esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization
## 5 Evaluation Metrics  
The objective function, which is used to evaluate the different solutions, combines the classification accuracy and the number of selected features. The near-optimal solution is the one that exhibits the highest classification accuracy, i.e., the lowest classification error, and the least number of features. The fitness function that evaluates each obtained solution is computed as follows:  
\[\text{Fit}=\text{v ClassErr}+(1-\text{v})\frac{|\text{q}|}{|\text{Q}|} \tag{15}\]  
where ClassErr is the classification error provided by any classifier, such as K-Nearest Neighbor (KNN) [71], which was used in this study; \(|\text{q}|\) is the number of selected features; and \(|\text{Q}|\) is the number of all features in the dataset. The parameter v is used to control the weights of the classification error and the feature subset length, respectively, when computing the fitness of the solution, where v \(\in\) [0, 1], as mentioned in [41; 72].  
The proposed approaches were evaluated based on a set of metrics, namely, average classification accuracy (ACA), average number of selected features (ASF), average fitness value (AFV), and average computational time (ACT). The mathematical formula of each of these metrics will be described in Equation (16), Equation (17), Equation (18), and Equation (19), respectively.  
The ACA evaluates the classifier's prediction ability using the group of features that is selected. It is defined by the following equation:  
\[\text{ACA}=\frac{1}{\text{R}}\sum_{\tau=1}^{\text{R}}\frac{1}{\text{S}}\sum_{ \text{s}=1}^{\text{S}}\left(\text{Pred}_{\text{i}}==\text{Act}_{\text{i}}\right) \tag{16}\]where R is the number of runs, S is the number of instances, and Predi and Acti are the predictive and actual classes, respectively. The ASF evaluates how well each proposed approach performs in terms of the number of relevant features that are selected when tackling the FS issue. It is defined by the following equation:  
\[\text{ASF}=\frac{1}{\text{R}}\sum_{n=1}^{\text{R}}\frac{\text{f}}{\text{F}} \tag{17}\]  
where R is the number of runs, f is the number of selected features, and F is the total number of features in the dataset. The AFV illustrates the average fitness value obtained over a set of runs. AFV is defined by the following equation:  
\[\text{AFV}=\frac{1}{\text{R}}\sum_{n=1}^{\text{R}}\text{Fit} \tag{18}\]  
where R is the number of runs; Fit was previously defined in Equation (15). Finally, the ACT is calculated using the following equation, where Time is the total computational time of each run and R is the number of runs:  
\[\text{ACT}=\frac{1}{\text{R}}\sum_{n=1}^{\text{R}}\text{Time} \tag{19}\]
## 5 Internet of Medical Things
### IoMT Systems Architecture
#### 5.3.7 Regulatory Layer  
The regulatory layer plays a crucial role in ensuring compliance with relevant regulations and standards within the IoMT system. It encompasses various components, including auditing systems, compliance frameworks, and certification programs. The primary objective of the regulatory layer is to ensure that the IoMT system adheres to regulations such as HIPAA, GDPR, and FDA regulations, thereby ensuring the system's safety, effectiveness, and compliance with healthcare requirements.  
In conclusion, the IoMT architecture represents a sophisticated system that facilitates the integration of medical devices, sensors, and healthcare systems with IoT technology. When designing the IoMT architecture, several crucial considerations must be taken into account, including security, privacy, scalability, and interoperability. Security measures need to be implemented to safeguard patient data against cyber threats, while privacy regulations should be adhered to in order to maintain the confidentiality of patient information. Scalability is essential to accommodate the increasing number of connected medical devices and sensors within the network, and interoperability is critical to enabling seamless exchange and analysis of data collected from diverse devices.
