esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Owing to the expansion of non-face-to-face activities, security issues in video conferencing systems are becoming more critical. In this paper, we focus on the end-to-end encryption (E2 EE) function among the security services of video conferencing systems. First, the E2 EE-related protocols of Zoom and Secure Frame (SFrame), which are representative video conferencing systems, are thoroughly investigated, and the two systems are compared and analyzed from the overall viewpoint. Next, the E2 EE protocol in a Government Public Key Infrastructure (GPKI)-based video conferencing system, in which the user authentication mechanism is fundamentally different from those used in commercial sector systems such as Zoom and SFrame, is considered. In particular, among E2 EE-related protocols, we propose a detailed mechanism in which the post-quantum cryptography (PQC) key encapsulation mechanism (KEM) is applied to the user key exchange process. Since the session key is not disclosed to the central server, even in futuristic quantum computers, the proposed mechanism, which includes the PQC KEM, still satisfies the E2 EE security requirements in the quantum environment. Moreover, our GPKI-based mechanism induces the effect of enhancing the security level of the next-generation video conferencing systems up to a quantum-safe level.  
end-to-end encryption (E2 EE); video conferencing system; Zoom; Secure Frame (SFrame); messaging layer security (MLS); Government Public Key Infrastructure (GPKI); post-quantum cryptography (PQC); key encapsulation mechanism (KEM) +
Footnote †: journal: Applied system  
0023
## 5 Conclusions  
In this study, material specifications of steel fiber-reinforced mortar to strengthen masonry prisms were investigated based on extensive test programs regarding both workability and strength. In addition, the strengthening effect of SFRM overlay for masonry prisms was investigated through tests for various strengthening schemes. Finally, applicability of existing design equations to predict enhanced strength is evaluated. The key findings from the experimental study are summarized as follows.  
1. The mixing ratios of SFRM appropriate for plasticing work on masonry walls and effective in strengthening were identified. Plastering the SFRM was more effective for the shear strength than the compressive strength and achieved 67% and 138% increases for red clay brick prisms and concrete brick prisms;
2. The higher volume fraction of amorphous steel fibers in SFRM e.g., 1.3% achieved considerably ductile behavior in the diagonal tension test in contrast to the brittle behavior observed for the plain mortar overlay;
3. The SFRM overlay was separated without damage from the masonry prisms at the failure in diagonal tension tests. For accurate estimation of enhanced shear strength, it is necessary to take into account the bond strength between the SFRM overlay and the masonry, instead of the shear strength of SFRM overlay itself. Additionally, enhancement of the bond strength is expected to improve the strengthening effects by fully exploiting the capacity of the SFRM overlay;
4. The masonry prisms used in this study have relatively good condition. In order to apply the proposed strengthening technique to existing masonry buildings in relatively poor conditions, additional verification for masonry in such a condition is necessary.  
[PERSON] programmed and conducted the experiment, analyzed the results of the experiment, and wrote the original draft. [PERSON] supervised the entire experimental procedure and revised and finalized the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (20 CTAP-C152105-02) from Technology Advancement Research Program (TARP) funded by Ministry of Land, Infrastructure and Transport of Korean government. The authors declare no conflict of interest.
## 4 Data Analysis and Results
### Test of the Structural Model  
SEM was used to test the significance for each hypothesised path (\(\beta\)) and the explained variance (R2) for each dependent variable, adopting the maximum likelihood estimation method. As shown in Table 5, five of the seven hypotheses are supported.  
The results displayed in Table 5 show that the model explained 34.2% of the variance in intention to continue to playing Pokemon Go. As expected, social interaction (\(\beta\) = 0.0627155, \(p\) < 0.01), social presence (\(\beta\) = 0.155, \(p\) < 0.001), achievement (\(\beta\) = 0.0814, \(p\) < 0.01), enjoyment (\(\beta\) = 0.484; \(p\) < 0.001) and escapism (\(\beta\)=0.0647, \(p\) < 0.01) have a positive impact in Pokemon Go continuance intention. However, fantasy and self-presentation do not obtain a significant path coefficient, hence these relationships are not supported.
