esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Attack Algorithms  
#### 2.2.1 Fast Gradient Sign Method  
The Fast Gradient Sign Method (FGSM) [6] is an algorithm that uses \(L_{\infty}\) distance to create an adversarial example. FGSM modifies all pixels in the input image by a given amount. The amount of modification for each pixel is determined in the way that the value of loss function is increased. Specifically, the adversarial example is created using Equation (2). In the equation, \(x\) is the input data and \(y\) is its label, which indicates the true class of \(x\). \(J\) is the loss function and \(\theta\) is the set of parameters in the target neural network. Since the loss function basically calculates the difference between the true class \(y\) and the predicted class of \(x\) using parameters \(\theta\), choosing \(x^{\prime}\) that increases \(J\) will also increase the chance that \(x^{\prime}\) is misclassified by the network.  
\[x^{\prime}=x+\epsilon\cdot sign\left(\bigtriangledown_{x}J_{\theta}\left(x,y \right)\right) \tag{2}\]  
FGSM first calculates the gradient of \(J\) with respect to the input \(x\), and then modifies each dimension of the input based on the sign of the gradient. For example, if the gradient is positive for some pixel, its value is increased. If the gradient is negative, its value is decreased. The amount of modification is determined by the parameter \(\epsilon\). A high \(\epsilon\) will increase the chance of misclassification, but the resulting image will look very different from the original image. A low \(\epsilon\) will produce an output image that is very similar to the input image, but the chance of misclassification becomes low as well.  
The second row of Figure 1 shows the images created using FGSM. We can observe that the background pixels that used to be black are now somewhat gray, and some parts of the digit that used to be white have also turned gray. There are other attack algorithms that use the same approach as FGSM, such as Basic Iterative Method (BIM) [19], Projected Gradient Descent (PGD) [10] and Momentum Iterative Attack [20]. Instead of changing the input image once, these methods modify the input multiple times iteratively, so that the distance between the input image and the adversarial image is reduced.
# Multi-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm
## 2 Materials and Methods
### MI-EEG Recognition Based on MTF-CSP
#### 2.2.3. SVM Classifier for Multi-Window EEG Classification  
SVM is a supervised machine learning algorithm that can be used for classification or regression tasks. Before this, a linear classifier was used to find a hyperplane that could distinguish the two types of data. However, in many cases, there are countless similar hyperplanes. The purpose of SVM is to find the optimal hyperplane to distinguish the two types of samples. It is formulated to find an optimal hyperplane to distinguish the two categories and tries to maximize the margin between those categories [46; 47]. The optimization objective is to find the hyperplane that is the farthest away from the support vector, which is the sample that is closest to the hyperplane that is decisive. The objective function is defined as follows:  
\[\underset{\mathbf{w},\mathbf{b}}{\operatorname{argmax}}\bigg{\{}\frac{1}{\|\mathbf{w}\|} \underset{i}{\min}\Big{[}y_{i}\cdot\Big{(}\mathbf{w}^{\intercal}\cdot\Phi(\mathbf{x} _{i})+\mathbf{b}\Big{)}\Big{]}\bigg{\}} \tag{5}\]  
where \(\mathbf{w}\) and \(\mathbf{b}\) are the decision surface parameters to be optimized for; \(\mathbf{x}_{i}\) and \(y_{i}\) represent the ith sample and its label, respectively, and \(\Phi\) is the kernel function, which maps the features of the samples to the higher dimensional space so as to transform the linearly indivisible problem in the lower dimensional space into a linearly separable problem in the higher dimensional space. In Formula (5), the subformula after min aims to find the vectors with the minimum distance from the hyperplane, namely the support vector, and the subformula after max aims to find the hyperplane with the maximum distance from the support vector. The Radial Basis Function (RBF) is used as the kernel function in this paper to map the EEG features to a higher dimension. The RBF function is represented by Formulas (6):  
\[\Phi_{\gamma}(x)=\exp\Bigl{(}-\gamma\|x-1\|^{2}\Bigr{)} \tag{6}\]  
where \(x\) represents the data that need to be mapped into higher dimensions, \(l\) represents the features of all of the samples, \(\gamma\) is an adjustable parameter that represents the complexity of the transformation, \(\exp\) computes exponential functions based on natural numbers, and \(\|x-l\|^{2}\) represents the similarity between the data. The reasons why one may use the RBF function is to map low-dimensional features into higher-dimensional space by calculating the similarity values between a sample feature and all of other sample features as a new higher-dimensional vector.  
