esa-sceva/satcom-chunk-collection huggingface dataset:
# MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks
## 3 Related Work
### Jamming Detectors  
Jamming detectors are relatively easy to design. In this section, we will review some of the techniques proposed by other authors.  
In [12], the jamming detector analyzes throughput, energy in the nodes, and end-to-end delay as performance metrics. In [PERSON] et al. [13], Packet Delivery Ratio (PDR) is used to detect jamming. Moreover, in [14], the author observes collisions rate and SNR to detect the jammer node [14]. Here, authors analyze widely known metrics; however, they overlook that these metrics can be altered by momentary interferences, or sudden changes in the communication channel. The performance metrics we have studied in our work are directly related to routing and traffic and are more specific with respect to changes in the network. In addition, they are linked to topology changes as a result of reconfigurations due to attacks.  
In [15], the author discusses two strategies: proactive and reactive channel-hopping in order to understand which of them is more resistant to a jamming attack. To test this hypothesis, they analyze two performance metrics: availability and energy. This work adequately complements ours because the authors contrast the two nature of the protocols in WSNs. It is precisely what we want to point out, that our proposed protocol, MPH, with proactive nature, presents better performance compared to AODV and DSR, two protocols widely studied with reactive nature. This performance is analyzed with more metrics such as: overhead, retransmissions, CSMA retries, energy, delay, and so on.  
[PERSON] et al. [16] propose to detect a proactive, bit reactive and packet reactive jammers by analyzing bit errors in each packet and signal strength. Once a node detects a jammer it sends an alarm messages to their neighbors. This perspective of sending an alarm message is interesting because in our proposal, when a node is attacked because its performance metrics drastically changed values, it sends a control packet. This way the network can isolate the jammer node.  
In [17], the jammer detector examines packet delivery ratio (PDR) [7] and received signal strength indicator (RSSI). In [18] the authors inspect Signal Strength, Autocorrelation of Signal Strength, Packet Delivery Ratio to detect jamming. In [19], the authors use throughput and packet drop ratio to detect a jammer attack. In these works, jammer presence analysis is performed based on known and related metrics to signal strength. Our work complements this perspective by analyzing routing metrics to give more power to the decision to isolate a node from the network.  
[PERSON] et al. in [20] studied path statistics and spectrum availability for the jammer detector. In [21], the authors assume a jammer that constantly emits signals, which can also attack several channels at the same time. The jammer detector is based on load balancing. This load balancing perspective is quite interesting and could be a future addition to our proposal when analyzing different traffic rates and the network is more versatile and dynamic.
## 3 Azimuth Spectrum Reconstruction
### Doppler Parameters Analysis  
As shown in Figure 1, the angular velocity of the hypersonic platform can be defined as \(\omega_{s}=V_{s}/L\), and the corresponding ground linear velocity of the illuminated target is defined as \(V_{G}=\omega_{s}r\). Then, the SAR Doppler rate can be derived based on the second-order Taylor series expansion of Equation (2), i.e.,  
\[K_{a}=-\frac{2\omega_{s}^{2}Lr}{\lambda R}=-\frac{2V_{s}V_{G}}{\lambda R}, \tag{4}\]  
where \(R\) is the closest slant range of targets. Due to \(V_{G}>V_{s}\), the Doppler rate of side-looking SAR for hypersonic platform will be larger compared with the traditional airborne SAR.  
In addition, the synthetic aperture time can be deduced according to the imaging geometry model of the hypersonic platform, which is given by  
\[T_{syn}=\frac{R\theta_{a}}{V_{G}}=\frac{R\theta_{a}}{\omega_{s}r}, \tag{5}\]  
where \(\theta_{a}\) denotes the beamwidth in azimuth. Similarly, due to \(r>L\), SAR synthetic aperture time for hypersonic platform will be shorter.  
Based on Equations (4) and (5), the Doppler bandwidth can be further obtained by  
\[B_{a}=K_{a}T_{syn}=\frac{2V_{s}}{D_{a}}, \tag{6}\]  
where \(D_{a}\) is the length of sub-aperture. From Equation (6), it can be noted that the SAR Doppler bandwidth for hypersonic platform holds as a constant compared with the conventional airborne SAR. Furthermore, the SAR theoretical minimum azimuth resolution can be given as  
\[\rho_{a}=\frac{V_{G}}{B_{a}}=\frac{\lambda r}{2L\theta_{a}}. \tag{7}\]
# Resilience in the Internet of Medical Things: A Review and Case Study
## 5 Analysis of Resilience in Existing Works
### Analysis of Existing Works That Used SDN or MSA Individually in Meeting Resilience Requirements  
To make the IoMT network resilient, researchers have proposed and implemented different methodologies by using the individual technologies, such as (1) SDN or (2) MSA. In this section, a review is conducted on each of these technologies and provides the advantages and disadvantages associated with each of these technologies concerning the functionalities mentioned above.
