esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Repeatability Study  
Thirty healthy adults (15 F, 15 M; age 51.5 \(\pm\) 6.2 years, BMI 24.5 \(\pm\) 4.2 kg/m\({}^{2}\)) were recruited and signed informed consent to participate in the experimental evaluation. The inclusion criteria were as follows: age between 40 and 60 years; absence of major foot and lower limb injuries/surgeries; absence of minor foot and lower limb injuries in the last three years.  
Basic morphometric analysis of the feet was performed by using the PodoBox, a plexiglass box fitted with adhesive rulers to measure the main foot dimensions [13]. Foot length, width, and the distance between the first metatarsal head and the center of the medial and lateral malleoli were recorded for each foot (Table A1, in Appendix A).  
Ankle force was acquired with the knee fully extended and with the knee flexed by around 15 deg (Figure 1, right). Straps were used to secure the leg just above the knee to limit the engagement of thigh muscles (Figure 1, see also Appendix A). Force data were collected for the ankle of both the dominant and non-dominant leg. Leg dominancy was identified by asking which leg was normally used to kick a ball [14].  
In each trial, temporal profiles of force data were recorded continuously for 30 s while the participants were alternating muscle contractions in plantar flexion and dorsiflexion in quasi-isometric conditions. Verbal encouragement was used to motivate subjects to generate the maximum voluntary ankle forces. For this acquisition time, a minimum of seven plantar flexion or dorsiflexion peak of forces were acquired in each trial. Participants were allowed one minute to rest between trials to avoid muscle fatigue. The healthy subjects were assessed in three sessions about one week apart.  
For each trial, the highest and lowest peak plantar flexion and dorsiflexion forces were excluded to enhance data consistency, and five measurements were used for statistical analysis. The coefficient of variation (CV, ratio between standard deviation and mean of the measurements) was calculated to determine intra-session (inter-trial) repeatability. For each participant, the CV computed for the 15 samples measured across the three sessions  
Figure 1: The ankle force measurement apparatus (AFT). (**Left**), the axial load cell fixated to an aluminum frame, allowing for three degrees of freedom to be obtained in the (I) antero-posterior, (II) medio-lateral, and (III) cranio-caudal directions. (**Right**), lateral view of the apparatus showing the knee fully extended (**top**) and flexed (**bottom**) by placing a cylinder under the joint.  
(five measurements times and three sessions) was used to determine the inter-session repeatability of ankle force data in each loading direction and knee condition. The effect of leg dominancy and the difference between plantar- and dorsiflexion forces were assessed via the non-parametric paired Friedman test (\(\alpha\) = 0.05) in both the fully extended and partially flexed knee positions. Force data were normalized to the subject's body weight.
# Radiator Enablers for Wireless Communication Evolution
## 2 Challenges, Enabling Technologies, and Antenna Developments
### Challenges and Enabling Antenna Technologies
#### 2.1.2 URLLC to HRLLC Challenge  
In the quest toward HRLLC, there are two main issues: the minimization of the time response of the network (so that V2X, robotic automation, and alike uses can be viable) and the low probability of error. As shown in the relative Equation (3), the network latency is composed of two terms: the \"processing\" latency and the \"propagation\" latency. To minimize the second, one needs to consider placing closer (minimizing the distance between Tx and RX) the BS to the user equipment (UE), which is the accepted measure adopted by edge computing technology. To minimize the \"processing\" latency, the main solution is to use a shorter transmission time interval (TTI). Indeed, a shorter TTI makes the transmission time shorter, and it also makes the buffering and processing times shorter: (ref. [13]). This means that a higher numerology (\(\mu>0\)) is used for the OFDM (although NOMA schemes can also be accommodated) recourse grid. This in turn calls for higher modulation and coding schemes, thus a higher SINR (to boost capacity), lower delay spread (thus smaller cell size), and longer coherent time (thus relatively reduced mobility). Nevertheless, it is important to note that higher SINR can be achieved by using directive antennas (directive patterns increase received power and, at the same time, reduce interference). Also, directive patterns are beneficial for lowering the delay spread (a directive beam explores, mainly, if not exclusively, the LOS direction). Furthermore, the directive beam is, in a way, insensitive to the out of LOS environment and thus preserves the coherent time for longer. Both previous sentences refer to reduced processor time since there is a reduced overhead due to channel state information varying slowly.  
Let us now focus on the reliability index. As understood by the relative Formula (4), reliability increases with SINR. Thus, again, by employing MIMO technology and directional antennas, the SINR is increased, which results in a far more reliable system. Furthermore, it is important to note that closer inspection of Equation (4) reveals that BER and thus reliability also depend on the H power channel gain. Thus, one can design optimized channels for better reliability. This goal can be achieved by RIS. Indeed, RIS can restore the existence of a NLOS path, establishing an otherwise unattainable communication link. In addition, this link usually varies slowly, allowing for larger frames (larger L) and thus again increase reliability by reducing FER.