# Bluetooth Low Energy Mesh Networks: A Survey
## 2 Bluetooth Low Energy (BLE): Overview and Mesh Network Support
### Bluetooth 4.1  
The Bluetooth 4.1 specification was released in 2013. Bluetooth 4.1 incorporates a fundamental change with regard to BLE mesh network support; a device, regardless of its Link Layer role, can run multiple Link Layer instances simultaneously without limitation. Thus, a slave is allowed to be simultaneously connected to more than one master. In addition, one device can act as a slave at certain intervals and as a master at others, keeping parallel communications with its neighbors. This opens the door to creating extended network topologies beyond the star topology such as the mesh topology. However, the architecture and mechanisms for the formation and operation of a BLE mesh network are not defined in the Bluetooth 4.1 specification.
## 4 The Role of UAV-Base Station in Wireless Communications
### Content Caching and Computation Offloading  
Content caching is an important process in modern-day wireless communication networks as users move from one location to another. It involves storing important information such as username, location, and popularly requested content at multiple BSs in order to provide seamless communication and minimize the latency involved in information retrieval and transmission to the user [80]. However, most of the caches are usually installed in a fixed location and would not be suitable for highly mobile users in vehicles and high-speed trains as the requested content would have to be stored in all BSs along the user path. In such situations, dynamic caching can be achieved by mounting the caches on UAVs, in what is known as UAV-enabled caching [81], and making the UAVs follow such highly mobile devices to provide the requested services. In this regard, the authors in [82] proposed a learning-based joint caching and trajectory optimization scheme in vehicular networks in order to enhance the throughput of the network.  
Mobile edge computing (MEC) was developed to assist user devices in offloading and processing computation-intensive tasks that are beyond their battery capacity [83]. However, these MEC servers are usually deployed in a fixed location within the network and may not always be accessible. To solve this problem, UAV-enabled MEC was proposed in [84] where UAVs equipped with edge servers can assist in computation offloading from ground user devices in order to minimize their energy consumption and maximize their battery lifetime as well as respond to real-time computationally intensive data processing demands. Regarding this, the authors in [85] proposed a UAV-enabled computation offloading framework based on deep RL to minimize the network latency, energy, and bandwidth cost.
# Wheel-Based MDM-PON System Incorporating OCDMA for Secure Network Resiliency
## 1 Introduction
### Motivation and Contributions  
Considering the large investments needed for MDM-PON transmission systems, using a defined topology would be of foremost significance. Moreover, except for the quality of service, complexity, power management, cost of bandwidth, defect detection, and management are challenging and critical for various service suppliers. A consumer at each receiver end in the network expects to acquire the highest bandwidth at an acceptable cost, but the conventional PON-MDM network topologies offer confined protection. The condition causes serious information loss because of failure in optical fiber medium or optical components. So, a required topology design that solves network resilience is crucial while keeping a uniform traffic transmission at a preferable and economic capacity. In one of the recent works in reference [34], ring-based PON and FSO using single weight ZCC code over 10 km fiber and 40 km FSO range at 1 Tbit/s bit rate has been suggested. Work in this reference offers high security, high data rate and long distance but lacks system scalability and supports less number of end users/nodes. Moreover, it poses high transmission losses due to the presence of unguided FSO links, such as weather and turbulence effects. Thus, there is a need for an adequate topology to overcome all these problems. Although the current developments in TWDM-PON defined the significance of ring, bus, tree, star and wheel-based topologies, no work investigated their credibility toward fault detection or failure management. Furthermore, an OCDMA is one of the promising candidates for NG-PONs. Thus, a study regarding the high-speed and secure wheel-based MDM-PON incorporating OCDMA technique for network resiliency in next future networks is needed.  
In this work, an MDM-based TWDM-PON system employing the SAC-OCDMA code is demonstrated. Donut modes 0 and 1 are utilized in the proposed system to transmit 10/2.5 Gbit/s bit rate per channel over bidirectional MMFs in wheel topology. Multi-weight zero cross-correlation (MWZCC) code is utilized to minimize the MAI effect and improve network security with '_n_' input channels. Additionally, the system performance has been evaluated in terms of received optical power and bit error rate (BER). The presence of fiber impairments and the presence of noise due to various active or passive devices cause degradation in system performance. Therefore, in addition to the design and simulation analysis, the impacts of four-wave mixing (FWM), fiber interference and dispersion, power dissipation due to power splitters/combiners, and mode selectors on the proposed scheme are discussed.  
