esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms
## 4 Performance Evaluation and Validation  
Generally, there are a variety of metrics that can be used to evaluate the performance of classification models. Those include classification accuracy, precision, and the F1-score. All those metrics depend on four expected outcomes: true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). In our particular model, TP represents the COVID-19 patients identified correctly by our system, FP represents healthy subjects identified as patients, TN is the healthy subjects identified correctly, and FN are COVID-19 patients categorised as healthy subjects.  
Based on those outcomes, various evaluation parameters can be measured. For example, the classification accuracy represents the number of labels correctly categorised divided by the total number of labels to be classified [40]. The recall (specificity) is another metric that calculates the fraction of positive (negative) patterns that are correctly categorised. The positive patterns that are correctly predicted from the total predicted patterns in the positive class are estimated by the precision metric. Finally, the harmonic mean between the REC and PER values is described by the F1-score. Those metrics are, respectively, defined mathematically by [41] as:  
\[ACC=\frac{TN+TP}{TN+TP+FN+FP} \tag{6}\]  
\[REC=\frac{TP}{TP+FN} \tag{7}\]  
\[PER=\frac{TP}{TP+FP} \tag{8}\]  
\[\text{F1-Score}=2*\frac{(PER\times REC)}{(PER+REC)} \tag{9}\]  
In addition to accuracy metrics, the receiver operating characteristics (ROC) curve is also employed to confirm and support the robustness and accuracy of our deep learning model. ROCs depict a model's classification output based on the true positive and false positive rates at various classification thresholds. Quantitatively, an additional evaluation metric is usually employed in classification systems using the area under the curve (AUC) of an ROC. The AUC value illustrates how well the model performs by discriminating between classes [42]. AUC can be calculated according to Equation (10):  
\[AUC=\frac{P_{s}-p_{n}(N_{n}+1)/2}{p_{n}N_{n}} \tag{10}\]  
where \(P_{s}\) is the sum of all the positive examples ranked, and \(p_{n}\), \(N_{n}\) signifies the number of positive and negative examples.
## 2 Materials and Methods
### Roof Suitability: Analysis of Criteria to Assess Rooftop Renovations' Feasibility  
This section presents the criteria used to evaluate the feasibility of rooftop renovation and to identify the correct rooftop strategy as a function of urban environment. These criteria refer to building architecture, morphological context (Table 2), building codes, and regulations. The information of buildings' architecture and morphological context were investigated using the DBT presented in Section 2.1. According to Italian Standard (UNI) 11235:2015 and to the literature review [13; 24; 25; 26; 27], the following criteria were identified to select the potential roofs. * Building height had to be higher than 3.5 m for green and solar roofs, while for albedo strategies (high-reflectance roof) it had to be less than 3.5 m in order to have the greatest effect on near-surface air temperatures. * Roof area had to be greater than 100 m\({}^{2}\) for green roofs; for high-reflectance roofs, greater than 20 m\({}^{2}\); and it had to be greater than 50 m\({}^{2}\) for solar roofs. * Roof material and color tones for green and high-reflectance roofs were excluded; roofs with high reflectance and vegetated roofs, solar roofs, roofs with red tiles and/or disturbing elements, such as dormers and/or antennas, were excluded. * Roof slope had to be less than 11\({}^{\circ}\) (flat roofs) for intensive green roofs and between 11\({}^{\circ}\) and 20\({}^{\circ}\) for extensive green roofs. There is no limit for high-reflectance roofs and it had to be between 20\({}^{\circ}\) and 45\({}^{\circ}\) (pitched roofs) for solar roofs. * Roof orientation with northern exposition was excluded for solar technology, as north-facing rooftops receive less sunlight. * Solar radiation: Roof area should receive at least 1200 kWh/m\({}^{2}\)/year of annual solar radiation for solar technologies. The solar energy potential was investigated, identifying the available rooftop areas and quantifying the total solar radiation on the rooftop. * Shadow effects: More than 3 h of sunlight for green roofs are necessary to allow the growth of vegetation. Therefore, the shaded roofs (less than 3 h of sunlight) were excluded. In addition, the shadowing effects are important for the selection of the most appropriate plant species for green roofs. The feasibility of energy efficiency interventions was assessed considering energy and environmental regulations at national and municipal levels. According to the Italian Decree 28/2011, some requirements were considered for the installation of solar energy technologies:  
* Production of thermal from solar thermal (ST) collectors' installation: At least 50% of the annual domestic hot water consumption must be covered by the ST production. * Production of electricity from photovoltaic (PV) panels: The installed electric power, \(P\), (in kW) must be greater than or equal to the value calculated with the following equation: \[P=(1/K)\cdot A\] (1) where:
###### Abstract  
The main objective of the S-STEP (the Small Synthetic Aperture Radar (SAR) Technology Experimental Project (S-STEP)) mission is developing an 80-kg-class active X-band SAR observation small satellite. For lighter, smaller, better, and cheaper development of the S-STEP system, a new thermal design strategy is essential. Therefore, we proposed a new thermal design strategy in this study. The main features of the proposed thermal design involve the minimization of heater power consumption by optimizing environmental heat fluxes on the satellite, the provision of long-term SAR imaging duration in both right- and left-looking modes, and the use of a lightweight flexible graphite sheet as a thermal interface for some high-power instruments. These features contribute to minimizing the satellite's mass budget through heater power minimization and achieving on-orbit system performance of S-STEP. The effectiveness of the proposed thermal design was numerically verified by on-orbit thermal analysis of the S-STEP system. In addition, the thermal design on a key payload component and the multifunctional transmit/receive module structure were verified through a space-simulated thermal vacuum test.  