The left- and right-hand MI features extracted by the MTF-CSP algorithm were classified by SVM. The multi-frequency band CSP features extracted from n time-window signals were fed into n SVM classifiers with the same parameters, obtaining n recognition values.
# Electromagnetic Induction Imaging with Atomic Magnetometers: Progress and Perspectives
## 3 Application Areas and Limitations of Inductive Coils
### Biomedical Imaging
#### 3.3.3 Atrial Fibrillation  
Cardiac arrhythmias are heart conditions characterized by irregular, slow, or accelerated, beating of the heart. Their causes are related to anomalies in the conduction of the electrical pulses that control the heart's activity [16]. Among the most diffused sustained arrhythmias is atrial fibrillation (AF), which affects a substantial fraction of population over 70. It manifests as a turbulent cardiac activity and represents one of the most important causes of morbidity and mortality in rich countries [17]. Nevertheless, its clinical treatment, either via drug therapy [18] or surgical procedure, is sub-optimal [19].  
Little is known about the fundamental causes and mechanisms of AF. Recently, the first indications of localized stable sources were found in patients referred for AF [20]. It was then argued that fibrillation is caused by permanent modification of the heart's local conductivity, which produces deterministic sources known as rotors, and by the stochastic interaction with the local anatomy, which creates randomly distributed re-entry paths (wavelets). However, the topic is still widely debated.  
Progress in understanding the mechanisms underlying atrial fibrillation, and improvements in the clinical practice, require new diagnostic tools. In fact, magnetocardiography (MCG) and electrocardiography (ECG) do not provide any direct information about the causes of the irregular beat and, more importantly, the structures producing it.  
In 2016 magnetic induction tomography was proposed as novel approach to the diagnosis of fibrillations [21; 22]. In fact, EMI has the potential to generate a non-invasive, space-resolved map of the heart's conductivity, thus addressing directly the presence of permanent conduction anomalies and their relationship with the on-set and the dynamics of fibrillation. However, the sensitivity required for EMI of the hearth surpasses by far the performances of conventional inductive sensors.
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
## 3 System Model Design
### Computation of Transmission Evaluation Value Based on CMFI
#### 3.4.3 The De-Blurring Component  
The proposed curve trapezoid Mamdani fuzzy inference system uses the maximum membership principle [13] to compute the corresponding membership degree, which can also be expressed as the method of \"first OR then AND\" operation [22]. The abscissa and ordinate in each of membership degree can produce a closed area with the corresponding level of membership function. Obeying the mathematics expression of De-Blurring theory [38], we adopt the centroid of the overlap of these six areas to represent the fuzzy control result for two-factor fuzzy inputs. The fuzzy control result is applied to optimize the classification of neighbor nodes, by which the source node or message carrier is able to make a suitable decision on data routing or forwarding in the data transmission process in opportunistic social networks.
# Real-Time Hand Gesture Recognition Based on Deep Learning YOLOv3 Model
## 6 Conclusions  
In this paper, we have proposed a lightweight model based on the YOLOv3 and DarkNet-53 deep learning models for hand gesture recognition. The developed hand gesture recognition system detects both real-time objects and gestures from video frames with an accuracy of 97.68%. Despite the accuracy obtained there is still room for improvement in the following model, as right now the model proposed detects static gestures. However, the model can be improved for detecting multiple gestures and can be improved by detecting more than one gesture at a time. The proposed method can be used for improving assisted living systems, which are used for human-computer interaction both by healthy and impaired people. Additionally, we compared the performance and execution of the YOLOv3 model with different methods and our proposed method achieved better results by extracting features from the hand and recognized hand gestures with the accuracy, precision, recall, and F-1 score of 97.68, 94.88, 98.66, and 96.70%, respectively. For future work, we will be focusing on hybrid methods with smart mobile applications or robotics with different scenarios. Furthermore, we will design a more advanced convolution neural network with data fusion, inspired by recent works [61; 62; 63], to enhance the precision of hand gesture recognition.  
