esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Evaluation
### Capacity Outage Probability  
The end-to-end CDF of the dual-hop FSO link from OGS to HAP under the AF scheme with a fixed relay gain \(Q\) is given by our previous work ([17], Equation (33))  
\[\begin{array}{l}F_{\gamma_{1}}(\gamma)=\exp\biggl{(}-\frac{\theta_{PAV}^{2}} {2\sigma_{angl,\alpha}^{2}}\biggr{)}+\biggl{(}1-\exp\biggl{(}-\frac{\theta_{ PAV}^{2}}{2\sigma_{angl,\alpha}^{2}}\biggr{)}\biggr{)}\biggl{(}F_{\gamma_{GS}} (\gamma)-\frac{\xi_{1}^{2}F^{2}}{\beta\Gamma(\alpha_{1})\Gamma(\beta_{1})}\\ \times G_{1,0,3;2;\ b,\ 3+1}^{0,1,0,3;3:3+1,0}\left[\begin{array}{cc}1&1-\xi_{1}^{2},1-\alpha_{1},1-\beta_{1}\\ -&-\xi_{1}^{2},0\end{array}\right]\left[\begin{array}{cc}E_{3}\\ E_{4},0\end{array}\right]\left[\begin{array}{cc}1\\ \overline{D_{1}}\left(\frac{\mu_{1}}{7}\right)^{\frac{1}{6}},\frac{D_{1}^{2} \overline{Q}}{\beta^{2}\mu_{2}}\end{array}\right]\biggr{)}\end{array} \tag{28}\]  
Capacity outage probability is defined as the probability that the instantaneous link capacity falls below a predefined capacity threshold \(C_{th}\). The capacity outage probability of our proposed three-hop hybrid FSO/RF system is defined as ([8], Equation (23))  
\[P_{out}=P_{out1}+P_{out2}-P_{out1}P_{out2} \tag{29}\]  
where \(P_{out2}\) is the capacity outage probability of HAP-to-AP parallel FSO/RF links, which can be obtained by using (27) shown as \(P_{out2}=F_{C_{t}}(C_{th})\). \(P_{out1}\) defines the capacity outage probability of a dual-hop FSO link from OGS to HAP and can be calculated by \(P_{out1}=F_{\gamma_{1}}(\gamma_{th})\). \(\gamma_{th}\) is the predefined SNR, which is determined capacity threshold \(C_{th}\). The instantaneous capacity of the dual-hop FSO link from OGS to HAP is shown as  
\[C_{GSH}=\min\{C_{GS},\ C_{SH}\} \tag{30}\]  
where \(C_{GS}\) is the instantaneous capacity of the OGS-to-satellite FSO link, \(C_{SH}\) is the instantaneous capacity of the satellite-to-HAP FSO link. When considering that the OGS-to-satellite FSO link is more affected by weather conditions compared to the satellite-to-HAP FSO link [8; 16], we have the relationship \(\alpha_{1}<\alpha_{2}\), \(\beta_{1}<\beta_{2}\) and \(\xi_{1}<\xi_{2}\). Thus, the relationship \(C_{GS}<C_{SH}\) holds true. \(C_{GSH}\) can be rewritten as \(C_{GSH}=C_{GS}\), which is calculated by  
\[C_{GS}=W_{f}\log_{2}\Bigl{(}1+\tau\gamma_{GS}^{f}\Bigr{)} \tag{31}\]  
Therefore, \(\gamma_{th}\) can be calculated from (31) with a predefined threshold \(C_{th}\) and the capacity outage probability in (29) can be obtained.
# FlowHaptics: Mid-Air Haptic Representation of Liquid Flow
## 5 Experiment 2: Different Velocity Profiles  
### Experimental Conditions and Settings  
In order to investigate how closely the proposed system can emulate the sensation of flowing water that matches human perception, we performed a subjective evaluation based on 3 velocity profiles, which were:  
* _Constant_: the velocity profile of controlling the air jet at a constant velocity (95.58 m/s) for a certain time. The conditions 1F and 3F are presented in Figures 7a and 8a, respectively;
* _FlowEst1_: the velocity profile of controlling the air jet of each control valve using the estimated flow velocity transitions with FluidNet. The velocity profiles of the conditions 1F and 3F are presented in Figures 7b and 8b, respectively;
* _FlowEst2_: the velocity profile of the air jets only presenting a flow velocity that exceeded the threshold value (54.3 m/s) among the estimated flow velocity changes by FluidNet. The velocity profiles of 1F and 3F are presented in Figures 7c and 8c, respectively.  