# Quantifying Nutrient Content in the Leaves of Cowpea Using Remote Sensing
## 3 Results and Discussion
### Model Validation
In a study with compacted alfalfa samples, [29] achieved respective RPD values of 3.6, 3.4, 3.1 and 2.9 for estimating the P, K, Ca and Zn content, which are different to the results obtained in this study, where an RPD value of 1.66 was found for P, values of between 0.6 and 2.06 for K, between 1.00 and 1.54 for Ca and between 0.36 and 1.66 for Zn. Using PLSR to predict the nutrient content of the dende leaf, measuring the reflectance with a spectroradiometer, [80] obtained R\({}^{2}\) values of between 0.49 and 0.90 for predicting phosphorus, and values ranging from 0.61 to 0.85 and from 0.69 to 0.86 for estimating potassium and calcium, respectively, agreeing with the results of the present study (P: 0.77, K: 0.84 and Ca: 0.73). When using PLSR to estimate nutrients in woody plant species, [81] obtained RPD values of 2.3 and 3.8, respectively, for predicting P and Ca, unlike the results of this study, which resulted in lower RPD values (1.66 for P, and between 1.10 and 1.54 for Ca).
## 3 Results and Discussion
### Stability Study  
#### 3.2.1 Particle Size  
As reported in our previous paper [16], the particle size of KTP in the nanosuspension was reduced to the nano range (238.30 \(\pm\) 1.42 nm), which enhanced the surface area and promoted a faster dissolution rate. Following spray-drying, and after incorporation of MNT particles, the D [0.5] value of the F1 sample increased to 2.325 \(\pm\) 0.14 \(\upmu\)m, indicating the formation of a nano-in-micro particle system. Further particle size analysis showed that the particle size of the formulation remained almost consistent throughout the testing period (Table 1), confirming the absence of particle agglomeration. Notably, the size range remained within the optimal 1-5 \(\upmu\)m range required for effective pulmonary delivery [24].  
While statistical analysis showed no significant variation in particle size during storage (Figure 5), the span values increased significantly. This suggests that particle size distribution was affected, likely due to humidity-induced uneven moisture absorption [24,25], which may have caused varying degrees of particle swelling or surface roughening, thereby broadening the distribution. Span, a parameter defining the breadth of size distribution, is critical in dry powder formulations. A wide particle size distribution (high span) can result in poor powder flow and uneven distribution during inhalation, potentially compromising inhaler performance and leading to drug deposition in non-target areas.
## Materials and Methods
### MOCT Laboratory Hardware
#### 2.1.1 Modulator  
A novel PPM modulator, with precise pulse duration control, has been developed for MOCT. The FPGA being used for the modulator on a Microsemi development board can be seen in Figure 3.  
The seed laser driver described later in this paper (Section 2.1.2) requires two digital signals with an adjustable phase difference. The signals also need a high time resolution to produce short time slots for the PPM modulation. Our current implementation uses two of the SERDES hardware modules of the Smartfusion 2 FPGA (Microsemi Corporation, Aliso Viejo, CA, USA). The two SERDES modules are synchronized with a single clock, and the phase of clock for the second SERDES can be adjusted with a variable delay chain. The PPM symbols to be transmitted are first split in a counter value and SERDES word position, then a second logic block generates 20-bit words for the SERDES modules. The resulting modulator has a resolution of 400 ps for the PPM slots because of the 2.5 GHz maximum SERDES frequency. The pulse duration is adjustable down to 12 ps increments by utilizing the delay chain resolution. In addition, the 20-bit words sent to the SERDES modules allow 400 ps adjustments. The additional jitter introduced by the delay chain is partially filtered by the SERDES module integrated Phase Locked Loops (PLLs). The resulting modulator is highly configurable: the guard time, the slot duration and the pulse duration can be adjusted. Data rate of up to 1.25 Gbps can be achieved with PPM4 at 625 MHz symbol rate, but such data throughputs are limited by the seed laser and the available optical power from the EDFA. Since all the timing circuitry is implemented inside the FPGA, and no external analog timing adjustments are required, the modulator can be efficiently implemented at no PCB area cost while using few FPGA resources. The power consumption of the modulator, which includes both the FPGA and development board, was measured to be 7.32 W. When the development board is powered on, it also powers USB ports, Ethernet ports, and other features that would not be used on a space version of the modulator. However, some additional memory and processors will be needed so the modulator as a whole is expected to consume at least 5 W.  