## 3 Materials and Methods
### Security Metrics for Satellite Networks
#### 3.5.2 Vulnerability Score
The risk (\(r_{h}\)) is quantified by combining the attack success probability and the impacts of the vulnerabilities, enabling a detailed assessment of the network's security state. Risk calculations are categorized into three cases based on the presence of host and protocol vulnerabilities:  
Host vulnerabilities: The risk for a host (\(h\)) with host-based vulnerabilities (\(v\in A_{h}\)) is calculated by multiplying the success probability (\(p_{v}\)) of each vulnerability by its corresponding risk (\(r_{v_{l}}\)) and then summing the results;Protocol vulnerabilities: The risk is determined by multiplying the success probability (\(p_{pv}\)) of each protocol vulnerability (\(pv\in P_{h}\)) by its corresponding risk (\(r_{p_{v_{i}}}\)) and summing all the values;  
Host and protocol vulnerabilities: The overall risk is computed by separately evaluating the risk contributions from the host vulnerabilities and protocol vulnerabilities and then summing them. This approach combines the individual evaluations to reflect the total security state of the node. Next, security metrics at the attack-path level are evaluated by incorporating the security status of each node in the path. The attack success probability (\(p_{ci}^{ap}\)) for a specific attack path (\(ap\)) is calculated by multiplying the attack success probabilities of all the nodes (\(h\)) along the path. Relevant formulae are provided in (5) and (6). \[p_{ci}^{ap}=\prod_{h\in ap}p_{h}^{ci},\quad ap\in AP_{ci} \tag{5}\]  
Additionally, the risk of the attack path (\(r_{ci}^{ap}\)) is calculated by summing the risk of each node within the path. \[r_{ci}^{ap}=\sum_{h\in ap}r_{h}^{ci},\quad ap\in AP_{ci} \tag{6}\]  
\(AP_{ci}\) represents all the possible attack paths for a specific threat vector (\(c_{i}\)). This calculation allows for a quantitative assessment of the security status not only of individual nodes but also of the entire attack path. Finally, the overall system security metric was evaluated by aggregating the security states of all the attack paths. The total attack success probability (\(p_{ci}\)) for a specific threat vector (\(c_{i}\)) is calculated based on the success probabilities of individual attack paths, as defined in Equation (7). Similarly, the system's total risk (\(r_{ci}\)) was evaluated by summing the risks of all the attack paths, as shown in Equation (8). \[p_{ci}=1-\prod_{ap\in AP_{ci}}(1-p_{ci}^{ap}) \tag{7}\]  
\[r_{ci}=\sum_{ap\in AP_{ci}}r_{ci}^{ap} \tag{8}\]  
This model provides a more complex analysis than traditional security evaluation models by incorporating the interaction between host and protocol vulnerabilities.
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 3 Methodology
### Deep Learning Models
#### 3.5.4 Gated Recurrent Units (GRUs)  
GRUs are a simplified variant of LSTM, reducing computational complexity while maintaining high accuracy. GRU-based models perform well in Roman Urdu text classification by efficiently capturing linguistic nuances [46].
# Dynamic Analysis of Cavitation Tip Vortex of Pump-Jet Propeller Based on DES
###### Abstract  
When a pump-jet propeller rotates at high speeds, a tip vortex is usually generated in the tip clearance region. This vortex interacts with the main channel fluid flow leading to the main energy loss of the rotor system. Moreover, operating at a high rotational speed can cause cavitation near the blades which may jeopardize the propulsion efficiency and induce noise. In order to effectively improve the propulsion efficiency of the pump-jet propeller, it is mandatory to research more about the energy loss mechanism in the tip clearance area. Due to the complex turbulence characteristics of the blade tip vortex, the widely used Reynolds averaged Navier-Stokes (RANS) method may not be able to accurately predict the multi-scale turbulent flow in the tip clearance. In this paper, an unsteady numerical simulation was conducted on the three-dimensional full flow field of a pump-jet propeller based on the DES (detached-eddy-simulation) turbulence model and the Z-G-B (Zwart-Gerber-Belamri) cavitation model. The simulation yielded the vortex shape and dynamic characteristics of the vortex core and the surrounding flow field in the tip clearance area. After cavitation occurred, the influence of cavitation bubbles on tip vortices was also studied. The results revealed two kinds of vortices in the tip clearance area, namely tip leakage vortex (TLV) and tip separation vortex (TSV). Slight cavitation at \(J=1.02\) led to low-frequency and high-frequency pulsation in the TLV vortex core. This occurrence of cavitation promotes the expansion and contraction of the tip vortex. Further, when the advance ratio changes into \(J=0.73\), a third type of vortex located between TLV and TSV appeared at the trailing edge which runs through the entire rotational cycle. This study has presented the dynamic characteristics of tip vortex including the relationship between cavitation bubbles and TLV inside the pump-jet propeller, which may provide a reference for the optimal design of future pump-jet propellers.  