thermal design; Synthetic Aperture Radar (SAR); small satellite; graphite sheet; thermal mathematical model +
Footnote †: journal: Aerospace
# Two-Stage Intelligent Model for Detecting Malicious DDoS Behavior
## 5 Experimental Results
### Offline Train
#### 5.2.1 Performance vs. Batch Size  
Figure 7 shows the impact of batch size on model accuracy and loss. Batch size is the number of samples selected for one epoch of training. Each color represents a different batch sample size. The \(x\)-axis is the epoch; the \(y\)-axis is the accuracy rate and the loss.  
As can be seen from Figure 7, with the increase in batch size, the training accuracy slowly increases and the loss slowly decreases. When the batch size is 15, the 10 epochs' loss is 0.05 and the accuracy is 0.98. When the batch size is 25, the minimum loss is 0.09 and the accuracy is 0.97. When the batch size is 35, the loss decreases from 0.39 to 0.12, and the accuracy increases from 0.90 to 0.95. The experimental results show that a small batch size tends to converge to flat minimization, which only changes slightly in the small neighborhood of minimization. Flat minimization is easier to converge, and can quickly find the direction of loss function decline, while large batch training converges to sharp minimization, which causes the loss to fluctuate greatly. Therefore, the training model with a small batch has better generalization performance and increases the model's robustness. The batch size selected for this paper is 15.
## 1 Introduction  
Wearable systems have several applications in personal communication devices and medical devices, as presented in [1; 2; 3; 4; 5; 6; 7; 8; 9]. Printed antennas are frequently used as wearable antennas in BANs and medical systems, see [1; 2; 3]. Printed antenna features, such as small volume and low production cost, are crucial in several wireless communication systems. Moreover, printed antennas are compact, flexible, and can be used as wearable antennas. Printed wearable antennas are presented in journals and books, as referred in [10; 11; 12; 13; 14; 15]. Investigation of RF transmission near the human body for wearable communication systems is presented in [16; 17; 18; 19; 20; 21; 22; 23]. However, small printed antennas suffer from low efficiency [24]. Active antennas for communication systems are presented in journals, as referredto in [25; 26; 27; 28; 29; 30; 31]. Novel active and tunable wearable antennas for BAN applications is presented in this paper. Active wearable antennas may be used in receiving or transmitting communication and medical systems. In transmitting channels, a power amplifier is connected to the antenna. In receiving channels, a low noise amplifier is connected to the receiving antenna. Amplifiers may be connected to the wearable antenna feed line to increase the system dynamic range. The active loop antenna gain is \(25\pm 2.5\) dB for frequencies ranging from 350 to 580 MHz. The active loop antenna noise figure is \(0.7\pm 0.2\) dB for frequencies ranging from 400 to 900 MHz. A varactor is used to control the antenna electrical parameters at different locations on the patient body. The antenna resonant frequency may be shifted by 10% by varying the varactor bias voltage. The presented active antennas electrical performance was optimized by using ADS software [32]. There is good agreement between the computed and measured results in all the active antennas and devices presented in this paper.