Similar to Experiment 1, we applied 3 velocity profiles with 4 conditions; 1F/1T, 1F/3T, 3F/1T, and 3F/3T, respectively. Each trial was repeated 3 times. Therefore, we had \(3\times 4\times 3=36\) trials for each participant. After each trial, we asked the subjects to respond to the question, \"How closely did the haptic rendering match your sensation of the flowing water?\" using a 7-point Likert scale (1--not at all realistic, 7--highly realistic). We had 8 participants in total.  
Note that we had an additional condition, _FlowEst2_, because we hypothesized that the participant would feel the difference between the exact estimation (from low to high velocity) in _FlowEst1_ and when the participant experienced only a high velocity (more than 54.3 m/s) in _FlowEst2_.
# A Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter for Spacecraft-Relative Navigation
## 6 Numerical Simulations  
To demonstrate the superiority of MRD-DCSCKF for handling multiple-step, randomly delayed measurements and suppressing outliers, we designed simulations to compare the properties of the proposed filter with CKF [15], the one-step randomly delayed CKF (ORD-CKF) [20], and the multiple-step randomly delayed CKF (MRD-CKF) [25].
## 2 Materials and Methods
### Identification of the Constants of GTN Model
#### 2.3.2 Surrogate-Based Optimization Method  
An effective and efficient method to have cheap approximations of expensive black-box functions is the surrogate-based algorithm [20; 21]. It is simply a numerical approximation of how an entity varies when the entities that affect it are varied. This method has also been widely employed to evaluate the numerical predictions which are calculated by expensive codes such as computational fluid dynamics (CFD) and nonlinear finite element method (NFEM) to speed up the analyzing process [22; 23; 24]. For implementing the surrogate-based optimization technique in a numerical simulation, the following steps should be taken [24; 25]:  
1. Selecting the initial sampling points with a design of experiments (DoE) technique
2. Performing the computationally expensive FE simulation for the selected points
3. Fitting the surrogate model
4. Optimizing the surrogate model and finding the new set of samples and
5. Repeating steps 2-4 to reach convergence.  
Figure 11 illustrates the flow chart of the surrogate-based optimization process.  
Figure 11: The flow chart of the surrogate-based optimization method.  
In surrogate-based optimization there are different methods for generation of the surrogate function. This paper employs two methods of polynomial regression and Kriging. Each method is described briefly and the results obtained from the two methods applied for optimization are compared.
## 5 Future Work  
In our project, we have demonstrated the potential for satellite-based communication systems to significantly enhance data collection and transmission in remote and extreme environments. Looking ahead, several avenues for future work will further optimize and expand our system's capabilities.  
First, we recognize that many regions suffer from limited or difficult-to-achieve Internet connectivity. These areas often require stable communication or data collection for research or other critical purposes. While our current solutions address some of these challenges, there is room for improvement. Future work will involve refining message transactions and enhancing notification systems to achieve more efficient and reliable communication.  
One promising direction for enhancement is the development of a sophisticated computer program that automates communication between SWARM devices and other platforms. This program would allow users to send and receive messages, information, and notifications with a single click, regardless of their location. It would also include features for data processing and generating comprehensive reports or files as desired. Such a tool would streamline operations and make the system more user-friendly and accessible globally.  
Additionally, we plan to explore improvements in hardware by integrating advanced measuring and processing boards with SWARM devices. This enhancement could lead to faster and more efficient data collection, enabling the system to handle more complex tasks and deliver more precise results. By adapting the hardware to meet specific project goals and desired outcomes, we can significantly boost the performance and versatility of our technology.  
Finally, we intend to extend our system's geographical reach and application. Future work will involve field trials in various environments to validate and optimize system performance and ensure that it meets users' needs across different geographic and climatic conditions. We will also seek collaborations with stakeholders in diverse sectors to explore new use cases and refine our technology to address specific challenges and opportunities.  
Through these future initiatives, we aim to enhance the effectiveness and versatility of our satellite-based communication system, contributing to more efficient data collection and analysis in a wide range of remote and extreme environments. It is important to note that the following research could continue the future improvements only if SWARM Technologies would continue its commercial service; however, based on current trends, SWARM Technologies would potentially stop their services [38].