This proposed work in the paper is organized as follows: Section 2 depicts the proposed design of wheel-based MDM-PON using MWZCC code. Section 3 describes the results and discussion. Section 4 concludes the work and its pros/cons and emphasizes future directions.
## 4 Analyses
### Analysis on Impacts of Welding Variables on Outcomes (YS, UTS and % E)
#### 4.4.2 Impact of Tool Rotation Speed and Weld Speed on YS, UTS and % E  
In FSW, at a higher tool rotation speed, more heat is generated. The heat helps to transform solid materials into semi-solid ones, which results in a proper fusion of two materials. However, if the tool rotation speed is very high and weld speed is lowered, extra heat is generated which creates a HAZ. Due to extra heat in the HAZ, the grain structure at the weld joint becomes coarse and brittle [30]. Thus, the HAZ is considered as a weak link in the weld joint and becomes prone to failure. Thus, when weld speed increases at a constant tool rotation speed and probe offset, tensile properties also improve [31]. The same was observed during the experimental study, as shown in Figure 12.  
The Table 9 shows the summary of the impact of different welding variables on the mechanical properties of FSW weld joint.  
Figure 11: UTS of weld joint with different probe offset values.  
Figure 12: (**a**) Tool RPM Vs. YS, (**b**) Weld Speed Vs. YS, (**c**) Tool RPM Vs. UTS, (**d**) Weld Speed Vs. UTS, (**e**) Tool RPM Vs. % E, and (**f**) Weld Speed Vs. % E.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Welding Variables** & **Values** & **Impact on Mechanical Properties** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Tool probe offset (mm) \\ (at constant tool rotational speed and \\ weld speed) \\ \end{tabular} } & 1.0 & \\ \cline{2-3}  & 1.5 & \\ \cline{2-3}  & 2.0 & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Tool rotational speed (RPM) \\ (at constant weld speed and tool probe offset) \\ \end{tabular} } & 710 & \\ \cline{2-3}  & 1000 & \\ \cline{2-3}  & 1400 & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Weld speed (mm/min) \\ (at constant tool rotational speed and tool \\ probe offset) \\ \end{tabular} } & 25 & \\ \cline{2-3}  & 40 & \\ \cline{1-1} \cline{2-3}  & 65 & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Summary on the impact of welding variables on mechanical properties.
# Advantages and Limitations of Current Microgravity Platforms for Space Biology Research
## 3 Ground Microgravity Analogues
### The Unilateral Lower Limb Suspension (ULLS)  
The unilateral lower limb suspension (ULLS) method was developed and validated for studying the effects of spaceflight on muscle size and function. Suspension is achieved with the use of one tall platform shoe and crutches for ambulation, preventing the other leg from touching the ground (Figure 2e). The weight bearing withdrawal for 2-3 days affects protein metabolism of postural muscles, simulating the skeletal muscle adaptation observed in microgravity condition (Table 2) [50]. During the first 2 months ULLS, muscle mass is lost at a constant rate of about 2-3% per week. On a microscopic level, muscle loss is accompanied by a corresponding decrease in the size of individual muscle fibres. As in spaceflight, ULLS shifts the metabolism, favouring carbohydrate over fat utilization.  
ULLS is a relatively inexpensive method compared with bed rest analogues and costs can be sustained by single scientific teams (Table 2). Indeed, in contrast to bed rest and neutral buoyancy, there is no need for volunteers to be confined into hospitals. As ULLS is compatible with a person's social life, it is easier to find a wider number of volunteers and increase sample size. In addition, load and overall mechanical stress are unaltered in the weight-bearing limb, which can work as a control.  
There are some obvious limitations and drawbacks with ULLS. Compared with bed rest, the ULLS volunteers are not subjected to standardized conditions. Therefore, any differences in the daily routine of the volunteers can impact the response to ULLS, adding variability to the data. Moreover, compliance with the use of the system cannot be entirely controlled, even if volunteers who intentionally avoid the imposed restrictions are typically ejected early in the study. Moreover, the use of the platform shoe, especially without the crouches, can lead to dangerous falls. Serious medical issues might also stem from an increased thromboembolism risk, and predisposed subjects should thus be excluded.