Conceptualization, A.M., M.J.A., A.Y., M.A.M., R.D. and K.H.A.; methodology, A.M., J.A., A.Y., M.A.M., R.D. and K.H.A.; software, A.M., M.J.A. and A.Y.; validation, M.J.A.; formal analysis, M.A.M.; writing--original draft preparation, A.M., M.J.A. and A.Y.; writing--review and editing. A.M, M.J.A., A.Y., M.A.M., R.D., R.M. and K.H.A.; supervision, M.A.M.; funding acquisition, R.D. and R.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable for this study.  
The dataset used is collected and labeled by the authors and is available at Google Drive at: [[https://drive.google.com/drive/folders/1X4q59](https://drive.google.com/drive/folders/1X4q59) iAUcPDY9 lsJaq2 HMb0 GLU6]([https://drive.google.com/drive/folders/1X4q59](https://drive.google.com/drive/folders/1X4q59) iAUcPDY9 lsJaq2 HMb0 GLU6) ckp-q?usp=sharing (accessed on 1 February 2021).
# AOA-Based Three-Dimensional Positioning and Tracking Using the Factor Graph Technique
## 6 Results  
In this section, simulation results are provided in order to prove the accuracy, convergence, and robustness of the proposed technique. The real path of the moving target is formulated by functions with regard to the time index \(k\), as:  
\[x_{k}=x_{k-1}+\cos\left(\frac{x_{k-1}\phi}{k}\right)+w_{x,k}, \tag{65}\] \[y_{k}=y_{k-1}+\sin\left(\frac{y_{k-1}\phi}{k}\right)+w_{y,k},\] (66) \[z_{k}=z_{k-1}+\cos\left(\frac{z_{k-1}\phi}{k}\right)+w_{z,k}, \tag{67}\]  
where \(\phi=\pi/60\) and \(\mathbf{s}_{0}=\left[x_{0},y_{0},z_{0}\right]^{T}=\left[0,0,0\right]^{T}\) indicating the initial location. According to the specific simulation scenarios, the setting of sensor positions, FG iteration time, and AOA measurement variances will be different.
# Interleaved Boost Converter with ZVT-ZCT for the Main Switches and ZCS for the Auxiliary Switch
## 4 Proposed Control Strategy  
Figure 15 shows the proposed control strategy block diagram. First, the output voltage is sensed by a voltage divider with a gain of \(k\). The sensed voltage is sent to the first analog-to-digital converter (ADC1) to obtain the sensed output voltage \(V^{\prime}_{o}\). After this, the error coming from \(V_{\textit{ref}}\) minus \(V^{\prime}_{o}\) is passed to the controller \(G_{\text{c1}}(z)\) so as to generate a control force. The sensed current after ADC2 is subtracted from this control effort. Therefore, a resulting error is created and sent to the controller \(G_{\text{c3}}(z)\) so as to yield one pulse-width modulated (PWM) signal after the first PWM generator. This signal will  
Figure 14: Current flow in state 11.  
Figure 13: Current flow in state 10.  
control the main switch of the first phase. On the other hand, the sensed current of the second phase after ADC3, called \(I_{L2}^{\prime}\), is subtracted from half of the sum of \(I_{L1}^{\prime}\) and \(I_{L2}^{\prime}\), and the corresponding error is sent to the controller \(G_{c2}(z)\) to obtain the other PWM signal after the second PWM generator. This PWM signal is shifted by 180 degrees and then used to drive the main switch of the second phase.  
The basic operating behavior is described as follows. Since \(0.5(I_{L1}+I_{L2})\) is used as the current reference for \(I_{L2}\), the difference between \(0.5(I_{L1}+I_{L2})\) and \(I_{L2}\) is \(0.5(I_{L1}-I_{L2})\) and this value will be sent to the feedback controller such that \(I_{L1}\) is almost equal to \(I_{L2}\). On the other hand, \(I_{01}=(1-D)I_{L1}\) and \(I_{02}=(1-D)I_{L2}\), where \(D\) is a duty cycle. Since \(I_{L1}=I_{L2}\) and \(I_{01}=I_{02}=0.5I_{o}\), the current sharing will be achieved.
## 5 The Concept of IoV
### Security Issues in IoV Communication
#### 5.5.3 Authentication and Key Management  
During IoV deployment, authentication and key management should be employed between the different devices. Moreover, when authentication and key management are employed between fog servers and cloud servers, they enhance communication in an IoV environment [97].