# Feature Optimization for Gait Phase Estimation with a Genetic Algorithm and Bayesian Optimization
## 3 Gait Phase Estimation Model
### Optimization Algorithms
#### 3.2.2 Bayesian Optimization for LTW  
In this study, BO was used as the optimizer for the LTWs of the TD features, and the loss values of the test datasets, obtained by (2), were the values to be minimized. The BO of these loss values was realized using a surrogate model and an acquisition function. A Gaussian process was used as the surrogate model, and an expected improvement function was used as the acquisition function. The parameters of the BO algorithm implemented in this study are summarized in Table 2.  
After the BGA assigns a feature set to the BO, the BO determines the LTW value of each TD feature. Subsequently, the MLP is trained with the features calculated using the LTW, as shown in Figure 6. Next, the MLP returns the performance of the test dataset. Then, the BO selects new LTW values based on the previous results. The procedure described above is repeated until a predefined number of iterations are conducted. Notably, in this study, the BO was conducted with five random initial points for every feature set to obtain reliable results. Through this procedure, the BO is able to obtain the optimal gait estimation performance for the assigned feature set, and the corresponding optimal performance is returned to the BGA.  
There are two special cases in which the BO needs to be operated differently. First, if every gene in a chromosome is zero, the MLP cannot estimate the gait phase. Second, if the selected TD feature set consists only of the final angle \(\theta\Big{(}t_{f}\Big{)}\) and angular velocity \(\dot{\theta}\Big{(}t_{f}\Big{)}\) value, the LTW determination is unnecessary because these final values are not affected by the LTW. Thus, BO does not iteratively optimize the LTWs in these cases.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Bayesian Optimization Parameter** & **Value** \\ \hline Number of initial points & 5 \\ Number of iterations & 20 \\ Search space of parameters & (100, 800) \\ Surrogate model & Gaussian process \\ Kernel function & Matern 5/2 \\ Acquisition function & Expected Improvement \\ Explore-exploit trade-off parameter & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: BO parameters.
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Security of AI Models
#### 5.6.3 Evaluation of the Existing Solutions  
To address security concerns in AI models within healthcare, each method has distinct advantages and limitations. Detecting and preventing sensor spoofing attacks through anomaly detection and data fusion ensures data integrity by identifying and mitigating falsified sensor data. However, these methods might struggle with high false positive rates and require integration with existing electronic health records, which can be complex [135]. Image scaling attacks can be managed with robust algorithms and median filters that improve image quality and diagnostic accuracy, though they may not fully restore original data and can add computational overhead [136]. For defending against adversarial attacks, techniques like adversarial retraining and generative adversarial networks (GANs) enhance model robustness against poisoning and evasion attacks, but they can be computationally expensive and require continuous updates to remain effective [137]. Overall, while each method contributes significantly to improving AI model security, its effectiveness can be influenced by implementation complexity, computational costs, and the need for ongoing adaptation. Combining these approaches, tailored to specific attack types and system requirements, offers a more comprehensive strategy to safeguard healthcare AI systems.
## 3 Results and Discussion
### Chemical Analysis  
The moisture content in the cereal bars from the control sample (Table 2) was at a level typical of this type of product and was approximately 16.3%. The addition of the fruit pomace resulted in a significant increase in the moisture content of the products. The increase in the moisture content of products containing dried/powdered fruit pomace is probably related to the high pectin content. These substances are responsible for the ability of products to retain water [22; 23]. Low-moisture bakery products, such as cereal bars and  
Figure 1: Pictures of the experimental products: CB—control bars; G1—bars with 10 g grape pomace addition; G2—bars with 20 g grape pomace addition; A1—bars with 10 g apple pomace addition; A2—bars with 20 g apple pomace addition.  
biscuits, are generally characterized by long life due to their low water activity. However, during storage, quality decay occurs due to moisture adsorption or the development of lipid oxidation. Oxidation is a complex set of reactions, which can be prevented by the addition of antioxidants in the formulation [24]. Therefore, moisture control plays a critical role in determining the quality and shelf life of cereal snacks. Additional protective measures are necessary for products with higher water content. Appropriate storage conditions, new aseptic technologies, and controlled atmosphere/modified atmosphere packaging can extend the product shelf life [25].  