pump-jet propeller; cavitation; tip leakage vortex; tip separation vortex +
Footnote †: journal: Applied
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 5 Scheme Implementation
### System Initialization Phase  
The initialization phase aims to establish the blockchain network. It involves setting up the Hyperledger Fabric blockchain network and then integrating HOs authorized by the HMC into this network. Hyperledger Fabric's Certificate Authority then allocates keys and certificates to them. Following that, the corresponding chaincode is deployed for each channel in the network.
## 4 Performance Evaluation
### LTE Mode 4  
Our interest was to perform simulations based on the use of LTE-V2V mode 4. This is because, through this unassisted mode, vehicles must manage the resources to be used in situations where there is no cellular network coverage. For this purpose, a scenario is considered in a highway whose conditions are based on the recommended in [21]. Thus, it is considered a two-way highway with three lanes in each direction (see Figure 5). The length of the highway is 2 km, and each lane has a width of 4 m. In this sense, different vehicle densities (in vehicles per meter) can be configured to consider highway fast (0.06 vehicles per meter (vpm)) and slow scenarios (0.12 vpm).  
Additional parameters to be considered are shown in the Table 3.  
Figure 5: Highway conditions considered for the simulations.
# Hybrid Crow Search Algorithm-LSTM System for Enhanced Stock Price Forecasting
## 2 Materials and Methods
### CSA (Crow Search Algorithm)  
This CSA heuristic algorithm is used to optimize the weights of the ensemble LSTM, leading to improved results.  
CSA, proposed by [PERSON] in 2016, is a heuristic algorithm inspired by the intelligent behavior of crows. It considers the following scenarios:  
1. Scenario 1: Crow \(j\) does not know that it is being followed by crow \(i\), who steals food from where \(j\) bid it and flies it to a new position. This scenario can be expressed as follows: \[x_{i}^{t+1}=x_{i}^{t}+r_{i}\times fl_{i}^{t}\times\left(m_{j}^{t}-x_{i}^{t} \right),\] (6) where \(fl_{i}^{t}\) represents the flight distance of \(i\) and \(r_{i}\) represents a random number from 0-1;
2. Scenario 2: \(j\) realizes that it is being followed by \(i\). To protect food from being stolen, \(j\) guides \(i\) to a random position. This movement of \(j\) can be described as follows: \[\left\{\begin{aligned} x_{i}^{t+1}=x_{i}^{t}+r_{i}\times fl_{i}^{t} \times\left(m_{j}^{t}-x_{i}^{t}\right),\ r_{j}>AP_{j}^{t}\\ a\ random\ position,\ otherwise\end{aligned}\right.,\] (7) where \(AP_{j}^{t}\) represents a probability of \(j\) identifying that \(i\) is following it. The detailed steps of CSA are as follows: 1. Initialize the crows with randomly generated positions and memory states; 2. Calculate the fitness values of all crows; 3. Update the positions of all crows; 4. Update the memory states of all crows; 5. Return to Step 2 and execute until a preset number of iterations is reached.  
Currently, many researchers use CSA in different fields, such as image and video classification [51], time series [52], and stock forecasting [53].
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 8 Conclusions
### Agenda for Future Research  
Converged optical and mmWave architectures provide additional capacity, and most works selected to this review try to make best use of this capacity boost with optimization algorithms to maximize QoS and EE. However, from an ANM perspective, the resource allocation solutions with different optimization objectives are not decomposed into clear functional components, but instead designed as solutions to specifically modeled problems. Converged optical and radio networks can greatly benefit from the integration of the provided AI-ML solutions to data analytics frameworks provided by ITU-T, 3 GPP, and other regulatory bodies; however, further research and cooperation is required to define the boundaries of the protocols of these frameworks and create self-adaptive interaction mechanisms between the network components and enabling technologies in order not to suffer from rigid architectural structures. As given in Section 7.2, such a framework should target bringing all physical and virtual network resources together with common abstractions and well-defined interfaces. The increasing amount of information can be exploited with a common framework that considers data measurement, storage, and processing libraries for diverse AI/ML methods, such as federated and deep learning methods discussed in Sections 4.2, 5.2 and 6.2. Furthermore, as ML components provide mechanisms generating knowledge with data, researchers cannot overlook privacy and security related issues during the acquisition, storage and transfer of these data when developing ML-based solutions. Applying _\"security by design\"_ at each data related operation in a service-based softwarized network design should therefore be considered as a critical future research direction. **Author Contributions:** [PERSON] and [PERSON] conceptualized the study and identified the research method; [PERSON] supervised the study; [PERSON] performed the database search, data collection, and record screening; [PERSON], [PERSON] and [PERSON] specified the eligibility criteria and selected the papers in the study; [PERSON] prepared the original draft of the manuscript and created the visual materials; [PERSON] and [PERSON] reviewed and corrected the manuscript; [PERSON] and [PERSON] were responsible for the project administration and funding acquisition. All authors have read and approved the final manuscript. **Funding:** This work was in part supported by the EC H2020 5G-MOBIX project under grant no. 825496. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The complete list of papers found in the database search and the full list of papers presented in this study are available in open access at [[http://doi.org/10.5281/zenodo.5643397](http://doi.org/10.5281/zenodo.5643397)]([http://doi.org/10.5281/zenodo.5643397](http://doi.org/10.5281/zenodo.5643397)), accessed on 3 November 2021 [41]. **Conflicts of Interest:** The authors declare no conflict of interest.