Figure 3: Here is the FPGA based modulator for MOCT on its development board. It is responsible for sending data to the seed laser in order to be transmitted to the receiver.
## 4 Results and Discussion
### Discussion
#### 4.2.4 Effect of Gasket Thickness  
The O-ring is only in contact with the gasket in the deflation state, so the change in the gasket thickness only affects the performance of the O-ring in the deflation state. Figure 18a shows the maximum von Mises stress curves of the O-ring with various gasket thicknesses. When the thickness of the gasket is within the range of 0.85-1.05 mm, the von Mises stress of the O-ring in the inflation state is greater and more likely to be damaged. In other ranges (0.3-0.85 mm or 1.05-1.2 mm), the von Mises stress of the O-ring in the deflation state is greater and more likely to be damaged. Additionally, as the gasket thickness increases from 0.3 mm to 1.2 mm at 15 \({}^{\circ}\)C and 70 MPa, the maximum von Mises stress of the O-ring in the deflation state initially experiences a sharp decrease from 137 MPa to 113 MPa, followed by a gradual rise to 116 MP. The results show that increasing the gasket thickness can effectively reduce the stress concentration of the O-ring, but excessive gasket thickness (more than 0.9 mm in this study) will aggravate the stress concentration.  
Figure 17: Influence of gasket fillet on O–ring performance: (**a**) max von Mises stress; (**b**) max contact pressure.  
Figure 18b shows the maximum contact pressure curves of O-rings with various gasket thicknesses. The maximum contact pressures of both contact surfaces I and II are greater than 70 MPa, indicating effective sealing performance across a wide range of gasket thicknesses. It can be clearly seen that as the gasket thickness increases from 0.3 mm to 1.2 mm, the gap between the contact pressure of contact surface I in the deflation state and the other three situations (inflation contact surface I, inflation contact surface II, and deflation contact II) gradually decreases. The results show that when the thickness of the gasket increases from 0.3 mm to 1.2 mm, although the sealing performance of surface I is always relatively poor in the deflated state, the gap between the sealing performance here and in the other three situations gradually diminishes. Furthermore, the maximum contact pressure of contact surface I in the deflation state increases from the minimum of 78 MPa to about 140 MPa, while the maximum contact pressure of contact surface II in the deflation state fluctuates by less than 3%. The results show that in the deflation state, the gasket thickness has a significant effect on the sealing performance of contact surface I, while the effect on the sealing performance of contact surface II is negligible.  
In summary, based on the influence of gasket thickness on maximum von Mises stress and maximum contact pressure, the optimal range is determined to be between 0.85 mm and 1.05 mm. Within this range, the stress concentration of the O-ring is lighter and the sealing performance of the combined sealing structure is better.
# A Study of Cislunar-Based Small Satellite Constellations with Sustainable Autonomy
## 4 Cislunar Dynamics (Constellation Scenario)
### Constellation Modelling
#### 4.1.1 Flower Constellations  
There are several ways to model the flower-type constellations, but for this paper, Ref. [28] is followed to define and operate a flower constellation.  
The Mean Anomaly and RAAN are termed as primary perturbative quantities, with \(J_{2}\) considered given as:  
\[\hat{\Omega}=-2\varphi n\cos i\approx\frac{-2\varphi\omega_{\otimes}[1-A(\varphi )]\cos i}{\Gamma-2\varphi\cos i} \tag{5}\]  
where the \(i\) is the inclination of the orbit; and  
\[\begin{array}{rl}A(\varphi)&=&\varphi[4+2\sqrt{1-e^{2}}-(5+3\sqrt{1-e^{2}} \sin^{2}i)]\\ &\varphi=\frac{3R_{\odot}^{2}J_{2}}{4p^{2}}\\ &\dot{M}_{0}=-\varphi n\sqrt{1-e^{2}}(3\sin^{2}i-2)\\ &\approx&\frac{-\varphi\omega_{\otimes}[1-A(\varphi)]\sqrt{1-e^{2}}(3\,\sin^{ 2}i-2)}{\tau-2\varphi\cos i}\end{array} \tag{6}\]  
where \(p=a(1-e^{2})\); \(J_{2}=1.08266269\times 10^{-3}\); and \(R_{\odot}=6378.1363\) Km which is equatorial Earth radius and \(n\) is the mean motion is expressed as  
\[n=\frac{\omega_{\otimes}[1+A(\varphi)]^{-1}}{\tau-2\varphi[1+A(\varphi)]^{-1} \cos i}\approx\frac{\omega_{\otimes}[1-A(\varphi)]}{\tau-2\varphi\cos i}\]  
where \(\tau=N_{d}/N_{p}\); \(N_{d}\) is the number sidereal days for ground repeats, and \(N_{p}\) is the number of petals; \(\omega_{\otimes}=7.2921158553\times 10^{-5}\) rad/s presents angular velocity of Earth.
