esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Current Off-Training Wellbeing/Wellness Profiling in Elite Sports
### Menstrual Cycle (MC)  
The MC stands as a valuable health marker in female athletes and is also considered in relation to training recommendations. Health-wise, menstrual dysfunction is linked to a diminished quality of life due to increased anxiety, fatigue, and pain interference [76]. In terms of performance, female athletes perceive that the MC impacts it [28; 77; 78], as a few studies have reported changes in muscle strength [79] and peak power performance [80] related to the changes in the reproductive hormones including testosterone.  
Recent challenges have been raised against resistance training recommendations based on the MC, citing methodological shortcomings. Criticisms emphasize the lack of robust evidence supporting the claim that training during the follicular phase is more efficient compared to the luteal phase [81]. Most reviews in this area [76; 81; 82; 83] highlight issues such as small sample sizes, failure to measure female sex hormones to confirm the MC phase, and inconsistencies in determining the phase, among other confounding factors in the reviewed studies.  
Therefore, addressing these methodological concerns is crucial for providing reliable and conclusive insights into the relationship between the MC and athletic performance. Currently, MC monitoring can be accomplished through various methods such as calendar-based counting, basal body temperature recording, and tracking technologies. The use of smartphone applications, for instance, has become popular for predicting menstrual phases based on unsupervised machine learning algorithms, with predictions improving over time as users track their data. However, studies have shown that predictions from these apps often deviate from the real period dates, ovulation day, and fertile window, indicating the need for improved accuracy [84; 85]. Achieving accuracy in MC tracking seems inherently challenging, as studies indicate that fewer than 20% of mobile app users have a regular 28-day cycle [86]. Urine hormone testing devices are considered the gold standard [87], and monitoring basal body temperature, cervical mucus, cervix position, and vaginal sensation (the Fertility Awareness Method) [88] might also help with determining the MC phases.  
The current use of menstrual cycle (MC) trackers is primarily motivated by interests in diagnosing diseases such as polycystic ovary syndrome and endometriosis, as well as for family planning purposes [89]. In female athletes, not only has their psychological well-being been found to correlate with MC in performance [90], but menstrual dysfunction has been associated with RED-S [69]. Thus, there is an emerging opportunity to leverage tracking methods for unveiling the detailed aspects of the connection between MC, wellness, and sports performance, particularly since existing practices rely on self-reported, non-validated questionnaires [91].
# Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture
## 2 Materials and Methods
### Description of the Base Station Coordinator Node  
The base station coordinator node, or local control node, consists of different components: an Arduino DUE platform (Arduino, Ivrea, Italy) based on the microcontroller Atmel SAM3X8E (Microchip Technology Inc., Chandler, AZ, USA), an Ethernet/WiFi module for the connectivity with the SCADA (Supervisory Control And Data Acquisition) web-based application, a ZigBee radio device XBee 868 LP for low-power-consumption wireless data communication, and a real-time clock module RTC DS3231 (Maxim Integrated, San Jose, CA, USA) to implement timestamps to image data files, and a microSD card memory module to store the received images in a database. The capacity of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Measured Current** & **Power on** & **Idle** & **Idle + Cam on** & \
\begin{tabular}{c} **High Rate** \\ **Transmission** \\ \end{tabular} &
\begin{tabular}{c} **Sleep** \\ **Mode** \\ \end{tabular} \\ \hline A20/A6C + XBee 868 LP + & 200 mA & 95 mA & 140 mA (180 & 200 mA (240 & 1.2 mA \\ Battery charger & & & mA peaks) & mA peaks) & 1.2 mA \\ XBee 868 LP & & 25 mA & 25 mA & 50 mA & 1.7 \(\mu\)A \\ Battery charger & - & - & - & - & 85 \(\mu\)A \\ A20/A6C camera & & 60 mA & 115 mA & 150 mA & \textless{}1 mA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measured currents of the remote node in different states.  
Figure 5: Power supply current profile for a 1-hour data cycle of remote node.  
card is 4 Gb (although other cards can be used), allowing storing around 52,000 images at an average size of 80 Kb. The assembly of all these elements can be seen in Figure 6.  
