esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving End-To-End Latency Fairness Using a Reinforcement-Learning-Based Network Scheduler
## 2 Materials and Methods
### SARL and MARL
In SARL, all the rewards obtained for each output port for each node are added up and used as the actual reward. In MARL, the reward of a single node is directly used, so the penalty can be set smaller. ```
M = number of the output port N = number of the input portOutputPort Module[1..M] OutputPortModuleQueue[1..N] reward = 0 for i in 1..M: if all OutputPortModule[i].Queue is not empty if packet transmission is complete: if all queues are empty: reward += estimated delay of the transmitted packet else: reward += estimated delay of the transmitted packet - max(estimated delay of packets in all queues) else: reward += -max(estimated delay of packets in all queues) return reward
```  
**Algorithm 1** Rewards in a single node. In SARL, the environment has a structure shown in Figure 3. The controller is physically connected to the nodes in the network, receives status information, and sends control messages to each node. In Figure 3, the action list means a control message. Since the queue to be serviced in the actual node must be determined for each output port, it is expressed as an action list. In this process, a transmission delay occurs. However, considering this transmission delay, the time unit of the event in the simulation cannot be fixed to T. To set the simulation time unit to T, the transmission delay of the control message can also be fixed to T, or it can be implemented so that the control message contains not only the action to be performed now but also the action to be performed at a more distant future time. Although this transmission delay is not negligible, it is a delay caused by a structural problem, not a delay caused by the performance of the scheduler. In the actual simulation, we wanted to see the maximum E2E latency difference due to the scheduler, so the transmission delay between the controller and the node was ignored. Additionally, the controller combines the state of each node and uses it as a single state. In the example topology, it is assumed that all events occur in units of T, so all nodes send state information to the controller at the same time. It is assumed that the processing delay occurring in the controller is small enough to be negligible. MARL uses the IQL method, so the node information is all of the states that the agent can observe, as shown in Figure 4. If one node is drawn including the RL model, it is shown in Figure 5. In Figure 5, the dotted line indicates logical division. In other words, there is no physical distinction between the RL model and the scheduler, and it is implemented in all nodes, so MARL does not need to consider transmission delay, unlike SARL. State information obtained from each output port is combined and used as the actual state,
# Review of 3D Imaging by Coded Aperture Correlation Holography (COACH) +
## 2 System Architectures
### Coded Aperture Digital Holography  
The use of coded aperture masks is common in X-rays since the end of the sixties. In 1968, [PERSON] and [PERSON], separately [47; 48], reported the first modern coded aperture for X-ray imaging. The coded aperture, as explained in [49] and illustrated in Figure 2, is a randomly arranged array of pinholes. The goals of such systems are to increase the signal to noise ratio (SNR), the field of view (FOV) and the low power efficiency, which are associated with the regular pinhole imaging [49]. In the coded aperture imaging, instead of imaging the object directly, a non-recognizable pattern is formed on the sensor plane by the superposition of many randomly arranged images, each of which is formed independently by a different pinhole. The coded aperture imaging is not a direct process as in the case of a single pinhole, but an indirect process in which the recorded pattern must be digitally processed to retrieve the image. The coded aperture imaging possesses 3D imaging capabilities as objects located at different distances appear with different magnifications and are shifted by different lateral distances. Therefore, in principle, the different planes of the object can be retrieved [49].
## 2 Experimental Setup and Measurement
### Uncertainty Analysis  
The uncertainty of all parameters was calculated using the method proposed by [PERSON] [33]. The calculated parameter \(R\) has the following functional relationship with a series of directly measured parameters (\(X_{1}\), \(X_{2}\), \(X_{3}\),..., \(X_{N}\)):  
\[R\ =\ (X_{1},X_{2},X_{3}\ldots X_{N}) \tag{9}\]  
The relative uncertainty of indirectly measured parameters can be calculated by Equation (10).  