###### Abstract  
Classification of remote sensing scene image (RSSI) has been broadly applied and has attracted increasing attention. However, scene classification methods based on convolutional neural networks (CNNs) require a large number of manually labeled samples as training data, which is time-consuming and costly. Therefore, generating labeled data becomes a practical approach. However, conventional scene generation based on generative adversarial networks (GANs) involve some significant limitations, such as distortion and limited size. To solve the mentioned problems, herein, we propose a method of RSSI generation using element geometric transformation and GAN-based texture synthesis. Firstly, we segment the RSSI, extracting the element information in the RSSI. Then, we perform geometric transformations on the elements and extract the texture information in them. After that, we use the GAN-based method to model and generate the texture. Finally, we fuse the transformed elements with the generated texture to obtain the generated RSSI. The geometric transformation increases the complexity of the scene. The GAN-based texture synthesis ensures the generated scene image is not distorted. Experimental results demonstrate that the RSSI generated by our method achieved a better visual effect than a GAN model. In addition, the performance of CNN classifiers was reduced by 0.44-3.41% on the enhanced data set, which is partly attributed to the complexity of the generated samples. The proposed method was able to generate diverse scene data with sufficient fidelity under conditions of small sample size and solve the accuracy saturation issues of the public scene data sets.  
GAN; CNN; data generation; texture synthesis; classification Article  
Remote Sensing Scene Data Generation Using Element Geometric Transformation and GAN-Based Texture Synthesis
## 5 Applications of Wireless EM Energy Transfer and Propagation in SCs
### Near and Onboard Living-Tissue Tools in SC Environment
#### 5.1.1 Onboard Tools Urban SC Perturbations Due to EM Radiations  
Exposures to external EMFs have diverse consequences on onboard tools, including the impairment of their function and possible heating, which can increase the temperature of the living tissues involved. Thus, both categories of adverse effects can be critical and contingent on the nature of the organ being monitored, such as, for example, organ fluid circulation rate, the organ's vital role, the tool task in the organ, etc. Figure 1 demonstrates the interaction of EMF exposure with the tool design, its duty, and its exposure side effects on biodiversity (OH approach) [41].
## 3 Results and Discussions
### Energy Consumption Comparison  
Figure 9 presents a comparison of device energy consumption of the routing approaches during network operation time. The result shows that EMBLR is superior to the other routing approaches due to the simultaneous consideration of intermediate devices estimated parameters value during multiple path computation process. The EMBLR routing approach selects the intermediate devices based on the low HETX metric through probe packets. In the EMBLR, the link quality is estimated by using the HETX metric that can mitigate the flooding impact of RREQs packets in the network. Therefore, the HETX metric decreases the chance of frequent route failures that lead to the mitigation of the network load as well as device energy consumption in the route selection process. When the device speed increases from (10, 20, 30, 40, 50, and 60), the device energy consumption also increases. The EMBLR increases from 53.454 to 56.124 mAh, MEQSA-OLSRv2 increases from 54.541 to 58.465 mAh, and MP-OLSRv2 increases from 56.145 to 59.648 mAh. It can be seen that the EMBLR routing approach has less energy consumption as compared to the MEQSA-OLSRv2 and MP-OLSRv2 routing approaches.
# MC_MoveAbsolute(0) 4 th Order Real-Time Trajectory Generation Function Algorithm and Implementation
## 3 Fourth Order Position Profile Generation  
In this work, the position profile will be understood as a setpoint trajectory with a sampling time specified for a real-time algorithm implementation. The following convention, adopted from [9], was used to denote the parameters: \(x\) - position, displacement (profile, \(m\)); \(v\) - velocity (profile, \(m/s\)); \(a\) - acceleration (profile, \(m/s^{2}\)); \(j\) - jerk (profile, \(m/s^{3}\)); \(d\) - derivative of jerk (profile, \(m/s^{4}\)); \(\overline{x}\), \(\overline{v}\), \(\overline{a}\), \(\overline{j}\), \(\overline{d}\) - bound on \(|x|\), \(|v|\), \(|a|\), \(|j|\), \(|d|\), respectively; \(t_{\overline{d}}\), \(t_{\overline{j}}\), \(\overline{u}\), \(t_{\overline{v}}\) - time interval during in which \(|d|\), \(|j|\), \(|a|\), \(|v|\) obtains its bound, respectively; \(T_{s}\) - sampling time in seconds.  