The coordinator node can be connected via USB to a PC with a serial terminal application where control commands, responses and data received can be monitored to check the system procedure and analyze failures. Depending on the operating mode, the microprocessor captures an image either periodically or on demand, analyzes the integrity, processes the information, controls the communication modules, and receives commands and sends information to the server.  
The system is configured so that all the remote nodes perform a measurement every hour. These measurements are synchronized and locally stored. The frequency of this process can be configured by the user. These tasks do not demand high computational requirements, but it is essential to use a robust PC and an uninterruptible power supply to ensure nonstop operation.
## 3 Preliminaries and System Model
### Relay Selection Scheme
\tag{17}\]  
The links with the best SINR are found among all the links; both from the source-to-relay cluster and from the relay-to-destination cluster. As our scheme is based on virtual duplexing, i.e., simultaneous transmission and reception at the relay cluster, but not to and from the same relay, we incorporate a modified feature of the scheme in [14]. First, the link with the highest SINR from the  
Figure 9: Flow diagram of the relay selection scheme. relay-to-destination is selected, and then, depending on the availability of links from the source-to-relay, a link that may have higher or lower SINR may be selected. Although, transmission and reception are still simultaneous, \(R\to D\) links are given priority, which results in smaller queue lengths as higher data rates are possible in transmission from the relay-to-destination.
## 2 Results and Discussion
### Analysis of Expression Profile of Pc-fps and Total Triterpenoids at Different Stages of Development
_Pc-fps_ transcripts accumulated following the addition of 100-300 \(\upmu\)M MeJA to the culture medium; the highest mRNA level was observed at a concentration of 200 \(\upmu\)M MeJA and the induction time was 72 h. Furthermore, the transcriptional level of _Pc-fps_ was also determined by real-time quantitative PCR. As shown in Figure 9c, transcription accumulation was obvious in the _Pc-fps_ expression under all MeJA stimulation conditions. The transcriptional level of _Pc-fps_ was highly enhanced following the addition of 100-300 \(\upmu\)M MeJA to the culture medium. The transcriptional level was approximately 4-fold higher than the control sample when the induction concentration was 200 \(\upmu\)M and the induction time was 72 h. Sequence analysis of the _Pc-fps_ promoter identified two potential MeJA responsive elements. MeJA are considered as potent lipid regulators that modulate various physiological processes in plants [21]. So for, MeJA has been used to induce or increase useful secondary metabolites [22,23]. However, there has been no such report on the effect of MeJA on the activities of the _fps_ in _P. cocos_. In this study, we found that the MeJA treatment could significantly increase the transcription level of _Pc-fps_ and content of total triterpenoids. Our results are consistent with previous reports, which demonstrated that MeJA could increase the biosynthesis of ganoderic acid as well as the transcript level of the _fps_ in _G. lucidum_[24]. These results suggest that _Pc-fps_ is probably involved in the triterpenoids biosynthesis regulation pathways of MeJA signalling via the putative MeJA-response elements. However, further studies are needed to define precisely and elucidate the _Pc-fps_ promoter elements involved in the regulation of _P. cocos_ triterpenoids biosynthesis. These results are useful to facilitate the investigation of the role of FPS in the control of triterpenoids biosynthesis in _P. cocos_.
# A Heuristic Methods-Based Power Distribution System Optimization Toolbox
## 5 Simulation and Test Results  
Several simulations were performed to test the correct functioning of the test systems, optimization methods, and settings used in the program.  
We ran simulations for Model I and Model II using a 33-bus test system. We set the number of search agents to 50, the number of iterations to 1000, and placed 8 PVs at each bus 15, 16, 19, 20, 25, and 33. The results of PSO, GWO, WOA, and ALO-based optimization are shown in Figure 6. In all the results, it could be seen that the final voltage magnitudes values have improved.  
For the simulations we run with Model II, we set the number of search agents to 50 and the number of iterations to 100. The initial power of the BESSs and their step values are set to 30 kW and 5 kWh, respectively. The lower and upper limits of SOC are 20 kW and 80 kW, respectively. There are tap changers on the line between the 5 th and 6 th buses and 24 th and 25 th buses. The two BESSs are installed on buses 5 and 13, and each BESS has the same characteristics, such as the initial value and SOC limits. We perform the tests using the GWO algorithm on a 33-bus test system. Figure 7 shows the improvement of the daily voltage profile when BESSs and tap changers are used. Figures 8 and 9 illustrate the daily change in the power states of the BESSs and the daily change in the tap changer settings.  