# Wintertime Local Wind Dynamics from Scanning Doppler Lidar and Air Quality in the Arve River Valley
## 5 Discussion
### Cause of the Observed Wind Patterns
#### 5.1.3 North-South Structure  
Figures 10 and 11 show sheared wind structures in the cross-basin direction during the night-time, with a down-valley jet close to the northern sidewalls and a wind blowing in the opposite up-valley direction close to the southern sidewalls. This sheared structure may be influenced by:  
* Dynamical effects due to the particular geometry of the basin (curvature and semi-closed structure), which may induce a re-circulation cell forced by the orography. Indeed, [PERSON] and [PERSON] [49] have shown that a sharp valley curvature may generate a secondary circulation, leading to strong shear in the cross-valley direction.
* The down-valley flows emerging from the tributary valleys (Saint-Gervais and Megeve valleys), both of which lie on the southern side of the Passy basin. Their flows may thus prevent the down-valley jet from extending southward.
* The daytime asymmetric solar heating of the northern and southern slopes, which could perturb the cross-basin temperature structure. This north-south gradient may generate a cross-basin circulation, which could influence the trajectory of the down-valley jet. Moreover, this differential heating has a strong influence on snow cover since the north-facing slopes remained snow covered while the snow progressively melted on south-facing slopes and basin bottom during the IOP1. This north-south gradient of snow cover, and thus of albedo, can be an additional source of north-south asymmetry as already observed by [PERSON] and [PERSON] [13].  
Further studies will be needed to understand the contribution of each of these processes, but, for the question that concerns us here, this sheared cross-basin structure can have a major importance for pollution re-distribution as discussed in the next section.
## 4 Experiment Results and Comparison
### The Experimental Setup
#### 4.4.1 Parameters Setting  
Choosing the correct combination of hyper-parameter is a difficult task. The process of deciding the best combination that allows improving the model performance is known as hyper-parameter tuning. Grid Search is one of the most popular heuristic-based algorithms for tuning hyper-parameters [34]. It works by running nested loops upon all possible values. Our proposed automatic labeling algorithm is influenced by the following hyper-parameters:  
1. The procedure for selecting classifiers for the ensemble learning phase.
2. The value of the \(\alpha\) threshold, which represents the minimum acceptable probability for labeling the observations at each iteration
3. The value of the \(\beta\) threshold, which denotes the maximum learning rate that allows \(\alpha\) threshold to be updated.
4. The \(\gamma\) threshold value, which indicates \(\alpha\) threshold decrements step.
# Online SOPDT Model Identification Method Using a Relay
## 3 Simulation Results and Discussion
### Identification of the Second-Order Time-Delayed Plant
The transfer function of a plant that is subjected to an identification test is  
\[G_{m22}(s)=\frac{1.995\:e^{-3.01s}}{(10.954s+1)(1.907s+1)} \tag{25}\]1  
The steady-state gain of the model was measured in the pretest as being 1.486 and the dead time as equal to 3.51 s. The following data were obtained during the critical oscillation measurements: the time delay was 6.5 s, an ultimate gain of \(K_{u}\) = 2.651 was reached, the ultimate frequency was \(\omega_{u}=\omega_{1}\) = 0.172 rad/s, and, after the introduction of the ideal integral term, the frequency of the sustained oscillation changed the value to \(\omega_{2}\) = 0.0681 rad/s. Since the measured time delay was found to be greater than the assumed maximum value \(\theta_{c}\), which was calculated from relation (29) to be equal to 6.1 s, the parameter \(\theta=\theta_{c}\) was adopted; we thus obtained the transfer function of the model as follows:  
\[G_{m22}(s)=\frac{1.995\:e^{-3.01s}}{(10.954s+1)(1.907s+1)} \tag{25}\]2  
The step responses of the plant and its model are shown in \[G_{m22}(s)=\frac{1.995\:e^{-3.01s}}{(10.954s+1)(1.907s+1)} \tag{25}\]3  
Comparing the responses of the plant and the model, it can be concluded that the time constant of the model's delay, although it shifts the model's response in time, allowed for proper coverage of the high-frequency dynamics.