\[U_{R}\ =\ \frac{\delta R}{R}\ =\ \frac{\sqrt{\left(\frac{\hat{\partial}R}{ \partial X_{1}}\delta X_{1}\right)^{2}+\left(\frac{\hat{\partial}R}{\partial X _{2}}\delta X_{2}\right)^{2}+\ldots+\left(\frac{\hat{\partial}R}{\partial X _{n}}\delta X_{n}\right)^{2}}}{R} \tag{10}\]  
According to Equations (9) and (10), the relative uncertainties of the pressure drops, frost formation rate, and heat transfer rate are calculated as follows:  
\[U_{\Delta p}\ =\ \frac{\delta\Delta p}{\Delta p}\ =\ \frac{\left\{\left(\frac{ \hat{\partial}\Delta p}{\partial p_{in}}\delta p_{in}\right)^{2}+\left(\frac{ \hat{\partial}\Delta p}{\partial p_{out}}\delta p_{out}\right)^{2}\right\}^{1/ 2}}{p_{in}-p_{out}}\ =\ \sqrt{\frac{\delta p_{in}^{2}+\delta p_{out} ^{2}}{\left(p_{in}-p_{out}\right)^{2}}} \tag{11}\]  
\[U_{in_{f}}\ =\ \frac{\delta\hat{m}_{f}}{\hat{m}_{f}}\ =\ \frac{\left\{\left(\frac{ \hat{\partial}\hat{m}_{f}}{\hat{m}_{a}}\delta\hat{m}_{a}\right)^{2}+\left( \frac{\hat{\partial}\hat{m}_{f}}{\hat{m}_{in}}\delta\hat{m}_{a,in}\right)^{2}+ \left(\frac{\hat{\partial}\hat{m}_{f}}{\hat{m}_{out}}\delta\hat{m}_{out}\right) ^{2}\right\}^{1/2}}{\frac{m_{f}}{m_{f}}} \tag{12}\]  
\[U_{\hat{Q}_{a}}\ =\ \frac{\delta\hat{Q}_{a}}{\hat{Q}_{a}}\ =\ \frac{\left\{\left(\frac{ \hat{\partial}\hat{Q}_{a}}{\hat{m}_{a}}\delta\hat{m}_{a}\right)^{2}+\left( \frac{\hat{\partial}\hat{Q}_{a}}{\hat{m}_{a}}\delta\hat{m}_{a,in}\right)^{2}+ \left(\frac{\hat{\partial}\hat{Q}_{a}}{\hat{m}_{a,out}}\delta\hat{m}_{b,out} \right)^{2}\right\}^{1/2}}{\frac{\hat{Q}_{a}}{\left(\hat{m}_{a}\delta_{a,in} \right)^{2}+\left(\hat{m}_{a}\delta\hat{m}_{b,out}\right)^{2}}} \tag{13}\]  
According to the above formulas and the calculation method of each measurement parameter, the relative uncertainty of each measurement parameter in this study is obtained and summarized in Table 4.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Relative Uncertainty** \\ \hline Pressure drops & \(\pm 0.4\%\)\(\pm 1.3\%\) \\ Frost formation rate & \(\pm 6.6\%\)\(\pm 9.8\%\) \\ Heat transfer rate & \(\pm 5.2\%\)\(\pm 6.9\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relative uncertainties of typical parameters.
## 4 Experiments
### Evaluation Metrics  
Five standard classification performance measurements are adopted in this study to comprehensively estimate the machine learning and deep learning models. The classification measures are all based on four elements: True Positives (TPs), True Negatives (TNs), False Positives (FPs), and False Negatives (FNs). The representations of the utilized metrics are as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Training Dataset** & **Testing Dataset** \\ \hline Normal & 56,000 & 37,000 \\ Generic & 40,000 & 18,871 \\ Exploits & 33,393 & 11,132 \\ Fuzzers & 18,184 & 6062 \\ DoS & 12,264 & 4089 \\ Reconnaissance & 10,491 & 3496 \\ Analysis & 2000 & 677 \\ Backdoor & 1746 & 583 \\ Shellcode & 1133 & 378 \\ Worms & 130 & 44 \\ Total & 175,341 & 82,332 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Details of attack type on the UNSW-NB15 dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Feature Numbers** & **Count** \\ \hline Flow feature & 2 & 1 \\ Basic feature & 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 & 14 \\ Content feature & 20, 21, 22, 23, 27, 28, 29, 30 & 8 \\ Time feature & 16, 17, 18, 19, 24, 25, 26, 42 & 8 \\ Additional feature & 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 3: UNSW-NB15 groups of features.  
\[Precision=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{3}\]  
\[Recall=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{4}\]  
\[F\text{1-}Score=\frac{2\text{ x Precision x Recall}}{Precision+Recall} \tag{5}\]  
\[FAR=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{6}\]  
Accuracy represents the proportion of network activities that are correctly classified, including both DoS attacks and normal traffic. Equation (2) defines its calculation. Equation (3) represents precision as the proportion of detected DoS attacks that are correctly classified. Recall measures the proportion of actual DoS attacks that are corrected and detected, as indicated by Equation (4). The F1-score is a thorough assessment metric. Its definition is given in Equation (5) as the weighted harmonic average of precision and recall. The percentage by which normal traffic is regarded as DoS attacks is known as the false alarm rate (FAR). Equation (6) illustrates how this can be quantified.