Figure 6: Model I, voltage magnitudes for the base case and the near-optimal case for (**a**) PSO, (**b**) GWO, (**c**) WOA, and (**d**) ALO.  
Figure 8: Change of BESSs’ power state for 24 h.  
Figure 7: Daily, (**a**) base case, and (**b**) near-optimal voltage magnitudes when BESSs and tap changers are used.  
Figure 9: Tap changer settings for 24 h.  
The convergence curves for the four methods using Model I with a maximum number of 100 runs using 33- and 69-bus test systems are shown in Figure 10. It is evident from the figure that all the methods arrive at a similar near optimal solution. From the figure, it can be seen that GWO converges faster compared to the other methods. The convergence speed of ALO was worst for the 33-bus test system, and the convergence speed of PSO was worst for the 69-bus test case.
# Blockchain-Based Licensed Spectrum Fair Distribution Method towards 6G-Envisioned Communications
###### Abstract  
Spectrum distribution is a classical licensed spectrum accessing method in mobile communication networks. The licensed idle spectrum resources are authorized and distributed from spectrum owners to mobile users. However, the exponential growth of user capacity brings excessive load pressure on the traditional centralized network architecture. With a lack of sufficient supervision and penalty measures, dishonest behaviors of spectrum owners and spectrum users will lead to an unfair status in the distribution process. As a result, the honest participants' interest will be harmed. As an important supporting infrastructure of Internet of Things technology, 6G cannot completely follow the existing spectrum distribution method. Towards 6G network spectrum distribution, a blockchain-based licensed spectrum fair distribution method is proposed. A lightweight consensus mechanism named proof of trust (PoT) is applied to reduce computational power consumption and consensus time overhead. We deploy the method on the Ethereum test chain; a theoretical analysis and experimental results demonstrate the fairness, effectiveness and security of the method.  
6G; licensed spectrum distribution; blockchain; fairness Article Blockchain-Based Licensed Spectrum Fair Distribution Method towards 6G-Envisioned Communications. April 2023, 13, 9231. [[https://doi.org/10.3390/appl3169231](https://doi.org/10.3390/appl3169231)]([https://doi.org/10.3390/appl3169231](https://doi.org/10.3390/appl3169231))
# Blockchain-Embedded Strategic Options for Satellite Operators: Sell, SataS, or Dual?
## 2 Literature Review and Marginal Contributions
### Marginal Contributions  
Our research presents significant contributions in three key areas, strengthening the foundation of knowledge and practice in the evolving satellite industry.  
Firstly, a critical gap in Satellite-as-a-Service is filled (SataaS). Our study addresses a noticeable void in the literature concerning SataaS by providing a comprehensive theoretical framework. We offer insights into the impact of selling satellites and SataaS, which, in turn, enriches our understanding of optimal channel distribution strategies within the satellite industry. By doing so, we bridge the knowledge gap that has hindered the industry's ability to harness the full potential of satellite services.  
Secondly, enriching dual-channel supply chain management in satellite data business. Our research goes beyond filling a gap; it enriches the field of dual-channel supply chain management, with a specific focus on the satellite data business. This novel perspective enhances the operations management literature. We delve into the intricacies of dual-channel strategies within the satellite data context, providing actionable insights and theoretical advancements that benefit both researchers and practitioners.  
Lastly, pioneering blockchain technology in satellite supply chains. Our study pioneers the application of blockchain technology in satellite supply chains. This innovative approach opens new vistas for enhancing data security, transparency, and efficiency. By exploring and introducing blockchain solutions in the satellite industry, we offer fresh perspectives and practical applications that can revolutionize the management and utilization of satellite data.  
In summary, our research not only adds to the body of knowledge but offers valuable insights and practical applications to the satellite industry, cementing our role in shaping its future. These contributions are designed to catalyze innovation, drive operational excellence, and secure the evolving satellite industry's sustainable growth.