## 5 Conclusions  
The presented experimental results of the innovative assist device show that it may be capable of supporting by increasing the ejection volume of blood and even replacing the left ventricle in a permanent manner for patients with end-stage heart failure accompanied by mitral valve regurgitation and pulmonary hypertension. The above statement is true at least on a laboratory scale on a specially designed experimental stand and should therefore be understood as an in vitro proof of concept. This experimental stand permits realistic reproductions of heart pressures over a wide range: from hypotension to hypertension.  
Importantly, the functioning of the proposed assist device is based on the strategy of pulsatile flow that can be easily synchronized with the heart cycle. Meanwhile, most of the available devices is based on the mechanism of continuous flow, which can contribute to the formation of arrhythmias.  
As the implantation of the presented assist devices may require less invasive implantation, there is no need to perform median sternotomy. This may be beneficial for frail patients with unstable circulation, which is an additional advantage of the proposed device. This also means that the proposed experimental stand can be used to test the safest method of implanting the device with continuous monitoring of flow rates and pressures in the heart cavities, which will be the subject of further publications in due time.  
Importantly, the introduced device is the only solution that can be used in the presence of mitral regurgitation, which is not achievable with other known devices. Finally, it may also be possible to increase the ejection volume of blood as well as to reduce the wedge and pulmonary trunk pressure in order to reduce the systolic pressure in the right ventricle and to reduce the triple feedback wave which leads to an improvement in the hemodynamic conditions of the heart and its efficiency.  
Figure 8: Experimental results—balloon pressure.  
**Author Contributions:** Conceptualization, K.T., L.D., R.J. and J.R.; methodology, K.T., L.D., R.J. and J.R.; validation, R.J.; formal analysis, K.T.; investigation, K.T., L.D. and R.J.; data curation, L.D. and R.J.; writing--original draft preparation, K.T.; visualization, R.J. and L.D.; supervision, K.T. and J.R.; project administration, K.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Centre for Research and Development, Poland, POIR.01.01.01-00-1026/18.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** Not applicable.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this article.
# Soft Fuzzy Reinforcement Neural Network Proportional-Derivative Controller
###### Abstract  
Controlling systems with highly nonlinear or uncertain dynamics present significant challenges, particularly when using conventional Proportional-Integral-Derivative (PID) controllers, as they can be difficult to tune. While PID controllers can be adapted for such systems using advanced tuning methods, they often struggle with lag and instability due to their integral action. In contrast, fuzzy Proportional-Derivative (PD) controllers offer a more responsive alternative by eliminating reliance on error accumulation and enabling rule-based adaptability. However, their industrial adoption remains limited due to challenges in manual rule design. To overcome this limitation, Fuzzy Neural Networks (FNNs) integrate neural networks with fuzzy logic, enabling self-learning and reducing reliance on manually crafted rules. However, most fuzzy neural network PD (FNNPD) controllers rely on mean square error (MSE)-based training, which can be inefficient and unstable in complex, dynamic systems. To address these challenges, this paper presents a Soft Fuzzy Reinforcement Neural Network PD (SFPD) controller, integrating the Soft Actor-Critic (SAC) framework into FNNPD control to improve training speed and stability. While the actor-critic framework is widely used in reinforcement learning, its application to FNNPD controllers has been unexplored. The proposed controller leverages reinforcement learning to autonomously adjust parameters, eliminating the need for manual tuning. Additionally, entropy-regularized stochastic exploration enhances learning efficiency. It can operate with or without expert knowledge, leveraging neural network-driven adaptation. While expert input is not required, its inclusion accelerates convergence and improves initial performance. Experimental results show that the proposed SFPD controller achieves fast learning, superior control performance, and strong robustness to noise, making it effective for complex control tasks.  
deep reinforcement learning; soft actor-critic; fuzzy neural networks; PD control