## 4 Hardware Description
### Description of Hardware Components
#### 4.2.3 pH Sensor  
The pH of a solution shows how the solution is acidic or basic (alkaline). The pH term converts the concentration of hydrogen ions. The value is from 0 to 14. There is a pH of 7 for a neutral solution such as water. This electronic circuit amplifies the voltage values to be fed to the microcontroller for further processing [38]. This sample is immersed in the pH measuring solution. The pH of a solution shows how the solution is acidic or basic (alkaline). The pH term converts the concentration of hydrogen ions. The value is from 0 to 14. There is a pH of 7 for a neutral solution such as water. The Components of the pH measurement loop are a pH sensor, preamplifier, and analyzer(transmitter). The pH sensor includes a measuring electrode, reference electrode, and temperature sensor [39]. The measuring loop is a battery in which the measuring electrode is the positive terminal, and the negative terminal is the reference electromagnetic. The electrode responds to the ion of the hydrogen. The measuring electrode produces a voltage directly linked to the solution's hydrogen ion concentration. The reference electrode gives a significant potential in comparison to the electrode measurement. The pH sensor components comprise a single device called a pH electrode combination.
## 2 Structural Design
### Hopping Mechanism Design
#### 2.1.2 Preliminary Configuration  
In order to design the configuration of the hopping mechanism by evolving the closed-chain multi-bar isomeric mechanisms with same kinematics, as well as satisfying the aforementioned design requirements (DR.), the degree of freedom (DOF) of the planar mechanism is first studied.  
Figure 1: Prototype of the RHop.  
The equation for calculating the DOF of a planar mechanism is:  
\[F=3n-2p_{\rm L}-p_{\rm H} \tag{1}\]  
where \(N\) represents the number of components in the planar mechanism, where \(n=N-1\) is the number of moving components, \(p_{\rm L}\) is the number of lower pairs and \(p_{\rm H}\) is the number of higher pairs.  
To satisfy DR.1, the DOF of the robot needs to be set to one. When using the pure linkage mechanism to design the hopping mechanism, the number of higher pairs (\(p_{\rm H}=0\)) can be omitted, i.e.,  
\[F=3n-2p_{\rm L}=1 \tag{2}\]  
Table 1 lists the desirable permutations of Equation (2).  
Config.1 does not exist; config.2 is a common single closed-chain four-bar mechanism; config.3 is a closed-chain six-bar linkage (Watt type and Stephenson type), as shown in Figure 2. Config.4 is a closed-chain eight-bar linkage, in which 16 main configurations of kinematical chain are shown in Figure 3[33]. Config.5 is a closed-chain 10-bar linkage, which includes 230 specific configurations [34].  
The four-bar mechanism is difficult to meet the requirements of complex motion performance and rich trajectory due to its few optimization parameters. The configuration of the 10-bar linkage is too complicated and changeable. Therefore, the single DOF six-bar linkage and eight-bar linkage are preselected for the mechanical design of RHop.  
DR.4 requires that the horizontal acceleration of the CM caused by the inertia of the moving component needs to be eliminated. Therefore, the bilateral symmetric kinematical chain configuration is chosen as the basic structural configuration of the hopping robot. Watt and Stephenson types of the six-bar linkage and the type (a), (b), (e), (f), (g), (h), (i), (m), (n), (o), and (p) of the eight-bar linkage shown in Figure 3 can all meet this requirement. According to the drive mode indicated in DR.2, the input bar needs the function of circumferential rotation to form the crank-rocker mechanism. The kinematical chain configurations in the eight-bar linkage satisfy the condition are (m), (n), and (p), and the six-bar linkage does not satisfy the condition of existing the crank-rocker mechanism.