## 2 Site and Measurements
### Image Processing  
Top-view images are used to determine particle size, cross-sectional area, area ratio, and aspect ratio by the automated process presented in detail in [PERSON] and Vazquez-Martin [22]. The first steps are to remove the background features from uneven illumination and to remove out-of-focus particles. The particles then have their boundaries traced. These particle boundaries are further used to determine the properties of the particle.  
The maximum dimension, \(D_{\text{max}}\), is used to describe particle size. Here, it is determined by the smallest diameter that completely encircles the particle on the image. The cross-sectional area, \(A\), is the area enclosed by the particle boundary on the image, determined from the number of enclosed pixels. Equation (1) defines the area ratio, \(A_{\text{r}}\), as follows:  
\[A_{\text{r}}=\frac{A}{\frac{\pi}{4}\cdot D_{\text{max}}^{2}}. \tag{1}\]  
Equation (2) defines the aspect ratio, \(A_{\text{s}}\), as follows:  
\[A_{\text{s}}=\frac{X}{Y}, \tag{2}\]  
where \(X\) is the width perpendicular to \(Y\), the longest distance between any two points of the particle boundary.
## 3 Results and Discussion
### Hydrotreater of Oil Refinery Plant
#### 3.1.2 Cluster Analysis of AE Data  
After preliminary filtering, a cluster analysis of the AE data was carried out. AE signals were clustered for each measuring channel separately. As a measure of the similarity for each pair of signals, the correlation coefficient of their waveforms was used (threshold value was set to 0.7), or a measure based on the similarity between their AE parameters. As a result, \"clusters of signals\" were formed [10].  
In total, about 500,000 AE signals were analyzed, including 65,000 waveforms. For AE signals registered by the channels testing zone #5, 23 clusters were formed. While conducting cluster analysis, 14 clusters was attributed to process-induced AE events, 4 clusters corresponded to the correlated noise and 5 clusters were characterized as the sources of AE with high probability.  
The most representative clusters were formed for signals registered by channels No 8 and No 17 (Figure 3, Table 1). These signals had an amplitude of more than 70 dB\({}_{\text{AE}}\) with a high probability they were generated by AE source located in zone #5.
## 5 Performance Evaluation
### Simulation Experiments
#### 5.1.2 Random Deployment  
In this group of experiments, 400 nodes are randomly deployed in 600 \(\times\) 600 two-dimensional square areas, due to the reasons of the block, the deployment shaped to C, S and Z-type (shown in Figure 9).  
As shown in Figure 10, we examine the final localization result of a set of ML-RELM methods (the number of anchor nodes is 40). It is clear from the figure that the proposed method performance is still superior to other localization methods.  
Figure 8: Comparison of different algorithms with different number of anchor nodes in the regular deployment.  
Figure 10: _Cont_.  
Figure 9: The random distribution of nodes.  
After 50 trials, the average RMS values of the four localization algorithms in the three type areas are shown in Figure 11. The proposed ML-RELM performance is 82.2%, 12.1%, and 20.5% higher than that of DV-hop, PDM, and LSVR respectively.  
Table 2 describes the running time of four algorithms in three random deployment environment. The ML-RELM method is more efficient and superior to the latest LSVR method.  
Bars in Figure 12 represent RMS values and standard deviations of the four algorithms varying with the number of anchor nodes in the three random deployment areas of the C, Z, and S shapes. It is also obvious that ML-RELM has the best performance, and LSVR is degraded due to over-fitting when the anchor nodes numbers are large.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**CPU TIME (m)** & & **DV-Hop** & **PDM** & **LSVR** & **ML-RELM** \\ \hline \multirow{4}{*}{40 anchors, 360 unknown nodes} & **Average** & 1.0561 & 1.09 & 1.5637 & 1.0852 \\  & **Median** & 1.0588 & 1.0854 & 1.5563 & 1.0855 \\ (C-shaped) & **Worst** & 1.1127 & 1.1391 & 1.7832 & 1.1334 \\  & **Standard deviation** & 0.0245 & 0.0235 & 0.0719 & 0.0320 \\ \hline \multirow{4}{*}{40 anchors, 360 unknown nodes (Z-shaped)} & **Average** & 1.0901 & 1.1048 & 1.5693 & 1.0913 \\  & **Median** & 1.0805 & 1.0981 & 1.5701 & 1.0839 \\  & **Worst** & 1.2112 & 1.1631 & 1.6103 & 1.1226 \\  & **Standard deviation** & 0.0376 & 0.0242 & 0.0189 & 0.0181 \\ \hline \multirow{4}{*}{40 anchors, 360 unknown nodes (S-shaped)} & **Average** & 1.0201 & 1.0346 & 1.4501 & 1.0256 \\  & **Median** & 1.0185 & 1.0343 & 1.4793 & 1.0287 \\ (S-shaped) & **Worst** & 1.0665 & 1.0657 & 1.5043 & 1.0518 \\  & **Standard deviation** & 0.0285 & 0.0185 & 0.1173 & 0.0219 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Running time in the random deployment.  