The fourth order position trajectory generation problem was considered within this document. One input was used to define the desired position (i.e., increment from an actual position to the new one) and another four input parameters described the movement profile including velocity, acceler-ation, jerk, and snap. These latter parameters described the boundaries which cannot be crossed while executing the movement. The generated trajectory was time-optimal, i.e., the position was reached in the shortest possible time. For simplicity we assumed that the shape of the profiles was symmetric no matter what the direction of movement was.  
\[\mathbf{u}=\left|\left[\begin{array}{cccc}\overline{x}&\overline{v}& \overline{a}&\overline{j}&\overline{d}\end{array}\right]^{T}\right| \tag{1}\]  
The direction of movement can be calculated based on the following equation:  
\[sd=\text{sgn}(\overline{x}) \tag{2}\]  
where the \(sd\) variable equals \(1\) if the movement direction is positive, or \(-1\) when it is negative.  
While considering the real-time implementation with respect to sampling time of code execu-tion, it is necessary to round up calculated time values:  
\[t_{corr}=T_{s}\text{ceil}\left(\frac{t_{calc}}{T_{s}}\right) \tag{3}\]  
Fourth order trajectory profiles are shown in Figure 4.  
Figure 3: MC_MoveAbsolutues() example. (**a**): first use scenario; (**b**): second use scenario.  
In the following sections, calculations for the type of trajectory generation for MC_MoveAbsolute() are shown in an algorithmic way.
## 4 Results  
### Test System  
Optimization is tested using the IEEE 9-bus test system shown in Figure 2. The system comprises nine buses, three VSGs, three loads, and three transformers. The parameters of the IEEE 9-bus system are listed in references [23; 24] and are used as initial parameters in the optimization.  
A simplified second-order model is used for the \(i\)-th VSG using the swing Equation (15), which has been linearized and expressed in the standard linear state-space form (1).  
\[\begin{array}{c}\frac{2H_{i}}{\omega_{R}}\frac{d\omega_{i}}{dt}+D_{i}\omega_ {i}=P_{mi}-\left[E_{i}^{2}G_{ii}+\sum_{\begin{subarray}{c}j=1\\ j\
eq i\end{subarray}}^{N}E_{i}E_{j}\gamma_{ij}cos\big{(}\theta_{ij}-\delta_ {i}+\delta_{j}\big{)}\right]\\ \frac{d\delta_{i}}{dt}=\omega_{i}-\omega_{R}\qquad i=1,2,\ldots,N\end{array} \tag{15}\]  
Parameters of the \(i(j)\)-th VSG are defined as follows: \(H_{i}\) denotes the inertia constant, \(D_{i}\) is the damping coefficient, \(\omega_{i}\) is the angular speed, \(\delta_{i(j)}\) is the rotor angle, \(P_{mi}\) is the mechanical input power, and \(E_{i(j)}\) denotes internal voltage. Here, \(i=1\), \(2\), \(\ldots\), \(N\) are the internal machine buses, and \(N\) represents the number of VSGs in the system. \(\omega_{R}=2\pi f\) is the rated angular speed and \(f\) is nominal frequency. Furthermore, grid parameters are given as follows: \(G_{ii}\) is self-conductance at node \(i\), \(Y_{ij}\) represents the off-diagonal elements of the admittance matrix, and \(\theta_{ij}\) is the admittance angle between bus \(i\) and bus \(j\). The state-space vector is given as \(\mathbf{x}=[\Delta\omega_{1},\Delta\omega_{2},\Delta\omega_{3},\Delta\delta_ {21},\Delta\delta_{31}]^{\mathrm{T}}\), while the input vector is \(\mathbf{u}=[\Delta P_{m1},\Delta P_{m2},\Delta P_{m3}]^{\mathrm{T}}\). Matrices \(\mathbf{A}\) and \(\mathbf{B}\) are computed as Jacobian matrices evaluated at equilibrium.  
Figure 2: IEEE 9-bus system.
## 8 Analysis of Structural Vibration Absorption Effect  
### Time History Analysis of Main Beam Control  
In this section, the PC continuous girder bridge with CSWs was excited by El Centro waves, the changes in displacement and acceleration time-history curves of main girder in uncontrolled state and semi-active control state were studied, and the vibration absorption effect of semi-active system of composite girder bridge was analyzed. The displacement and acceleration of the main girder are expressed as mid-span node, and the analysis results are shown in Figure 6.  