# Gyre Precoding and T-Transformation-Based GFDM System for UAV-Aided mMTC Network
## 1 Introduction
Among the mentioned, GFDM is a symbol-based multicarrier multiplexing technique which keeps the signal well confined in time and frequency domains. It utilizes circular filter for effective reduction in the low out-of-band (OOB) emission and ensures simultaneous transmission of multiple symbols at different time slots. By taking all these properties into account, a GFDM-aided system is presented in place of the OFDM-based signaling technique. As far as the novelty of this work is concerned, designing a system for the mMTC network utilizing a CP-less UAV-aided GFDM system has not been well investigated before. Significant contributions of the paper are outlined as follows:  
* T-transformation spreading codes in combination with the walsh Hadamard transform (WHT) and discrete Fourier transform (DFT) is proposed along with recently the introduced gyre precoding (GP) [21] technique. This reduces multi-user interference (MUI) and computational complexity. * The BTRC pulse shaping filter and concatenated channel-coding techniques are utilized to reduce OOB power emission and improve BER performance. * [PERSON] et al. 's proposed a 3D controlled Lorenz mapping system [10] is implemented as a cryptographic technique in this proposed system for PLS encryption. * The 3D mobility model is also introduced to observe 3D positional uncertainty of the UAVs due to Brownian motion and analyzed its impact on the achievable ergodic rate. * Simulation results verify the significant enhancement of the achievable ergodic rate, bit error rate (BER) performance, OOB reduction at a reasonably acceptable PAPR. The structure of this paper is as follows. Section 2 describes the system model, including the block diagram and insights of the signal model considered. In Section 3, simulation parameters, numerical results, and outcomes of the proposed system are presented. Finally, a brief discussion on final remarks and future directions are provided in Section 4.
## 2 Materials and Methods  
### E-Field Measurement Equipment Mounted on a Car  
A commercial hybrid car was modified for measuring the E-field strength of RF EMF (Figure 1). Two three-axis isotropic probes (Narda S.T.S. GmbH) for a low-frequency range (low-frequency probe) of 27 MHz to 3 GHz and for a high-frequency range (high-frequency probe) of 420 MHz to 6 GHz were installed in a radome on the roof of the car. The height of the probe was about 2.2 m from the ground. It is reported that the E-field strength tends to be slightly higher than that at the height of a person [2]. The E-field strengths detected by the probes were recorded together with information, including global positioning system (GPS) data through a spectrum analyzer (Narda S.T.S. GmbH) installed in the cabin. One of the unique points is that no additional engine was installed since the power supply capacity from the originally installed battery was sufficient to operate the measuring equipment. This is a benefit that ensures workability without reducing the space in the cabin. Note that it was confirmed that there was almost no difference in the noise level between when the equipment was mounted in the car, including driving, and when it was not.
# HCLE: Hybrid Clustering Approach for Extending WBAN Lifetime
## 3 Preliminaries
### Performance Metrics
#### 3.4.1 Network Lifetime and Stability Period  
The network lifetime and stability period are closely related metrics in WBANs, both vital for ensuring the overall performance and reliability of the network. The network lifetime refers to the duration for which the network can operate before the energy of the nodes is completely depleted. At the same time, the stability period represents the time from network initiation until the first node becomes non-operational due to energy exhaustion. Maximizing the network lifetime is a fundamental objective in WBAN design, and it directly impacts the stability period. Prolonging the stability period, in turn, contributes to extending the overall lifetime of the network, allowing continuous monitoring and data transmission, which is particularly necessary in applications such as healthcare systems.  