Figure 10: The localization results of randomly distributed nodes.
## Appendix B Linear Constitutive Material Model between Strain and Stress FRFs  
Any constitutive material model makes a functional relation between the deformations and the stress in the material. Here, a linear isotropic constitutive model is introduced between the _total Strain FRFs_\(\mathbf{H_{\varepsilon}}(x,y,\omega)\in\mathbb{C}\) and the _Stress FRF tensor_\(\mathbf{H_{\sigma}}(x,y,\omega)\in\mathbb{C}\), as:  
\[\mathbf{H_{\sigma}}(x,y,\omega)=\mathcal{S}_{cm}(\mathbf{H_{\varepsilon}}(x,y,\omega) )\in\mathbb{C}. \tag{11}\]  
Specifically, the functional relation \(\mathcal{S}_{cm}()\) of linear elasticity can be deployed, with the aid of the material parameters (\(E\) elastic modulus, \(\
u\) Poisson ratio, \(G\) shear modulus, \(\Lambda\) Lame constant), as:  
\[\begin{split} H_{\sigma}(x,y,\omega)_{kk}&=2 GH_{ \varepsilon}(x,y,\omega)_{kk}+\Lambda\big{(}H_{\varepsilon}(x,y,\omega)_{xx}+ H_{\varepsilon}(x,y,\omega)_{yy}\big{)}\in\mathbb{C};\\ H_{\sigma}(x,y,\omega)_{kj}&=2 GH_{\varepsilon}(x,y, \omega)_{kj}\in\mathbb{C};\\ G&=E/2(1+\
u)\in\mathbb{R};\qquad\Lambda=Ev \big{/}((1+\
u)(1-2\
u))\in\mathbb{R}.\end{split} \tag{12}\]
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 5 MAC Protocols for Satellite IoT and M2M
### Aloha-Based Protocols  
The most representative random access protocol--and the inspiration for many other MAC protocols--is Aloha, developed in 1970. Although this protocol is quite old and simple, in current IoT developments Aloha plays a fundamental role. For example, leading IoT technologies that use variations of this protocol are LoRa and SigFox. Furthermore, there are several applications and modifications to Aloha reported for satellite environments in the literature. Some of them can be found in [41; 42; 43; 44] for the interested reader.  
In Pure Aloha, nodes send data when they have data to send, hoping that a collision does not occur. When the reception of a packet is successful, the receiver sends an acknowledgement (ACK); otherwise, nodes retransmit the same packet after a random time [45; 46]. The performance of this protocol is quite modest. It achieves a maximum normalized throughput of \(S=0.18\) when \(C=0.5\). In terms of packet loss rate, it achieves a \(PLR=10^{-3}\) for an extremely low normalized load of \(C<10^{-3}\). The advantage of this protocol lies in its simplicity of implementation, since it does not require pre-coordination or extra access control signaling [47]. When there is low load in the channel,the energy consumption of Aloha is efficient, since it only requires sending data and the reception of an ACK, so the active consumption due to transmission and reception is proportional to the data and ACKs transmission delays. Nevertheless, for high channel loads, the packet losses due to collisions become high, which causes more retransmissions, overloading the channel with the associated wasting of energy.
## 2 Methods  
### BP Imaging Algorithm  
The BP imaging algorithm is a simple and effective migration imaging algorithm for radar reflected waves. It can restore complex radar-reflected-wave events to a space reflector, thereby greatly reducing the difficulty in recognizing GPR detection results. The algorithm originated from synthetic aperture radar detection [20] and was introduced to process GPR detection data, achieving desirable results [21].  