## 3 Computational Examples and Analysis
### Case Study III: Optimization with Four Objectives
#### 3.3.2 MOO Problem Formulation for Rubber-Basalt Fibre Composite Modified Concrete  
(1) Objective Function  
According to the requirements of rubber and basalt fibre-modified concrete, the objective functions are calculated as follows:  
\[\left\{\begin{array}{c}\max\text{F}_{1}(\mathbf{X})=\max\text{Y}_{1}\\ \max\text{F}_{2}(\mathbf{X})=\max\text{Y}_{2}\\ \max\text{F}_{3}(\mathbf{X})=\max\text{Y}_{3}\\ \min\text{F}_{4}(\mathbf{X})=\min(12\text{X}_{2}+1.6\text{X}_{3})\end{array}\right. \tag{25}\]  
(2) Constraints  
Following the previous study [54], the range constraints for concretes modified by rubber and basalt fibre are summarized and written as:  
\[\left\{\begin{array}{c}0.39\leq\mathbf{X}_{1}\leq 0.49\\ 2\leq\mathbf{X}_{2}\leq 8\\ 10\leq\mathbf{X}_{3}\leq 30\end{array}\right. \tag{26}\]
# A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System
## 4 Experiments and Results
### Correlation Assessment  
We also conducted a correlation assessment experiment to analyze the correlation between investor personality traits and risk tolerance in this case study. First, we compared the predicted results of the Big Five personality traits in the developed multimodal system with the results of self-assessed personality traits, as shown in Figure 9. In Figure 9, the prediction results of the developed multimodal system were obtained by averaging dialogue scores, which were similar to the results for self-assessment. In Figure 10, we show the actual prediction results of our developed personality prediction system for the five customer personality traits. We then analyzed the results of the investor personality traits questionnaire and risk tolerance questionnaire using the Pearson correlation method.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **MAE** & **MSE** & **RMSE** & **R2** \\ \hline Openness & 1.403 & 4.563 & 2.136 & 0.827 \\ Conscientiousness & 1.418 & 3.699 & 1.923 & 0.823 \\ Extraversion & 1.310 & 3.936 & 1.984 & 0.832 \\ Agreeableness & 1.414 & 3.517 & 1.875 & 0.821 \\ Neuroticism & 1.314 & 4.337 & 2.083 & 0.830 \\ \hline \end{tabular}
\end{table}
Table 6: Evaluation results for the multimodal model based on feature fusion approach.  
Figure 7: Comparison of loss values of text features using Bidirectional LSTM model.  
Table 7 shows that openness, extraversion, and neuroticism are directly correlated with risk tolerance, with openness and extraversion showing a positive correlation, and neuroticism having a negative correlation. The other two personality traits (i.e., agreeableness and conscientiousness) are not directly reflected in risk tolerance.  
Figure 10: Sample predicted scores for customer personality traits.  
\begin{table}
\begin{tabular}{l l r r r r r r} \hline \hline  & \multicolumn{1}{c}{**Openness**} & \multicolumn{1}{c}{**Conscientiousness**} & \multicolumn{1}{c}{**Extraversion**} & \multicolumn{1}{c}{**Agreeableness**} & \multicolumn{1}{c}{**Neuroticism**} & \multicolumn{1}{c}{**Risk Tolerance**} \\ \hline \multirow{4}{*}{Agreeableness} & Pearson Correlation & 0.247 & 0.226 & \(-\)0.141 & 1 & \(-\)0.024 & 0.249 \\ \cline{2-7}  & Sig. (2-tailed) & 0.174 & 0.214 & 0.441 & & 0.896 & 0.169 \\ \cline{2-7}  & N & 32 & 32 & 32 & 32 & 32 & 32 \\ \hline \multirow{4}{*}{Neuroticism} & Pearson Correlation & \(-\)0.627  & \(-\)0.024 & 1 & \(-\)0.426  & 0.020 & 0.336 & 0.249 & \(-\)0.426 [FOOTNOTE:]Footnote *: Correlation is significant at the 0.01 level (2-tailed). [ENDFOOTNOTE] & 1 \\ \cline{2-7}  & Sig. (2-tailed) & 0.004 & 0.913 & 0.060 & 0.169 & 0.015 \\ \cline{2-7}  & N & 32 & 32 & 32 & 32 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._  
Figure 9: An example of a personality trait assessment (system predicted score vs. customer self-assessment score).
