optimal threshold value \(t^{*}\) is the argument of the maximum of the similarity function, and it is subject to one of the following conditions:  
\[if\{arg(max(\Psi(I_{n},I_{n-1})))\}\leq\mu\Rightarrow t^{*}=arg(max(\Psi(I_{n},I_{ n-1})))+\kappa\sigma\]  
\[if\{arg(max(\Psi(I_{n},I_{n-1})))\}>\mu\Rightarrow t^{*}=arg(max(\Psi(I_{n},I_{ n-1})))-\kappa\sigma\]  
where \(\mu\) is the mean and \(\sigma\) the standard deviation of the image, and \(\kappa\) is a normalisation factor to be tuned. \(\kappa\) was found by the PST in the range: \(0.6<\kappa<5\). The value \(\kappa\sigma\) defines the quality of the binarisation.
## 3 Validation  
### Case Study 1  
For the purpose of verifying the effectiveness of the proposed method in this work, an engineering practice of a bridge pile in a steep slope illustrated in work of [PERSON] et al. [34] was adopted for relevant analysis herein.  
Specifically, in reference to this example, the basic parameters are listed as below, in Table 1:  
Additionally, the angle for the slope is approximately 40\({}^{\circ}\), the distributions of soil deposits along with the pile shaft from top to the bottom are planting soil, gravelly silty clay, strongly weathered dolomitic limestone and moderately weathered dolomitic limestone, with thicknesses of 1.8 m, 9.7 m, 6.1 m and 7.4 m, respectively.  
Therefore, according to the national code [48], in this paper, the proportional coefficients of horizontal resistance for the foundation against soil layer \(m_{1}\), \(m_{2}\) and the coefficients of horizontal resistance against rock layer \(C_{3}\), \(C_{4}\) can be determined as 1.5 MN/m\({}^{4}\), \(m_{2}\) = 4 MN/m\({}^{4}\), \(C_{3}\) = 80 MN/m\({}^{3}\) and \(C_{4}\) = 420 MN/m\({}^{3}\), respectively. By noticing the fact that, for the pile shaft, the passive segment occurs in the soil layers, the horizontal resistance reduction coefficient for foundation \(\delta_{p}\), the vertical subgrade coefficients for the foundation in both the planting soil layer and the gravelly silty-clay soil layer are able to be obtained as 0.8 and 40 MN/m\({}^{3}\)[48], respectively.  
Hence, the predictions of the bending moment for each segment of the pile shaft yielded by the proposed approach in this work is illustrated diagrammatically in Figure 8, along with the site measured results reported by [PERSON] et al. [34]:  
In Figure 8, it is found that the predictions highly coincide as satisfactorily as possible with the site measured results. The error between predicted results and measured value in the maximum bending moment of pile shaft is about 6.19%. In detail, although there are some underestimations for the predicted results compared with the measured results, especially around the locations near the interface between the layers of the highly weathered lime stone and the moderately weathered limestone, from the viewpoint of applicability,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Type of soil stress distribution & Parabola \\ Dameter of pile shaft (m) & 2.0 \\ Length of pile shaft (m) & 25.0 \\ Elastic modulus (GPa) & 29.6 \\ Subjected axial force at top of pile shaft (kN) & 7312.0 \\ Subjected bending moment at top of pile shaft (kN \(\times\) m) & 530.0 \\ Subjected horizontal force at top of pile shaft (kN) & 50.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed parameters in work of [PERSON] et al. [34].  
Figure 8: Comparison of present study prediction and measured values.  
the majority of the predicted curve is capable of indeed delineating the bending moments for the pile shaft in real engineering practice.
## 2 Overview of Compressive Sensing Algorithm
### Sensing Matrices in CS  
The sensing matrix plays a vital role in designing the CS algorithm, and the most important aspect in CS theory is to design efficient sensing matrices. A few important sensing matrices are the Random sensing matrix, the Deterministic sensing matrix, the Structural sensing matrix, the Optimized sensing matrix and the Binary sensing matrix.  
Compressed sensing commonly makes use of random sensing matrices such as Gaussian and Bernoulli. These matrices are unstructured type and they need large memory storage and involve high computational complexity. Hence they are not feasible for hardware implementation. Contrary to this, the deterministic sensing matrix is simple to implement and widely used for practical applications. Though it does not satisfy RIP it is preferred due its compatibility with hardware application. Toeplitz and circulant are the types of structured sensing matrix with minimal parameters, and these measurement matrices can be accomplished for different applications [46; 47; 48; 49; 50; 51; 52].
# A Novel MPPT Based Reptile Search Algorithm for Photovoltaic System under Various Conditions
###### Abstract
The main contribution of this study is to develop a large-scale PV system with an effective MPPT controller based on the new RSA algorithm to track GMP under various climatic circumstances. The efficiency and performance of the proposed RSA algorithm were tested under varying solar irradiance and partial shading conditions, and then compared with different bio-inspired optimization MPPT methods. The suggested RSA-MPPT based method is found to have remarkable performance in terms of tracking efficiency, faster convergence time, and robustness (under fast and slow variation of irradiance), higher tracking accuracy, and better global tracking capabilities. The rest of the paper is organized as follows: the PV system description and the impact of variable irradiance and partial shading on PV characteristics are discussed in Section 2, proposed RSA-MPPT algorithm is presented in Section 3, simulation results are presented and analyzed in Section 4, and finally, conclusion remarks are given in Section 5.
## 4 Results and Discussions
### Case Study
#### 4.1.1 First Phase  
In the first phase, the association scores are computed between each pair of elements. Few of the cases are shown in this section to explain how association scores are actually computed. In this phase we identified two separate cases:  
* Outliers
* Need to be investigated  
Association score among service Optical Coherence Tomography OCT scan and patients are shown in Figure 8. Total 21 patients avail this service of OCT scan and an average of all association scores is 0.052. We set this average value as a threshold. It can be seen from the Figure 8 that two patients are identified as \"need to be investigated\", and rating of this service is decremented to 98 from 100. Total score of rating is 100. Similarly, association score for all services and patients are computed in the same manner and the rating score is also adjusted accordingly.  
Figure 8: Service with respect to Patient association scores.  
Figure 7: Yearwise insurance amount utilization.  
Figure 9 explains doctor_id association scores with respect to patients. Total 36 number of patients are examined by the doctor_id 131. Two cases are identified as \"need to be investigated\" and the rating score of this doctor is decreased to 98 from 100.  
Figure 10 shows association scores of services with respect to doctors. Service Routine Electroencephalogram \"EEG\" prescribed by 50 different doctors and six cases are identified as 'need to be investigated\". The threshold value is 0.0476. The Rating score of this service is 94, which is decreased by 6. Complete output is shown in Appendix A.1.  
Figure 11 is explaining patients with respect to doctors association scores, Patient MR_no is 959705 visited 126 times to the hospital and he is examined by 12 different doctors. This patient visited doctor_id 1511, forty eight times. Four cases of \"need to be investigated\" are identified. The Rating score of this patient is decreased to 96.  
Figure 10: Service with respect to doctor association scores.  
Figure 9: Doctor with respect to Patient association scores.