The working principle of the BP algorithm is to use the ray tracing method to calculate the propagation time of the electromagnetic wave transmitted from the transmitting antenna, which is reflected at a certain point in the detection area and received by the receiving antenna [13]. With this propagation time, the amplitude can be extracted from the measured radar data, and the imaging value can be obtained by adding the extracted amplitudes of each channel. Assuming that the transmitting antenna position is (\(x_{si}\),0), the receiving antenna position is (\(x_{ri}\),0), the coordinate of any point \(A\) in the imaging region is (\(x_{a}\),\(y_{a}\)) and the relative permittivity of the background is \(\varepsilon_{r}\), then the propagation time of the electromagnetic wave at that point is (see Figure 1):  
\[T_{i}=\frac{\sqrt{\left(x_{a}-x_{si}\right)^{2}+{y_{a}}^{2}}+\sqrt{\left(x_{a }-x_{ri}\right)^{2}+{y_{a}}^{2}}}{c/\sqrt{\varepsilon_{r}}},\ \ \ i=1,\ldots,N \tag{1}\]where \(c\) is the velocity of vacuum light; \(i\) is the detected position of the GPR, where each position corresponds to a piece of data of a certain channel; and \(N\) is the total channel number of GPR detection [7]. Assuming that \(S_{i}\) is the \(i\)-th channel data of the GPR, the BP imaging value corresponding to point \(A\) is:  
\[P(A)=\sum_{i=1}^{N}S_{i}(t=T_{i}) \tag{2}\]  
When the detected target is simple and the signal-to-noise ratio of the GPR data is high, the detected target position can be clearly restored by using the BP imaging algorithm alone. However, when the detected target is complex or the data quality is poor, the multipath ghosts brought by multipath propagation of rays become severe, interfering with the identification and interpretation of the targets.
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
## 2 Piezoresistive Mechanisms
### Tunneling Effect  
As the strain increases, parts of the contacting conductive fillers begin to separate, and, hence, the tunneling effect appears. With this mechanism, the electrons between the conductive fillers can pass through the so-called energy barrier between adjacent but uncontacted conductive particles, resulting in tunneling currents [35; 36].  
[PERSON] proposed an improved 3D statistical resistance network model, which can evaluate the tunneling effect between randomly distributed CNTs in a polymer matrix and predict the change in the CNT network [35]. In terms of a small strain level, the change in resistivity of a CNT nanocomposite sensor is mainly due to the tunneling effect between CNTs, leading to its nonlinear piezoresistive characteristics. Scanning electron microscopy was used to realize random distribution and a possible tunnel path between adjacent CNTs (Figure 2a). This physical diagram is simplified into a model for evaluating resistance (Figure 2b). The tunneling resistance between two adjacent CNTs is estimated as:  
\[R_{tunnel}=\frac{V}{AJ}=\frac{(h^{2}d)}{(Ae^{2}\sqrt{2m\lambda})}\exp\left( \frac{4\pi d}{h}\sqrt{2m\lambda}\right) \tag{1}\]  
where \(J\) is the tunneling current density, \(V\) is the potential difference, \(e\) is the quantum of electricity, \(m\) is the mass of electrons, \(h\) is the Planck constant, \(d\) is the distance between CNTs, \(\lambda\) is the energy barrier height and \(A\) is the cross-sectional area of the tunnel (the cross-sectional area of CNTs is used as an approximate value).  
[PERSON] et al. introduced the quantum tunneling effect to establish a representative volume element model to predict the conductivity, percolation value and mechanical properties of graphene rubber composites accurately [37]. In this model, graphene was randomly and uniformly distributed and large deformation characteristics of the rubber matrix were considered. Additionally, current density calculation results with and without considering tunneling effect were compared for further illustration of the influence of the tunneling effect. As shown in Figure 3, the current density of the rubber matrix without tunneling effect was the smallest, resulting in an insulated state of the graphene rubber composite (Figure 3a). By contrast, the tunnel current density of the composites increased  
Figure 1: (**a**) conductive paths via the contact points [31] (Copyright 2017, Elsevier Ltd.); (**b**) variation of the contact resistance under pressure [33] (Copyright 2019, American Chemical Society); (**c**) interface effect of CN-coated fibers under external pressure [34] (Copyright 2018, American Chemical Society).  
significantly by about 106 times when the tunneling effect was considered (Figure 3b). Therefore, the tunneling effect is beneficial to format conductive paths in composites and is very important for accurately predicting the conductivity and seepage behavior of conductive polymer composites (CPCs).