## 3 DCPSO with Cooperation
### Distributed Cooperative Particle Swarm Optimization with Cooperation
#### 3.2.3 Fitness Function  
In order to generate a flyable and safe PH path for UAV \(i\) with a short length and appropriate smoothness, the fitness function for UAV \(i\) is designed as:  
\[f_{i}=1/(w_{1}I_{i,\text{fuel}}+(1-w_{1})I_{i,\text{curve}}+I_{i,\text{obsc}}+ J_{i,\text{nofly}}+J_{i,\text{terr}}+J_{i,\text{unflyble}}), \tag{11}\]  
where \(w_{1}\) is a user-defined weight parameter. \(J_{i,\text{fuel}}\) is the cost of fuel consumption, which is approximately proportional to the path length of UAV \(i\). Here, let \(J_{i,\text{fuel}}=L_{i}\), where path length \(L_{i}\) can be calculated by Equation (4). \(J_{i,\text{curve}}\) is the cost of path smoothness, which can be described by the elastic energy of the PH path. Hence, let \(J_{i,\text{curve}}=E_{i}\), where the elastic energy \(E_{i}\) is calculated by Equation (7). \(J_{i,\text{obsc}}\), \(J_{i,\text{nofly}}\), \(J_{i,\text{terr}}\), and \(J_{i,\text{unflyble}}\) are the penalty functions for avoiding the obstacles, no-fly zones, collisions with terrain, and meeting the maximum curvature and torsion constraints, respectively.  
For simplicity, the PH path is discretized as \(N_{\text{P}}\) points. If all the discretized points do not collided with the obstacles or pass through the no-fly zones, \(J_{i,\text{obsc}}=0\) or \(J_{i,\text{nofly}}=0\); otherwise, a large positive number (like \(10^{5}\)) is assigned to \(J_{i,\text{obsc}}\) or \(J_{i,\text{nofly}}\). Similarly, if all the discretized points can avoid collision with the terrain, \(J_{i,\text{terr}}=0\); otherwise, a large positive number (like \(10^{5}\)) is assigned. If the curvature and torsion at all discretized points satisfy the maximum curvature and torsion constraints, the PH path is considered flyable and \(J_{i,\text{unflyble}}=0\); otherwise a large positive number is assigned, which can be formulized as:  
\[J_{i,\text{unflyble}}=\begin{cases}10^{5},&\max\limits_{q}\abs{x_{i}(q)}>\kappa _{\text{max}}\quad\|\ \max\limits_{q}\abs{\tau_{i}(q)}>\tau_{\text{max}}\\ 0,&\max\limits_{q}\abs{x_{i}(q)}\leq\kappa_{\text{max}}\quad\&\max\limits_{q} \abs{\tau_{i}(q)}\leq\tau_{\text{max}}\end{cases}, \tag{12}\]  
where \(\kappa_{\text{max}}\) and \(\tau_{\text{max}}\) are the maximum curvature and torsion, respectively.  
In a 3-D environment, the terrain is simulated by following function:  
\[H(x,y)=a\cdot\sin(x+b)+c\cdot\sin(y+d)+e\cdot\cos(f\cdot\sqrt{x^{2}+y^{2}})+g \cdot\sin(h\cdot x+l\cdot y), \tag{13}\]  
where \(a,b,c,d,e,f,g,h\), and \(l\) are custom constants.
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 5 Prototype Development and Validation
### Test Deployment and Improvements
#### 5.3.2 Improvements in Usability and Compatibility of the Prototype  
During the deployment and initial testing of the prototype, we found some usability and compatibility issues. Here, we explain some of them, and in the following section, we include the final system validation and usability experiment.  
During FIDO2 authentication, there are several errors that can occur, so the user should be notified. However, we found that the user is not properly notified of all errors through their web browser. This decreases the usability of the authentication flow UI. In fact, the browsers raise different browser exceptions upon the same error. For instance, Firefox and Chrome in Android differ when the user disconnects a FIDO2 security key during authentication. At the time of writing, Linux raises the \"Unknown Error\" exception, while the Chrome browser raises \"Not Readable Error\".  
For this reason, we are conducting a study of all these differences, and finding some guidance, which will be published in the near future. In the prototype proposed in this paper, we addressed some of these issues. Some of the errors display a non-intrusive message, like a \"Try again\" button, without identifying the error and allowing the user to repeat the action. Other errors that occur when the operation is cancelled include this information to the user, allowing them to have another try.