The highest average polyphenol content was recorded in the sample of bars with the unmodified recipe. The addition of grape and apple pomace reduced the content of antioxidant compounds. It was further observed that the increase in the proportion of fruit residues from 10 to 20 g had no significant effect on changes in the number of polyphenols in the cereal bars. It should be noted that Sultanas, or dried grapes, were substituted with the fruit residue, i.e., raw materials with very high polyphenol content (10.7 mg of GAE/g wet weight) [26]. In studies conducted by other authors, where pomace was used to substitute cereal raw materials, the polyphenol content increased significantly in the fortified products [27; 28; 29].  
The addition of the apple and grape pomace resulted in a significant increase in soluble dietary fibre (SDF). The relatively high SDF content in the fruit residue-supplemented bars was probably related to the high pectin content, i.e., over 14 g/100 g of dried apple pomace [30]. The average insoluble fibre and total fibre content also increased after the addition of 10 g of the apple residue. However, the statistical analysis did not confirm the significance of these changes. In contrast, it was observed that the introduction of 20 g of the grape residue into the recipe reduced the amount of total dietary fibre (TDF).
# Current Sensorless MPPT Control for PV Systems Based on Robust Observer
## 2 System Modeling
#### Boost Converter Model  
Power converters, specifically DC-DC type, are widely used in PV generation systems as an adaptation stage between PV panels and the load being supplied [27; 28]. The main objective of such converters is to adjust the PV array output voltage in order to maximize the power generation. One of the most used topologies for PV systems is the boost configuration which is shown in Figure 2, where \(V_{pv}\) is the PV array voltage, \(V_{s}\) is the output voltage and \(i_{L}\) which is the inductor current is assumed to be equal to \(I_{pv}\). The remaining components \(C\), \(L\) and \(R\) are the output capacitor, inductor and converter load, respectively.  
Depending on the switch (S) state and considering only the continuous conduction mode, the boost converter can be represented by means of state equations by the following cases.  
Case 1: Switch is in OFF state, the load is supplied by energy stored in the inductor, applying [PERSON]'s laws, voltage and current dynamics can be described as  
\[\frac{d}{dt}\begin{bmatrix}I_{L}\\ V_{s}\end{bmatrix}=\underbrace{\begin{bmatrix}0&-\frac{1}{t}\\ \frac{1}{C}&-\frac{1}{RC}\end{bmatrix}}_{A_{OFF}}\begin{bmatrix}I_{L}\\ V_{s}\end{bmatrix}+\underbrace{\begin{bmatrix}V_{pv}\\ 0\\ 0\end{bmatrix}}_{b_{OFF}} \tag{1}\]  
Case 2: Switch is in ON state, the inductor is charged by the input voltage \(V_{pv}\) and the load is disconnected, applying [PERSON]'s laws, voltage and current dynamics can be described as  
\[\frac{d}{dt}\begin{bmatrix}I_{L}\\ V_{s}\end{bmatrix}=\underbrace{\begin{bmatrix}0&0\\ 0&-\frac{1}{RC}\end{bmatrix}}_{A_{ON}}\begin{bmatrix}I_{L}\\ V_{s}\end{bmatrix}+\underbrace{\begin{bmatrix}V_{pv}\\ 0\\ 0\end{bmatrix}}_{b_{ON}} \tag{2}\]  
From the above cases, defining \(x_{a}=[I_{L},V_{s}]^{T}\) the dynamic behavior during ON-OFF periods can be expressed as follows  
\[\dot{x}_{a}=Ax_{a}+b \tag{3}\]  
Considering that \(\mu=1\) when the switch is ON and \(\mu=0\) when switch is OFF, Equation (3) can be expressed as [29; 30]  
\[\dot{x}_{a}=A_{OFF}x_{a}+b_{OFF}+(A_{ON}-A_{OFF})x_{a}\mu+(b_{ON}-b_{OFF})\mu \tag{4}\]  
In boost topology \(b_{ON}=b_{OFF}\), which leads to  
\[\dot{x}_{a}=A_{OFF}x_{a}+b_{OFF}+(A_{ON}-A_{OFF})x_{a}\mu \tag{5}\]  
Under the assumption that a high switching frequency is present the corresponding average state space model [31; 32] for (1) and (2) is described by  
Figure 2: Boost converter topology.  
\[\dot{x}_{1} =\frac{V_{pv}}{L}-\frac{x_{2}(1-u)}{L} \tag{6}\] \[\dot{x}_{2} =\frac{x_{1}(1-u)}{C}-\frac{x_{2}}{RC}\]  
where \(u\in[0\ 1]\) is the average value of converter's duty cycle and \(x=[l_{L},V_{s}]^{\top}\) represents the average values of inductor current and converter output voltage.  