###### Abstract  
The volume of data generated by earth observation satellites has increased tremendously over the last few decades and will increase further in the coming decade thanks in particular to the launch of nanosatellites constellations. These data should open new avenues for Earth surface monitoring due to highly improved spectral, spatial and temporal resolution. Many applications depend, however, on the accuracy of the image geometric model. The geometry of optical images, whether acquired from pushbroom or frame systems, is now commonly represented using a Rational Function Model (RFM). While the formalism has become standard, the procedures used to generate these models and their accuracies are diverse. As a result, the RFM models delivered with commercial data are commonly not accurate enough for 3-D extraction, subpixel registration or ground deformation measurements. In this study, we present a methodology for RFM optimization and demonstrate its potential for 3D reconstruction using tri-stereo and multi-date Cubesat images provided by SkySat and PlanetScope, respectively. We use SkySat data over the Morenci Mine, Arizona, which is the largest copper mine in the United States. The re-projection error after the RFM refinement is 0.42 pix without using ground control points (GCPs). Comparison of our Digital Elevation Model (DEM with \(\sim\)3 m GSD) with a reference DEM obtained from an airborne LiDAR survey (with \(\sim\)1 m GSD) over stable areas yields a standard deviation of the elevation differences of \(\sim\)3.9 m. The comparison of the two DEMs allows detecting and measuring the topographic changes due to the mine activity (excavation and stockpiles). We assess the potential of PlanetScope data, using multi-date DOVE-C images from the Shisper glacier, located in the Karakoram (Pakistan), which is known for its recent surge. We extracted DEMs in 2017 and 2019 before and after the surge. The re-projection error after the RFM refinement is 0.38 pix without using GCPs. The accuracy of our DEMs (with \(\sim\)9 m GSD) is evaluated through comparison with the SRTM DEM (GSD \(\sim\)30 m) and with a DEM (GSD \(\sim\)2 m) calculated from Geoeye-1 (GE-1) and World-View-2 (WV-2) stereo images. The standard deviation of the elevation differences in stable areas between the PlanetScope DEM and SRTM is \(\sim\)12 m, and \(\sim\)7 m with the GE-1&WV-2 DEM. The mass transfer due to the surge is clearly revealed from a comparison of the 2017 and 2019 DEMs. The study demonstrates that, with the proposed scheme for RFM optimization, times series of DEM extracted from SkySat and PlanetScope images can be used to measure topographic changes due to mining activities or ice flow, and could also be used to monitor geographic processes such as landslides, or coastal erosion for example.  
DEM-extraction; Cubesats; PlanetScope; SkySat; RFM sensor model optimization 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020, Vol. 10, No. 1, 2020, pp. 1-5.
# Amplification Ratio of a Recycled Plastics-Compliant Mechanism Flexure Hinge
###### Abstract  
This research focuses on the fabrication of plastic flexure hinges from diverse plastics such as ABS, PP, HDPE, and LDPE. To enhance hinge efficiency, the recycling ratios are also investigated. The amplification ratio of different recycle ratios and plastic types are measured. The results show that the input and output displacements of all PP, ABS, and HDPE hinges are linear. The presence of recycled plastics has no impact on this basis. The pure PP, ABS, and HDPE flexure hinges achieve the highest amplification ratios of 5.728, 8.249, and 5.668. The addition of recycled plastics reduces the amplification ratio. This decrease in the amplification ratio, however, is small. At a 25% recycle ratio, the PP, ABS, and HDPE flexure hinges have 12%, 13.3%, and 21.7% lower amplification ratios than the pure plastic hinges. Furthermore, the utilization of recycled plastics may lessen the need for new plastic made from raw materials. With the PP flexure hinge, a maximum input value of 157 \(\upmu\)m could result in an output value of 886 \(\upmu\)m. At a maximum input value of 115 \(\upmu\)m, the ABS flexure hinge could achieve an output value of 833 \(\upmu\)m. Finally, a maximum input value of 175 \(\upmu\)m might result in an output value of 857 \(\upmu\)m when using the HDPE flexure hinge. The average amplification ratio values for all recycling ratios for PP, ABS, and HDPE flexure hinges are, respectively, 5.35, 7.60, and 5.02. The ABS flexure hinge frequently outperforms the PP and HDPE flexure hinge in terms of amplification ratios. Among these plastics, HDPE flexure hinges have the lowest amplification ratio. In general, increasing the thermoplastic polyurethane (TPU) content of the LDPE/TPU blend increases the amplification ratio. The cause could be the TPU's high compatibility with the LDPE polymer. The LDPE/TPU blend hinge offers a broader range of the amplification ratio of 2.85-10.504 than the PP, ABS, and HDPE flexure hinges. It is interesting that changing the blend percentage has a much greater impact on the amplification ratio than changing the recycling ratio. The findings broaden the range of applications for plastic flexure hinges by identifying optimal plastic types. The impact of the hinge shape on the performance of the injected plastic flexure hinge might be studied in further research.  