Several factors influence both the network lifetime and the stability period in WBANs. One significant factor is the energy consumption of the sensor nodes, which occurs during data transmission, reception, processing, and sensing activities. Efficient routing protocols play a vital role in balancing the energy consumption among nodes and optimizing the utilization of energy resources. Uneven energy consumption can lead to premature depletion of certain nodes, creating energy holes and reducing the overall network lifetime [25; 47]. To address this, load balancing techniques can distribute the workload evenly across the nodes, preventing energy exhaustion in specific nodes, particularly those near the sink. Furthermore, selecting appropriate parent nodes for data forwarding is paramount for maximizing the network lifetime.
## 3 Results
### Distinguishing Resource Types
#### 3.2.1 Evaluation Grid  
As stated above, the actual work achieved by any process is ultimately done by the resources that perform the tasks making up the process. The goal is to take into consideration the characteristics of each type of resource in order to calculate their efficacy within a process.  
To do so, this work proposes the use of an evaluation grid containing the criteria to be taken into account for each resource. The evaluation grid allows an assessment to be made on the quality and performance of a resource that cannot be simply judged good or bad. It is composed of different columns:  
* Criterion: The criterion used to evaluate a resource. Each criterion can be divided into sub-criteria;
* Score: A score between 0 and 2 is given to each criterion, with 2 being the best score;
* Weight: The weight of a criterion is intended to capture its relative importance.  
The first step consists of choosing the right efficacy measures of each resource. Several characteristics defining the efficacy of human, physical means, and IT resources were provided in the literature. For this work, they were sorted by taking into consideration two criteria:  
* Common measurement: we work with several business sectors, so we needed to choose a measurement that is common to all business activities, with the possibility of new ones being added at the client's demand;
* Objective measurement: choosing a clearly defined and easily calculable criterion. Thereby, the stakeholders involved with filling in the grid will reach the same results because they are quantifiable in nature.  
Regarding the rating scale, we chose a score between 0 and 2, with 2 being the best score.  
The weight of each criterion must reflect its importance regarding the goal to be reached; hence we believe that it makes more sense to let the client choose the weight of each criterion, in order to match their goals.
# Flexible Antennas: A Review
## 1 Introduction
In another article [15], the main focus was the different fabrication technologies for flexible antennas along with a design and analysis of a printed monopole antenna. A complete survey of recently used materials and fabrication methods of wearable antennas, ranging from Very High Frequency (VHF) to millimeter-wave band was presented earlier [16]. In another article [17], the main focus of the review was on materials and fabrication techniques for only wearable antennas designs with their applications, limitations, and solutions. The recent progress in the field of wearable ultrawideband antennas and their application in wireless body area network (WBAN) systems were demonstrated in an earlier study [18]. Different types of implantable antennas, their design requirements, and performance comparison was surveyed in a prior article [19]. The uniqueness of this article stems from the fact that this paper is the fact that we cover the entire area of flexible antenna rather than focusing on any subset such as wearables. Further, the authors discuss antenna applications covering a wide frequency bands unlike other articles restricting the scope on the working frequency. In addition, the original of this article is the focus on the investigations in the last 5 years. In any wireless application, the choice and design of the antenna vary depending on the environment, transmission strength, and frequency range [20]. Further, the performance of the antenna depends on the material used, the type of fabrication technique employed, and the substrate properties. In this context, the article reviews the research trend in conductive materials used, the substrates, the different fabrication techniques to realize the flexible antenna, and their diverse applications. Besides, the challenges in flexible antenna research and future directions are highlighted.
# Development of a Self-Powered Magnetorheological Damper System for Cable Vibration Control
## 3 Experimental Setup  
### Experimental Platform  
To evaluate the vibration control performance of the presented self-powered MR damper system, an experimental platform with a 21.6 m long scaled cable was established in the laboratory. Figure 7 illustrates the general layout of the experimental setup, while corresponding photos are shown in Figure 8. The main properties of the model cable are listed in Table 1. Lumped masses at 160 mm intervals were arranged along the cable to ensure that the model cable had similar dynamic characteristics with those on real cable-stayed bridges. The RD-1005-3 MR damper, manufactured by the Lord Corporation (Cary, NC, USA) (http:www.lord.com), was transversely attached 0.48 m (i.e., 2.2% of the cable length) from the end of the cable, incorporating a load cell and a displacement sensor to monitor the response of the MR damper. This damper type was once applied to suppress the cable vibration in the Dongting Lake Bridge (2010). The rotary DC generator was attached 1.08 m (i.e., 6% of the cable length) from the end of the cable; A laser vibrometer and a load cell were also attached to measure the linear displacement and the total force of the generator, respectively. The elastic force due to the pre-tensioned spring was also measured with a load cell. The output voltage of the DC generator applied to the MR damper was recorded. An accelerator was installed at 12.5% relative to the cable length to measure the cable acceleration, which was then adopted to identify the modal damping ratios of the cable.  