# Experimental Performance Analysis of Wi-SUN Channel Modelling Applied to Smart Grid Applications
###### Abstract  
The grid operation and communication network are essential for smart grids (SG). Wi-SUN channel modelling is used to evaluate the performance of Wi-SUN smart grid networks, especially in the last-mile communication. In this article, the distribution approximation of the received signal strength for IEEE 802.15.4g Wi-SUN smart grid networks was investigated by using the Rician distribution curve fitting with the accuracy improvement by the biased approximation methodology. Specifically, the Rician distribution curve fitting was applied to the received signal strength indicator (RSSI) measurement data. With the biased approximation method, the Rician K-factor, a non-centrality parameter (r\({}_{\text{s}}\)), and a scale parameter (\(\sigma\)) are optimized such that the lower value of the root-mean squared error (RMSE) is achieved. The environments for data collection are selected for representing the location of the data concentrator unit (DCU) and the smart meter installation in the residential area. In summary, the experimental results with the channel model parameters are expanded to the whole range of Wi-SUN's frequency bands and data rates, including 433.92, 443, 448, 923, and 2440 MHz, which are essential for the successful data communication in multiple frequency bands. The biased distribution approximation models have improved the accuracy of the conventional model, by which the root mean-squared error (RMSE) is reduced in the percentage range of 0.47-3.827%. The proposed channel models could be applied to the Wi-SUN channel simulation, smart meter installation, and planning in smart grid networks.  
recieved signal strength distribution approximation; Wi-SUN channel modeling; smart grid networks +
Footnote †: journal: _Energies_  
1
Footnote 1: institutetext: The Sirinthhorn International Thai-German Graduate School of Engineering (TGGS), King Mongkut’s University of Technology North Bangkok, 1518 Pracharat 1 Rd. Bangsue, Bangkok 10800, Thailand; [EMAIL_ADDRESS]
## 5 Conclusions  
This paper proposes a stochastic MPC approach to investigate the modeling, coordinated optimal economic scheduling, and analysis of the MES. The stochastic nature of EVs available charging/discharging electric power respectively in offices and residential buildings is investigated considering EVs owners' commuting pattern and represented by a number of scenarios that are generated by LHS and scenario reduction techniques. The charging/discharging degradation cost of EVs batteries is also taken into consideration. Two types of CHP units with complementary properties are applied to supply electrical and thermal energy, considering their different operational characteristics. The stochastic MPC scheme is formulated by a MILP problem, taking the coordinated economic operation as an objective, while considering various operational constraints and uncertain EV availability. The MPC base MES scheduling optimization can be solved by Cplex. The simulations of two representative cases, office and residential building, are conducted to evaluate the proposed approach, in which the TOU pricing of electricity energy purchasing from external main grid and two CHPs with fixed and variable heat-to-electricity ratios are included [45; 46; 47]. In office buildings, the aggregated EVs are scheduled to serve as an electricity supplier during the daytime when the electricity price stays at peak level--moreover, CHP units, aggregated EVs and the connected main grid coordinates to satisfy the thermal and electricity demands simultaneously. In residential buildings, the aggregated EVs are scheduled to be charged during the night when the electricity price is lower, and the coordination among several energy suppliers is achieved. The simulations results demonstrate the feasibility and effectiveness of the proposed approach on the multi-energy coordination while considering the uncertainties and battery degradation of EVs.  
In future work, the proposed solution can be further improved through incorporating realistic energy systems and using a massive number of field statistics [46; 47; 48]. Renewable energy such as wind power and photovoltaic energy will be considered to investigate how intermittency affects the economic goal and the balance of the MES.  
the algorithm performance, helped verify the idea and partially wrote the paper. All of the authors worked together to finish the implementation of the whole algorithm and paper writing.  
This work was supported by the National Natural Science Foundation of China under Grant No. 51777182.  
The authors declare no conflict of interest.