Keywords:ABS; amplification ratio; recycle ratio; HDPE; PP; LDPE/TPU +
Footnote †: journal: Applied Sciences
## 2 Fn as a Pertinent Key Actor in the ECM
### Fn's Role and the Structure-Function Relationship within the ECM and Cells
The accessibility of these regions necessitates a specific Fn dynamic conformation and structural rearrangement. Initially secreted from cells and, in solution, organized as a compressed, soluble dimer connected by disulfide linkages, Fn is naturally stimulated in an active state by interactions between its integrin-affinity sequences and synergy domains of the integrin present on the cell surface. This initiates a gathering of several Fn molecules near the cell surface. Although each molecule has a row of five Fn-specific binding sites, the primary matrix-assembly domain, located near the disulfide bonds, is essential for fiber formation. The high concentration of activated Fn molecules promotes the linkages between each of these and molecules from surrounding cells. The process generates a network of interconnected, stabilized, and insoluble proteins connecting cells throughout the matrix (Figure 3). Figure 3: Schematic representation of the major steps that mediate an Fn fibrillar assembly at the interfaces and lead to a structural Fn reorganization from a soluble compact form to aligned Fn molecules with fibrils. First, the III-9 and III-10 modules of each chain of the soluble Fn bind to the integrins clustered at the surface (**1**). This triggers a reorganization of both the type III modules around the cell-binding domain and that of the disulfide bridge cross-links at the C-terminal part of each chain in a double-folded loop orientated to face the integrins and a dissociation of the N-terminal dimer assembly with a motion of the row of the six type I modules at the N-terminal site that now become aligned in parallel to the membrane, opposite to the orientation in a solution (**2**). This opening and new orientation of the N-terminal Fn-assembly domain favors interactions with the N-terminal domain of another Fn molecule and, consequently, initiates Fn fibrillogenesis (**3**).
# RF Energy Harvesting Wireless Communications:
## 3 RF-EHWC Hardware  
An RF-EHWC device combines the RF energy harvest and wireless information transmission techniques. To better understand the working principle of the RF-EHWC device, we briefly introduce the hardware architectures of RF-EHWC devices. Depending on whether the energy harvesting circuit and the communication module share the antenna, RF-EHWC systems can be divided into the separate system or co-located system.  
In a separated system, the energy harvester has a dedicated antenna and therefore can be considered as a battery, which replenishes energy automatically and supplies power to the wireless communication module. In a co-located system, the energy harvester and the communication module share the same antenna, as shown in Figure 8. It switches between energy harvesting and information communication modes in a time-division manner. Sharing an antenna between the energy harvester and the communication module can reduce the system size of a co-located system. However, it usually requires the energy stream and the information stream to have the same frequency band. Meanwhile, it will incur additional requirement on the RF-EHWC hardware design, as introduced below.
# Performance and Parameter Sensitivity Analysis of the PEMFC Flow Channel with Porous Baffles
## 3 Establishment of Numerical Model  
### Model Assumptions  
A 3D, isothermal steady-state PEMFC model was established to verify the proposed channel design. As shown in Figure 1, in order to obtain the effect of the channel with the porous baffles on the performance of the PEMFC, a basic straight channel was used for comparison. To simplify the model, the following assumptions were made:  
1. The reaction gas is the ideal gas equation;
2. The gas flow in the PEMFC is laminar flow;
3. The porosity, conductivity, and permeability of the GDL and CL is isotropic;
4. The contact resistance between the layers is ignored;
5. Gravity is ignored;
6. Only protons can pass through the proton exchange membrane;
7. The system is ideal and binary.