Figure 6: The output voltage and output power of the generator versus the excitation frequency (\(R_{\text{L}}=15\)\(\Omega\)). (**a**) The output voltage versus the excitation frequency; (**b**) The harvested electrical energy versus the excitation frequency.  
Figure 8: Experimental setup photos for cable vibration control. (**a**) Stay cable; (**b**) Self-powered MR damper installed on the cable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Cable length (\(l\)) & 21.6 m \\ Cable cross section area (\(A\)) & 1.374 cm\({}^{2}\) \\ Mass per unit length (\(m\)) & 11.01 kg/m \\ Elastic modulus (\(E\)) & 200 GPa \\ Static tension (\(T\)) & 20.5 kN \\ Inclination angle (\(\theta\)) & 14.2\({}^{\circ}\) \\ Sag parameter (\(\lambda^{2}\)) & 16.28 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the model cable.  
Figure 7: General layout of the experimental setup for cable vibration control.
## 4 Analysis and Comparison of Air-Gap Flux Density and Torque Performance
### Distribution of Air-Gap Flux Density
\(k_{seg}\) and \(k_{arc}\) have slightly different effects on region B4 because it can be seen that when \(k_{arc}\) is constant, the value of flux density decreases with the increase of \(k_{seg}\). In contrast, the value of flux density increases with the increase of \(k_{arc}\) when \(k_{seg}\) is small and remains unchanged when \(k_{seg}\) is large. It can be seen from the observation of Figure 8e that cross-shaped flux-barrier parameters exert a similar influence on the value of flux density on region B5 to region A5. Figure 9 shows the influence of parameters of cross-shaped flux-barriers on the air-gap flux density of region C1-C5 which are mainly located in the air gap corresponding to the lower half of the end of the flux barriers. As can be seen from the figure, the impact of \(k_{seg}\) and \(k_{arc}\) on regions C2 and C3 is consistent with that of regions B2 and B3 analyzed above, and the same tendency with the region C4 and C5 with that of region B4. When one of the two variables \(k_{seg}\) and \(k_{arc}\) is fixed, and the other one increases, the value of flux density shows a changing trend of increasing first and then decreasing, thus forming an umbrella-shaped trend as shown in Figure 10c. Moreover, the flux density reaches its maximum value when \(k_{seg}=k_{arc}=0.4\). It can be observed from the Figures 7-9 that the magnetic density value of each region is affected by the change of equivalent reluctance of flux barriers, and some of the changes are nonlinear.
# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 3 Methodology
### Ensemble Methods
#### 3.1.3 Stacking Methods  
Stacking is an ensemble learning method [24]. The model has two layers, the first consisting of multiple base learners and the second being a single learner, called the meta-learner. Base learners consist of different types of learners with high performance. The meta-learner should be a relatively simple machine-learning model, to avoid overfitting.
# Solar Filament Detection Based on an Improved Deep Learning Model
## 1 Introduction
We built a new solar filament dataset and proposes a Hybrid CNN-Transformer model for filament detection. The model introduces Transformer, designs a multi-scale residual block, and introduces deformable large kernel attention along with the res path. All of the structures are specifically designed for the characteristics of solar filaments. Experiments were conducted on full-disk H\(\alpha\) images observed by the Big Bear Solar Observatory (BBSO) from 2012 to 2014. Our method achieved accurate detection of solar filaments, especially in the presence of sunspot interference and background noise, with detection performance superior to the existing methods Improved U-Net and Improved V-Net. In practical applications, it can detect filaments from observed images by BBSO automatically and accurately. The structure of this study is as follows: Section 2 shows the problems of existing datasets, as well as the process of building a new dataset. Section 3 introduces the Hybrid CNN-Transformer Model and explains why it works well for the task of filament detection. Section 4 delves into the method and introduces the structure of the model. In Section 5, discussions and explanations are provided regarding the detection results. Finally, the conclusion is presented in Section 6.