The relation between boost converter output voltage (\(V_{s}\)) and the PV array voltage (\(V_{pv}\)) is described by  
\[\frac{V_{s}}{V_{pv}}=\frac{1}{1-u} \tag{7}\]
## 1 Introduction
### The Waist Type Laminated Membrane Coupling  
Figure 1 illustrates the configuration of the six-hole waist type laminated membrane coupling. It is observed that the coupling consists of an active flange, bolt connection component and metal laminated group. Moreover, the working principle of this type of coupling can be described as the following: The torque is input from the flange at the drive end, and then transmitted to the metal laminate group by means of precision bolts through reaming holes at circular cross intervals. The output torque of the flange is then applied to the driven end by adjacent precision bolts.  
Figure 1 indicates that the assembly of the lamination coupling is composed of 16 pieces of hexagonal stainless steel rings with the same thickness. It should be indicated that the thickness of each piece is 0.4 mm. Figure 2 shows that these rings are assembled to the flange plate through bolts,  
Figure 1: Configuration of the waist type laminated membrane coupling.  
flange bushing, spherical washer, bushing and self-locking nut. Moreover, Figure 3 illustrates the lamination group used in this study.  
Figure 4 shows the principle of the lamination group. Assuming that bolt holes 1, 3, and 5 (shown in shade) are connected to the side of the driving shaft, bolt holes 2, 4, and 6 (shown in hollow) are connected to the side of the driven shaft, and the driving shaft rotates clockwise. Based on the above, the arc segments on the laminated sheet are divided into two statuses: the interval is compressed and stretched. The torque transmission is mainly based on the stretch arc. This structure can optimize the force distribution in the lamination when the torque is loaded. The main geometric parameter identification diagram of the lamination is shown in Figure 5, and the values of each geometric parameter are shown in Table 1.  
Figure 4: Principle of lamination transfer torque.  
Figure 3: Assembly of the waist type lamination.  
Figure 2: Partial assembly of the lamination group.  
Due to various errors between the driving and driven shafts, the laminated coupling bears the stresses caused by axial, radial and angular displacements under un-working conditions. The laminated coupling will be affected by torque and centrifugal force in working state, and its stress state is very complex. In order to ensure the maximum stress under each load does not exceed the strength limit of the laminate itself, the effects of each load need to be calculated separately.
## 2 Materials and Methods
### Irradiation Conditions  
Room-temperature irradiations with 0.5 MeV He\({}^{+}\) and 2 MeV Ar\({}^{9+}\) ions were performed by a 320 kV electron cyclotron resonance (ECR) ion source. All experiments were carried out in a heavy-ion accelerator research facility, Lanzhou (HIRFL). During the experiment, beam currents for He and Ar ion irradiations were set to 15.0 \(\upmu\)A and 6.0 \(\upmu\)A, respectively. It is worth noting that different scenarios were followed in experiments. First, part of the samples were irradiated by He ions with different fluences. In the second scenario, the other part of the samples were irradiated by Ar ions. Finally, the samples pre-irradiated with 1.1 \(\times\) 10\({}^{13}\), 1.7 \(\times\) 10\({}^{14}\), 1.1 \(\times\) 10\({}^{15}\) and 1.7 \(\times\) 10\({}^{16}\) Ar cm\({}^{-2}\) were irradiated with 3.3 \(\times\) 10\({}^{16}\) He cm\({}^{-2}\). In this regard, Figure 1 illustrates the stopping power against the projectile ranges. It should be indicated that the SRIM2010 code was used in the present study to calculate the total displacements of the collision events in the irradiated samples with 0.5 MeV He\({}^{+}\) and 2 MeV Ar\({}^{9+}\) ions. Table 2 presents details of irradiation and stopping power of ions.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Composition.** & **SiO\({}_{2}\)** & **Na\({}_{2}\)O** & **Al\({}_{2}\)O\({}_{3}\)** & **B\({}_{2}\)O\({}_{3}\)** \\ \hline mol\% & 83.06 & 4.09 & 1.46 & 11.39 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition (mol%) of the studied borosilicate glass.
# Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy
## 4 Experimental Results and Analysis  
In this section, we conduct several experiments to evaluate the proposed method. In Sections 4.1 and 4.2, we introduce the maximum embedding rate in tested images, and then a detailed example for the proposed method is presented. Finally, the proposed method is compared with other methods in Section 4.3.