It can be seen from Figure 6 that the vibration absorption effect of the displacement time history of the PC continuous girder bridge with CSWs subjected to semi-active control under the action of El Centro wave is more obvious--the average vibration rate absorption is 71%--while the vibration absorption effect of the acceleration time history is relatively weaker. Although the vibration absorption effect of peak acceleration is \(-2\%\) (Table 4), a few time points of the acceleration values under semi-active control exceed the values of uncontrolled state, and most of the time, the acceleration response presents a positive shock absorbing state, which is also indicated by its average vibration rate absorption of 20%.
## 1 Introduction
### Frequency Containment Reserves (FCR)
#### 3.3.3 Costs and Benefits  
The cost for providing this AS is actually the lost opportunity cost as described above for each DRES. However, when considering the primary frequency response of the whole distribution grid by aggregating the individual DRES this cost may be slightly increased or decreased by the losses incurred within the distribution grid. The increase or decrease depends on the loading of the feeders and actually if the power flow is reversed or not. It should be mentioned that the evaluation of an aggregated _P-f_ droop for the whole distribution grid based on the individual _P-f_ droop characteristics of the DRES while respecting technical constrains within the grid is still a research issue. The financial benefits are obviously the deferral of commitment of respective reserves from conventional PPs.
## 3 Architecture and System Evaluation
### Measurements of the Different Delay Components
#### 3.3.1 Encoding Delay  
The edge server makes the JPEG compression before the transmission (offloading) to the end devices. Image encoding is a very important step in our system because it makes the size of the image sets much smaller which has an enormous effect on the transmission delay. The compression of the image sets is made by the encoding rate value \(q\)[10; 95]. The higher the encoding rate value the bigger the image set sizes. Figures 2-5 present how the encoding rate affects the encoding delay and how much the encoding delay affects the image set sizes.  
Figure 2: Image size for different batch sizes (1500 COCO images) for encoding rate \(q\) = [50; 75; 95].  
Figure 4: Encoding delay for every batch size (\(q=75\)).  
Figure 5: Encoding delay for every batch size (\(q=95\)).  
Figure 3: Encoding delay for every batch size (\(q=50\)).
# The Effects of Tai Chi Chuan Exercise Training on the Lower Extremities of Middle-Aged and Elderly
## 2 Materials and Methods  
### Participants  
According to the definition of The World Health Organization, more than 7% of the population over the age of 65 is an \"aging society\". Therefore, this study divided the age categories into 55-64 years old and over 65 years old.  
A total of 48 participants in this research were divided into four groups according to their age and exercise type. Individuals who practiced Tai Chi were included in the Tai Chi group (TCG), and the others who had regular physical activity were included in the healthy group (HG). The TCG and HG were further divided into middle-aged (55 to 64 years old) and elderly (65 years old and older) groups.  
In middle-aged groups, there were 5 males and 7 females in TCG (age: 60.67 \(\pm\) 2.71 years, height: 162.25 \(\pm\) 8.16 cm, weight: 64.79 \(\pm\) 11.09 kg) and 5 males and 7 females in HG (age: 62.33 \(\pm\) 1.61 years old, height: 157.67 \(\pm\) 8.55 cm, weight: 60.75 \(\pm\) 8.08 kg). In elderly groups, there were 5 males and 7 females in TCG (age: 71.08 \(\pm\) 5.55 years, height: 162.75 \(\pm\) 6.40 cm,weight: 56.67 \(\pm\) 6.27 kg) and 4 males and 8 females in HG (age: 69.00 \(\pm\) 3.81 years, height: 160.25 \(\pm\) 5.72 cm, weight: 59.17 \(\pm\) 6.95 kg).  
The TCG participants had been regularly practicing Yang-style Tai Chi (108 forms) at least three times a week for over 5 years. The HG was the control group, and the individuals of which were healthy and regularly participated in recreational activities such as walking, jogging or mountain climbing. None of the participants had a history of surgery or sports injuries of the lower limbs, lower back, back, or upper limbs in the past year. Before the experiment, the required sample size was calculated by G*power software, and the power was set at 0.8.