## 4 Data Processing and Imaging Results
### Data Processing of the GAFEM System  
For the navigation data collected by the GAFEM system, we first converted the coordinate data of the navigation system into geodetic coordinates. A fast Fourier transform (FFT) was performed on the transmission current data to obtain the effective value of the current that corresponded to each transmission frequency. Second, for the data obtained from the receiving coil sensor, the wavelet and full-period superposition methods were used to correct the motion baseline and suppress random noise, respectively. Then, FFT was used to obtain the effective signal value of each frequency. Finally, we determined the magnetic field amplitude through magnetic field calibration and current normalization. The magnetic field data of measuring lines L3 and L4 are presented in Figure 6.
# Multifunctional Graphene-Concrete Composites: Performance and Mechanisms
## 1 Introduction
This study investigated the microstructure, mechanical properties, thermal properties, and impermeability of the concrete, exploring the effects of the graphene content and curing duration on these properties. Furthermore, the working mechanism of graphene-reinforced concrete was elucidated. The results demonstrated that the incorporation of an appropriate amount of graphene into concrete enhances its mechanical properties, photothermal performance, and impermeability.
# A Lightweight AI-Based Approach for Drone Jamming Detection
## 3 Topic Overview
### Detection and Mitigation of Jamming Attacks  
The state-of-the-art approaches for jamming detection involve monitoring of the communication link and the analysis of metrics such as the Signal to Noise Ratio (SNR), the Received Signal Strength (RSS) or the Packet Delivery Ratio (PDR) [25]. The simplest approach when developing a method to detect jamming attacks is to set a static threshold for these metrics and trigger an alarm when the threshold is crossed. While this approach is easy to implement and might be effective in some cases, it is not able to adapt to changes in the communication channel and might be prone to false positives. Implementing a ML-based approach for jamming detection would allow the system to adapt to the changes in the communication channel and would be able to provide a more accurate detection of jamming attacks [5]. This is especially useful in mobile scenarios like drone networks, where the environment is constantly changing and the signal strength can vary greatly.  
Once an attack is successfully detected, state-of-the-art jamming mitigation techniques, like Direct Sequence Spread Spectrum (DSSS), Frequency Hopping (FHSS) and advanced signal processing techniques can be put in place to mitigate the effects of the attack.  
The use of an AI model for jamming detection is particularly suitable for a 6G network, as the improved sensing capabilities would allow the model to train on a wide variety of diagnostic data, improving the detection rate and accuracy of the model. Furthermore, as the network is expected to be integrated with AI from the ground up, it is likely that in the future drones will be equipped with AI-specific chips for a more efficient execution of AI models.
## 3 Results
### Parameters for Assessing RV Function  
Evaluation of the right ventricle is paramount in PAH patients, considering that right ventricular function determines the prognosis and the treatment responsiveness in these patients [16].  
Some conventional echocardiographic parameters such as RA dimension, RV dimension, TAPSE, S/D ratio, FAC, pulmonary artery acceleration time, have been suggested as predictors of outcome in PAH patients (Nice, 2018) [3; 4]; however, the need for identification of more valid, easy-to-determine predictors and treatment goals in pediatric patients with PAH is emphasized [3].  
In our study, we found a significant decrease in the following conventional parameters RV-FAC, TAPSE, S' or increase in the following conventional parameters RV-MPI, RV S/D ratio, LV-EI between non-worsening PAH children compared to controls and between worsening PAH children compared to controls. In addition, these parameters were more affected in the worsening group in comparison with the non-worsening group, however, in our study, there was no significant difference between the two groups. We speculate that this could be explained by the fact that in our study, the majority of patients in the worsening group has a baseline WHO functional class \(\leq 2\), so in that phase of the disease, the conventional echocardiographic parameters are not sensitive enough to predict worsening.  
Figure 7: Receiver-operator characteristic analysis. ROC curves result from BNP level that showed significant difference between non-worsening and worsening PAH groups. The optimum cut-off value for BNP level to predict clinical worsening was 34.35 pg/mL; the sensitivity and specificity were 77.80% and 72.70%, respectively.