# Comparing Three Machine Learning Techniques for Building Extraction from a Digital Surface Model
###### Abstract  
Automatic building extraction from high-resolution remotely sensed data is a major area of interest for an extensive range of fields (e.g., urban planning, environmental risk management) but challenging due to urban morphology complexity. Among the different methods proposed, the approaches based on supervised machine learning (ML) achieve the best results. This paper aims to investigate building footprint extraction using only high-resolution raster digital surface model (DSM) data by comparing the performance of three different popular supervised ML models on a benchmark dataset. The first two methods rely on a histogram of oriented gradients (HOG) feature descriptor and a classical ML (support vector machine (SVM)) or a shallow neural network (extreme learning machine (ELM)) classifier, and the third model is a fully convolutional network (FCN) based on deep learning with transfer learning. Used data were obtained from the International Society for Photogrammetry and Remote Sensing (ISPRS) and cover the urban areas of Vaihingen an der Enz, Potsdam, and Toronto. The results indicated that performances of models based on shallow ML (feature extraction and classifier training) are affected by the urban context investigated (F1 scores from 0.49 to 0.81), whereas the FCN-based model proved to be the most robust and best-performing method for building extraction from a high-resolution raster DSM (F1 scores from 0.80 to 0.86).  
automated building extraction; machine learning (ML); deep learning (DL); digital surface model (DSM); histogram of oriented gradients (HOG); support vector machine (SVM); extreme learning machine (ELM); fully convolutional network (FCN)
## 4 Discussion
This indicates that the model has a smaller range of differences in detecting all patients. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Extracted Features** & **Classifier** & \begin{tabular}{c} **Prediction** \\ **Time (min)** \\ \end{tabular} & \begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Sen** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Spec** \\ **(\%)** \\ \end{tabular} \\ \hline [PERSON] et al. [19] & \begin{tabular}{c} STFT \\ Supervised \\ Handcraft extraction + \\ Using SAE \\ unsupervised \\ \end{tabular} & CNN & SPH = 5 & 83.39 & 81.20 & 84.00 \\ \begin{tabular}{c} [PERSON] et al. [26] \\ \end{tabular} & \begin{tabular}{c} Using SAE \\ unsupervised \\ \end{tabular} & SVM & & & 95.00 & 94.00 \\ \begin{tabular}{c} [PERSON] et al. [28] \\ \end{tabular} & \begin{tabular}{c} Using DCGAN \\ unsupervised \\ \end{tabular} & \begin{tabular}{c} Two fully \\ connected \\ layers \\ \end{tabular} & SPH = 5 & 72.63 & 68.21 & 33.03 \\ \
\begin{tabular}{c} This study \\ \end{tabular} &
\begin{tabular}{c} Using WGAN-GP \\ unsupervised \\ \end{tabular} & Bi-LSTM & SPH = 5 & 90.08 & 82.84 & 85.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between this study and existing methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Patients** & **CNN** & **DCGAN** & **WGAN-GP** & **WGAN-GP-BI-LSTM** \\ \hline Patient 1 & 92.48 & 99.52 & 99.78 & 99.81 \\ Patient 2 & 37.50 & 28.52 & 65.34 & 74.59 \\ Patient 3 & 96.66 & 92.43 & 80.27 & 84.92 \\ Patient 5 & 87.80 & 48.83 & 75.33 & 92.06 \\ Patient 9 & 74.41 & 57.99 & 80.57 & 92.75 \\ Patient 10 & 55.59 & 52.38 & 80.11 & 85.10 \\ Patient 13 & 97.21 & 98.04 & 94.37 & 96.98 \\ Patient 14 & 67.16 & 52.28 & 49.69 & 50.72 \\ Patient 18 & 93.29 & 63.27 & 92.61 & 95.62 \\ Patient 19 & 99.48 & 85.93 & 99.34 & 99.72 \\ Patient 20 & 98.67 & 90.70 & 99.89 & 99.91 \\ Patient 21 & 90.47 & 78.71 & 99.67 & 99.89 \\ Patient 23 & 99.90 & 98.59 & 97.99 & 99.03 \\ Range & [37.50, 99.90] & [28.52, 99.52] & [49.69, 99.89] & [50.72, 99.91] \\ Average & 83.89 & 72.63 & 85.77 & 90.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of seizure prediction performance AUC (%) on the CHB-MIT dataset under different unsupervised learning models. Compared to the fully supervised prediction model, this method in the paper did not achieve optimal classification, but its results were comparable to the supervised prediction model CNN, and it achieved lower differences in detecting different patients. This research also has certain limitations and shortcomings. On the CHB-MIT dataset, the WGAN-GP-Bi-LSTM model demonstrated superior performance compared to existing semi-supervised epileptic seizure prediction methods. However, this method still needs to be further tested with more case data and validated using clinical data. Therefore, the generalizability of the proposed method needs to be further verified.